diff --git a/NER_SRL/accuracy_plot.png b/NER_SRL/accuracy_plot.png
index 366a1b3..6942adf 100644
Binary files a/NER_SRL/accuracy_plot.png and b/NER_SRL/accuracy_plot.png differ
diff --git a/NER_SRL/adjst_model_lstm.ipynb b/NER_SRL/adjst_model_lstm.ipynb
index c27a556..12e3ab9 100644
--- a/NER_SRL/adjst_model_lstm.ipynb
+++ b/NER_SRL/adjst_model_lstm.ipynb
@@ -2,10 +2,30 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 548,
+ "execution_count": 1,
"id": "263af9e9",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-05-12 02:34:48.724737: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+ "2025-05-12 02:34:48.725382: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
+ "2025-05-12 02:34:48.727445: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
+ "2025-05-12 02:34:48.733495: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+ "E0000 00:00:1746992088.743638 16048 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+ "E0000 00:00:1746992088.746546 16048 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+ "W0000 00:00:1746992088.754190 16048 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "W0000 00:00:1746992088.754206 16048 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "W0000 00:00:1746992088.754207 16048 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "W0000 00:00:1746992088.754208 16048 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "2025-05-12 02:34:48.756870: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
"source": [
"import pickle, tensorflow as tf, numpy as np\n",
"from tensorflow.keras.models import Model\n",
@@ -26,7 +46,7 @@
},
{
"cell_type": "code",
- "execution_count": 549,
+ "execution_count": 2,
"id": "4fc87f1b",
"metadata": {},
"outputs": [
@@ -34,38 +54,28 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "total kalimat 628\n",
+ "total kalimat 638\n",
"NER Label Count || SRL Label Count \n",
"-------------------------------------------------------\n",
- "O 4165 || O 2146 \n",
- "B-TIME 189 || ARGM-TMP 1282 \n",
- "B-PER 313 || ARG0 939 \n",
- "B-LOC 551 || V 719 \n",
- "I-PER 226 || ARG1 1277 \n",
- "B-DATE 335 || ARGM-LOC 495 \n",
- "I-DATE 643 || ARG2 285 \n",
- "B-ETH 213 || ARGM-MOD 39 \n",
- "I-ETH 217 || ARGM-MNR 37 \n",
- "B-EVENT 60 || ARGM-NEG 6 \n",
- "I-EVENT 38 || ARGM-DIR 41 \n",
- "I-LOC 32 || ARGM-CAU 21 \n",
- "B-MISC 14 || \n",
- "I-MISC 3 || \n",
- "I-TIME 46 || \n",
- "B-ORG 20 || \n",
- "I-ORG 17 || \n",
- "B-QUANT 46 || \n",
- "B-MAT 97 || \n",
- "B-UNIT 44 || \n",
- "I-UNIT 1 || \n",
- "I-MAT 16 || \n",
- "I-QUANT 1 || \n"
+ "O 4251 || O 2178 \n",
+ "TIME 235 || ARGM-TMP 1291 \n",
+ "PER 539 || ARG0 962 \n",
+ "LOC 586 || V 737 \n",
+ "DATE 985 || ARG1 1305 \n",
+ "ETH 430 || ARGM-LOC 503 \n",
+ "EVENT 125 || ARG2 292 \n",
+ "MISC 17 || ARGM-MOD 39 \n",
+ "ORG 37 || ARGM-MNR 37 \n",
+ "QUANT 47 || ARGM-NEG 6 \n",
+ "MAT 115 || ARGM-DIR 41 \n",
+ "UNIT 45 || ARGM-CAU 21 \n"
]
}
],
"source": [
"data = []\n",
- "with open(\"../dataset/new_ner_srl.tsv\", encoding=\"utf-8\") as f:\n",
+ "# with open(\"../dataset/new_ner_srl.tsv\", encoding=\"utf-8\") as f:\n",
+ "with open(\"../dataset/ner_srl_without_bio.tsv\", encoding=\"utf-8\") as f:\n",
" tok, ner, srl = [], [], []\n",
" for line in f:\n",
" line = line.strip()\n",
@@ -89,13 +99,6 @@
"\n",
"srl_counter = Counter(label for seq in labels_srl for label in seq)\n",
"\n",
- "# print(\"Total per label NER:\")\n",
- "# for label, count in ner_counter.items():\n",
- "# print(f\"{label}: {count}\")\n",
- "\n",
- "# print(\"\\nTotal per label SRL:\")\n",
- "# for label, count in srl_counter.items():\n",
- "# print(f\"{label}: {count}\")\n",
"\n",
"print(f\"{'NER Label':<15} {'Count':<10} || {'SRL Label':<15} {'Count':<10}\")\n",
"print(\"-\" * 55)\n",
@@ -106,7 +109,42 @@
},
{
"cell_type": "code",
- "execution_count": 550,
+ "execution_count": 3,
+ "id": "8dda2d6c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "NER -> Total Labels: 7412, O Count: 4251, O Percentage: 57.35%\n",
+ "SRL -> Total Labels: 7412, O Count: 2178, O Percentage: 29.38%\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "def calculate_o_percentage(labels):\n",
+ " counter = Counter(label for seq in labels for label in seq)\n",
+ " total = sum(counter.values())\n",
+ " count_o = counter.get(\"O\", 0)\n",
+ " percent_o = (count_o / total) * 100 if total > 0 else 0\n",
+ " return percent_o, total, count_o\n",
+ "\n",
+ "# Hitung persentase 'O' untuk NER\n",
+ "o_ner_percent, total_ner, o_ner_count = calculate_o_percentage(labels_ner)\n",
+ "\n",
+ "# Hitung persentase 'O' untuk SRL\n",
+ "o_srl_percent, total_srl, o_srl_count = calculate_o_percentage(labels_srl)\n",
+ "\n",
+ "print(f\"NER -> Total Labels: {total_ner}, O Count: {o_ner_count}, O Percentage: {o_ner_percent:.2f}%\")\n",
+ "print(f\"SRL -> Total Labels: {total_srl}, O Count: {o_srl_count}, O Percentage: {o_srl_percent:.2f}%\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
"id": "48553e6b",
"metadata": {},
"outputs": [],
@@ -132,7 +170,7 @@
},
{
"cell_type": "code",
- "execution_count": 551,
+ "execution_count": 5,
"id": "096967e8",
"metadata": {},
"outputs": [],
@@ -148,12 +186,12 @@
"y_ner = pad_sequences(y_ner, maxlen=maxlen, padding=\"post\", value=pad_id)\n",
"y_srl = pad_sequences(y_srl, maxlen=maxlen, padding=\"post\", value=pad_id)\n",
"\n",
- "mask = (y_ner != pad_id).astype(\"float32\") # shape (N, L)"
+ "mask = (y_ner != pad_id).astype(\"float32\")"
]
},
{
"cell_type": "code",
- "execution_count": 552,
+ "execution_count": 6,
"id": "a26893cc",
"metadata": {},
"outputs": [],
@@ -166,18 +204,25 @@
},
{
"cell_type": "code",
- "execution_count": 553,
+ "execution_count": 7,
"id": "1b4a1c61",
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-05-12 02:34:51.102561: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
+ ]
+ },
{
"data": {
"text/html": [
- "Model: \"functional_39\"\n",
+ "Model: \"functional\"\n",
"\n"
],
"text/plain": [
- "\u001b[1mModel: \"functional_39\"\u001b[0m\n"
+ "\u001b[1mModel: \"functional\"\u001b[0m\n"
]
},
"metadata": {},
@@ -191,31 +236,31 @@
"┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
"│ tokens (InputLayer) │ (None, 34) │ 0 │ - │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ embed (Embedding) │ (None, 34, 64) │ 84,864 │ tokens[0][0] │\n",
+ "│ embed (Embedding) │ (None, 34, 64) │ 86,208 │ tokens[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ spatial_dropout1d_… │ (None, 34, 64) │ 0 │ embed[0][0] │\n",
+ "│ spatial_dropout1d │ (None, 34, 64) │ 0 │ embed[0][0] │\n",
"│ (SpatialDropout1D) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ not_equal_38 │ (None, 34) │ 0 │ tokens[0][0] │\n",
+ "│ not_equal │ (None, 34) │ 0 │ tokens[0][0] │\n",
"│ (NotEqual) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ bidirectional_76 │ (None, 34, 128) │ 66,048 │ spatial_dropout1… │\n",
- "│ (Bidirectional) │ │ │ not_equal_38[0][… │\n",
+ "│ bidirectional │ (None, 34, 128) │ 66,048 │ spatial_dropout1… │\n",
+ "│ (Bidirectional) │ │ │ not_equal[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ bidirectional_77 │ (None, 34, 128) │ 98,816 │ bidirectional_76… │\n",
- "│ (Bidirectional) │ │ │ not_equal_38[0][… │\n",
+ "│ bidirectional_1 │ (None, 34, 128) │ 98,816 │ bidirectional[0]… │\n",
+ "│ (Bidirectional) │ │ │ not_equal[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ time_distributed_74 │ (None, 34, 64) │ 8,256 │ bidirectional_77… │\n",
- "│ (TimeDistributed) │ │ │ not_equal_38[0][… │\n",
+ "│ time_distributed │ (None, 34, 64) │ 8,256 │ bidirectional_1[… │\n",
+ "│ (TimeDistributed) │ │ │ not_equal[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ time_distributed_75 │ (None, 34, 64) │ 8,256 │ bidirectional_77… │\n",
- "│ (TimeDistributed) │ │ │ not_equal_38[0][… │\n",
+ "│ time_distributed_1 │ (None, 34, 64) │ 8,256 │ bidirectional_1[… │\n",
+ "│ (TimeDistributed) │ │ │ not_equal[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ ner_output │ (None, 34, 24) │ 1,560 │ time_distributed… │\n",
- "│ (TimeDistributed) │ │ │ not_equal_38[0][… │\n",
+ "│ ner_output │ (None, 34, 13) │ 845 │ time_distributed… │\n",
+ "│ (TimeDistributed) │ │ │ not_equal[0][0] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ srl_output │ (None, 34, 13) │ 845 │ time_distributed… │\n",
- "│ (TimeDistributed) │ │ │ not_equal_38[0][… │\n",
+ "│ (TimeDistributed) │ │ │ not_equal[0][0] │\n",
"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
"
\n"
],
@@ -225,31 +270,31 @@
"┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
"│ tokens (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ embed (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m84,864\u001b[0m │ tokens[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ embed (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m86,208\u001b[0m │ tokens[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ spatial_dropout1d_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ embed[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ spatial_dropout1d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ embed[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mSpatialDropout1D\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ not_equal_38 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ tokens[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ not_equal │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ tokens[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mNotEqual\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ bidirectional_76 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m66,048\u001b[0m │ spatial_dropout1… │\n",
- "│ (\u001b[38;5;33mBidirectional\u001b[0m) │ │ │ not_equal_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ bidirectional │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m66,048\u001b[0m │ spatial_dropout1… │\n",
+ "│ (\u001b[38;5;33mBidirectional\u001b[0m) │ │ │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ bidirectional_77 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m98,816\u001b[0m │ bidirectional_76… │\n",
- "│ (\u001b[38;5;33mBidirectional\u001b[0m) │ │ │ not_equal_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ bidirectional_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m98,816\u001b[0m │ bidirectional[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ (\u001b[38;5;33mBidirectional\u001b[0m) │ │ │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ time_distributed_74 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │ bidirectional_77… │\n",
- "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ time_distributed │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n",
+ "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ time_distributed_75 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │ bidirectional_77… │\n",
- "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ time_distributed_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n",
+ "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ ner_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m24\u001b[0m) │ \u001b[38;5;34m1,560\u001b[0m │ time_distributed… │\n",
- "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ ner_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m13\u001b[0m) │ \u001b[38;5;34m845\u001b[0m │ time_distributed… │\n",
+ "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ srl_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m13\u001b[0m) │ \u001b[38;5;34m845\u001b[0m │ time_distributed… │\n",
- "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
]
},
@@ -259,11 +304,11 @@
{
"data": {
"text/html": [
- " Total params: 268,645 (1.02 MB)\n",
+ " Total params: 269,274 (1.03 MB)\n",
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- "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m268,645\u001b[0m (1.02 MB)\n"
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@@ -272,11 +317,11 @@
{
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- " Trainable params: 268,645 (1.02 MB)\n",
+ " Trainable params: 269,274 (1.03 MB)\n",
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- "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m268,645\u001b[0m (1.02 MB)\n"
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@@ -352,7 +397,7 @@
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{
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- "execution_count": 554,
+ "execution_count": 8,
"id": "f41d6012",
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@@ -360,26 +405,79 @@
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"text": [
- "Epoch 1/10\n",
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- "Epoch 2/10\n",
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- "Epoch 3/10\n",
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- "Epoch 4/10\n",
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- "Epoch 5/10\n",
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- "Epoch 6/10\n",
- "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 25ms/step - loss: 1.9864 - ner_output_loss: 0.9526 - ner_output_sparse_categorical_accuracy: 0.2486 - srl_output_loss: 1.0338 - srl_output_sparse_categorical_accuracy: 0.2096 - val_loss: 1.8169 - val_ner_output_loss: 0.8461 - val_ner_output_sparse_categorical_accuracy: 0.2829 - val_srl_output_loss: 0.9708 - val_srl_output_sparse_categorical_accuracy: 0.2337 - learning_rate: 3.0000e-04\n",
- "Epoch 7/10\n",
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- "Epoch 8/10\n",
- "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 30ms/step - loss: 1.7250 - ner_output_loss: 0.8011 - ner_output_sparse_categorical_accuracy: 0.2681 - srl_output_loss: 0.9239 - srl_output_sparse_categorical_accuracy: 0.2264 - val_loss: 1.5871 - val_ner_output_loss: 0.7190 - val_ner_output_sparse_categorical_accuracy: 0.2901 - val_srl_output_loss: 0.8681 - val_srl_output_sparse_categorical_accuracy: 0.2512 - learning_rate: 3.0000e-04\n",
- "Epoch 9/10\n",
- "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 30ms/step - loss: 1.6804 - ner_output_loss: 0.7667 - ner_output_sparse_categorical_accuracy: 0.2711 - srl_output_loss: 0.9137 - srl_output_sparse_categorical_accuracy: 0.2291 - val_loss: 1.4840 - val_ner_output_loss: 0.6594 - val_ner_output_sparse_categorical_accuracy: 0.2937 - val_srl_output_loss: 0.8246 - val_srl_output_sparse_categorical_accuracy: 0.2558 - learning_rate: 3.0000e-04\n",
- "Epoch 10/10\n",
- "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 29ms/step - loss: 1.3918 - ner_output_loss: 0.6476 - ner_output_sparse_categorical_accuracy: 0.2810 - srl_output_loss: 0.7442 - srl_output_sparse_categorical_accuracy: 0.2508 - val_loss: 1.3737 - val_ner_output_loss: 0.6048 - val_ner_output_sparse_categorical_accuracy: 0.2979 - val_srl_output_loss: 0.7690 - val_srl_output_sparse_categorical_accuracy: 0.2591 - learning_rate: 3.0000e-04\n"
+ "Epoch 1/30\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-05-12 02:34:58.809821: E tensorflow/core/util/util.cc:131] oneDNN supports DT_BOOL only on platforms with AVX-512. Falling back to the default Eigen-based implementation if present.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ "Epoch 2/30\n",
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+ "Epoch 3/30\n",
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+ "Epoch 4/30\n",
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+ "Epoch 5/30\n",
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+ "Epoch 6/30\n",
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+ "Epoch 7/30\n",
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+ "Epoch 8/30\n",
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+ "Epoch 9/30\n",
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+ "Epoch 10/30\n",
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+ "Epoch 11/30\n",
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+ "Epoch 12/30\n",
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+ "Epoch 13/30\n",
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+ "Epoch 14/30\n",
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+ "Epoch 15/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.9059 - ner_output_loss: 1.3101 - ner_output_sparse_categorical_accuracy: 0.1994 - srl_output_loss: 1.5958 - srl_output_sparse_categorical_accuracy: 0.1454 - val_loss: 2.9432 - val_ner_output_loss: 1.3262 - val_ner_output_sparse_categorical_accuracy: 0.2027 - val_srl_output_loss: 1.6170 - val_srl_output_sparse_categorical_accuracy: 0.1459 - learning_rate: 3.0000e-04\n",
+ "Epoch 16/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.9055 - ner_output_loss: 1.2973 - ner_output_sparse_categorical_accuracy: 0.1947 - srl_output_loss: 1.6083 - srl_output_sparse_categorical_accuracy: 0.1426 - val_loss: 2.9266 - val_ner_output_loss: 1.3165 - val_ner_output_sparse_categorical_accuracy: 0.2015 - val_srl_output_loss: 1.6101 - val_srl_output_sparse_categorical_accuracy: 0.1420 - learning_rate: 3.0000e-04\n",
+ "Epoch 17/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.8617 - ner_output_loss: 1.2864 - ner_output_sparse_categorical_accuracy: 0.1981 - srl_output_loss: 1.5752 - srl_output_sparse_categorical_accuracy: 0.1503 - val_loss: 2.9099 - val_ner_output_loss: 1.3069 - val_ner_output_sparse_categorical_accuracy: 0.2011 - val_srl_output_loss: 1.6030 - val_srl_output_sparse_categorical_accuracy: 0.1438 - learning_rate: 3.0000e-04\n",
+ "Epoch 18/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.8199 - ner_output_loss: 1.2665 - ner_output_sparse_categorical_accuracy: 0.1957 - srl_output_loss: 1.5536 - srl_output_sparse_categorical_accuracy: 0.1460 - val_loss: 2.8892 - val_ner_output_loss: 1.2952 - val_ner_output_sparse_categorical_accuracy: 0.2008 - val_srl_output_loss: 1.5940 - val_srl_output_sparse_categorical_accuracy: 0.1512 - learning_rate: 3.0000e-04\n",
+ "Epoch 19/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.8937 - ner_output_loss: 1.2911 - ner_output_sparse_categorical_accuracy: 0.1984 - srl_output_loss: 1.6025 - srl_output_sparse_categorical_accuracy: 0.1455 - val_loss: 2.8620 - val_ner_output_loss: 1.2787 - val_ner_output_sparse_categorical_accuracy: 0.2006 - val_srl_output_loss: 1.5834 - val_srl_output_sparse_categorical_accuracy: 0.1533 - learning_rate: 3.0000e-04\n",
+ "Epoch 20/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.8192 - ner_output_loss: 1.2545 - ner_output_sparse_categorical_accuracy: 0.1988 - srl_output_loss: 1.5647 - srl_output_sparse_categorical_accuracy: 0.1529 - val_loss: 2.8301 - val_ner_output_loss: 1.2602 - val_ner_output_sparse_categorical_accuracy: 0.2013 - val_srl_output_loss: 1.5698 - val_srl_output_sparse_categorical_accuracy: 0.1540 - learning_rate: 3.0000e-04\n",
+ "Epoch 21/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.7594 - ner_output_loss: 1.2302 - ner_output_sparse_categorical_accuracy: 0.1960 - srl_output_loss: 1.5291 - srl_output_sparse_categorical_accuracy: 0.1559 - val_loss: 2.7871 - val_ner_output_loss: 1.2358 - val_ner_output_sparse_categorical_accuracy: 0.2038 - val_srl_output_loss: 1.5512 - val_srl_output_sparse_categorical_accuracy: 0.1565 - learning_rate: 3.0000e-04\n",
+ "Epoch 22/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.7990 - ner_output_loss: 1.2422 - ner_output_sparse_categorical_accuracy: 0.1976 - srl_output_loss: 1.5568 - srl_output_sparse_categorical_accuracy: 0.1513 - val_loss: 2.7361 - val_ner_output_loss: 1.2072 - val_ner_output_sparse_categorical_accuracy: 0.2171 - val_srl_output_loss: 1.5289 - val_srl_output_sparse_categorical_accuracy: 0.1613 - learning_rate: 3.0000e-04\n",
+ "Epoch 23/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.6867 - ner_output_loss: 1.1842 - ner_output_sparse_categorical_accuracy: 0.2120 - srl_output_loss: 1.5023 - srl_output_sparse_categorical_accuracy: 0.1636 - val_loss: 2.6829 - val_ner_output_loss: 1.1777 - val_ner_output_sparse_categorical_accuracy: 0.2199 - val_srl_output_loss: 1.5052 - val_srl_output_sparse_categorical_accuracy: 0.1618 - learning_rate: 3.0000e-04\n",
+ "Epoch 24/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.6152 - ner_output_loss: 1.1454 - ner_output_sparse_categorical_accuracy: 0.2129 - srl_output_loss: 1.4698 - srl_output_sparse_categorical_accuracy: 0.1625 - val_loss: 2.6377 - val_ner_output_loss: 1.1529 - val_ner_output_sparse_categorical_accuracy: 0.2268 - val_srl_output_loss: 1.4847 - val_srl_output_sparse_categorical_accuracy: 0.1677 - learning_rate: 3.0000e-04\n",
+ "Epoch 25/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 2.5303 - ner_output_loss: 1.0935 - ner_output_sparse_categorical_accuracy: 0.2241 - srl_output_loss: 1.4368 - srl_output_sparse_categorical_accuracy: 0.1707 - val_loss: 2.5891 - val_ner_output_loss: 1.1272 - val_ner_output_sparse_categorical_accuracy: 0.2318 - val_srl_output_loss: 1.4619 - val_srl_output_sparse_categorical_accuracy: 0.1700 - learning_rate: 3.0000e-04\n",
+ "Epoch 26/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.5297 - ner_output_loss: 1.0978 - ner_output_sparse_categorical_accuracy: 0.2190 - srl_output_loss: 1.4318 - srl_output_sparse_categorical_accuracy: 0.1648 - val_loss: 2.5424 - val_ner_output_loss: 1.1029 - val_ner_output_sparse_categorical_accuracy: 0.2383 - val_srl_output_loss: 1.4394 - val_srl_output_sparse_categorical_accuracy: 0.1721 - learning_rate: 3.0000e-04\n",
+ "Epoch 27/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.4495 - ner_output_loss: 1.0693 - ner_output_sparse_categorical_accuracy: 0.2250 - srl_output_loss: 1.3802 - srl_output_sparse_categorical_accuracy: 0.1723 - val_loss: 2.4932 - val_ner_output_loss: 1.0773 - val_ner_output_sparse_categorical_accuracy: 0.2399 - val_srl_output_loss: 1.4159 - val_srl_output_sparse_categorical_accuracy: 0.1737 - learning_rate: 3.0000e-04\n",
+ "Epoch 28/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.4468 - ner_output_loss: 1.0679 - ner_output_sparse_categorical_accuracy: 0.2334 - srl_output_loss: 1.3787 - srl_output_sparse_categorical_accuracy: 0.1790 - val_loss: 2.4424 - val_ner_output_loss: 1.0533 - val_ner_output_sparse_categorical_accuracy: 0.2403 - val_srl_output_loss: 1.3891 - val_srl_output_sparse_categorical_accuracy: 0.1772 - learning_rate: 3.0000e-04\n",
+ "Epoch 29/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.4241 - ner_output_loss: 1.0521 - ner_output_sparse_categorical_accuracy: 0.2321 - srl_output_loss: 1.3720 - srl_output_sparse_categorical_accuracy: 0.1788 - val_loss: 2.4031 - val_ner_output_loss: 1.0369 - val_ner_output_sparse_categorical_accuracy: 0.2394 - val_srl_output_loss: 1.3663 - val_srl_output_sparse_categorical_accuracy: 0.1790 - learning_rate: 3.0000e-04\n",
+ "Epoch 30/30\n",
+ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.3343 - ner_output_loss: 1.0166 - ner_output_sparse_categorical_accuracy: 0.2329 - srl_output_loss: 1.3177 - srl_output_sparse_categorical_accuracy: 0.1790 - val_loss: 2.3498 - val_ner_output_loss: 1.0117 - val_ner_output_sparse_categorical_accuracy: 0.2410 - val_srl_output_loss: 1.3381 - val_srl_output_sparse_categorical_accuracy: 0.1836 - learning_rate: 3.0000e-04\n"
]
}
],
@@ -395,8 +493,8 @@
" sample_weight=[m_tr, m_tr], # sama‑persis urutan\n",
" validation_data=(X_te, [ner_te, srl_te], [m_te, m_te]),\n",
" \n",
- " batch_size=2,\n",
- " epochs=10,\n",
+ " batch_size=64,\n",
+ " epochs=30,\n",
" callbacks=callbacks,\n",
" verbose=1,\n",
")\n",
@@ -417,13 +515,13 @@
},
{
"cell_type": "code",
- "execution_count": 555,
+ "execution_count": 9,
"id": "430794b9",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -433,7 +531,7 @@
},
{
"data": {
- "image/png": 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",
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",
"text/plain": [
""
]
@@ -445,82 +543,52 @@
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
- "\n",
"def plot_training_history(history):\n",
- " # Extract data from history\n",
" history_data = history.history\n",
- " epochs = range(1, len(history_data[\"ner_output_sparse_categorical_accuracy\"]) + 1)\n",
+ " epochs = range(1, len(next(iter(history_data.values()))) + 1)\n",
+ "\n",
+ " # Coba deteksi metric secara dinamis agar fleksibel\n",
+ " ner_acc_key = next((k for k in history_data if \"ner_output\" in k and \"accuracy\" in k), None)\n",
+ " srl_acc_key = next((k for k in history_data if \"srl_output\" in k and \"accuracy\" in k), None)\n",
+ " val_ner_acc_key = f\"val_{ner_acc_key}\" if ner_acc_key else None\n",
+ " val_srl_acc_key = f\"val_{srl_acc_key}\" if srl_acc_key else None\n",
"\n",
" # --- Plot Accuracy ---\n",
" plt.figure(figsize=(10, 6))\n",
- " plt.plot(\n",
- " epochs,\n",
- " history_data[\"ner_output_sparse_categorical_accuracy\"],\n",
- " marker=\"o\",\n",
- " label=\"NER Accuracy (Train)\",\n",
- " )\n",
- " plt.plot(\n",
- " epochs,\n",
- " history_data[\"srl_output_sparse_categorical_accuracy\"],\n",
- " marker=\"s\",\n",
- " label=\"SRL Accuracy (Train)\",\n",
- " )\n",
- "\n",
- " if \"val_ner_output_sparse_categorical_accuracy\" in history_data:\n",
- " plt.plot(\n",
- " epochs,\n",
- " history_data[\"val_ner_output_sparse_categorical_accuracy\"],\n",
- " marker=\"o\",\n",
- " linestyle=\"--\",\n",
- " label=\"NER Accuracy (Val)\",\n",
- " )\n",
- " plt.plot(\n",
- " epochs,\n",
- " history_data[\"val_srl_output_sparse_categorical_accuracy\"],\n",
- " marker=\"s\",\n",
- " linestyle=\"--\",\n",
- " label=\"SRL Accuracy (Val)\",\n",
- " )\n",
+ " if ner_acc_key:\n",
+ " plt.plot(epochs, history_data[ner_acc_key], marker=\"o\", label=\"NER Accuracy (Train)\")\n",
+ " if srl_acc_key:\n",
+ " plt.plot(epochs, history_data[srl_acc_key], marker=\"s\", label=\"SRL Accuracy (Train)\")\n",
+ " if val_ner_acc_key in history_data:\n",
+ " plt.plot(epochs, history_data[val_ner_acc_key], marker=\"o\", linestyle=\"--\", label=\"NER Accuracy (Val)\")\n",
+ " if val_srl_acc_key in history_data:\n",
+ " plt.plot(epochs, history_data[val_srl_acc_key], marker=\"s\", linestyle=\"--\", label=\"SRL Accuracy (Val)\")\n",
"\n",
" plt.title(\"Accuracy per Epoch\")\n",
" plt.xlabel(\"Epochs\")\n",
" plt.ylabel(\"Accuracy\")\n",
" plt.legend()\n",
" plt.grid(True)\n",
- " plt.savefig(\"accuracy_plot.png\") # Save the accuracy plot\n",
+ " plt.savefig(\"accuracy_plot.png\")\n",
" plt.show()\n",
"\n",
" # --- Plot Loss ---\n",
" plt.figure(figsize=(10, 6))\n",
- " plt.plot(\n",
- " epochs, history_data[\"ner_output_loss\"], marker=\"o\", label=\"NER Loss (Train)\"\n",
- " )\n",
- " plt.plot(\n",
- " epochs, history_data[\"srl_output_loss\"], marker=\"s\", label=\"SRL Loss (Train)\"\n",
- " )\n",
- "\n",
+ " if \"ner_output_loss\" in history_data:\n",
+ " plt.plot(epochs, history_data[\"ner_output_loss\"], marker=\"o\", label=\"NER Loss (Train)\")\n",
+ " if \"srl_output_loss\" in history_data:\n",
+ " plt.plot(epochs, history_data[\"srl_output_loss\"], marker=\"s\", label=\"SRL Loss (Train)\")\n",
" if \"val_ner_output_loss\" in history_data:\n",
- " plt.plot(\n",
- " epochs,\n",
- " history_data[\"val_ner_output_loss\"],\n",
- " marker=\"o\",\n",
- " linestyle=\"--\",\n",
- " label=\"NER Loss (Val)\",\n",
- " )\n",
- " plt.plot(\n",
- " epochs,\n",
- " history_data[\"val_srl_output_loss\"],\n",
- " marker=\"s\",\n",
- " linestyle=\"--\",\n",
- " label=\"SRL Loss (Val)\",\n",
- " )\n",
+ " plt.plot(epochs, history_data[\"val_ner_output_loss\"], marker=\"o\", linestyle=\"--\", label=\"NER Loss (Val)\")\n",
+ " if \"val_srl_output_loss\" in history_data:\n",
+ " plt.plot(epochs, history_data[\"val_srl_output_loss\"], marker=\"s\", linestyle=\"--\", label=\"SRL Loss (Val)\")\n",
"\n",
" plt.title(\"Loss per Epoch\")\n",
" plt.xlabel(\"Epochs\")\n",
" plt.ylabel(\"Loss\")\n",
" plt.legend()\n",
" plt.grid(True)\n",
- " plt.savefig(\"loss_plot.png\") # Save the loss plot\n",
+ " plt.savefig(\"loss_plot.png\")\n",
" plt.show()\n",
"\n",
"\n",
@@ -529,13 +597,13 @@
},
{
"cell_type": "code",
- "execution_count": 556,
+ "execution_count": 10,
"id": "e690a0e0",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -545,7 +613,7 @@
},
{
"data": {
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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -632,7 +700,7 @@
},
{
"cell_type": "code",
- "execution_count": 557,
+ "execution_count": 11,
"id": "a49f1dfe",
"metadata": {},
"outputs": [
@@ -640,8 +708,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "NER TAG accuracy : 85.12%\n",
- "SRL TAG accuracy : 74.05%\n"
+ "NER TAG accuracy : 70.26%\n",
+ "SRL TAG accuracy : 53.52%\n"
]
}
],
@@ -660,7 +728,7 @@
},
{
"cell_type": "code",
- "execution_count": 558,
+ "execution_count": 12,
"id": "9adad755",
"metadata": {},
"outputs": [
@@ -672,33 +740,22 @@
"[NER] Classification report:\n",
" precision recall f1-score support\n",
"\n",
- " B-DATE 0.94 0.92 0.93 71\n",
- " B-ETH 0.95 0.83 0.89 47\n",
- " B-EVENT 0.00 0.00 0.00 11\n",
- " B-LOC 0.89 0.59 0.71 113\n",
- " B-MAT 0.00 0.00 0.00 20\n",
- " B-MISC 0.00 0.00 0.00 3\n",
- " B-ORG 0.00 0.00 0.00 5\n",
- " B-PER 0.77 0.73 0.75 60\n",
- " B-QUANT 0.00 0.00 0.00 7\n",
- " B-TIME 0.82 0.40 0.54 45\n",
- " B-UNIT 0.00 0.00 0.00 7\n",
- " I-DATE 0.92 0.97 0.94 135\n",
- " I-ETH 0.92 0.86 0.89 51\n",
- " I-EVENT 0.00 0.00 0.00 3\n",
- " I-LOC 0.00 0.00 0.00 9\n",
- " I-MAT 0.00 0.00 0.00 1\n",
- " I-MISC 0.00 0.00 0.00 0\n",
- " I-ORG 0.00 0.00 0.00 3\n",
- " I-PER 0.94 0.79 0.86 43\n",
- " I-QUANT 0.00 0.00 0.00 0\n",
- " I-TIME 0.00 0.00 0.00 9\n",
- " I-UNIT 0.00 0.00 0.00 1\n",
- " O 0.83 0.98 0.90 855\n",
+ " DATE 0.63 0.84 0.72 184\n",
+ " ETH 0.59 0.51 0.54 87\n",
+ " EVENT 0.00 0.00 0.00 14\n",
+ " LOC 0.00 0.00 0.00 112\n",
+ " MAT 0.00 0.00 0.00 32\n",
+ " MISC 0.00 0.00 0.00 3\n",
+ " O 0.72 0.92 0.81 877\n",
+ " ORG 0.00 0.00 0.00 0\n",
+ " PER 0.80 0.38 0.52 102\n",
+ " QUANT 0.00 0.00 0.00 13\n",
+ " TIME 0.00 0.00 0.00 55\n",
+ " UNIT 0.00 0.00 0.00 14\n",
"\n",
- " accuracy 0.85 1499\n",
- " macro avg 0.35 0.31 0.32 1499\n",
- "weighted avg 0.81 0.85 0.82 1499\n",
+ " accuracy 0.70 1493\n",
+ " macro avg 0.23 0.22 0.22 1493\n",
+ "weighted avg 0.59 0.70 0.63 1493\n",
"\n"
]
},
@@ -743,7 +800,7 @@
},
{
"cell_type": "code",
- "execution_count": 559,
+ "execution_count": 13,
"id": "7cd28380",
"metadata": {},
"outputs": [
@@ -755,22 +812,22 @@
"[SRL] Classification report:\n",
" precision recall f1-score support\n",
"\n",
- " ARG0 0.83 0.85 0.84 186\n",
- " ARG1 0.72 0.59 0.65 245\n",
- " ARG2 1.00 0.44 0.61 71\n",
- " ARGM-CAU 0.00 0.00 0.00 1\n",
- " ARGM-DIR 0.00 0.00 0.00 6\n",
- " ARGM-LOC 0.81 0.61 0.70 108\n",
+ " ARG0 0.68 0.75 0.71 195\n",
+ " ARG1 0.43 0.51 0.47 257\n",
+ " ARG2 0.00 0.00 0.00 55\n",
+ " ARGM-CAU 0.00 0.00 0.00 0\n",
+ " ARGM-DIR 0.00 0.00 0.00 11\n",
+ " ARGM-LOC 0.00 0.00 0.00 98\n",
" ARGM-MNR 0.00 0.00 0.00 7\n",
- " ARGM-MOD 0.00 0.00 0.00 8\n",
- " ARGM-NEG 0.00 0.00 0.00 2\n",
- " ARGM-TMP 0.89 0.84 0.87 274\n",
- " O 0.64 0.89 0.75 450\n",
- " V 0.71 0.55 0.62 141\n",
+ " ARGM-MOD 0.00 0.00 0.00 2\n",
+ " ARGM-NEG 0.00 0.00 0.00 0\n",
+ " ARGM-TMP 0.65 0.64 0.65 254\n",
+ " O 0.48 0.67 0.56 469\n",
+ " V 0.63 0.29 0.40 145\n",
"\n",
- " accuracy 0.74 1499\n",
- " macro avg 0.47 0.40 0.42 1499\n",
- "weighted avg 0.75 0.74 0.73 1499\n",
+ " accuracy 0.54 1493\n",
+ " macro avg 0.24 0.24 0.23 1493\n",
+ "weighted avg 0.49 0.54 0.50 1493\n",
"\n"
]
},
@@ -780,9 +837,21 @@
"text": [
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
- "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+ " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+ " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+ " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
+ " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+ " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+ " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+ "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
]
}
@@ -802,7 +871,7 @@
},
{
"cell_type": "code",
- "execution_count": 560,
+ "execution_count": 14,
"id": "333745fd",
"metadata": {},
"outputs": [],
@@ -840,7 +909,7 @@
},
{
"cell_type": "code",
- "execution_count": 561,
+ "execution_count": 15,
"id": "df36e200",
"metadata": {},
"outputs": [],
@@ -887,7 +956,7 @@
},
{
"cell_type": "code",
- "execution_count": 562,
+ "execution_count": 16,
"id": "9127cce0",
"metadata": {},
"outputs": [],
@@ -898,7 +967,7 @@
},
{
"cell_type": "code",
- "execution_count": 563,
+ "execution_count": 17,
"id": "300897b8",
"metadata": {},
"outputs": [],
diff --git a/NER_SRL/loss_plot.png b/NER_SRL/loss_plot.png
index d04bdc2..d5a9752 100644
Binary files a/NER_SRL/loss_plot.png and b/NER_SRL/loss_plot.png differ
diff --git a/NER_SRL/tag2idx_ner.pkl b/NER_SRL/tag2idx_ner.pkl
index f619f58..fd2aa6e 100644
Binary files a/NER_SRL/tag2idx_ner.pkl and b/NER_SRL/tag2idx_ner.pkl differ
diff --git a/NER_SRL/word2idx.pkl b/NER_SRL/word2idx.pkl
index 85715e7..c10dbc9 100644
Binary files a/NER_SRL/word2idx.pkl and b/NER_SRL/word2idx.pkl differ
diff --git a/QC/new_model_lstm_qg.keras b/QC/new_model_lstm_qg.keras
deleted file mode 100644
index ffd0687..0000000
Binary files a/QC/new_model_lstm_qg.keras and /dev/null differ
diff --git a/QC/tokenizers.pkl b/QC/tokenizers.pkl
deleted file mode 100644
index 9338361..0000000
Binary files a/QC/tokenizers.pkl and /dev/null differ
diff --git a/dataset/dev_dataset_qg.json b/dataset/dev_dataset_qg.json
new file mode 100644
index 0000000..566f2bc
--- /dev/null
+++ b/dataset/dev_dataset_qg.json
@@ -0,0 +1,14591 @@
+[
+ {
+ "tokens": [
+ "raden",
+ "ajeng",
+ "kartini",
+ "lahir",
+ "pada",
+ "21",
+ "april",
+ "1879",
+ "di",
+ "jepara"
+ ],
+ "ner": [
+ "B-PER",
+ "I-PER",
+ "I-PER",
+ "V",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "I-DATE",
+ "O",
+ "B-LOC"
+ ],
+ "srl": [
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "V",
+ "O",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O",
+ "ARGM-LOC"
+ ],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["dimana", "kartini", "lahir", "___"],
+ "answer": ["jepara"]
+ },
+ {
+ "type": "true_false",
+ "question": [
+ "kartini",
+ "lahir",
+ "pada",
+ "tanggal",
+ "21",
+ "mei",
+ "1879",
+ "___"
+ ],
+ "options": ["true", "false"],
+ "answer": ["false"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "kerajaan",
+ "majapahit",
+ "berdiri",
+ "pada",
+ "tahun",
+ "1293",
+ "di",
+ "trowulan"
+ ],
+ "ner": ["O", "B-ORG", "V", "O", "O", "B-DATE", "O", "B-LOC"],
+ "srl": ["ARG1", "ARG1", "V", "O", "O", "ARGM-TMP", "O", "ARGM-LOC"],
+ "quiz_posibility": [
+ {
+ "type": "opsi",
+ "question": ["dimana", "kerajaan", "majapahit", "berdiri", "___"],
+ "options": ["trowulan", "singasari", "kuta", "banten"],
+ "answer": ["trowulan"]
+ },
+ {
+ "type": "true_false",
+ "question": [
+ "kerajaan",
+ "majapahit",
+ "berdiri",
+ "pada",
+ "tahun",
+ "1300",
+ "___"
+ ],
+ "options": ["true", "false"],
+ "answer": ["false"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "soekarno",
+ "dan",
+ "mohammad",
+ "hatta",
+ "memproklamasikan",
+ "kemerdekaan",
+ "indonesia",
+ "pada",
+ "17",
+ "agustus",
+ "1945"
+ ],
+ "ner": [
+ "B-PER",
+ "O",
+ "B-PER",
+ "I-PER",
+ "V",
+ "O",
+ "B-LOC",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "I-DATE"
+ ],
+ "srl": [
+ "ARG0",
+ "O",
+ "ARG0",
+ "ARG0",
+ "V",
+ "ARG1",
+ "ARGM-LOC",
+ "O",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP"
+ ],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "pada",
+ "tanggal",
+ "berapa",
+ "kemerdekaan",
+ "indonesia",
+ "diproklamasikan",
+ "___"
+ ],
+ "answer": ["17", "agustus", "1945"]
+ },
+ {
+ "type": "opsi",
+ "question": [
+ "siapa",
+ "yang",
+ "memproklamasikan",
+ "kemerdekaan",
+ "indonesia",
+ "___"
+ ],
+ "options": ["soekarno", "mohammad hatta", "sudirman", "ahmad yani"],
+ "answer": ["soekarno", "mohammad hatta"]
+ }
+ ]
+ },
+ {
+ "tokens": ["bumi", "mengelilingi", "matahari", "selama", "365", "hari"],
+ "ner": ["B-MISC", "O", "B-MISC", "O", "B-DATE", "I-DATE"],
+ "srl": ["ARG1", "V", "ARG2", "O", "ARGM-TMP", "ARGM-TMP"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "berapa",
+ "lama",
+ "bumi",
+ "mengelilingi",
+ "matahari",
+ "___"
+ ],
+ "answer": ["365", "hari"]
+ }
+ ]
+ },
+ {
+ "tokens": ["presiden", "pertama", "indonesia", "adalah", "soekarno"],
+ "ner": ["O", "O", "B-LOC", "O", "B-PER"],
+ "srl": ["ARG1", "ARG1", "ARGM-LOC", "V", "ARG2"],
+ "quiz_posibility": [
+ {
+ "type": "opsi",
+ "question": ["siapa", "presiden", "pertama", "indonesia", "___"],
+ "options": ["soekarno", "suharto", "bj habibie", "gus dur"],
+ "answer": ["soekarno"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "merkurius",
+ "adalah",
+ "planet",
+ "yang",
+ "paling",
+ "dekat",
+ "dengan",
+ "matahari"
+ ],
+ "ner": ["B-LOC", "O", "O", "O", "O", "O", "O", "B-LOC"],
+ "srl": ["ARG1", "O", "O", "O", "ARGM-MNR", "ARGM-MNR", "O", "ARGM-LOC"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "planet",
+ "apa",
+ "yang",
+ "paling",
+ "dekat",
+ "dengan",
+ "matahari",
+ "___"
+ ],
+ "answer": ["merkurius"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "venus",
+ "memiliki",
+ "suhu",
+ "permukaan",
+ "yang",
+ "sangat",
+ "tinggi"
+ ],
+ "ner": ["B-LOC", "V", "O", "O", "O", "O", "O"],
+ "srl": ["ARG0", "V", "ARG1", "ARG1", "O", "ARGM-EXT", "ARGM-EXT"],
+ "quiz_posibility": [
+ {
+ "type": "true_false",
+ "question": ["venus", "memiliki", "suhu", "permukaan", "tinggi", "___"],
+ "options": ["true", "false"],
+ "answer": ["true"]
+ }
+ ]
+ },
+ {
+ "tokens": ["mars", "dikenal", "sebagai", "planet", "merah"],
+ "ner": ["B-LOC", "V", "O", "O", "O"],
+ "srl": ["ARG1", "V", "O", "O", "ARG1"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "planet",
+ "apa",
+ "yang",
+ "dikenal",
+ "sebagai",
+ "planet",
+ "merah",
+ "___"
+ ],
+ "answer": ["mars"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "jupiter",
+ "adalah",
+ "planet",
+ "terbesar",
+ "di",
+ "tata",
+ "surya"
+ ],
+ "ner": ["B-LOC", "O", "O", "O", "O", "O", "O"],
+ "srl": ["ARG1", "O", "O", "ARG1", "O", "ARGM-LOC", "ARGM-LOC"],
+ "quiz_posibility": [
+ {
+ "type": "opsi",
+ "question": [
+ "planet",
+ "apa",
+ "yang",
+ "terbesar",
+ "di",
+ "tata",
+ "surya",
+ "___"
+ ],
+ "options": ["mars", "jupiter", "saturnus", "neptunus"],
+ "answer": ["jupiter"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saturnus", "terkenal", "dengan", "cincin", "yang", "indah"],
+ "ner": ["B-LOC", "V", "O", "O", "O", "O"],
+ "srl": ["ARG1", "V", "O", "ARG1", "O", "ARGM-MNR"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "planet",
+ "apa",
+ "yang",
+ "terkenal",
+ "dengan",
+ "cincin",
+ "indah",
+ "___"
+ ],
+ "answer": ["saturnus"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "uranus",
+ "memiliki",
+ "warna",
+ "biru",
+ "karena",
+ "gas",
+ "metana"
+ ],
+ "ner": ["B-LOC", "V", "O", "O", "O", "O", "O"],
+ "srl": ["ARG0", "V", "ARG1", "ARG1", "O", "ARGM-CAU", "ARGM-CAU"],
+ "quiz_posibility": [
+ {
+ "type": "opsi",
+ "question": ["planet", "apa", "yang", "berwarna", "biru", "___"],
+ "options": ["uranus", "neptunus", "venus", "mars"],
+ "answer": ["uranus"]
+ }
+ ]
+ },
+ {
+ "tokens": ["neptunus", "adalah", "planet", "terjauh", "dari", "matahari"],
+ "ner": ["B-LOC", "O", "O", "O", "O", "B-LOC"],
+ "srl": ["ARG1", "O", "O", "ARG1", "O", "ARGM-LOC"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "planet",
+ "apa",
+ "yang",
+ "paling",
+ "jauh",
+ "dari",
+ "matahari",
+ "___"
+ ],
+ "answer": ["neptunus"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "pluto",
+ "dulu",
+ "dianggap",
+ "sebagai",
+ "planet",
+ "namun",
+ "statusnya",
+ "sudah",
+ "berubah"
+ ],
+ "ner": ["B-LOC", "B-TMP", "V", "O", "O", "O", "O", "B-TMP", "V"],
+ "srl": ["ARG1", "ARGM-TMP", "V", "O", "ARG1", "O", "ARG1", "ARGM-TMP", "V"],
+ "quiz_posibility": [
+ {
+ "type": "true_false",
+ "question": ["apakah", "pluto", "masih", "dianggap", "planet", "___"],
+ "options": ["true", "false"],
+ "answer": ["false"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "soekarno",
+ "membacakan",
+ "teks",
+ "proklamasi",
+ "pada",
+ "17",
+ "agustus",
+ "1945"
+ ],
+ "ner": ["B-PER", "O", "O", "O", "O", "B-DATE", "I-DATE", "I-DATE"],
+ "srl": [
+ "ARG0",
+ "V",
+ "ARG1",
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+ "O",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP"
+ ],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "pada",
+ "tanggal",
+ "berapa",
+ "soekarno",
+ "membacakan",
+ "teks",
+ "proklamasi",
+ "___"
+ ],
+ "answer": ["17", "agustus", "1945"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "pada",
+ "17",
+ "agustus",
+ "1945",
+ "soekarno",
+ "membacakan",
+ "teks",
+ "proklamasi"
+ ],
+ "ner": ["O", "B-DATE", "I-DATE", "I-DATE", "B-PER", "O", "O", "O"],
+ "srl": [
+ "O",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARG0",
+ "V",
+ "ARG1",
+ "ARG1"
+ ],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "kapan",
+ "soekarno",
+ "membacakan",
+ "teks",
+ "proklamasi",
+ "___"
+ ],
+ "answer": ["17", "agustus", "1945"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "teks",
+ "proklamasi",
+ "dibacakan",
+ "oleh",
+ "soekarno",
+ "pada",
+ "17",
+ "agustus",
+ "1945"
+ ],
+ "ner": ["O", "O", "V", "O", "B-PER", "O", "B-DATE", "I-DATE", "I-DATE"],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "O",
+ "ARG0",
+ "O",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP"
+ ],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "sebutkan",
+ "tanggal",
+ "teks",
+ "proklamasi",
+ "dibacakan",
+ "___"
+ ],
+ "answer": ["17", "agustus", "1945"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "teks",
+ "proklamasi",
+ "dibacakan",
+ "pada",
+ "17",
+ "agustus",
+ "1945",
+ "oleh",
+ "soekarno"
+ ],
+ "ner": ["O", "O", "V", "O", "B-DATE", "I-DATE", "I-DATE", "O", "B-PER"],
+ "srl": [
+ "ARG1",
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+ "V",
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+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O",
+ "ARG0"
+ ],
+ "quiz_posibility": [
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+ "answer": ["17", "agustus", "1945"]
+ }
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+ "soekarno"
+ ],
+ "ner": [
+ "O",
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+ "B-DATE",
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+ "V",
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+ "B-PER"
+ ],
+ "srl": [
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+ "ARGM-TMP",
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+ "ARGM-TMP",
+ "V",
+ "ARG1",
+ "ARG1",
+ "O",
+ "ARG0"
+ ],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["kapan", "teks", "proklamasi", "dibacakan", "___"],
+ "answer": ["17", "agustus", "1945"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "andi",
+ "setiawan",
+ "pergi",
+ "ke",
+ "jakarta",
+ "pada",
+ "5",
+ "januari",
+ "2020"
+ ],
+ "ner": ["B-PER", "I-PER", "O", "O", "O", "O", "B-DATE", "I-DATE", "I-DATE"],
+ "srl": [
+ "ARG0",
+ "ARG0",
+ "V",
+ "O",
+ "ARGM-LOC",
+ "O",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP"
+ ],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "sebutkan",
+ "tanggal",
+ "andi",
+ "melakukan",
+ "pergi",
+ "ke",
+ "jakarta",
+ "___"
+ ],
+ "answer": ["5", "januari", "2020"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "berangkat",
+ "ke",
+ "bandung",
+ "ditemani",
+ "oleh",
+ "siti",
+ "maharani",
+ "pada",
+ "12",
+ "februari",
+ "2019"
+ ],
+ "ner": [
+ "O",
+ "O",
+ "B-LOC",
+ "O",
+ "O",
+ "B-PER",
+ "I-PER",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "I-DATE"
+ ],
+ "srl": [
+ "V",
+ "ARGM-LOC",
+ "O",
+ "O",
+ "ARG0",
+ "ARG0",
+ "O",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP"
+ ],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "pada",
+ "tanggal",
+ "berapa",
+ "siti",
+ "berangkat",
+ "ke",
+ "bandung",
+ "___"
+ ],
+ "answer": ["12", "februari", "2019"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "pindah",
+ "ke",
+ "bali",
+ "pada",
+ "23",
+ "maret",
+ "2021",
+ "bersama",
+ "budi",
+ "santoso"
+ ],
+ "ner": [
+ "O",
+ "O",
+ "B-LOC",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "I-DATE",
+ "O",
+ "B-PER",
+ "I-PER"
+ ],
+ "srl": [
+ "V",
+ "ARGM-LOC",
+ "O",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O",
+ "ARG0",
+ "ARG0"
+ ],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "pada",
+ "tanggal",
+ "berapa",
+ "budi",
+ "pindah",
+ "ke",
+ "bali",
+ "___"
+ ],
+ "answer": ["23", "maret", "2021"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "lina",
+ "wati",
+ "liburan",
+ "ke",
+ "medan",
+ "pada",
+ "1",
+ "april",
+ "2022"
+ ],
+ "ner": ["B-PER", "I-PER", "O", "O", "O", "O", "B-DATE", "I-DATE", "I-DATE"],
+ "srl": [
+ "ARG0",
+ "ARG0",
+ "V",
+ "O",
+ "ARGM-LOC",
+ "O",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP"
+ ],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "kapan",
+ "lina",
+ "dan",
+ "wati",
+ "melakukan",
+ "liburan",
+ "ke",
+ "medan",
+ "___"
+ ],
+ "answer": ["1", "april", "2022"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "melanjutkan",
+ "studi",
+ "ke",
+ "surabaya",
+ "ditemani",
+ "oleh",
+ "agus",
+ "wijaya",
+ "pada",
+ "15",
+ "mei",
+ "2023"
+ ],
+ "ner": [
+ "O",
+ "O",
+ "O",
+ "B-LOC",
+ "O",
+ "O",
+ "B-PER",
+ "I-PER",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "I-DATE"
+ ],
+ "srl": [
+ "V",
+ "ARGM-LOC",
+ "O",
+ "O",
+ "ARG0",
+ "ARG0",
+ "O",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP"
+ ],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": [
+ "kapan",
+ "agus",
+ "dan",
+ "wijaya",
+ "melakukan",
+ "melanjutkan",
+ "ke",
+ "surabaya",
+ "___"
+ ],
+ "answer": ["15", "mei", "2023"]
+ }
+ ]
+ },
+ {
+ "tokens": [
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+ }
+ ]
+ },
+ {
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+ "lestari",
+ "pindah",
+ "ke",
+ "makassar",
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+ "maret",
+ "2021"
+ ],
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+ "ARG0",
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+ "ARGM-LOC",
+ "O",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP"
+ ],
+ "quiz_posibility": [
+ {
+ "type": "isian",
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+ "kapan",
+ "aktivitas",
+ "pindah",
+ "ke",
+ "makassar",
+ "dilakukan",
+ "oleh",
+ "maya",
+ "lestari",
+ "___"
+ ],
+ "answer": ["23", "maret", "2021"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "liburan",
+ "ke",
+ "jakarta",
+ "ditemani",
+ "oleh",
+ "roni",
+ "firmansyah",
+ "pada",
+ "1",
+ "april",
+ "2022"
+ ],
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+ "O",
+ "B-LOC",
+ "O",
+ "O",
+ "B-PER",
+ "I-PER",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "I-DATE"
+ ],
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+ "ARGM-TMP",
+ "ARGM-TMP"
+ ],
+ "quiz_posibility": [
+ {
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+ "roni",
+ "dan",
+ "firmansyah",
+ "melakukan",
+ "liburan",
+ "ke",
+ "jakarta",
+ "___"
+ ],
+ "answer": ["1", "april", "2022"]
+ }
+ ]
+ },
+ {
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+ "ke",
+ "bandung",
+ "pada",
+ "15",
+ "mei",
+ "2023",
+ "bersama",
+ "tiara",
+ "amelia"
+ ],
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+ "O",
+ "O",
+ "B-LOC",
+ "O",
+ "B-DATE",
+ "I-DATE",
+ "I-DATE",
+ "O",
+ "B-PER",
+ "I-PER"
+ ],
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+ "ARG0",
+ "ARG0"
+ ],
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+ {
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+ "tanggal",
+ "tiara",
+ "melakukan",
+ "melanjutkan",
+ "ke",
+ "bandung",
+ "___"
+ ],
+ "answer": ["15", "mei", "2023"]
+ }
+ ]
+ },
+ {
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+ "everest",
+ "adalah",
+ "gunung",
+ "tertinggi",
+ "di",
+ "dunia"
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+ "answer": ["gunung", "everest"]
+ }
+ ]
+ },
+ {
+ "tokens": [
+ "fakta",
+ "menyebutkan",
+ "amazon",
+ "merupakan",
+ "sungai",
+ "terpanjang",
+ "di",
+ "dunia"
+ ],
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+ "srl": ["O", "V", "ARG1", "V", "ARG1", "ARG1", "ARG1", "ARG1"],
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+ "answer": ["amazon"]
+ }
+ ]
+ },
+ {
+ "tokens": [
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+ "tentang",
+ "sekitar",
+ "tahun",
+ "2560",
+ "SM",
+ "dibangun",
+ "piramida",
+ "giza"
+ ],
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+ "apa",
+ "yang",
+ "dibangun",
+ "sekitar",
+ "tahun",
+ "2560",
+ "SM",
+ "?"
+ ],
+ "answer": ["piramida", "giza"]
+ }
+ ]
+ },
+ {
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+ "answer": ["benua", "asia"]
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+ ]
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+ "Keajaiban",
+ "dunia",
+ "apa",
+ "yang",
+ "berada",
+ "di",
+ "italia",
+ "?"
+ ],
+ "answer": ["colosseum"]
+ }
+ ]
+ },
+ {
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+ "answer": ["colosseum"]
+ }
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+ },
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+ }
+ ]
+ },
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+ },
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+ "dunia",
+ "apa",
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+ "india",
+ "?"
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+ }
+ ]
+ },
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+ "keajaiban",
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+ }
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+ },
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+ "keajaiban",
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+ }
+ ]
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+ "di",
+ "china",
+ "?"
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+ }
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+ },
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+ "chichen",
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+ "?"
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+ ]
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+ "?"
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+ "dunia",
+ "apa",
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+ "berada",
+ "di",
+ "brasil",
+ "?"
+ ],
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+ ]
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+ "di",
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+ "?"
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+ "?"
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+ "?"
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+ "dunia",
+ "apa",
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+ "berada",
+ "di",
+ "kamodja",
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+ ]
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+ {
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+ "ARGM-MNR",
+ "ARGM-MNR"
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+ "quiz_posibility": [
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+ {
+ "type": "true_false",
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+ "everest",
+ "memiliki",
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+ "9000",
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+ },
+ {
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+ "quiz_posibility": [
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+ ]
+ }
+]
diff --git a/dataset/ner_srl_without_bio.tsv b/dataset/ner_srl_without_bio.tsv
index 2ee2b05..c787c5a 100644
--- a/dataset/ner_srl_without_bio.tsv
+++ b/dataset/ner_srl_without_bio.tsv
@@ -2963,319 +2963,215 @@ Juni DATE ARGM-TMP
2013 DATE ARGM-TMP
. O O
-Suku ETH ARG0
-Mentawai ETH ARG0
-menyelenggarakan O V
-Tradisi O ARG1
-Warisan O ARG1
-pada O O
+Pada O ARGM-TMP
2 DATE ARGM-TMP
Januari DATE ARGM-TMP
2010 DATE ARGM-TMP
-di O O
+, O O
+suku ETH ARG0
+Mentawai ETH ARG0
+menggelar O V
+Tradisi O ARG1
+Warisan O ARG1
+di O ARGM-LOC
Banjarmasin LOC ARGM-LOC
. O O
-Suku ETH ARG0
-Dani ETH ARG0
-merayakan O V
-Upacara O ARG1
-Adat O ARG1
-pada O O
+Pada O ARGM-TMP
21 DATE ARGM-TMP
November DATE ARGM-TMP
2016 DATE ARGM-TMP
-di O O
+, O O
+suku ETH ARG0
+Dani ETH ARG0
+mempersembahkan O V
+Upacara O ARG1
+Adat O ARG1
+di O ARGM-LOC
Bandung LOC ARGM-LOC
. O O
Suku ETH ARG0
Rote ETH ARG0
-mengadakan O V
+menyelenggarakan O V
Upacara O ARG1
Adat O ARG1
-pada O O
+pada O ARGM-TMP
28 DATE ARGM-TMP
September DATE ARGM-TMP
2014 DATE ARGM-TMP
-di O O
+di O ARGM-LOC
Surabaya LOC ARGM-LOC
. O O
-Suku ETH ARG0
-Minangkabau ETH ARG0
-menyelenggarakan O V
Festival O ARG1
Budaya O ARG1
-pada O O
+digelar O V
+oleh O O
+suku ETH ARG0
+Minangkabau ETH ARG0
+pada O ARGM-TMP
16 DATE ARGM-TMP
April DATE ARGM-TMP
2025 DATE ARGM-TMP
-di O O
+di O ARGM-LOC
Ambon LOC ARGM-LOC
. O O
-Suku ETH ARG0
-Dayak ETH ARG0
-merayakan O V
Tradisi O ARG1
Warisan O ARG1
-pada O O
+diperingati O V
+oleh O O
+suku ETH ARG0
+Dayak ETH ARG0
+pada O ARGM-TMP
12 DATE ARGM-TMP
Maret DATE ARGM-TMP
2010 DATE ARGM-TMP
-di O O
+di O ARGM-LOC
Ambon LOC ARGM-LOC
. O O
-Suku ETH ARG0
-Aceh ETH ARG0
-memperingati O V
Pameran O ARG1
Seni O ARG1
-pada O O
+diselenggarakan O V
+oleh O O
+suku ETH ARG0
+Aceh ETH ARG0
+pada O ARGM-TMP
26 DATE ARGM-TMP
April DATE ARGM-TMP
2017 DATE ARGM-TMP
-di O O
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Bandung LOC ARGM-LOC
. O O
-Suku ETH ARG0
-Melayu ETH ARG0
-mengadakan O V
-Ritual O ARG1
-Tradisional O ARG1
-pada O O
+Pada O ARGM-TMP
20 DATE ARGM-TMP
Agustus DATE ARGM-TMP
2014 DATE ARGM-TMP
-di O O
+, O O
+suku ETH ARG0
+Melayu ETH ARG0
+melaksanakan O V
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+Tradisional O ARG1
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Makassar LOC ARGM-LOC
. O O
-Suku ETH ARG0
-Aceh ETH ARG0
-merayakan O V
Hari O ARG1
Jadi O ARG1
-pada O O
+diperingati O V
+oleh O O
+suku ETH ARG0
+Aceh ETH ARG0
+pada O ARGM-TMP
12 DATE ARGM-TMP
Mei DATE ARGM-TMP
2012 DATE ARGM-TMP
-di O O
+di O ARGM-LOC
Padang LOC ARGM-LOC
. O O
-Suku ETH ARG0
-Kaili ETH ARG0
-merayakan O V
-Hari O ARG1
-Jadi O ARG1
-pada O O
+Pada O ARGM-TMP
15 DATE ARGM-TMP
Maret DATE ARGM-TMP
2012 DATE ARGM-TMP
-di O O
+, O O
+suku ETH ARG0
+Kaili ETH ARG0
+melaksanakan O V
+Hari O ARG1
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+di O ARGM-LOC
Banjarmasin LOC ARGM-LOC
. O O
-Suku ETH ARG0
-Muna ETH ARG0
-menggelar O V
Ritual O ARG1
Tradisional O ARG1
-pada O O
+dipersembahkan O V
+oleh O O
+suku ETH ARG0
+Muna ETH ARG0
+pada O ARGM-TMP
24 DATE ARGM-TMP
Mei DATE ARGM-TMP
2021 DATE ARGM-TMP
-di O O
+di O ARGM-LOC
Balikpapan LOC ARGM-LOC
. O O
-Suku ETH ARG0
-Sunda ETH ARG0
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-Adat O ARG1
-pada O O
+Pada O ARGM-TMP
9 DATE ARGM-TMP
November DATE ARGM-TMP
2011 DATE ARGM-TMP
-di O O
+, O O
+suku ETH ARG0
+Sunda ETH ARG0
+melaksanakan O V
+Upacara O ARG1
+Adat O ARG1
+di O ARGM-LOC
Balikpapan LOC ARGM-LOC
. O O
-Suku ETH ARG0
-Dani ETH ARG0
-menggelar O V
-Ritual O ARG1
-Tradisional O ARG1
-pada O O
+Pada O ARGM-TMP
8 DATE ARGM-TMP
Oktober DATE ARGM-TMP
2010 DATE ARGM-TMP
-di O O
+, O O
+suku ETH ARG0
+Dani ETH ARG0
+memperingati O V
+Ritual O ARG1
+Tradisional O ARG1
+di O ARGM-LOC
Pekanbaru LOC ARGM-LOC
. O O
Suku ETH ARG0
Dayak ETH ARG0
Kenyah ETH ARG0
-menggelar O V
+menyelenggarakan O V
Ritual O ARG1
Tradisional O ARG1
-pada O O
+pada O ARGM-TMP
7 DATE ARGM-TMP
September DATE ARGM-TMP
2012 DATE ARGM-TMP
-di O O
+di O ARGM-LOC
Palembang LOC ARGM-LOC
. O O
-Suku ETH ARG0
+Pada O ARGM-TMP
+17 DATE ARGM-TMP
+April DATE ARGM-TMP
+2021 DATE ARGM-TMP
+, O O
+suku ETH ARG0
Nias ETH ARG0
menggelar O V
Ritual O ARG1
Tradisional O ARG1
-pada O O
-17 DATE ARGM-TMP
-April DATE ARGM-TMP
-2021 DATE ARGM-TMP
-di O O
+di O ARGM-LOC
Mataram LOC ARGM-LOC
. O O
-Suku ETH ARG0
-Banjar ETH ARG0
-mengadakan O V
Festival O ARG1
Budaya O ARG1
-pada O O
+diselenggarakan O V
+oleh O O
+suku ETH ARG0
+Banjar ETH ARG0
+pada O ARGM-TMP
9 DATE ARGM-TMP
November DATE ARGM-TMP
2015 DATE ARGM-TMP
-di O O
+di O ARGM-LOC
Balikpapan LOC ARGM-LOC
. O O
-Suku ETH ARG0
-Betawi ETH ARG0
-menggelar O V
-Pameran O ARG1
-Seni O ARG1
-pada O O
-22 DATE ARGM-TMP
-Juli DATE ARGM-TMP
-2020 DATE ARGM-TMP
-di O O
-Makassar LOC ARGM-LOC
-. O O
-
-Suku ETH ARG0
-Dani ETH ARG0
-mengadakan O V
-Upacara O ARG1
-Adat O ARG1
-pada O O
-12 DATE ARGM-TMP
-Mei DATE ARGM-TMP
-2025 DATE ARGM-TMP
-di O O
-Pontianak LOC ARGM-LOC
-. O O
-
-Suku ETH ARG0
-Tolaki ETH ARG0
-mengadakan O V
-Pameran O ARG1
-Seni O ARG1
-pada O O
-12 DATE ARGM-TMP
-Agustus DATE ARGM-TMP
-2025 DATE ARGM-TMP
-di O O
-Denpasar LOC ARGM-LOC
-. O O
-
-Suku ETH ARG0
-Madura ETH ARG0
-mengadakan O V
-Ritual O ARG1
-Tradisional O ARG1
-pada O O
-27 DATE ARGM-TMP
-Maret DATE ARGM-TMP
-2018 DATE ARGM-TMP
-di O O
-Palembang LOC ARGM-LOC
-. O O
-
-Suku ETH ARG0
-Rote ETH ARG0
-merayakan O V
-Tradisi O ARG1
-Warisan O ARG1
-pada O O
-12 DATE ARGM-TMP
-Desember DATE ARGM-TMP
-2020 DATE ARGM-TMP
-di O O
-Makassar LOC ARGM-LOC
-. O O
-
-Suku ETH ARG0
-Sangir ETH ARG0
-memperingati O V
-Tradisi O ARG1
-Warisan O ARG1
-pada O O
-6 DATE ARGM-TMP
-Juni DATE ARGM-TMP
-2016 DATE ARGM-TMP
-di O O
-Jakarta LOC ARGM-LOC
-. O O
-
-Suku ETH ARG0
-Muna ETH ARG0
-mengadakan O V
-Hari O ARG1
-Jadi O ARG1
-pada O O
-20 DATE ARGM-TMP
-Januari DATE ARGM-TMP
-2017 DATE ARGM-TMP
-di O O
-Pontianak LOC ARGM-LOC
-. O O
-
-Suku ETH ARG0
-Gorontalo ETH ARG0
-menggelar O V
-Tradisi O ARG1
-Warisan O ARG1
-pada O O
-13 DATE ARGM-TMP
-Agustus DATE ARGM-TMP
-2022 DATE ARGM-TMP
-di O O
-Medan LOC ARGM-LOC
-. O O
-
-Suku ETH ARG0
-Sasak ETH ARG0
-memperingati O V
-Pameran O ARG1
-Seni O ARG1
-pada O O
-13 DATE ARGM-TMP
-Oktober DATE ARGM-TMP
-2013 DATE ARGM-TMP
-di O O
-Medan LOC ARGM-LOC
-. O O
-
Suku ETH ARG0
Banjar ETH ARG0
menggelar O V
@@ -5238,67 +5134,56 @@ pada O O
Juni DATE ARGM-TMP
1970 DATE ARGM-TMP
-Pada O O
-28 DATE ARGM-TMP
-Oktober DATE ARGM-TMP
-1928 DATE ARGM-TMP
-, O O
-Mohammad PER ARG0
-Yamin PER ARG0
-mengusulkan O V
-Sumpah EVENT ARG1
-Pemuda EVENT ARG1
-. O O
+Mohammad PER ARG0
+Yamin PER ARG0
+mengusulkan O V
+sumpah O ARG1
+pemuda O ARG1
+pada O O
+28 DATE ARGM-TMP
+Oktober DATE ARGM-TMP
+1928 DATE ARGM-TMP
-Pada O O
-tahun DATE ARGM-TMP
-1902 DATE ARGM-TMP
-, O O
-R.A. PER ARG0
-Kartini PER ARG0
-menulis O V
-surat O ARG1
-. O O
+R.A. PER ARG0
+Kartini PER ARG0
+menulis O V
+surat O ARG1
+pada O O
+tahun DATE ARGM-TMP
+1902 DATE ARGM-TMP
-Perang EVENT ARG1
-gerilya EVENT ARG1
-yang O O
-dipimpin O V
-oleh O O
-Jenderal PER ARG0
-Soedirman PER ARG0
-terjadi O V
-pada O O
+Jenderal PER ARG0
+Soedirman PER ARG0
+memimpin O V
+perang O ARG1
+gerilya O ARG1
+pada O O
Desember DATE ARGM-TMP
-1948 DATE ARGM-TMP
-. O O
+1948 DATE ARGM-TMP
-Pada O O
-17 DATE ARGM-TMP
-Agustus DATE ARGM-TMP
-1945 DATE ARGM-TMP
-, O O
-Presiden PER ARG0
-Soekarno PER ARG0
-dan O O
-Mohammad PER ARG0
-Hatta PER ARG0
-membacakan O V
-proklamasi EVENT ARG1
-. O O
+Presiden PER ARG0
+Soekarno PER ARG0
+dan O O
+Mohammad PER ARG0
+Hatta PER ARG0
+membacakan O V
+proklamasi O ARG1
+pada O O
+17 DATE ARGM-TMP
+Agustus DATE ARGM-TMP
+1945 DATE ARGM-TMP
-Jenderal PER ARG0
-A.H. PER ARG0
-Nasution PER ARG0
-selamat O V
-dari O O
-percobaan EVENT ARG1
-pembunuhan EVENT ARG1
-pada O O
-1 DATE ARGM-TMP
-Oktober DATE ARGM-TMP
-1965 DATE ARGM-TMP
-. O O
+Jenderal PER ARG0
+A.H. PER ARG0
+Nasution PER ARG0
+selamat O V
+dari O O
+percobaan O ARG1
+pembunuhan O ARG1
+pada O O
+1 DATE ARGM-TMP
+Oktober DATE ARGM-TMP
+1965 DATE ARGM-TMP
Sutomo PER ARG0
atau O O
@@ -5818,10 +5703,10 @@ tadi TIME ARGM-TMP
malam TIME ARGM-TMP
hingga O O
membuat O V
-beberapa O ARG1
+beberapa QUANT ARG1
jalan O ARG1
tergenang O V
-air O ARG1
+air MAT ARG1
Proyek O ARG1
itu O ARG1
@@ -5863,7 +5748,6 @@ berjuang O V
mempertahankan O V
kemerdekaan O ARG1
-
Lusa TIME ARGM-TMP
pagi TIME ARGM-TMP
rombongan O ARG0
@@ -5900,7 +5784,7 @@ masih O ARGM-MOD
sepi O ARG1
dan O O
belum O ARGM-NEG
-banyak O ARG1
+banyak QUANT ARG1
penduduk O ARG1
Festival EVENT ARG1
@@ -5946,106 +5830,47 @@ hari TIME ARGM-TMP
Petani O ARG0
mulai O ARGM-TMP
panen O V
-padi O ARG1
+padi MAT ARG1
ketika O O
musim TIME ARGM-TMP
kemarau TIME ARGM-TMP
tiba O V
-Tadi TIME ARGM-TMP
-pagi TIME ARGM-TMP
-terjadi O V
-hujan O ARG1
-deras O ARG1
-di O O
-daerah O ARGM-LOC
-pegunungan LOC ARGM-LOC
-
-Pada O O
-siang TIME ARGM-TMP
-hari TIME ARGM-TMP
-aktivitas O ARG1
-perdagangan O ARG1
-di O O
-pasar LOC ARGM-LOC
-semakin O ARGM-MNR
-ramai O ARG1
-
-Menjelang TIME ARGM-TMP
-sore TIME ARGM-TMP
-kabut O ARG1
-mulai O ARGM-TMP
-turun O V
-di O O
-kawasan O ARGM-LOC
-perbukitan LOC ARGM-LOC
-
-Pada O O
-malam TIME ARGM-TMP
-hari TIME ARGM-TMP
-suhu O ARG1
-udara O ARG1
-di O O
-pegunungan LOC ARGM-LOC
-turun O V
-drastis O ARGM-MNR
-
-Gempa O ARG1
-bumi O ARG1
-terjadi O V
-pada O O
-dini TIME ARGM-TMP
-hari TIME ARGM-TMP
-saat O O
-warga O ARG0
-masih O ARGM-MOD
-terlelap O V
-
-Hari TIME ARGM-TMP
-ini TIME ARGM-TMP
-suhu O ARG1
-di O O
-Jakarta LOC ARGM-LOC
-mencapai O V
-34 O ARG1
-derajat O ARG1
-Celcius O ARG1
-
-Tadi B-TIME ARGM-TMP
-pagi I-TIME ARGM-TMP
+Tadi TIME ARGM-TMP
+pagi TIME ARGM-TMP
terjadi O V
hujan O ARG1
deras O ARG1
di O O
daerah O ARGM-LOC
-pegunungan B-LOC ARGM-LOC
+pegunungan LOC ARGM-LOC
Pada O O
-siang B-TIME ARGM-TMP
-hari I-TIME ARGM-TMP
+siang TIME ARGM-TMP
+hari TIME ARGM-TMP
aktivitas O ARG1
perdagangan O ARG1
di O O
-pasar B-LOC ARGM-LOC
+pasar LOC ARGM-LOC
semakin O ARGM-MNR
ramai O ARG1
-Menjelang B-TIME ARGM-TMP
-sore I-TIME ARGM-TMP
+Menjelang TIME ARGM-TMP
+sore TIME ARGM-TMP
kabut O ARG1
mulai O ARGM-TMP
turun O V
di O O
kawasan O ARGM-LOC
-perbukitan B-LOC ARGM-LOC
+perbukitan LOC ARGM-LOC
Pada O O
-malam B-TIME ARGM-TMP
-hari I-TIME ARGM-TMP
+malam TIME ARGM-TMP
+hari TIME ARGM-TMP
suhu O ARG1
udara O ARG1
di O O
-pegunungan B-LOC ARGM-LOC
+pegunungan LOC ARGM-LOC
turun O V
drastis O ARGM-MNR
@@ -6053,19 +5878,2187 @@ Gempa O ARG1
bumi O ARG1
terjadi O V
pada O O
-dini B-TIME ARGM-TMP
-hari I-TIME ARGM-TMP
+dini TIME ARGM-TMP
+hari TIME ARGM-TMP
saat O O
warga O ARG0
masih O ARGM-MOD
terlelap O V
-Hari B-TIME ARGM-TMP
-ini I-TIME ARGM-TMP
+Hari TIME ARGM-TMP
+ini TIME ARGM-TMP
suhu O ARG1
di O O
-Jakarta B-LOC ARGM-LOC
+Jakarta LOC ARGM-LOC
mencapai O V
-34 B-QUANT ARG1
-derajat B-UNIT ARG1
-Celcius I-UNIT ARG1
+34 QUANT ARG1
+derajat UNIT ARG1
+Celcius UNIT ARG1
+
+Kereta O ARG1
+pertama O ARG1
+berangkat O V
+dari O O
+stasiun LOC ARGM-LOC
+pusat LOC ARGM-LOC
+pada O O
+jam TIME ARGM-TMP
+07.00 TIME ARGM-TMP
+
+Rapat EVENT ARG1
+dimulai O V
+pukul TIME ARGM-TMP
+12.30 TIME ARGM-TMP
+di O O
+ruang LOC ARGM-LOC
+aula LOC ARGM-LOC
+utama LOC ARGM-LOC
+
+Acara O ARG1
+pembukaan O ARG1
+akan O ARGM-MOD
+dimulai O V
+15 TIME ARGM-TMP
+menit TIME ARGM-TMP
+lagi O ARGM-TMP
+
+Perjalanan O ARG1
+ke O O
+lokasi LOC ARGM-LOC
+wisata LOC ARGM-LOC
+memakan O V
+waktu O ARG1
+setengah TIME ARGM-TMP
+jam TIME ARGM-TMP
+
+Tiga TIME ARGM-TMP
+hari TIME ARGM-TMP
+yang TIME ARGM-TMP
+lalu TIME ARGM-TMP
+terjadi O V
+letusan EVENT ARG1
+gunung EVENT ARGM-LOC
+api EVENT ARGM-LOC
+di O O
+wilayah O ARGM-LOC
+timur LOC ARGM-LOC
+
+Surabaya LOC ARG1
+adalah O V
+tempat O ARG2
+yang O O
+dituju O V
+Dani PER ARG0
+. O O
+
+Malang LOC ARG1
+menjadi O V
+kota O ARG2
+tujuan O ARG2
+perjalanan O ARG2
+Dani PER ARG0
+. O O
+
+Dani PER ARG0
+berangkat O V
+menuju O O
+Bandung LOC ARGM-DIR
+. O O
+
+Yogyakarta LOC ARG1
+menjadi O V
+destinasi O ARG2
+pilihan O ARG2
+Dani PER ARG0
+. O O
+
+Semarang LOC ARG1
+adalah O V
+lokasi O ARG2
+yang O O
+ingin O V
+dikunjungi O V
+Dani PER ARG0
+. O O
+
+Dani PER ARG0
+memutuskan O V
+untuk O O
+pergi O V
+ke O O
+Nganjuk LOC ARGM-DIR
+. O O
+
+Denpasar LOC ARG1
+menjadi O V
+arah O ARG2
+perjalanan O ARG2
+Dani PER ARG0
+. O O
+
+Dani PER ARG0
+menetapkan O V
+Balikpapan LOC ARG1
+sebagai O O
+tujuannya O ARG2
+. O O
+
+Pontianak LOC ARG1
+adalah O V
+destinasi O ARG2
+yang O O
+ditetapkan O V
+Dani PER ARG0
+. O O
+
+Dani PER ARG0
+dalam O ARGM-LOC
+perjalanannya O ARGM-LOC
+menuju O O
+Makassar LOC ARGM-DIR
+. O O
+
+Rangkaian O ARG1
+Percobaan EVENT ARG1
+Pengukuran O ARG1
+Volume O ARG1
+Pernapasan O ARG1
+dengan O O
+Botol MAT ARG2
+Air MAT ARG2
+Mineral MAT ARG2
+Terisi O V
+Udara MAT ARG2
+dengan O O
+Volume O ARG1
+1.500 QUANT ARG1
+mL UNIT ARG1
+. O O
+
+Kondisi O ARG1
+atmosfer O ARG1
+Bumi LOC ARGM-LOC
+yang O O
+mengalami O V
+peningkatan O ARG1
+suhu O ARG1
+hingga O O
+1,5 QUANT ARGM-TMP
+°C UNIT ARGM-TMP
+dapat O ARGM-MOD
+mempengaruhi O V
+ekosistem O ARG2
+global O ARG2
+. O O
+
+Jarak O ARG1
+bayangan O ARG1
+yang O O
+terbentuk O V
+pada O O
+percobaan EVENT ARGM-MNR
+cermin MAT ARG2
+cembung O ARG2
+adalah O O
+25 QUANT ARG2
+cm UNIT ARG2
+saat O O
+jarak O ARG1
+benda O ARG1
+15 QUANT ARGM-TMP
+cm UNIT ARGM-TMP
+. O O
+
+Suhu O ARG1
+air MAT ARG1
+dalam O O
+gelas MAT ARGM-LOC
+diukur O V
+menggunakan O O
+termometer MAT ARGM-MNR
+dan O O
+didapatkan O V
+suhu O ARG1
+sebesar O O
+75 QUANT ARG2
+°C UNIT ARG2
+. O O
+
+Seekor O O
+kucing O ARG0
+memiliki O V
+massa O ARG1
+tubuh O ARG1
+4 QUANT ARG1
+kg UNIT ARG1
+, O O
+dan O O
+saat O O
+melompat O V
+mampu O ARGM-MOD
+mencapai O V
+ketinggian O ARG2
+2 QUANT ARG2
+meter UNIT ARG2
+. O O
+
+Besi MAT ARG1
+Merupakan O V
+Bahan O ARG2
+Feromagnetik O ARG2
+. O O
+
+Pipa MAT ARG1
+Besi MAT ARG1
+Digunakan O V
+dalam O O
+Instalasi ORG ARGM-LOC
+Air MAT ARGM-LOC
+Bersih O ARGM-LOC
+dan O O
+Limbah MAT ARGM-LOC
+. O O
+
+Teknisi O ARG0
+Menguji O V
+Kebocoran O ARG1
+pada O O
+Pipa MAT ARG2
+Besi MAT ARG2
+Menggunakan O O
+Alat O ARGM-MNR
+Uji MAT ARGM-MNR
+Ultrasonik MAT ARGM-MNR
+. O O
+
+Pipa MAT ARG1
+Besi MAT ARG1
+Digunakan O V
+dalam O O
+Instalasi O ARGM-CAU
+Air MAT ARGM-CAU
+Bersih O ARGM-CAU
+dan O O
+Limbah MAT ARGM-CAU
+. O O
+
+Besi MAT ARG1
+dapat O ARGM-MOD
+dijadikan O V
+magnet O ARG2
+dengan O O
+cara O O
+menggosok O O
+. O O
+
+Besi MAT ARG1
+digosok O V
+dengan O O
+arah O ARGM-MNR
+yang O O
+tetap O ARGM-MNR
+, O O
+agar O O
+magnet O ARG1
+elementer O ARG1
+dapat O ARGM-MOD
+diatur O V
+untuk O O
+menuju O V
+ke O O
+satu O O
+arah O ARGM-DIR
+saja O ARGM-DIR
+. O O
+
+Magnet O ARG1
+juga O O
+dapat O ARGM-MOD
+dibuat O V
+dengan O O
+cara O O
+meliliti O V
+besi MAT ARG2
+atau O O
+baja MAT ARG2
+dengan O O
+kawat MAT ARGM-MNR
+penghantar O ARGM-MNR
+yang O O
+dialiri O V
+arus O ARG2
+DC O O
+. O O
+
+Pemasangan O ARG1
+jembatan O ARG1
+umumnya O ARGM-MOD
+dibuat O V
+dari O O
+besi MAT ARG2
+baja MAT ARG2
+yang O O
+saling O ARGM-MNR
+disambungkan O V
+satu O O
+dengan O O
+lainnya O O
+. O O
+
+Inti O ARG1
+besi MAT ARG1
+digunakan O V
+dengan O O
+tujuan O O
+untuk O O
+memperkuat O V
+medan O ARG1
+magnet O ARG1
+yang O O
+dihasilkan O V
+dalam O O
+transformator O ARGM-LOC
+. O O
+
+Massa O ARG1
+tubuhmu O ARG1
+52 QUANT ARG2
+kg UNIT ARG2
+, O O
+massa O ARG1
+seekor O O
+kelinci O ARG1
+3 QUANT ARG2
+kg UNIT ARG2
+, O O
+massa O ARG1
+sekantong O O
+gula MAT ARG1
+1 QUANT ARG2
+kg UNIT ARG2
+. O O
+
+1 QUANT ARG1
+kilometer UNIT ARG1
+( O O
+km UNIT O
+) O O
+= O O
+1.000 QUANT ARG2
+meter UNIT ARG2
+( O O
+m UNIT O
+) O O
+. O O
+
+Volume O ARG1
+air MAT ARG1
+dengan O O
+gelas MAT ARGM-MNR
+ukur O ARGM-MNR
+di O O
+atas O ARGM-LOC
+memiliki O V
+satuan O ARG2
+mL UNIT ARG2
+, O O
+kependekan O O
+dari O O
+mililiter UNIT ARG2
+. O O
+
+Jam O ARG1
+atom O ARG1
+Cesium MAT ARG1
+bergetar O V
+9.192.631.770 QUANT ARG2
+kali O ARG2
+untuk O O
+mendefinisikan O V
+1 QUANT ARG2
+sekon UNIT ARG2
+( O O
+detik O O
+) O O
+. O O
+
+Massa O ARG1
+jenis O ARG1
+air MAT ARG1
+adalah O V
+1 QUANT ARG2
+g/cm³ UNIT ARG2
+. O O
+
+Waktu O ARG1
+perjalanan O ARG1
+diukur O V
+sejak O ARGM-TMP
+mulai O V
+bergerak O ARG1
+sampai O O
+dengan O O
+akhir O ARGM-TMP
+gerak O ARG1
+( O O
+berhenti O O
+) O O
+, O O
+biasanya O ARGM-MOD
+menggunakan O V
+satuan O ARG2
+detik UNIT ARG2
+. O O
+
+Panjang O ARG1
+meja O ARG1
+adalah O V
+120 QUANT ARG2
+cm UNIT ARG2
+. O O
+
+Dalam O O
+waktu O ARGM-TMP
+10 TIME ARGM-TMP
+hari TIME ARGM-TMP
+, O O
+tingginya O ARG1
+menjadi O V
+60 QUANT ARG2
+cm UNIT ARG2
+. O O
+
+Satu QUANT ARG1
+kilogram UNIT ARG1
+standar O ARG1
+yang O O
+disimpan O V
+di O O
+Sevres LOC ARGM-LOC
+, O O
+Paris LOC ARGM-LOC
+, O O
+Prancis LOC ARGM-LOC
+. O O
+
+1 QUANT ARG1
+kg UNIT ARG1
+= O O
+1.000 QUANT ARG2
+g UNIT ARG2
+, O O
+1 QUANT ARG1
+g UNIT ARG1
+= O O
+1.000 QUANT ARG2
+mg UNIT ARG2
+. O O
+
+1 QUANT ARG1
+menit UNIT ARG1
+= O O
+60 QUANT ARG2
+sekon UNIT ARG2
+, O O
+1 QUANT ARG1
+jam UNIT ARG1
+= O O
+60 QUANT ARG2
+menit UNIT ARG2
+. O O
+
+1 QUANT ARG1
+kilometer UNIT ARG1
+( O O
+km O O
+) O O
+= O O
+1.000 QUANT ARG2
+meter UNIT ARG2
+( O O
+m O O
+) O O
+. O O
+
+Bakteri O ARG0
+memiliki O V
+panjang O ARG1
+sampai O O
+dengan O O
+10 QUANT ARG2
+µm UNIT ARG2
+, O O
+virus O ARG0
+sampai O O
+100 QUANT ARG2
+nm UNIT ARG2
+. O O
+
+Jarak O ARG1
+Bumi LOC ARG1
+dan O O
+Pluto LOC ARG1
+adalah O V
+5.900 QUANT ARG2
+juta O O
+km UNIT ARG2
+. O O
+
+Volume O ARG1
+logam MAT ARG1
+= O O
+100 QUANT ARG2
+mL UNIT ARG2
+– O O
+60 QUANT ARG2
+mL UNIT ARG2
+= O O
+40 QUANT ARG2
+mL UNIT ARG2
+= O O
+40 QUANT ARG2
+cm³ UNIT ARG2
+. O O
+
+Contoh O O
+pengukuran O O
+: O O
+panjang O ARG1
+meja O ARG1
+120 QUANT ARG2
+cm UNIT ARG2
+. O O
+
+Kecepatan O ARG1
+cahaya O ARG1
+adalah O V
+299.792.458 QUANT ARG2
+m/s UNIT ARG2
+. O O
+
+Rel O ARG1
+kereta O ARG1
+api O ARG1
+dapat O ARGM-MOD
+melengkung O V
+akibat O O
+pemuaian O ARGM-CAU
+, O O
+panjangnya O ARG1
+bertambah O V
+0,000009 QUANT ARG2
+meter UNIT ARG2
+untuk O O
+setiap O O
+kenaikan O ARGM-CAU
+1 QUANT ARGM-CAU
+°C UNIT ARGM-CAU
+. O O
+
+Pembacaan O ARG1
+teks O ARG1
+proklamasi EVENT ARG1
+kemerdekaan EVENT ARG1
+Indonesia LOC ARG1
+tanggal O O
+17 DATE ARGM-TMP
+Agustus DATE ARGM-TMP
+1945 DATE ARGM-TMP
+dilakukan O V
+oleh O O
+Ir. O O
+Soekarno PER ARG0
+di O O
+Jalan LOC ARGM-LOC
+Pegangsaan LOC ARGM-LOC
+Timur LOC ARGM-LOC
+No. O O
+56 O O
+, O O
+Jakarta LOC ARGM-LOC
+. O O
+
+Peristiwa O ARG1
+Sumpah EVENT ARG1
+Pemuda EVENT ARG1
+merupakan O V
+klimaks O ARG2
+dari O O
+perjuangan O ARGM-CAU
+untuk O O
+mempersatukan O V
+seluruh O O
+bangsa O ARG2
+menuju O O
+cita-cita O ARG2
+kemerdekaan EVENT ARG2
+Indonesia LOC ARG2
+. O O
+
+Organisasi O ARG1
+Budi ORG ARG1
+Utomo ORG ARG1
+didirikan O V
+pada O O
+tanggal O O
+20 DATE ARGM-TMP
+Mei DATE ARGM-TMP
+1908 DATE ARGM-TMP
+sebagai O O
+tonggak O ARG2
+awal O O
+kebangkitan O ARG2
+nasional O ARG2
+Indonesia LOC ARG2
+. O O
+
+Pada O O
+masa O O
+penjajahan EVENT ARGM-TMP
+, O O
+rakyat O ARG0
+Indonesia LOC ARG0
+hidup O V
+menderita O V
+akibat O O
+kebijakan O ARGM-CAU
+monopoli EVENT ARGM-CAU
+, O O
+tanam EVENT ARGM-CAU
+paksa EVENT ARGM-CAU
+, O O
+beban O ARGM-CAU
+pajak O ARGM-CAU
+, O O
+dan O O
+kerja EVENT ARGM-CAU
+rodi EVENT ARGM-CAU
+. O O
+
+Pendidikan O ARG1
+di O O
+Indonesia LOC ARGM-LOC
+setelah O O
+merdeka EVENT ARGM-TMP
+terbagi O V
+atas O O
+pendidikan O ARG2
+rendah O ARG2
+, O O
+menengah O ARG2
+pertama O ARG2
+, O O
+menengah O ARG2
+atas O ARG2
+, O O
+dan O O
+pendidikan O ARG2
+tinggi O ARG2
+. O O
+
+Diplomasi EVENT ARG1
+Beras EVENT ARG1
+yang O O
+dilakukan O V
+pada O O
+tahun O O
+1946 DATE ARGM-TMP
+berhasil O ARGM-MOD
+menjadikan O V
+Indonesia LOC ARG1
+semakin O ARGM-MOD
+mendapat O V
+simpati O ARG2
+dunia O ARGM-LOC
+internasional O ARGM-LOC
+. O O
+
+Politik EVENT ARG1
+Etis EVENT ARG1
+berdampak O V
+positif O ARGM-MNR
+untuk O O
+jangka O O
+panjang O ARGM-TMP
+bagi O O
+bangsa ETH ARG1
+Indonesia ETH ARG1
+. O O
+
+Pada O O
+bidang O O
+pendidikan O ARGM-LOC
+, O O
+politik EVENT ARG1
+etis EVENT ARG1
+melahirkan O V
+golongan O ARG2
+terpelajar O ARG2
+seperti O O
+Sutomo PER ARG2
+dan O O
+Wahidin PER ARG2
+Soedirohusodo PER ARG2
+yang O O
+kemudian O ARGM-TMP
+membentuk O V
+organisasi O ARG2
+Budi ORG ARG2
+Utomo ORG ARG2
+, O O
+Sarikat ORG ARG2
+Islam ORG ARG2
+, O O
+hingga O O
+Perhimpunan ORG ARG2
+Indonesia ORG ARG2
+. O O
+
+Konferensi EVENT ARG1
+Meja EVENT ARG1
+Bundar EVENT ARG1
+( O O
+KMB EVENT O
+) O O
+diadakan O V
+di O O
+Den LOC ARGM-LOC
+Haag LOC ARGM-LOC
+, O O
+Belanda LOC ARGM-LOC
+, O O
+dari O O
+23 DATE ARGM-TMP
+Agustus DATE ARGM-TMP
+sampai O O
+2 DATE ARGM-TMP
+November DATE ARGM-TMP
+1949 DATE ARGM-TMP
+. O O
+KMB EVENT ARG1
+menjadi O V
+titik O ARG2
+terang O ARG2
+bagi O O
+bangsa ETH ARG1
+Indonesia ETH ARG1
+untuk O O
+mempertahankan O V
+kemerdekaan EVENT ARG2
+. O O
+
+Perang EVENT ARG1
+Gerilya EVENT ARG1
+yang O O
+dipimpin O V
+oleh O O
+Jenderal O O
+Sudirman PER ARG0
+berhasil O ARGM-MOD
+mematahkan O V
+propaganda O ARG2
+Belanda LOC ARG2
+yang O O
+menyatakan O V
+pemerintah O ARG1
+Indonesia LOC ARG1
+telah O ARGM-TMP
+tiada O ARGM-NEG
+. O O
+
+Perjanjian EVENT ARG1
+Roem-Royen EVENT ARG1
+dilaksanakan O V
+di O O
+Hotel LOC ARGM-LOC
+Des LOC ARGM-LOC
+Indes LOC ARGM-LOC
+Jakarta LOC ARGM-LOC
+pada O O
+14 DATE ARGM-TMP
+April DATE ARGM-TMP
+1949 DATE ARGM-TMP
+. O O
+Hasilnya O ARG1
+pemerintah O ARG1
+Republik LOC ARG1
+Indonesia LOC ARG1
+kembali O V
+ke O O
+Yogyakarta LOC ARGM-LOC
+. O O
+
+Perang EVENT ARG1
+Paderi EVENT ARG1
+di O O
+Sumatra LOC ARGM-LOC
+Barat LOC ARGM-LOC
+( O O
+1821 DATE ARGM-TMP
+– O O
+1838 DATE ARGM-TMP
+) O O
+dipimpin O V
+oleh O O
+Tuanku PER ARG0
+Imam PER ARG0
+Bonjol PER ARG0
+melawan O V
+dominasi O ARG2
+Belanda ETH ARG2
+. O O
+
+Pada O O
+7 DATE ARGM-TMP
+September DATE ARGM-TMP
+1944 DATE ARGM-TMP
+, O O
+Perdana O O
+Menteri O O
+Koiso PER ARG0
+menjanjikan O V
+kemerdekaan EVENT ARG2
+kepada O O
+Indonesia LOC ARG2
+untuk O O
+menarik O V
+simpati O ARG2
+rakyat O ARG2
+, O O
+yang O O
+kemudian O ARGM-TMP
+menjadi O V
+awal O ARG2
+pembentukan O ARG2
+BPUPKI ORG ARG2
+. O O
+
+Organisasi O ARG1
+Pergerakan ORG ARG1
+Nasional ORG ARG1
+Indonesia ORG ARG1
+Muda ORG ARG1
+dibentuk O V
+untuk O O
+meningkatkan O V
+semangat O ARG2
+persatuan O ARG2
+, O O
+yang O O
+akhirnya O ARGM-TMP
+melahirkan O V
+Sumpah EVENT ARG2
+Pemuda EVENT ARG2
+28 DATE ARGM-TMP
+Oktober DATE ARGM-TMP
+1928 DATE ARGM-TMP
+. O O
+
+Peristiwa O ARG1
+Bandung EVENT ARG1
+Lautan EVENT ARG1
+Api EVENT ARG1
+menjadi O V
+simbol O ARG2
+perlawanan O ARG2
+rakyat O ARG0
+Indonesia LOC ARG0
+terhadap O O
+penjajah O ARG2
+dengan O O
+membumihanguskan O V
+kota O ARG2
+Bandung LOC ARG2
+agar O O
+tidak O ARGM-NEG
+dikuasai O V
+Belanda LOC ARG2
+. O O
+
+Perjanjian EVENT ARG1
+Linggajati EVENT ARG1
+pada O O
+10 DATE ARGM-TMP
+November DATE ARGM-TMP
+1946 DATE ARGM-TMP
+menjadi O V
+kesepakatan O ARG2
+awal O O
+pengakuan O ARG2
+kedaulatan O ARG2
+Indonesia LOC ARG2
+meskipun O O
+menimbulkan O V
+pro O ARG2
+dan O O
+kontra O ARG2
+di O O
+kalangan O O
+rakyat O ARG2
+. O O
+
+Ir. O O
+Soekarno PER ARG0
+mendirikan O V
+Partai ORG ARG1
+Nasional ORG ARG1
+Indonesia ORG ARG1
+( O O
+PNI ORG ARG1
+) O O
+pada O O
+4 DATE ARGM-TMP
+Juli DATE ARGM-TMP
+1927 DATE ARGM-TMP
+yang O O
+bersifat O V
+revolusioner O ARG2
+dan O O
+menjadi O V
+tonggak O ARG2
+pergerakan O ARG2
+nasional O ARG2
+. O O
+
+Peristiwa O ARG1
+meletusnya O V
+Gunung LOC ARG1
+Krakatau LOC ARG1
+pada O O
+tahun O O
+1883 DATE ARGM-TMP
+berdampak O V
+besar O ARGM-MNR
+pada O O
+perjuangan O ARG2
+rakyat O ARG2
+Banten LOC ARG2
+tahun O O
+1888 DATE ARGM-TMP
+melawan O V
+pemerintah O ARG2
+kolonial O O
+Hindia LOC ARGM-LOC
+Belanda LOC ARGM-LOC
+. O O
+
+Kongres EVENT ARG1
+GAPI EVENT ARG1
+tahun O O
+1939 DATE ARGM-TMP
+menjadi O V
+tonggak O ARG2
+penting O ARG2
+dalam O O
+penguatan O ARG2
+jati O ARG2
+diri O ARG2
+keindonesiaan O ARG2
+sebelum O O
+kemerdekaan EVENT ARGM-TMP
+. O O
+
+Besok TIME ARGM-TMP
+pagi TIME ARGM-TMP
+, O O
+Andi PER ARG0
+berencana O V
+jogging O ARG1
+di O O
+taman LOC ARGM-LOC
+kota LOC ARGM-LOC
+. O O
+
+Pertemuan O ARG1
+itu O ARG1
+akan O ARGM-MOD
+diadakan O V
+pukul O O
+14.30 TIME ARGM-TMP
+di O O
+ruang LOC ARGM-LOC
+rapat LOC ARGM-LOC
+utama LOC ARGM-LOC
+. O O
+
+Musim O ARG1
+hujan O ARG1
+diperkirakan O V
+akan O ARGM-MOD
+tiba O V
+lebih O ARGM-MNR
+awal O ARGM-TMP
+tahun TIME ARGM-TMP
+ini TIME ARGM-TMP
+. O O
+
+Dia O ARG0
+baru O ARGM-TMP
+saja O ARGM-TMP
+tiba O V
+di O O
+stasiun LOC ARGM-LOC
+tadi TIME ARGM-TMP
+malam TIME ARGM-TMP
+. O O
+
+Tiga QUANT ARGM-TMP
+hari TIME ARGM-TMP
+yang O O
+lalu O ARGM-TMP
+, O O
+kami O ARG0
+mengunjungi O V
+museum LOC ARG1
+sejarah LOC ARG1
+nasional LOC ARG1
+. O O
+
+Ayah O ARG0
+biasanya O ARGM-MOD
+membaca O V
+koran O ARG1
+di O O
+teras LOC ARGM-LOC
+setiap O ARGM-TMP
+sore TIME ARGM-TMP
+. O O
+
+Kami O ARG0
+akan O ARGM-MOD
+berangkat O V
+ke O O
+Bali LOC ARGM-DIR
+dua QUANT ARGM-TMP
+minggu TIME ARGM-TMP
+ke O O
+depan O ARGM-TMP
+. O O
+
+Pada O O
+abad O O
+ke-19 DATE ARGM-TMP
+, O O
+teknologi O ARG1
+kereta MAT ARG1
+api O ARG1
+mulai O ARGM-MOD
+berkembang O V
+pesat O ARGM-MNR
+di O O
+Eropa LOC ARGM-LOC
+. O O
+
+Mereka O ARG0
+sedang O ARGM-MOD
+menunggu O V
+hasil O ARG1
+ujian O ARG1
+yang O O
+akan O ARGM-MOD
+diumumkan O V
+nanti TIME ARGM-TMP
+malam TIME ARGM-TMP
+. O O
+
+Mereka O ARG0
+sedang O ARGM-MOD
+menunggu O V
+hasil O ARG1
+ujian O ARG1
+yang O O
+akan O ARGM-MOD
+diumumkan O V
+nanti TIME ARGM-TMP
+malam TIME ARGM-TMP
+. O O
+
+Tengah TIME ARGM-TMP
+malam TIME ARGM-TMP
+tadi O ARGM-TMP
+, O O
+terjadi O V
+pemadaman O ARG1
+listrik O ARG1
+di O O
+seluruh O O
+kompleks LOC ARGM-LOC
+. O O
+
+Rani PER ARG0
+berangkat O V
+dari O O
+Bandung LOC ARGM-LOC
+menuju O O
+Semarang LOC ARGM-DIR
+untuk O O
+menghadiri O V
+seminar O ARG1
+budaya O ARG1
+. O O
+
+Pagi TIME ARGM-TMP
+tadi TIME ARGM-TMP
+, O O
+Pak O O
+Darto PER ARG0
+terbang O V
+dari O O
+Jakarta LOC ARGM-LOC
+ke O O
+Makassar LOC ARGM-DIR
+untuk O O
+urusan O ARG1
+bisnis O ARG1
+. O O
+
+Dina PER ARG0
+memutuskan O V
+pindah O V
+dari O O
+Surabaya LOC ARGM-LOC
+ke O O
+Denpasar LOC ARGM-DIR
+setelah O O
+mendapatkan O V
+pekerjaan O ARG1
+baru O ARG1
+. O O
+
+Para O O
+wisatawan O ARG0
+itu O O
+bertolak O V
+dari O O
+Yogyakarta LOC ARGM-LOC
+ke O O
+Lombok LOC ARGM-DIR
+menggunakan O O
+kapal O ARGM-MNR
+pesiar O ARGM-MNR
+. O O
+
+Amir PER ARG0
+dan O O
+keluarganya O ARG0
+mudik O V
+dari O O
+Bekasi LOC ARGM-LOC
+ke O O
+Padang LOC ARGM-DIR
+saat O O
+libur O ARGM-TMP
+Lebaran EVENT ARGM-TMP
+. O O
+
+Kemarin TIME ARGM-TMP
+sore TIME ARGM-TMP
+, O O
+Andika PER ARG0
+mengendarai O V
+motor O ARG1
+dari O O
+Malang LOC ARGM-LOC
+ke O O
+Blitar LOC ARGM-DIR
+untuk O O
+menjenguk O V
+neneknya O ARG2
+. O O
+
+Tim O ARG0
+peneliti O ARG0
+itu O O
+berangkat O V
+dari O O
+Pontianak LOC ARGM-LOC
+ke O O
+Palangkaraya LOC ARGM-DIR
+untuk O O
+melakukan O V
+riset O ARG1
+lingkungan O ARG1
+. O O
+
+Setelah O O
+lulus O V
+kuliah O ARG1
+, O O
+Iqbal PER ARG0
+merantau O V
+dari O O
+Medan LOC ARGM-LOC
+ke O O
+Balikpapan LOC ARGM-DIR
+mencari O V
+pekerjaan O ARG1
+. O O
+
+Rombongan O ARG0
+siswa O ARG0
+melakukan O V
+studi O ARG1
+banding O ARG1
+dari O O
+Solo LOC ARGM-LOC
+ke O O
+Bandung LOC ARGM-DIR
+menggunakan O O
+kereta O ARGM-MNR
+api O ARGM-MNR
+. O O
+
+Minggu TIME ARGM-TMP
+depan TIME ARGM-TMP
+, O O
+Sinta PER ARG0
+akan O ARGM-MOD
+melakukan O V
+perjalanan O ARG1
+dinas O ARG1
+dari O O
+Manado LOC ARGM-LOC
+ke O O
+Jayapura LOC ARGM-DIR
+. O O
+
+Rombongan O ARG0
+turis O ARG0
+itu O O
+memulai O V
+perjalanan O ARG1
+dari O O
+Jakarta LOC ARGM-LOC
+, O O
+singgah O V
+sebentar O ARGM-TMP
+di O O
+Surabaya LOC ARGM-LOC
+, O O
+lalu O O
+melanjutkan O V
+ke O O
+Bali LOC ARGM-DIR
+. O O
+
+Pagi TIME ARGM-TMP
+ini TIME ARGM-TMP
+, O O
+Dimas PER ARG0
+berangkat O V
+dari O O
+Bandung LOC ARGM-LOC
+ke O O
+Semarang LOC ARGM-DIR
+, O O
+lalu O O
+malamnya TIME ARGM-TMP
+akan O ARGM-MOD
+bertolak O V
+ke O O
+Yogyakarta LOC ARGM-DIR
+. O O
+
+Setelah O O
+berkunjung O V
+ke O O
+Makassar LOC ARGM-LOC
+, O O
+Rudi PER ARG0
+melanjutkan O V
+perjalanannya O ARG1
+ke O O
+Manado LOC ARGM-DIR
+dan O O
+Ambon LOC ARGM-DIR
+. O O
+
+Mereka O ARG0
+memulai O V
+ekspedisi O ARG1
+dari O O
+Palembang LOC ARGM-LOC
+, O O
+melintasi O V
+Jambi LOC ARGM-DIR
+, O O
+Pekanbaru LOC ARGM-DIR
+, O O
+hingga O O
+berakhir O V
+di O O
+Medan LOC ARGM-DIR
+. O O
+
+Kemarin TIME ARGM-TMP
+malam TIME ARGM-TMP
+, O O
+Mia PER ARG0
+terbang O V
+dari O O
+Denpasar LOC ARGM-LOC
+ke O O
+Jakarta LOC ARGM-DIR
+, O O
+lalu O O
+transit O V
+sebentar O ARGM-TMP
+sebelum O O
+melanjutkan O V
+ke O O
+Singapura LOC ARGM-DIR
+. O O
+
+Petualangan O ARG1
+Arif PER ARG0
+dimulai O V
+dari O O
+Balikpapan LOC ARGM-LOC
+, O O
+berlanjut O V
+ke O O
+Banjarmasin LOC ARGM-DIR
+, O O
+dan O O
+berakhir O V
+di O O
+Pontianak LOC ARGM-DIR
+. O O
+
+Dua QUANT ARGM-TMP
+hari TIME ARGM-TMP
+yang O O
+lalu O ARGM-TMP
+, O O
+rombongan O ARG0
+peziarah O ARG0
+berangkat O V
+dari O O
+Cirebon LOC ARGM-LOC
+, O O
+kemudian O O
+ke O O
+Tegal LOC ARGM-DIR
+, O O
+Pekalongan LOC ARGM-DIR
+, O O
+dan O O
+berakhir O V
+di O O
+Semarang LOC ARGM-DIR
+. O O
+
+Setelah O O
+mengunjungi O V
+Kupang LOC ARGM-LOC
+, O O
+tim O ARG0
+relawan O ARG0
+melanjutkan O V
+misi O ARG1
+kemanusiaan O ARG1
+ke O O
+Maumere LOC ARGM-DIR
+, O O
+Ende LOC ARGM-DIR
+, O O
+dan O O
+Labuan LOC ARGM-DIR
+Bajo LOC ARGM-DIR
+. O O
+
+Kevin PER ARG0
+memulai O V
+perjalanan O ARG1
+bisnis O ARG1
+dari O O
+Batam LOC ARGM-LOC
+, O O
+lalu O O
+ke O O
+Padang LOC ARGM-DIR
+, O O
+Palembang LOC ARGM-DIR
+, O O
+dan O O
+akhirnya O ARGM-TMP
+kembali O V
+ke O O
+Jakarta LOC ARGM-DIR
+. O O
+
+Minggu TIME ARGM-TMP
+depan TIME ARGM-TMP
+, O O
+rombongan O ARG0
+mahasiswa O ARG0
+akan O ARGM-MOD
+melakukan O V
+studi O ARG1
+lapangan O ARG1
+dari O O
+Malang LOC ARGM-LOC
+, O O
+menuju O O
+Probolinggo LOC ARGM-DIR
+, O O
+Lumajang LOC ARGM-DIR
+, O O
+dan O O
+berakhir O V
+di O O
+Banyuwangi LOC ARGM-DIR
+. O O
+
+Rapat O O
+koordinasi O O
+dimulai O V
+pukul TIME ARGM-TMP
+0900 TIME ARGM-TMP
+WIB O O
+
+Pertunjukan O O
+dimulai O V
+jam TIME ARGM-TMP
+7 TIME ARGM-TMP
+malam TIME ARGM-TMP
+di O O
+aula O O
+utama O O
+
+Kereta O O
+api O O
+berangkat O V
+pukul TIME ARGM-TMP
+0545 TIME ARGM-TMP
+dari O O
+stasiun O O
+
+Andi O O
+tiba O V
+di O O
+hotel O O
+jam TIME ARGM-TMP
+3 TIME ARGM-TMP
+sore TIME ARGM-TMP
+
+Pelatihan O O
+online O O
+akan O O
+dimulai O V
+pukul TIME ARGM-TMP
+1030 TIME ARGM-TMP
+WIB O O
+
+Mobil O O
+rombongan O O
+berangkat O V
+jam TIME ARGM-TMP
+6 TIME ARGM-TMP
+pagi TIME ARGM-TMP
+
+Mia O O
+sudah O O
+sampai O O
+di O O
+bandara O O
+pukul TIME ARGM-TMP
+1300 TIME ARGM-TMP
+
+Kami O O
+bertemu O V
+kembali O O
+jam TIME ARGM-TMP
+8 TIME ARGM-TMP
+malam TIME ARGM-TMP
+di O O
+restoran O O
+
+Seminar O O
+berlangsung O V
+pukul TIME ARGM-TMP
+1415 TIME ARGM-TMP
+WIB O O
+
+Penerbangan O O
+itu O O
+dijadwalkan O V
+jam TIME ARGM-TMP
+4 TIME ARGM-TMP
+sore TIME ARGM-TMP
+
+Sedimen MAT ARG0
+adalah O V
+material O ARG1
+atau O O
+pecahan O ARG1
+batuan MAT ARG1
+, O O
+mineral MAT ARG1
+dan O O
+material MAT ARG1
+organik MAT ARG1
+yang O O
+melayang-layang O V
+di O O
+dalam O O
+air MAT ARGM-LOC
+, O O
+udara MAT ARGM-LOC
+, O O
+maupun O O
+yang O O
+dikumpulkan O V
+di O O
+dasar O ARGM-LOC
+sungai O ARGM-LOC
+atau O O
+laut O ARGM-LOC
+oleh O O
+pembawa O ARG0
+atau O O
+perantara O ARG0
+alami O ARG0
+lainnya O O
+. O O
+
+Lapisan O ARG0
+tanah MAT ARG0
+vulkanik MAT ARG0
+berasal O V
+dari O O
+material O ARG1
+letusan EVENT ARG1
+gunung EVENT ARGM-LOC
+api EVENT ARGM-LOC
+. O O
+
+Horizon O ARG0
+C O ARG0
+merupakan O V
+lapisan O ARG1
+yang O O
+tersusun O V
+atas O O
+batuan MAT ARG1
+, O O
+yang O O
+berperan O V
+sebagai O O
+penyedia O ARG1
+utama O ARG1
+material O ARG1
+untuk O O
+tanah MAT ARG2
+bagian O ARG2
+paling O ARG2
+atas O ARG2
+. O O
+
+Peralatan O ARG0
+zaman TIME ARGM-TMP
+Paleolitikum TIME ARGM-TMP
+dibuat O V
+dari O O
+batu MAT ARG1
+yang O O
+masih O O
+bersifat O V
+kebetulan O ARG1
+dan O O
+seadanya O ARG1
+serta O O
+bersifat O V
+trial O ARG1
+and O ARG1
+error O ARG1
+, O O
+menggunakan O V
+material O ARG1
+dari O O
+alam O ARGM-LOC
+terutama O O
+batu MAT ARG1
+. O O
+
+Material O ARG0
+logam MAT ARG0
+seperti O O
+aluminium MAT ARG0
+banyak O O
+digunakan O V
+sebagai O O
+bahan O ARG1
+pembuatan O ARG1
+alat O ARG1
+memasak O ARG1
+dan O O
+tempat O ARG1
+menjemur O V
+pakaian O ARG2
+karena O O
+sifatnya O ARGM-CAU
+yang O O
+tidak O ARGM-NEG
+mudah O O
+berkarat O V
+. O O
+
+Dalam O O
+proses O ARGM-MNR
+fotosintesis EVENT ARGM-MNR
+, O O
+tumbuhan O ARG0
+hijau O ARG0
+memanfaatkan O V
+material O ARG1
+berupa O O
+air MAT ARG1
+dan O O
+karbon MAT ARG1
+dioksida MAT ARG1
+untuk O O
+menghasilkan O V
+oksigen O ARG2
+dan O O
+glukosa O ARG2
+. O O
+
+Besi MAT ARG0
+dan O O
+baja MAT ARG0
+dapat O O
+dijadikan O V
+magnet O ARG1
+dengan O O
+cara O ARGM-MNR
+menginduksi O V
+atau O O
+mendekatkannya O V
+dengan O O
+magnet O ARG1
+selama O O
+beberapa O O
+waktu O ARGM-TMP
+. O O
+
+Komponen O ARG0
+tanah MAT ARG0
+berupa O O
+batuan MAT ARG1
+, O O
+udara MAT ARG1
+, O O
+air MAT ARG1
+, O O
+humus MAT ARG1
+, O O
+mineral MAT ARG1
+, O O
+dan O O
+komponen O ARG1
+organik O ARG1
+. O O
+
+Kaca MAT ARG0
+memiliki O V
+massa O ARG1
+jenis O ARG1
+sebesar O O
+2,6 QUANT ARG1
+g/cm³ QUANT ARG1
+dan O O
+digunakan O V
+untuk O O
+pembuatan O ARG1
+jendela O ARG1
+serta O O
+berbagai O O
+perlengkapan O ARG1
+rumah O ARG1
+tangga O ARG1
+. O O
+
+Bahan O ARG0
+plastik MAT ARG0
+banyak O O
+digunakan O V
+untuk O O
+kemasan O ARG1
+makanan O ARG1
+, O O
+namun O O
+tidak O ARGM-NEG
+semua O O
+jenis O ARG1
+plastik MAT ARG1
+aman O O
+untuk O O
+makanan O ARG2
+panas O ARG2
+. O O
+
+Aluminium MAT ARG0
+banyak O O
+digunakan O V
+sebagai O O
+bahan O ARG1
+pembuatan O ARG1
+alat O ARG1
+memasak O ARG1
+karena O O
+memiliki O V
+titik O ARGM-CAU
+leleh O ARGM-CAU
+yang O O
+tinggi O ARGM-CAU
+dan O O
+tidak O ARGM-NEG
+mudah O O
+berkarat O V
+. O O
+
+Karet MAT ARG0
+memiliki O V
+elastisitas O ARG1
+yang O O
+lebih O O
+tinggi O ARG1
+dibandingkan O O
+dengan O O
+es MAT ARG1
+batu MAT ARG1
+, O O
+kayu MAT ARG1
+, O O
+dan O O
+gelas MAT ARG1
+. O O
+
+Tanah MAT ARG0
+liat MAT ARG0
+, O O
+tanah MAT ARG0
+lempung MAT ARG0
+, O O
+dan O O
+pasir MAT ARG0
+merupakan O V
+contoh O ARG1
+jenis-jenis O ARG1
+tanah MAT ARG1
+berdasarkan O O
+ukuran O ARGM-MNR
+butirannya O ARGM-MNR
+. O O
+
+Pada O O
+zaman O ARGM-TMP
+Neolitikum DATE ARGM-TMP
+, O O
+manusia O ARG0
+telah O O
+memanfaatkan O V
+bahan O ARG1
+batu MAT ARG1
+untuk O O
+peralatan O ARG2
+, O O
+seperti O O
+kapak O ARG2
+dan O O
+gelang O ARG2
+dari O O
+batu MAT ARG2
+. O O
+
+Dalam O O
+termometer O ARGM-LOC
+zat O ARGM-LOC
+cair O ARGM-LOC
+, O O
+digunakan O V
+raksa MAT ARG1
+atau O O
+alkohol MAT ARG1
+sebagai O O
+zat O ARG1
+cair O ARG1
+pengisi O ARG1
+yang O O
+dapat O O
+merespons O V
+perubahan O ARG2
+suhu O ARG2
+. O O
+
+Logam MAT ARG0
+seperti O O
+besi MAT ARG0
+, O O
+tembaga MAT ARG0
+, O O
+dan O O
+emas MAT ARG0
+banyak O O
+digunakan O V
+dalam O O
+kehidupan O ARGM-LOC
+seharhari O ARGM-LOC
+untuk O O
+alat O ARG1
+perkakas O ARG1
+dan O O
+rangka O ARG1
+kendaraan O ARG1
+. O O
+
+Rangka O ARG0
+atap O ARG0
+pabrik O ARGM-LOC
+itu O O
+menggunakan O V
+baja MAT ARG1
+ringan O ARG1
+. O O
+
+Paku O ARG0
+dari O O
+besi MAT ARG1
+itu O O
+mulai O O
+berkarat O V
+setelah O O
+lama O O
+terendam O V
+air MAT ARGM-LOC
+. O O
+
+Botol O ARG0
+minuman O ARG0
+ini O O
+dibuat O V
+dari O O
+plastik MAT ARG1
+daur O ARG1
+ulang O ARG1
+. O O
+
+Jendela O ARG0
+rumahnya O ARGM-LOC
+terbuat O V
+dari O O
+kaca MAT ARG1
+tebal O ARG1
+yang O O
+tahan O V
+pecah O ARG2
+. O O
+
+Perhiasan O ARG0
+dari O O
+emas MAT ARG1
+selalu O O
+diminati O V
+saat O O
+musim O ARGM-TMP
+pernikahan O ARGM-TMP
+. O O
+
+Dia O ARG0
+menambal O V
+ban O ARG1
+sepeda O ARG1
+dengan O O
+potongan O ARG1
+karet MAT ARG1
+. O O
+
+Kabel O ARG0
+tembaga MAT ARG0
+itu O O
+digunakan O V
+untuk O O
+instalasi O ARG1
+listrik O ARG1
+di O O
+gedung O ARGM-LOC
+baru O ARGM-LOC
+. O O
+
+Patung O ARG0
+kecil O O
+di O O
+ruang O ARGM-LOC
+tamu O ARGM-LOC
+itu O O
+terbuat O V
+dari O O
+batu MAT ARG1
+marmer MAT ARG1
+. O O
+
+Air MAT ARG0
+bersih O ARG0
+disalurkan O V
+ke O O
+rumah-rumah O ARGM-LOC
+melalui O O
+pipa MAT ARG1
+bawah O ARGM-LOC
+tanah O ARGM-LOC
+. O O
+
+Meja O ARG0
+belajar O ARG0
+ini O O
+terbuat O V
+dari O O
+kayu MAT ARG1
+mahoni MAT ARG1
+yang O O
+kokoh O ARG1
+. O O
+
+Dinding O ARG0
+bata MAT ARG0
+itu O O
+diperkuat O V
+dengan O O
+lapisan O ARG1
+semen MAT ARG1
+. O O
+
+Raksa MAT ARG0
+digunakan O V
+dalam O O
+termometer O ARGM-LOC
+untuk O O
+mengukur O V
+suhu O ARG1
+badan O ARG1
+. O O
+
+Dia O ARG0
+menggali O V
+tanah MAT ARG1
+di O O
+kebun O ARGM-LOC
+untuk O O
+menanam O V
+pohon O ARG2
+mangga O ARG2
+. O O
+
+Pasir MAT ARG0
+di O O
+pantai O ARGM-LOC
+ini O O
+sangat O O
+halus O ARG1
+dan O O
+bersih O ARG1
+. O O
+
+Lantai O ARG0
+rumahnya O ARGM-LOC
+dipoles O V
+menggunakan O O
+keramik MAT ARG1
+berbahan O O
+kaca MAT ARG1
+. O O
+
+Rak O ARG0
+sepatu O ARG0
+itu O O
+terbuat O V
+dari O O
+logam MAT ARG1
+ringan O ARG1
+namun O O
+kuat O ARG1
+. O O
+
+Karbon MAT ARG0
+aktif MAT ARG0
+digunakan O V
+dalam O O
+filter O ARGM-LOC
+air O ARGM-LOC
+minum O ARGM-LOC
+. O O
+
+Pabrik O ARGM-LOC
+mobil O ARGM-LOC
+itu O O
+menggunakan O V
+silikon MAT ARG1
+untuk O O
+membuat O V
+chip O ARG2
+kendaraan O ARG2
+pintar O ARG2
+. O O
+
+Asbes MAT ARG0
+di O O
+atap O ARGM-LOC
+lama O ARGM-LOC
+itu O O
+sudah O O
+diganti O V
+karena O O
+berbahaya O V
+bagi O O
+kesehatan O ARG2
+. O O
+
+Tukang O ARG0
+bangunan O ARG0
+itu O O
+mencampur O V
+semen MAT ARG1
+dan O O
+pasir MAT ARG1
+untuk O O
+membuat O V
+pondasi O ARG2
+. O O
+
+Peristiwa EVENT ARG0
+Sumpah EVENT ARG0
+Pemuda EVENT ARG0
+menjadi O V
+momen O ARG1
+penting O ARG1
+yang O O
+menyatukan O V
+semangat O ARG2
+kebangsaan O ARG2
+. O O
+
+Pada O O
+10 DATE ARGM-TMP
+November DATE ARGM-TMP
+1945 DATE ARGM-TMP
+, O O
+terjadi O V
+Pertempuran EVENT ARG1
+Surabaya EVENT ARG1
+yang O O
+kemudian O O
+diperingati O V
+sebagai O O
+Hari EVENT ARG1
+Pahlawan EVENT ARG1
+. O O
+
+Konferensi EVENT ARG0
+Meja EVENT ARG0
+Bundar EVENT ARG0
+yang O O
+diadakan O V
+pada O O
+1949 DATE ARGM-TMP
+menjadi O V
+tonggak O ARG1
+diplomasi O ARG1
+kemerdekaan O ARG1
+Indonesia LOC ARG1
+. O O
+
+Festival EVENT ARG0
+Ekonomi EVENT ARG0
+Kreatif EVENT ARG0
+digelar O V
+untuk O O
+mempromosikan O V
+produk O ARG1
+lokal O ARG1
+ke O O
+pasar O ARGM-LOC
+internasional O ARGM-LOC
+. O O
+
+Peristiwa EVENT ARG0
+Proklamasi EVENT ARG0
+Kemerdekaan EVENT ARG0
+Indonesia EVENT ARG0
+pada O O
+17 DATE ARGM-TMP
+Agustus DATE ARGM-TMP
+1945 DATE ARGM-TMP
+menjadi O V
+tonggak O ARG1
+sejarah O ARG1
+berdirinya O V
+negara O ARG2
+Indonesia LOC ARG2
+. O O
+
+Pameran O ARG0
+seni O ARG0
+budaya O ARG0
+digelar O V
+untuk O O
+memperkenalkan O V
+kearifan O ARG1
+lokal O ARG1
+kepada O O
+wisatawan O ARG2
+mancanegara O ARG2
+. O O
+
+Pada O O
+masa O ARGM-TMP
+kolonial O ARGM-TMP
+, O O
+diadakan O V
+Kongres EVENT ARG1
+Pemuda EVENT ARG1
+II EVENT ARG1
+yang O O
+menghasilkan O V
+ikrar O ARG1
+Sumpah EVENT ARG1
+Pemuda EVENT ARG1
+. O O
+
+Upacara EVENT ARG0
+Ngaben EVENT ARG0
+merupakan O V
+tradisi O ARG1
+penting O ARG1
+dalam O O
+masyarakat O ARGM-LOC
+Hindu O ARGM-LOC
+di O O
+Bali LOC ARGM-LOC
+. O O
+
+Pertunjukan EVENT ARG0
+wayang EVENT ARG0
+kulit EVENT ARG0
+menjadi O V
+acara O ARG1
+rutin O ARG1
+dalam O O
+festival O ARGM-LOC
+budaya O ARGM-LOC
+daerah O ARGM-LOC
+. O O
+
+Organisasi O ARG0
+Putri ORG ARG0
+Mardika ORG ARG0
+aktif O O
+mengadakan O V
+kegiatan O ARG1
+pendidikan O ARG1
+dan O O
+pemberdayaan O ARG1
+perempuan O ARG2
+sejak O O
+1912 DATE ARGM-TMP
+. O O
\ No newline at end of file
diff --git a/dataset/new_ner_srl.tsv b/dataset/new_ner_srl.tsv
index 20c180f..13565f7 100644
--- a/dataset/new_ner_srl.tsv
+++ b/dataset/new_ner_srl.tsv
@@ -7997,7 +7997,7 @@ negara O ARG2
Indonesia B-LOC ARG2
. O O
-Pameran O EVENT ARG0
+Pameran O ARG0
seni O ARG0
budaya O ARG0
digelar O V
@@ -8037,7 +8037,7 @@ di O O
Bali B-LOC ARGM-LOC
. O O
-Pertunjukan O EVENT ARG0
+Pertunjukan B-EVENT ARG0
wayang I-EVENT ARG0
kulit I-EVENT ARG0
menjadi O V
diff --git a/dataset/test_dataset_qg.json b/dataset/test_dataset_qg.json
new file mode 100644
index 0000000..abe5448
--- /dev/null
+++ b/dataset/test_dataset_qg.json
@@ -0,0 +1,12002 @@
+[
+ {
+ "tokens": ["halo", "nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Lestari", "Lestari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Dewi", "Dewi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Rizky", "Rizky", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Vina", "Vina", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Zulkifli", "Zulkifli", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Putri", "Putri", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Lestari", "Lestari"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Rizky", "Rizky", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Sari", "Sari", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Rizky", "Rizky", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Indah", "Indah"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Nurul", "Nurul"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "utomo"]
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+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "ramadhan"]
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "quiz_posibility": [
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+ "answer": ["wahyu", "putra"]
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+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "kusuma"]
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "irawan"]
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+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "utomo"]
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+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "wijaya"]
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "pratama"]
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+ {
+ "tokens": ["saya", "Maya", "Maya", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Tono", "Tono"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Budi", "Budi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Hendra", "Hendra"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Qori", "Qori", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Yani", "Yani"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Indah", "Indah", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Qori", "Qori", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Fajar", "Fajar", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "irawan"]
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+ ]
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+ {
+ "tokens": ["halo", "nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Maya", "Maya", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Lestari", "Lestari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Nurul", "Nurul"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Utami", "Utami"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Utami", "Utami"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "nugroho"]
+ }
+ ]
+ },
+ {
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+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Indah", "Indah"],
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "utomo"]
+ }
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+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "syahputra"]
+ }
+ ]
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+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "santoso"]
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "rahmawati"]
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+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "saputra"]
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "susanti"]
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "saputra"]
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+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "nugroho"]
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+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "saputra"]
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "amelia"]
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+ {
+ "tokens": ["nama", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "nugroho"]
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+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "syahputra"]
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+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "rahmawati"]
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "ramadhan"]
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "nugroho"]
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+ "tokens": ["nama", "lengkap", "saya", "Budi", "Budi"],
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "utomo"]
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+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "ramadhan"]
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+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "utomo"]
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+ "tokens": ["nama", "saya", "Joko", "Joko"],
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+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "ramadhan"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "saputra"]
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+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "kusuma"]
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "rahmawati"]
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "irawan"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "putra"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "kusuma"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "wijaya"]
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "answer": ["putri", "utomo"]
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "ramadhan"]
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
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+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "putra"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "halim"]
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+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "pratama"]
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "ramadhan"]
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+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
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+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Indah", "Indah"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Indah", "Indah"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Sari", "Sari", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Lestari", "Lestari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Nurul", "Nurul", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Zulkifli", "Zulkifli", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Lestari", "Lestari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Fajar", "Fajar", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Gita", "Gita"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Maya", "Maya", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Qori", "Qori", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Kurnia", "Kurnia", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Nurul", "Nurul", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Tono", "Tono"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Wahyu", "Wahyu", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Lestari", "Lestari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "santoso"]
+ }
+ ]
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+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Citra", "Citra", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Yani", "Yani"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Yani", "Yani"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Yani", "Yani", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Yani", "Yani"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Rizky", "Rizky"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Indah", "Indah"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Tono", "Tono"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Citra", "Citra"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Yani", "Yani", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Eka", "Eka", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Qori", "Qori"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Kurnia", "Kurnia", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Utami", "Utami"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Indah", "Indah"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Qori", "Qori"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Wahyu", "Wahyu", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Lestari", "Lestari"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Indah", "Indah"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "syahputra"]
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+ ]
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+ {
+ "tokens": ["saya", "bernama", "Utami", "Utami"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "rahmawati"]
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+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Joko", "Joko"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Joko", "Joko"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Wahyu", "Wahyu", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Nurul", "Nurul"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Dewi", "Dewi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Tono", "Tono", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Vina", "Vina"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Ahmad", "Ahmad", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Dewi", "Dewi", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Sari", "Sari", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Eka", "Eka", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Dewi", "Dewi", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Tono", "Tono", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Vina", "Vina"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Lestari", "Lestari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Fajar", "Fajar", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Nurul", "Nurul", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Sari", "Sari"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Eka", "Eka", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Gita", "Gita"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Gita", "Gita"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Lestari", "Lestari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Joko", "Joko"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Rizky", "Rizky", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Zulkifli", "Zulkifli", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "kusuma"]
+ }
+ ]
+ },
+ {
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+ "tokens": ["saya", "dikenal", "sebagai", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Eka", "Eka", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Budi", "Budi", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Eka", "Eka", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Gita", "Gita", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Qori", "Qori"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Fajar", "Fajar", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Tono", "Tono", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Putri", "Putri", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Nurul", "Nurul", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Nurul", "Nurul", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Dewi", "Dewi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Rizky", "Rizky"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Tono", "Tono"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Budi", "Budi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Utami", "Utami", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Citra", "Citra"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Lestari", "Lestari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Yani", "Yani"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Citra", "Citra"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Yani", "Yani"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "syahputra"]
+ }
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+ },
+ {
+ "tokens": ["saya", "bernama", "Eka", "Eka"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Dewi", "Dewi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "saputra"]
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+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "putra"]
+ }
+ ]
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+ {
+ "tokens": ["saya", "Nurul", "Nurul", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "santoso"]
+ }
+ ]
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+ {
+ "tokens": ["saya", "Tono", "Tono", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "syahputra"]
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+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "utomo"]
+ }
+ ]
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+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "irawan"]
+ }
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+ "tokens": ["saya", "bernama", "Eka", "Eka"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Lestari", "Lestari"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "putra"]
+ }
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+ "tokens": ["nama", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Maya", "Maya", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Gita", "Gita", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Hendra", "Hendra"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Lestari", "Lestari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Zulkifli", "Zulkifli", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Oscar", "Oscar", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Sari", "Sari", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Citra", "Citra", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Gita", "Gita"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Utami", "Utami"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Dewi", "Dewi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Citra", "Citra"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Oscar", "Oscar", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Citra", "Citra", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Putri", "Putri", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Maya", "Maya", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Tono", "Tono"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Eka", "Eka", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Nurul", "Nurul"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Sari", "Sari", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Rizky", "Rizky", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Vina", "Vina", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Oscar", "Oscar", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Gita", "Gita"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Zulkifli", "Zulkifli", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Qori", "Qori", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Utami", "Utami", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Gita", "Gita"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Vina", "Vina", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Tono", "Tono", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Tono", "Tono"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Maya", "Maya"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "putra"]
+ }
+ ]
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+ {
+ "tokens": ["saya", "bernama", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Joko", "Joko", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Qori", "Qori", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Yani", "Yani"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Lestari", "Lestari", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Hendra", "Hendra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Indah", "Indah", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "nugroho"]
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+ ]
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+ "tokens": ["saya", "Lestari", "Lestari", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "halim"]
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+ "tokens": ["perkenalkan", "saya", "adalah", "Hendra", "Hendra"],
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "rahmawati"]
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+ {
+ "tokens": ["saya", "bernama", "Maya", "Maya"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "syahputra"]
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+ },
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+ "tokens": ["nama", "saya", "Citra", "Citra"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "putra"]
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+ ]
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+ "tokens": ["nama", "lengkap", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "pratama"]
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+ ]
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+ {
+ "tokens": ["saya", "Citra", "Citra", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "ramadhan"]
+ }
+ ]
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+ {
+ "tokens": ["saya", "bernama", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "santoso"]
+ }
+ ]
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+ {
+ "tokens": ["nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "wijaya"]
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+ ]
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+ {
+ "tokens": ["halo", "nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "halim"]
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+ "tokens": ["halo", "nama", "saya", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "wijaya"]
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+ ]
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+ {
+ "tokens": ["nama", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "amelia"]
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+ ]
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+ "tokens": ["kenalkan", "nama", "saya", "Indah", "Indah"],
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "syahputra"]
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+ "tokens": ["saya", "dikenal", "sebagai", "Indah", "Indah"],
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+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "rahmawati"]
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+ "tokens": ["saya", "Eka", "Eka", "senang", "berkenalan"],
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "pratama"]
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+ "tokens": ["kenalkan", "nama", "saya", "Zulkifli", "Zulkifli"],
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "halim"]
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+ {
+ "tokens": ["saya", "Joko", "Joko", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "utomo"]
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+ ]
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+ {
+ "tokens": ["halo", "nama", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "wijaya"]
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+ {
+ "tokens": ["saya", "Rizky", "Rizky", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "pratama"]
+ }
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+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "saputra"]
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+ "tokens": ["perkenalkan", "saya", "adalah", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "nugroho"]
+ }
+ ]
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+ {
+ "tokens": ["saya", "Fajar", "Fajar", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "halim"]
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+ "tokens": ["saya", "dikenal", "sebagai", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Eka", "Eka"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "halim"]
+ }
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+ },
+ {
+ "tokens": ["saya", "Zulkifli", "Zulkifli", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "nugroho"]
+ }
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+ },
+ {
+ "tokens": ["nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "susanti"]
+ }
+ ]
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+ {
+ "tokens": ["halo", "nama", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "amelia"]
+ }
+ ]
+ },
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+ "tokens": ["kenalkan", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Ahmad", "Ahmad", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Fajar", "Fajar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Rizky", "Rizky", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Lestari", "Lestari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Dewi", "Dewi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Citra", "Citra"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Lestari", "Lestari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "syahputra"]
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+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Vina", "Vina", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Putri", "Putri", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "syahputra"]
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+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "wijaya"]
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+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Indah", "Indah"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "rahmawati"]
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+ ]
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+ {
+ "tokens": ["saya", "bernama", "Tono", "Tono"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "amelia"]
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+ "tokens": ["perkenalkan", "saya", "adalah", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "amelia"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "halim"]
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "irawan"]
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+ "tokens": ["nama", "saya", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "wijaya"]
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+ "tokens": ["nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "wijaya"]
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+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "ramadhan"]
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+ "tokens": ["saya", "dikenal", "sebagai", "Sari", "Sari"],
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+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "amelia"]
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+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "syahputra"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "nugroho"]
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+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "santoso"]
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "saputra"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "amelia"]
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+ "tokens": ["perkenalkan", "saya", "adalah", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "syahputra"]
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+ ]
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+ {
+ "tokens": ["nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "wijaya"]
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+ ]
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+ {
+ "tokens": ["saya", "bernama", "Utami", "Utami"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "santoso"]
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+ {
+ "tokens": ["saya", "Dewi", "Dewi", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "susanti"]
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+ ]
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+ {
+ "tokens": ["nama", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "saputra"]
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+ "tokens": ["nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "irawan"]
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+ ]
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+ "tokens": ["nama", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "halim"]
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+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "wijaya"]
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+ "tokens": ["nama", "saya", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "irawan"]
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+ "tokens": ["kenalkan", "nama", "saya", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "irawan"]
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+ "tokens": ["saya", "dikenal", "sebagai", "Sari", "Sari"],
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+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Indah", "Indah"],
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "ramadhan"]
+ }
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+ {
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+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Rizky", "Rizky", "senang", "berkenalan"],
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "rahmawati"]
+ }
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+ {
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+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "santoso"]
+ }
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "irawan"]
+ }
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+ },
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "santoso"]
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "amelia"]
+ }
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "amelia"]
+ }
+ ]
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+ "tokens": ["nama", "saya", "Joko", "Joko"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "syahputra"]
+ }
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+ {
+ "tokens": ["saya", "bernama", "Dewi", "Dewi"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "utomo"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "nugroho"]
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+ ]
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+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "irawan"]
+ }
+ ]
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+ {
+ "tokens": ["saya", "Joko", "Joko", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "rahmawati"]
+ }
+ ]
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+ "tokens": ["nama", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "saputra"]
+ }
+ ]
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+ {
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+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "nugroho"]
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "susanti"]
+ }
+ ]
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+ {
+ "tokens": ["saya", "Nurul", "Nurul", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "saputra"]
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+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "saputra"]
+ }
+ ]
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+ {
+ "tokens": ["halo", "nama", "saya", "Kurnia", "Kurnia"],
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "halim"]
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+ "tokens": ["saya", "Nurul", "Nurul", "senang", "berkenalan"],
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "halim"]
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+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "halim"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "rahmawati"]
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+ {
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "putra"]
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "kusuma"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "ramadhan"]
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "syahputra"]
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+ {
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "rahmawati"]
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+ {
+ "tokens": ["saya", "Zulkifli", "Zulkifli", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "wijaya"]
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+ ]
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+ {
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "putra"]
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "saputra"]
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+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "nugroho"]
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+ "tokens": ["saya", "Tono", "Tono", "senang", "berkenalan"],
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "pratama"]
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "halim"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "kusuma"]
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+ {
+ "tokens": ["saya", "Citra", "Citra", "senang", "berkenalan"],
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "rahmawati"]
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+ "tokens": ["nama", "lengkap", "saya", "Ahmad", "Ahmad"],
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "saputra"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "susanti"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "susanti"]
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "utomo"]
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+ "tokens": ["saya", "dikenal", "sebagai", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "ramadhan"]
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+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "halim"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "pratama"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "amelia"]
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+ "tokens": ["nama", "saya", "Vina", "Vina"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["vina", "amelia"]
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+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "amelia"]
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+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "nugroho"]
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+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "irawan"]
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+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "syahputra"]
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+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "nugroho"]
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+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
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+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "ramadhan"]
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+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Fajar", "Fajar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Joko", "Joko"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Putri", "Putri"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Putri", "Putri"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["putri", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Lestari", "Lestari"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Yani", "Yani"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Qori", "Qori"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["qori", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Lestari", "Lestari", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Lestari", "Lestari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["lestari", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Eka", "Eka", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "susanti"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Indah", "Indah"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["indah", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Joko", "Joko", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Zulkifli", "Zulkifli"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["zulkifli", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Eka", "Eka"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["eka", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Tono", "Tono"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Citra", "Citra"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "ramadhan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Utami", "Utami", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Ahmad", "Ahmad"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["ahmad", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Joko", "Joko"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["joko", "nugroho"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "irawan"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Tono", "Tono"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["tono", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Yani", "Yani"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["yani", "wijaya"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Maya", "Maya"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["maya", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["perkenalkan", "saya", "adalah", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Hendra", "Hendra", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["hendra", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Oscar", "Oscar"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "saya", "Wahyu", "Wahyu"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "halim"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "putra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Wahyu", "Wahyu", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["wahyu", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Citra", "Citra", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["citra", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "bernama", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "rahmawati"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Oscar", "Oscar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["oscar", "syahputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Utami", "Utami"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["utami", "pratama"]
+ }
+ ]
+ },
+ {
+ "tokens": ["kenalkan", "nama", "saya", "Rizky", "Rizky"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["rizky", "amelia"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Budi", "Budi"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["budi", "santoso"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Fajar", "Fajar"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["fajar", "utomo"]
+ }
+ ]
+ },
+ {
+ "tokens": ["nama", "lengkap", "saya", "Sari", "Sari"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["sari", "kusuma"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Kurnia", "Kurnia"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["kurnia", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["halo", "nama", "saya", "Nurul", "Nurul"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["O", "O", "ARG0", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["nurul", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "Dewi", "Dewi", "senang", "berkenalan"],
+ "ner": ["O", "B-PER", "B-PER", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "O", "O"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["dewi", "saputra"]
+ }
+ ]
+ },
+ {
+ "tokens": ["saya", "dikenal", "sebagai", "Gita", "Gita"],
+ "ner": ["O", "O", "O", "B-PER", "B-PER"],
+ "srl": ["ARG0", "O", "O", "ARG0", "ARG0"],
+ "quiz_posibility": [
+ {
+ "type": "isian",
+ "question": ["siapa", "nama", "anda", "?"],
+ "answer": ["gita", "saputra"]
+ }
+ ]
+ }
+]
diff --git a/dataset/test_dts.tsv b/dataset/test_dts.tsv
new file mode 100644
index 0000000..8e571ab
--- /dev/null
+++ b/dataset/test_dts.tsv
@@ -0,0 +1,1007 @@
+Ir. PER ARG0
+Soekarno PER ARG0
+membacakan O V
+teks O ARG1
+Proklamasi O ARG1
+pada O O
+17 DATE ARGM-TMP
+Agustus DATE ARGM-TMP
+1945 DATE ARGM-TMP
+. O O
+
+R.A. PER ARG0
+Kartini PER ARG0
+memperjuangkan O V
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+Indonesia LOC ARG2
+. O O
+
+Pangeran PER ARG0
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+1825 DATE ARGM-TMP
+. O O
+
+B.J. PER ARG0
+Habibie PER ARG0
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+. O O
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+Jenderal PER ARG0
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+. O O
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+Hasyim PER ARG0
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+. O O
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+Cut PER ARG0
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+. O O
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+. O O
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+. O O
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+. O O
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+Penghargaan O O
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+. O O
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+Trofi O O
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+. O O
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+Keputusan O O
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+. O O
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+Keputusan O O
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+. O O
+
+Pada O O
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+. O O
+
+Keputusan O O
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+. O O
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+Kemarin O O
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+Bill PER ARG0
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+. O O
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+Kemarin O O
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+. O O
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+Kemarin O O
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+. O O
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+Ridwan PER ARG0
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+Trofi O O
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+Keputusan O O
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+ditandatangani O O
+oleh O O
+Nadiem PER ARG0
+Makarim PER ARG0
+. O O
+
+Sultan PER ARG0
+Hasanuddin PER ARG0
+menghadiri O O
+konferensi O O
+internasional O O
+di O O
+Jepang O O
+. O O
+
+Taylor PER ARG0
+Swift PER ARG0
+memimpin O O
+proyek O O
+besar O O
+di O O
+Silicon O O
+Valley O O
+. O O
+
+Kemarin O O
+Ariana PER ARG0
+Grande PER ARG0
+tampil O O
+di O O
+konser O O
+amal O O
+. O O
+
+Sri PER ARG0
+Mulyani PER ARG0
+menghadiri O O
+konferensi O O
+internasional O O
+di O O
+Jepang O O
+. O O
+
+Elon PER ARG0
+Musk PER ARG0
+memimpin O O
+proyek O O
+besar O O
+di O O
+Silicon O O
+Valley O O
+. O O
+
+Keputusan O O
+itu O O
+ditandatangani O O
+oleh O O
+Najwa PER ARG0
+Shihab PER ARG0
+. O O
+
+Trofi O O
+juara O O
+berhasil O O
+diraih O O
+oleh O O
+B.J. PER ARG0
+Habibie PER ARG0
+. O O
+
+Kemarin O O
+Cristiano PER ARG0
+Ronaldo PER ARG0
+tampil O O
+di O O
+konser O O
+amal O O
+. O O
+
+Trofi O O
+juara O O
+berhasil O O
+diraih O O
+oleh O O
+Stephen PER ARG0
+Hawking PER ARG0
+. O O
+
+Sri PER ARG0
+Mulyani PER ARG0
+dikenal O O
+sebagai O O
+tokoh O O
+inspiratif O O
+dunia O O
+. O O
+
+Nelson PER ARG0
+Mandela PER ARG0
+memimpin O O
+proyek O O
+besar O O
+di O O
+Silicon O O
+Valley O O
+. O O
+
+Taylor PER ARG0
+Swift PER ARG0
+menghadiri O O
+konferensi O O
+internasional O O
+di O O
+Jepang O O
+. O O
+
+Ridwan PER ARG0
+Kamil PER ARG0
+dikenal O O
+sebagai O O
+tokoh O O
+inspiratif O O
+dunia O O
+. O O
+
+Di O O
+acara O O
+tersebut O O
+Elon PER ARG0
+Musk PER ARG0
+memberikan O O
+pidato O O
+penting O O
+. O O
+
+Penghargaan O O
+Nobel O O
+diberikan O O
+kepada O O
+Usain PER ARG0
+Bolt PER ARG0
+. O O
+
+Trofi O O
+juara O O
+berhasil O O
+diraih O O
+oleh O O
+Albert PER ARG0
+Einstein PER ARG0
+. O O
+
+Mahathir PER ARG0
+Mohamad PER ARG0
+memimpin O O
+proyek O O
+besar O O
+di O O
+Silicon O O
+Valley O O
+. O O
+
+Trofi O O
+juara O O
+berhasil O O
+diraih O O
+oleh O O
+Mark PER ARG0
+Zuckerberg PER ARG0
+. O O
+
+Pada O O
+tahun O O
+2020 O O
+Lionel PER ARG0
+Messi PER ARG0
+meraih O O
+penghargaan O O
+tertinggi O O
+. O O
+
+Elon PER ARG0
+Musk PER ARG0
+dikenal O O
+sebagai O O
+tokoh O O
+inspiratif O O
+dunia O O
+. O O
+
+Narendra PER ARG0
+Modi PER ARG0
+dikenal O O
+sebagai O O
+tokoh O O
+inspiratif O O
+dunia O O
+. O O
+
+Pangeran PER ARG0
+Diponegoro PER ARG0
+memimpin O O
+proyek O O
+besar O O
+di O O
+Silicon O O
+Valley O O
+. O O
+
+Kemarin O O
+Joko PER ARG0
+Widodo PER ARG0
+tampil O O
+di O O
+konser O O
+amal O O
+. O O
+
+Ki PER ARG0
+Hajar PER ARG0
+Dewantara PER ARG0
+dikenal O O
+sebagai O O
+tokoh O O
+inspiratif O O
+dunia O O
+. O O
+
+Dewi PER ARG0
+Persik PER ARG0
+dikenal O O
+sebagai O O
+tokoh O O
+inspiratif O O
+dunia O O
+. O O
+
+Pada O O
+tahun O O
+2020 O O
+Cut PER ARG0
+Nyak PER ARG0
+Dien PER ARG0
+meraih O O
+penghargaan O O
+tertinggi O O
+. O O
+
+Penghargaan O O
+Nobel O O
+diberikan O O
+kepada O O
+Bill PER ARG0
+Gates PER ARG0
+. O O
+
+Keputusan O O
+itu O O
+ditandatangani O O
+oleh O O
+Pangeran PER ARG0
+Diponegoro PER ARG0
+. O O
+
+Keputusan O O
+itu O O
+ditandatangani O O
+oleh O O
+Mark PER ARG0
+Zuckerberg PER ARG0
+. O O
+
+Di O O
+acara O O
+tersebut O O
+Nadiem PER ARG0
+Makarim PER ARG0
+memberikan O O
+pidato O O
+penting O O
+. O O
+
+Penghargaan O O
+Nobel O O
+diberikan O O
+kepada O O
+Albert PER ARG0
+Einstein PER ARG0
+. O O
+
+Keputusan O O
+itu O O
+ditandatangani O O
+oleh O O
+Ariana PER ARG0
+Grande PER ARG0
+. O O
+
+Di O O
+acara O O
+tersebut O O
+Dewi PER ARG0
+Persik PER ARG0
+memberikan O O
+pidato O O
+penting O O
+. O O
diff --git a/QC/convert_dts.py b/old/QC/convert_dts.py
similarity index 100%
rename from QC/convert_dts.py
rename to old/QC/convert_dts.py
diff --git a/old/QC/model_tr.ipynb b/old/QC/model_tr.ipynb
new file mode 100644
index 0000000..3237423
--- /dev/null
+++ b/old/QC/model_tr.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "94d3889b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-05-10 14:49:40.993078: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+ "2025-05-10 14:49:40.996369: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
+ "2025-05-10 14:49:41.002001: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
+ "2025-05-10 14:49:41.015917: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+ "E0000 00:00:1746863381.035097 166971 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+ "E0000 00:00:1746863381.038978 166971 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+ "W0000 00:00:1746863381.049265 166971 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "W0000 00:00:1746863381.049288 166971 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "W0000 00:00:1746863381.049289 166971 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "W0000 00:00:1746863381.049290 166971 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+ "2025-05-10 14:49:41.052642: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -------------------------------------------------\n",
+ "# 0. Import & Konfigurasi\n",
+ "# -------------------------------------------------\n",
+ "import json, pickle\n",
+ "import numpy as np\n",
+ "from pathlib import Path\n",
+ "from collections import Counter\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.keras.preprocessing.text import Tokenizer\n",
+ "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
+ "from tensorflow.keras.utils import to_categorical\n",
+ "from tensorflow.keras.layers import (\n",
+ " Input, Embedding, LSTM, Bidirectional, Dense, Concatenate,\n",
+ " TimeDistributed\n",
+ ")\n",
+ "from tensorflow.keras.models import Model\n",
+ "from tensorflow.keras.callbacks import EarlyStopping\n",
+ "\n",
+ "PAD_TOKEN = \"\"\n",
+ "UNK_TOKEN = \"UNK\"\n",
+ "START_TOKEN = \"\"\n",
+ "END_TOKEN = \"\"\n",
+ "MAXLEN_SRC = 100 # Panjang paragraf maksimal\n",
+ "MAXLEN_TGT = 40 # Panjang pertanyaan/jawaban maksimal\n",
+ "BATCH = 32\n",
+ "EPOCHS = 30"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "b528b34e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Valid 325 / 325 (invalid index: [])\n"
+ ]
+ }
+ ],
+ "source": [
+ "raw = json.loads(Path(\"normalize_dataset.json\").read_text(encoding=\"utf-8\"))\n",
+ "\n",
+ "req = {\"tokens\",\"ner\",\"srl\",\"question\",\"answer\",\"type\"}\n",
+ "valid, bad = [], []\n",
+ "for i,item in enumerate(raw):\n",
+ " if (isinstance(item,dict) and not (req-item.keys())\n",
+ " and all(isinstance(item[k],list) for k in req-{\"type\"})\n",
+ " and isinstance(item[\"type\"],str)):\n",
+ " valid.append(item)\n",
+ " else:\n",
+ " bad.append(i)\n",
+ "\n",
+ "print(f\"Valid {len(valid)} / {len(raw)} (invalid index: {bad[:10]})\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b18e4617",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for ex in valid:\n",
+ " ex[\"question_in\"] = [START_TOKEN] + ex[\"question\"]\n",
+ " ex[\"question_out\"] = ex[\"question\"] + [END_TOKEN]\n",
+ "\n",
+ " ex[\"answer_in\"] = [START_TOKEN] + ex[\"answer\"]\n",
+ " ex[\"answer_out\"] = ex[\"answer\"] + [END_TOKEN]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "faa30b82",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tok_token = Tokenizer(oov_token=UNK_TOKEN, filters=\"\")\n",
+ "tok_ner = Tokenizer(lower=False, filters=\"\")\n",
+ "tok_srl = Tokenizer(lower=False, filters=\"\")\n",
+ "tok_q = Tokenizer(oov_token=UNK_TOKEN, filters=\"\")\n",
+ "tok_a = Tokenizer(oov_token=UNK_TOKEN, filters=\"\")\n",
+ "tok_type = Tokenizer(lower=False, filters=\"\")\n",
+ "\n",
+ "tok_token.fit_on_texts([ex[\"tokens\"] for ex in valid])\n",
+ "tok_ner.fit_on_texts([ex[\"ner\"] for ex in valid])\n",
+ "tok_srl.fit_on_texts([ex[\"srl\"] for ex in valid])\n",
+ "tok_q.fit_on_texts([ex[\"question_in\"]+ex[\"question_out\"] for ex in valid])\n",
+ "tok_a.fit_on_texts([ex[\"answer_in\"]+ex[\"answer_out\"] for ex in valid])\n",
+ "tok_type.fit_on_texts([ex[\"type\"] for ex in valid])\n",
+ "\n",
+ "# +1 utk padding\n",
+ "vocab_token = len(tok_token.word_index)+1\n",
+ "vocab_ner = len(tok_ner.word_index)+1\n",
+ "vocab_srl = len(tok_srl.word_index)+1\n",
+ "vocab_q = len(tok_q.word_index)+1\n",
+ "vocab_a = len(tok_a.word_index)+1\n",
+ "vocab_type = len(tok_type.word_index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "c83ce734",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def seqs(field, tok, maxlen):\n",
+ " return pad_sequences(\n",
+ " tok.texts_to_sequences([ex[field] for ex in valid]),\n",
+ " maxlen=maxlen, padding=\"post\"\n",
+ " )\n",
+ "\n",
+ "X_tok = seqs(\"tokens\", tok_token, MAXLEN_SRC)\n",
+ "X_ner = seqs(\"ner\", tok_ner, MAXLEN_SRC)\n",
+ "X_srl = seqs(\"srl\", tok_srl, MAXLEN_SRC)\n",
+ "\n",
+ "Q_in = seqs(\"question_in\", tok_q, MAXLEN_TGT)\n",
+ "Q_out = seqs(\"question_out\", tok_q, MAXLEN_TGT)\n",
+ "A_in = seqs(\"answer_in\", tok_a, MAXLEN_TGT)\n",
+ "A_out = seqs(\"answer_out\", tok_a, MAXLEN_TGT)\n",
+ "\n",
+ "y_type = to_categorical(\n",
+ " np.array([seq[0]-1 for seq in tok_type.texts_to_sequences([ex[\"type\"] for ex in valid])]),\n",
+ " num_classes=vocab_type\n",
+ ")\n",
+ "\n",
+ "# Expand dims → (batch, seq, 1) agar cocok dgn sparse_cce\n",
+ "Q_out = np.expand_dims(Q_out, -1)\n",
+ "A_out = np.expand_dims(A_out, -1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "ad3fe7f2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "(X_tok_tr, X_tok_te,\n",
+ " X_ner_tr, X_ner_te,\n",
+ " X_srl_tr, X_srl_te,\n",
+ " Q_in_tr, Q_in_te,\n",
+ " Q_out_tr, Q_out_te,\n",
+ " A_in_tr, A_in_te,\n",
+ " A_out_tr, A_out_te,\n",
+ " y_type_tr,y_type_te) = train_test_split(\n",
+ " X_tok, X_ner, X_srl, Q_in, Q_out, A_in, A_out, y_type,\n",
+ " test_size=0.2, random_state=42\n",
+ " )\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "f20abfb5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-05-10 14:49:43.127764: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "too many values to unpack (expected 3)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[7], line 10\u001b[0m\n\u001b[1;32m 7\u001b[0m emb_srl \u001b[38;5;241m=\u001b[39m Embedding(vocab_srl, \u001b[38;5;241m16\u001b[39m, mask_zero\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)(enc_srl)\n\u001b[1;32m 9\u001b[0m enc_cat \u001b[38;5;241m=\u001b[39m Concatenate()([emb_tok, emb_ner, emb_srl])\n\u001b[0;32m---> 10\u001b[0m enc_out, state_h, state_c \u001b[38;5;241m=\u001b[39m Bidirectional(\n\u001b[1;32m 11\u001b[0m LSTM(\u001b[38;5;241m256\u001b[39m, return_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, return_sequences\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 12\u001b[0m )(enc_cat)\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# ---------- Klasifikasi tipe ----------\u001b[39;00m\n\u001b[1;32m 15\u001b[0m type_out \u001b[38;5;241m=\u001b[39m Dense(vocab_type, activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msoftmax\u001b[39m\u001b[38;5;124m\"\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype_output\u001b[39m\u001b[38;5;124m\"\u001b[39m)(enc_out)\n",
+ "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 3)"
+ ]
+ }
+ ],
+ "source": [
+ "enc_tok = Input(shape=(None,), name=\"enc_tok\")\n",
+ "enc_ner = Input(shape=(None,), name=\"enc_ner\")\n",
+ "enc_srl = Input(shape=(None,), name=\"enc_srl\")\n",
+ "\n",
+ "emb_tok = Embedding(vocab_token, 128, mask_zero=True)(enc_tok)\n",
+ "emb_ner = Embedding(vocab_ner, 16, mask_zero=True)(enc_ner)\n",
+ "emb_srl = Embedding(vocab_srl, 16, mask_zero=True)(enc_srl)\n",
+ "\n",
+ "enc_cat = Concatenate()([emb_tok, emb_ner, emb_srl])\n",
+ "enc_out, state_h, state_c = Bidirectional(\n",
+ " LSTM(256, return_state=True, return_sequences=False)\n",
+ ")(enc_cat)\n",
+ "\n",
+ "# ---------- Klasifikasi tipe ----------\n",
+ "type_out = Dense(vocab_type, activation=\"softmax\", name=\"type_output\")(enc_out)\n",
+ "\n",
+ "# ---------- Decoder QUESTION ----------\n",
+ "dec_q_in = Input(shape=(None,), name=\"dec_q_in\")\n",
+ "dec_q_emb = Embedding(vocab_q, 128, mask_zero=True)(dec_q_in)\n",
+ "dec_q_lstm = LSTM(256, return_sequences=True)\n",
+ "dec_q_out = dec_q_lstm(dec_q_emb, initial_state=[state_h, state_c])\n",
+ "q_out = TimeDistributed(Dense(vocab_q, activation=\"softmax\"), name=\"question_output\")(dec_q_out)\n",
+ "\n",
+ "# ---------- Decoder ANSWER ----------\n",
+ "dec_a_in = Input(shape=(None,), name=\"dec_a_in\")\n",
+ "dec_a_emb = Embedding(vocab_a, 128, mask_zero=True)(dec_a_in)\n",
+ "dec_a_lstm = LSTM(256, return_sequences=True)\n",
+ "dec_a_out = dec_a_lstm(dec_a_emb, initial_state=[state_h, state_c])\n",
+ "a_out = TimeDistributed(Dense(vocab_a, activation=\"softmax\"), name=\"answer_output\")(dec_a_out)\n",
+ "\n",
+ "# ---------- Build & compile ----------\n",
+ "model = Model(\n",
+ " inputs=[enc_tok, enc_ner, enc_srl, dec_q_in, dec_a_in],\n",
+ " outputs=[q_out, a_out, type_out]\n",
+ ")\n",
+ "\n",
+ "model.compile(\n",
+ " optimizer=\"adam\",\n",
+ " loss={\n",
+ " \"question_output\": \"sparse_categorical_crossentropy\",\n",
+ " \"answer_output\" : \"sparse_categorical_crossentropy\",\n",
+ " \"type_output\" : \"categorical_crossentropy\"\n",
+ " },\n",
+ " loss_weights={\n",
+ " \"question_output\": 1.0,\n",
+ " \"answer_output\" : 1.0,\n",
+ " \"type_output\" : 0.3\n",
+ " },\n",
+ " metrics={\n",
+ " \"question_output\": \"accuracy\",\n",
+ " \"answer_output\" : \"accuracy\",\n",
+ " \"type_output\" : \"accuracy\"\n",
+ " }\n",
+ ")\n",
+ "\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c348406e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "early = EarlyStopping(patience=3, restore_best_weights=True)\n",
+ "\n",
+ "model.fit(\n",
+ " [X_tok_tr, X_ner_tr, X_srl_tr, Q_in_tr, A_in_tr],\n",
+ " {\"question_output\": Q_out_tr,\n",
+ " \"answer_output\" : A_out_tr,\n",
+ " \"type_output\" : y_type_tr},\n",
+ " batch_size=BATCH,\n",
+ " epochs=EPOCHS,\n",
+ " validation_split=0.1,\n",
+ " callbacks=[early]\n",
+ ")\n",
+ "\n",
+ "# -------------------------------------------------\n",
+ "# 8. Simpan model & tokenizer\n",
+ "# -------------------------------------------------\n",
+ "model.save(\"qg_multitask.keras\")\n",
+ "with open(\"tokenizers.pkl\", \"wb\") as f:\n",
+ " pickle.dump({\n",
+ " \"token\": tok_token,\n",
+ " \"ner\" : tok_ner,\n",
+ " \"srl\" : tok_srl,\n",
+ " \"q\" : tok_q,\n",
+ " \"a\" : tok_a,\n",
+ " \"type\" : tok_type\n",
+ " }, f)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "myenv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/QC/new_LNS.tsv b/old/QC/new_LNS.tsv
similarity index 100%
rename from QC/new_LNS.tsv
rename to old/QC/new_LNS.tsv
diff --git a/QC/normalize.py b/old/QC/normalize.py
similarity index 100%
rename from QC/normalize.py
rename to old/QC/normalize.py
diff --git a/QC/normalize_dataset.json b/old/QC/normalize_dataset.json
similarity index 99%
rename from QC/normalize_dataset.json
rename to old/QC/normalize_dataset.json
index 953e975..222ebdc 100644
--- a/QC/normalize_dataset.json
+++ b/old/QC/normalize_dataset.json
@@ -13716,10 +13716,10 @@
"type": "tof"
},
{
- "tokens": ["Indonesia", "terletak", "di", "Benua", "Afrika", "."],
+ "tokens": ["Indonesia", "terletak", "di", "Benua", "asia", "."],
"ner": ["B-LOC", "O", "O", "O", "B-LOC", "O"],
"srl": ["ARG1", "V", "ARGM-LOC", "ARGM-LOC", "ARGM-LOC", "O"],
- "question": ["Indonesia", "terletak", "di", "Benua", "Afrika", "."],
+ "question": ["Indonesia", "terletak", "di", "Benua", "asia", "."],
"answer": ["false"],
"type": "tof"
}
diff --git a/QC/old/dataset_combination.json b/old/QC/old/dataset_combination.json
similarity index 100%
rename from QC/old/dataset_combination.json
rename to old/QC/old/dataset_combination.json
diff --git a/QC/old/dataset_indonesia_history_factual_5000.json b/old/QC/old/dataset_indonesia_history_factual_5000.json
similarity index 100%
rename from QC/old/dataset_indonesia_history_factual_5000.json
rename to old/QC/old/dataset_indonesia_history_factual_5000.json
diff --git a/QC/old/dataset_natural_ner_srl_pendidikan.json b/old/QC/old/dataset_natural_ner_srl_pendidikan.json
similarity index 100%
rename from QC/old/dataset_natural_ner_srl_pendidikan.json
rename to old/QC/old/dataset_natural_ner_srl_pendidikan.json
diff --git a/QC/old/dataset_perkenalan_1000_variatif.json b/old/QC/old/dataset_perkenalan_1000_variatif.json
similarity index 100%
rename from QC/old/dataset_perkenalan_1000_variatif.json
rename to old/QC/old/dataset_perkenalan_1000_variatif.json
diff --git a/QC/old/dataset_perkenalan_500.json b/old/QC/old/dataset_perkenalan_500.json
similarity index 100%
rename from QC/old/dataset_perkenalan_500.json
rename to old/QC/old/dataset_perkenalan_500.json
diff --git a/QC/old/dataset_qc.json b/old/QC/old/dataset_qc.json
similarity index 100%
rename from QC/old/dataset_qc.json
rename to old/QC/old/dataset_qc.json
diff --git a/QC/old/dataset_qc_tokenized.json b/old/QC/old/dataset_qc_tokenized.json
similarity index 100%
rename from QC/old/dataset_qc_tokenized.json
rename to old/QC/old/dataset_qc_tokenized.json
diff --git a/QC/old/new_dataset.json b/old/QC/old/new_dataset.json
similarity index 100%
rename from QC/old/new_dataset.json
rename to old/QC/old/new_dataset.json
diff --git a/QC/old/normalized_dataset.json b/old/QC/old/normalized_dataset.json
similarity index 100%
rename from QC/old/normalized_dataset.json
rename to old/QC/old/normalized_dataset.json
diff --git a/QC/output.tsv b/old/QC/output.tsv
similarity index 100%
rename from QC/output.tsv
rename to old/QC/output.tsv
diff --git a/QC/qc_v2.py b/old/QC/qc_v2.py
similarity index 100%
rename from QC/qc_v2.py
rename to old/QC/qc_v2.py
diff --git a/old/QC/qg_dataset.json b/old/QC/qg_dataset.json
new file mode 100644
index 0000000..063ffdc
--- /dev/null
+++ b/old/QC/qg_dataset.json
@@ -0,0 +1,1914 @@
+[
+ {
+ "tokens": ["Nama", "saya", "Aditya", "Pratama", "."],
+ "ner": ["O", "O", "B-PER", "I-PER", "O"],
+ "srl": ["ARG1", "ARG2", "ARG0", "ARG0", "O"],
+ "question": ["Nama", "saya", "Aditya", "Pratama", "."],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": [
+ "Aku",
+ "lahir",
+ "di",
+ "Kota",
+ "Padang",
+ ",",
+ "Sumatra",
+ "Barat",
+ "."
+ ],
+ "ner": ["O", "O", "O", "B-LOC", "I-LOC", "O", "B-LOC", "I-LOC", "O"],
+ "srl": [
+ "ARG0",
+ "V",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": [
+ "Aku",
+ "lahir",
+ "di",
+ "Kota",
+ "Padang",
+ "Sumatra",
+ "Barat",
+ "."
+ ],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": ["Dia", "tinggal", "di", "Benua", "Afrika", "."],
+ "ner": ["O", "O", "O", "O", "B-LOC", "O"],
+ "srl": ["ARG0", "V", "ARGM-LOC", "ARGM-LOC", "ARGM-LOC", "O"],
+ "question": ["Dia", "tinggal", "di", "Benua", "Afrika", "."],
+ "answer": ["false"],
+ "type": "tof"
+ },
+ {
+ "tokens": ["Usia", "saya", "enam", "belas", "tahun", "."],
+ "ner": ["O", "O", "O", "O", "O", "O"],
+ "srl": ["ARG1", "ARG2", "ARG0", "ARG0", "ARG0", "O"],
+ "question": ["Usia", "saya", "enam", "belas", "tahun", "."],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": ["Rina", "kuliah", "di", "Universitas", "Brawijaya", "."],
+ "ner": ["B-PER", "O", "O", "B-ORG", "I-ORG", "O"],
+ "srl": ["ARG0", "V", "ARGM-LOC", "ARGM-LOC", "ARGM-LOC", "O"],
+ "question": ["Rina", "kuliah", "di", "Universitas", "Brawijaya", "."],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": [
+ "Hobi",
+ "utama",
+ "saya",
+ "adalah",
+ "berenang",
+ "di",
+ "laut",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "O", "O", "O"],
+ "srl": ["ARG1", "ARG1", "ARG0", "V", "ARG2", "ARGM-LOC", "ARGM-LOC", "O"],
+ "question": [
+ "Hobi",
+ "utama",
+ "saya",
+ "adalah",
+ "berenang",
+ "di",
+ "laut",
+ "."
+ ],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": [
+ "Saya",
+ "menguasai",
+ "tiga",
+ "bahasa",
+ "yaitu",
+ "Spanyol",
+ ",",
+ "Jepang",
+ ",",
+ "dan",
+ "Arab",
+ "."
+ ],
+ "ner": [
+ "O",
+ "O",
+ "O",
+ "O",
+ "O",
+ "B-MISC",
+ "O",
+ "B-MISC",
+ "O",
+ "O",
+ "B-MISC",
+ "O"
+ ],
+ "srl": [
+ "ARG0",
+ "V",
+ "ARG1",
+ "ARG1",
+ "O",
+ "ARG2",
+ "O",
+ "ARG2",
+ "O",
+ "O",
+ "ARG2",
+ "O"
+ ],
+ "question": [
+ "Saya",
+ "menguasai",
+ "tiga",
+ "bahasa",
+ "Spanyol",
+ ",",
+ "Jepang",
+ "dan",
+ "Arab",
+ "."
+ ],
+ "answer": ["false"],
+ "type": "tof"
+ },
+ {
+ "tokens": ["Andi", "dan", "Siti", "bersaudara", "."],
+ "ner": ["B-PER", "O", "B-PER", "O", "O"],
+ "srl": ["ARG0", "O", "ARG0", "V", "O"],
+ "question": ["Andi", "dan", "Siti", "bersaudara", "."],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": ["Kami", "sedang", "belajar", "di", "Pantai", "Kuta", "."],
+ "ner": ["O", "O", "O", "O", "B-LOC", "I-LOC", "O"],
+ "srl": ["ARG0", "V", "V", "ARGM-LOC", "ARGM-LOC", "ARGM-LOC", "O"],
+ "question": ["Kami", "sedang", "belajar", "di", "Pantai", "Kuta", "."],
+ "answer": ["false"],
+ "type": "tof"
+ },
+ {
+ "tokens": ["Pekerjaan", "saya", "adalah", "dokter", "hewan", "."],
+ "ner": ["O", "O", "O", "O", "O", "O"],
+ "srl": ["ARG1", "ARG2", "V", "ARG0", "ARG0", "O"],
+ "question": ["Pekerjaan", "saya", "adalah", "dokter", "hewan", "."],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": ["Azizah", "berasal", "dari", "Negara", "Singapura", "."],
+ "ner": ["B-PER", "O", "O", "O", "B-LOC", "O"],
+ "srl": ["ARG0", "V", "ARGM-SOURCE", "ARGM-SOURCE", "ARGM-SOURCE", "O"],
+ "question": ["Azizah", "berasal", "dari", "Negara", "Singapura", "."],
+ "answer": ["false"],
+ "type": "tof"
+ },
+ {
+ "tokens": [
+ "Sekarang",
+ "aku",
+ "tinggal",
+ "di",
+ "Jalan",
+ "Mawar",
+ "Nomor",
+ "dua",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "B-LOC", "O", "O", "O"],
+ "srl": [
+ "ARGM-TMP",
+ "ARG0",
+ "V",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": ["Aku", "tinggal", "di", "Jalan", "Mawar", "Nomor", "dua", "."],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": ["Saya", "lulusan", "SMA", "Negeri", "1", "Bandung", "."],
+ "ner": ["O", "O", "B-ORG", "I-ORG", "I-ORG", "I-LOC", "O"],
+ "srl": ["ARG0", "V", "ARG1", "ARG1", "ARG1", "ARG1", "O"],
+ "question": ["Saya", "lulusan", "SMA", "Negeri", "1", "Bandung", "."],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": [
+ "Dia",
+ "bekerja",
+ "sebagai",
+ "pilot",
+ "di",
+ "Garuda",
+ "Indonesia",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "B-ORG", "I-ORG", "O"],
+ "srl": [
+ "ARG0",
+ "V",
+ "ARG2",
+ "ARG2",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": [
+ "Dia",
+ "bekerja",
+ "sebagai",
+ "pilot",
+ "di",
+ "Garuda",
+ "Indonesia",
+ "."
+ ],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": ["Umurku", "dua", "puluh", "tahun", "pada", "tahun", "2020", "."],
+ "ner": ["O", "O", "O", "O", "O", "O", "O", "O"],
+ "srl": [
+ "ARG1",
+ "ARG0",
+ "ARG0",
+ "ARG0",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": [
+ "Umurku",
+ "dua",
+ "puluh",
+ "tahun",
+ "pada",
+ "tahun",
+ "2020",
+ "."
+ ],
+ "answer": ["false"],
+ "type": "tof"
+ },
+ {
+ "tokens": [
+ "Saya",
+ "suka",
+ "mendengarkan",
+ "musik",
+ "klasik",
+ "setiap",
+ "malam",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "O", "O", "O"],
+ "srl": ["ARG0", "V", "V", "ARG1", "ARG1", "ARGM-TMP", "ARGM-TMP", "O"],
+ "question": [
+ "Saya",
+ "suka",
+ "mendengarkan",
+ "musik",
+ "klasik",
+ "setiap",
+ "malam",
+ "."
+ ],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": [
+ "Kamil",
+ "pernah",
+ "tinggal",
+ "di",
+ "Antartika",
+ "selama",
+ "setahun",
+ "."
+ ],
+ "ner": ["B-PER", "O", "O", "O", "B-LOC", "O", "O", "O"],
+ "srl": [
+ "ARG0",
+ "ARGM-TMP",
+ "V",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": [
+ "Kamil",
+ "pernah",
+ "tinggal",
+ "di",
+ "Antartika",
+ "selama",
+ "setahun",
+ "."
+ ],
+ "answer": ["false"],
+ "type": "tof"
+ },
+ {
+ "tokens": [
+ "Mereka",
+ "bertiga",
+ "menyusun",
+ "proyek",
+ "robotika",
+ "di",
+ "SMK",
+ "N",
+ "2",
+ "Yogyakarta",
+ "."
+ ],
+ "ner": [
+ "O",
+ "O",
+ "O",
+ "O",
+ "O",
+ "O",
+ "B-ORG",
+ "I-ORG",
+ "I-ORG",
+ "I-LOC",
+ "O"
+ ],
+ "srl": [
+ "ARG0",
+ "ARG0",
+ "V",
+ "ARG1",
+ "ARG1",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": [
+ "Mereka",
+ "menyusun",
+ "proyek",
+ "robotika",
+ "di",
+ "SMK",
+ "N",
+ "2",
+ "Yogyakarta",
+ "."
+ ],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": ["Saya", "tidak", "pernah", "mengunjungi", "Pulau", "Bali", "."],
+ "ner": ["O", "O", "O", "O", "B-LOC", "I-LOC", "O"],
+ "srl": ["ARG0", "O", "ARGM-NEG", "V", "ARG1", "ARG1", "O"],
+ "question": ["Saya", "mengunjungi", "Pulau", "Bali", "."],
+ "answer": ["false"],
+ "type": "tof"
+ },
+ {
+ "tokens": [
+ "Ia",
+ "sudah",
+ "menulis",
+ "dua",
+ "buku",
+ "tentang",
+ "kuliner",
+ "nusantara",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O"],
+ "srl": [
+ "ARG0",
+ "ARGM-TMP",
+ "V",
+ "ARG1",
+ "ARG1",
+ "ARG2",
+ "ARG2",
+ "ARG2",
+ "O"
+ ],
+ "question": [
+ "Ia",
+ "menulis",
+ "dua",
+ "buku",
+ "tentang",
+ "kuliner",
+ "nusantara",
+ "."
+ ],
+ "answer": ["true"],
+ "type": "tof"
+ },
+ {
+ "tokens": ["Nama", "lengkap", "saya", "adalah", "Bayu", "Prabowo", "."],
+ "ner": ["O", "O", "O", "O", "B-PER", "I-PER", "O"],
+ "srl": ["ARG1", "ARG1", "ARG2", "V", "ARG0", "ARG0", "O"],
+ "question": ["Nama", "lengkap", "saya", "adalah", "____", "____", "."],
+ "answer": ["Bayu", "Prabowo"],
+ "type": "isian"
+ },
+ {
+ "tokens": ["Saya", "lahir", "di", "Kota", "Makassar", "."],
+ "ner": ["O", "O", "O", "B-LOC", "I-LOC", "O"],
+ "srl": ["ARG0", "V", "ARGM-LOC", "ARGM-LOC", "ARGM-LOC", "O"],
+ "question": ["Saya", "lahir", "di", "Kota", "____", "."],
+ "answer": ["Makassar"],
+ "type": "isian"
+ },
+ {
+ "tokens": ["Usia", "saya", "dua", "puluh", "satu", "tahun", "."],
+ "ner": ["O", "O", "O", "O", "O", "O", "O"],
+ "srl": ["ARG1", "ARG2", "ARG0", "ARG0", "ARG0", "ARG0", "O"],
+ "question": ["Usia", "saya", "____", "____", "____", "tahun", "."],
+ "answer": ["dua", "puluh", "satu"],
+ "type": "isian"
+ },
+ {
+ "tokens": ["Aku", "kuliah", "di", "Universitas", "Gadjah", "Mada", "."],
+ "ner": ["O", "O", "O", "B-ORG", "I-ORG", "I-ORG", "O"],
+ "srl": ["ARG0", "V", "ARGM-LOC", "ARGM-LOC", "ARGM-LOC", "ARGM-LOC", "O"],
+ "question": ["Aku", "kuliah", "di", "Universitas", "____", "____", "."],
+ "answer": ["Gadjah", "Mada"],
+ "type": "isian"
+ },
+ {
+ "tokens": ["Hobi", "utama", "saya", "adalah", "berenang", "."],
+ "ner": ["O", "O", "O", "O", "O", "O"],
+ "srl": ["ARG1", "ARG1", "ARG2", "V", "ARG0", "O"],
+ "question": ["Hobi", "utama", "saya", "adalah", "____", "."],
+ "answer": ["berenang"],
+ "type": "isian"
+ },
+ {
+ "tokens": ["Saya", "fasih", "berbahasa", "Jepang", "dan", "Korea", "."],
+ "ner": ["O", "O", "O", "B-MISC", "O", "B-MISC", "O"],
+ "srl": ["ARG0", "ARGM-MNR", "V", "ARG2", "O", "ARG2", "O"],
+ "question": ["Saya", "fasih", "berbahasa", "____", "dan", "____", "."],
+ "answer": ["Jepang", "Korea"],
+ "type": "isian"
+ },
+ {
+ "tokens": [
+ "Dia",
+ "bekerja",
+ "sebagai",
+ "analis",
+ "data",
+ "di",
+ "PT",
+ "Astra",
+ "Internasional",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "O", "B-ORG", "I-ORG", "I-ORG", "O"],
+ "srl": [
+ "ARG0",
+ "V",
+ "ARG2",
+ "ARG2",
+ "ARG2",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": [
+ "Dia",
+ "bekerja",
+ "sebagai",
+ "____",
+ "____",
+ "di",
+ "PT",
+ "Astra",
+ "Internasional",
+ "."
+ ],
+ "answer": ["analis", "data"],
+ "type": "isian"
+ },
+ {
+ "tokens": [
+ "Sekarang",
+ "saya",
+ "tinggal",
+ "di",
+ "Jalan",
+ "Merpati",
+ "Nomor",
+ "tiga",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "B-LOC", "I-LOC", "O", "O", "O"],
+ "srl": [
+ "ARGM-TMP",
+ "ARG0",
+ "V",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": [
+ "Sekarang",
+ "saya",
+ "tinggal",
+ "di",
+ "Jalan",
+ "____",
+ "Nomor",
+ "____",
+ "."
+ ],
+ "answer": ["Merpati", "tiga"],
+ "type": "isian"
+ },
+ {
+ "tokens": ["Ibuku", "berasal", "dari", "Kabupaten", "Sleman", "."],
+ "ner": ["O", "O", "O", "O", "B-LOC", "O"],
+ "srl": ["ARG0", "V", "ARGM-SOURCE", "ARGM-SOURCE", "ARGM-SOURCE", "O"],
+ "question": ["Ibuku", "berasal", "dari", "Kabupaten", "____", "."],
+ "answer": ["Sleman"],
+ "type": "isian"
+ },
+ {
+ "tokens": [
+ "Kami",
+ "mendirikan",
+ "komunitas",
+ "Koding",
+ "Nusantara",
+ "tahun",
+ "2023",
+ "."
+ ],
+ "ner": ["O", "O", "O", "B-ORG", "I-ORG", "B-DATE", "I-DATE", "O"],
+ "srl": ["ARG0", "V", "ARG1", "ARG1", "ARG1", "ARGM-TMP", "ARGM-TMP", "O"],
+ "question": [
+ "Kami",
+ "mendirikan",
+ "komunitas",
+ "____",
+ "____",
+ "tahun",
+ "2023",
+ "."
+ ],
+ "answer": ["Koding", "Nusantara"],
+ "type": "isian"
+ },
+ {
+ "tokens": [
+ "Dia",
+ "gemar",
+ "bermain",
+ "sepak",
+ "bola",
+ "setiap",
+ "Sabtu",
+ "pagi",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "O", "B-DATE", "B-TIME", "O"],
+ "srl": [
+ "ARG0",
+ "V",
+ "V",
+ "ARG1",
+ "ARG1",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": [
+ "Dia",
+ "gemar",
+ "bermain",
+ "____",
+ "____",
+ "setiap",
+ "Sabtu",
+ "pagi",
+ "."
+ ],
+ "answer": ["sepak", "bola"],
+ "type": "isian"
+ },
+ {
+ "tokens": ["Alamat", "email", "saya", "adalah", "rizky@mail.com", "."],
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+ "."
+ ],
+ "answer": ["Putih"],
+ "type": "isian"
+ },
+ {
+ "tokens": [
+ "Ibu",
+ "Kota",
+ "provinsi",
+ "Jawa",
+ "Tengah",
+ "adalah",
+ "Semarang",
+ "."
+ ],
+ "ner": ["O", "O", "O", "B-LOC", "I-LOC", "O", "B-LOC", "O"],
+ "srl": ["ARG1", "ARG1", "ARG1", "ARG1", "ARG1", "V", "ARG2", "O"],
+ "question": [
+ "Ibu",
+ "Kota",
+ "provinsi",
+ "Jawa",
+ "Tengah",
+ "adalah",
+ "____",
+ "."
+ ],
+ "options": ["Surabaya", "Semarang", "Solo", "Magelang"],
+ "answer": ["Semarang"],
+ "type": "opsi"
+ },
+ {
+ "tokens": [
+ "Badan",
+ "Antariksaa",
+ "milik",
+ "Amerika",
+ "Serikat",
+ "disebut",
+ "NASA",
+ "."
+ ],
+ "ner": ["O", "B-ORG", "O", "B-LOC", "I-LOC", "O", "B-ORG", "O"],
+ "srl": ["ARG1", "ARG1", "O", "ARG1", "I-ARG1", "V", "ARG2", "O"],
+ "question": [
+ "Badan",
+ "Antariksaa",
+ "milik",
+ "Amerika",
+ "Serikat",
+ "disebut",
+ "____",
+ "."
+ ],
+ "options": ["ESA", "JAXA", "NASA", "ISRO"],
+ "answer": ["NASA"],
+ "type": "opsi"
+ },
+ {
+ "tokens": ["Pulau", "terbesar", "di", "Indonesia", "adalah", "Papua", "."],
+ "ner": ["O", "O", "O", "B-LOC", "O", "B-LOC", "O"],
+ "srl": ["ARG1", "ARG1", "O", "ARG1", "V", "ARG2", "O"],
+ "question": ["Pulau", "terbesar", "di", "Indonesia", "adalah", "____", "."],
+ "options": ["Sumatra", "Papua", "Kalimantan", "Sulawesi"],
+ "answer": ["Papua"],
+ "type": "opsi"
+ },
+ {
+ "tokens": [
+ "Presiden",
+ "pertama",
+ "Republik",
+ "Indonesia",
+ "adalah",
+ "Soekarno",
+ "."
+ ],
+ "ner": ["O", "O", "O", "B-LOC", "O", "B-PER", "O"],
+ "srl": ["ARG1", "ARG1", "ARG1", "ARG1", "V", "ARG2", "O"],
+ "question": [
+ "Presiden",
+ "pertama",
+ "Republik",
+ "Indonesia",
+ "adalah",
+ "____",
+ "."
+ ],
+ "options": ["Soekarno", "Suharto", "Habibie", "Megawati"],
+ "answer": ["Soekarno"],
+ "type": "opsi"
+ },
+ {
+ "tokens": ["Ibukota", "Australia", "adalah", "Canberra", "."],
+ "ner": ["O", "B-LOC", "O", "B-LOC", "O"],
+ "srl": ["ARG1", "ARG1", "V", "ARG2", "O"],
+ "question": ["Ibukota", "Australia", "adalah", "____", "."],
+ "options": ["Sydney", "Melbourne", "Perth", "Canberra"],
+ "answer": ["Canberra"],
+ "type": "opsi"
+ },
+ {
+ "tokens": [
+ "Suhu",
+ "air",
+ "mendidih",
+ "pada",
+ "tekanan",
+ "normal",
+ "adalah",
+ "100",
+ "derajat",
+ "Celsius",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
+ "srl": [
+ "ARG1",
+ "ARG1",
+ "V",
+ "O",
+ "ARGM-MNR",
+ "I-ARGM-MNR",
+ "V",
+ "ARG2",
+ "I-ARG2",
+ "I-ARG2",
+ "O"
+ ],
+ "question": [
+ "Suhu",
+ "air",
+ "mendidih",
+ "pada",
+ "tekanan",
+ "normal",
+ "adalah",
+ "____",
+ "derajat",
+ "Celsius",
+ "."
+ ],
+ "options": ["90", "95", "100", "110"],
+ "answer": ["100"],
+ "type": "opsi"
+ },
+ {
+ "tokens": [
+ "Planet",
+ "terdekat",
+ "dengan",
+ "Matahari",
+ "adalah",
+ "Merkurius",
+ "."
+ ],
+ "ner": ["O", "O", "O", "B-MISC", "O", "B-MISC", "O"],
+ "srl": ["ARG1", "ARG1", "O", "ARG1", "V", "ARG2", "O"],
+ "question": [
+ "Planet",
+ "terdekat",
+ "dengan",
+ "Matahari",
+ "adalah",
+ "____",
+ "."
+ ],
+ "options": ["Venus", "Mars", "Merkurius", "Bumi"],
+ "answer": ["Merkurius"],
+ "type": "opsi"
+ },
+ {
+ "tokens": [
+ "Candi",
+ "Borobudur",
+ "terletak",
+ "di",
+ "Magelang",
+ ",",
+ "Jawa",
+ "Tengah",
+ "."
+ ],
+ "ner": ["B-LOC", "I-LOC", "O", "O", "B-LOC", "O", "B-LOC", "I-LOC", "O"],
+ "srl": [
+ "ARG0",
+ "ARG0",
+ "V",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O",
+ "ARGM-LOC",
+ "ARGM-LOC",
+ "O"
+ ],
+ "question": [
+ "Candi",
+ "Borobudur",
+ "terletak",
+ "di",
+ "____",
+ "Jawa",
+ "Tengah",
+ "."
+ ],
+ "options": ["Solo", "Magelang", "Kediri", "Semarang"],
+ "answer": ["Magelang"],
+ "type": "opsi"
+ },
+ {
+ "tokens": ["Sistem", "angka", "biner", "menggunakan", "basis", "____", "."],
+ "ner": ["O", "O", "O", "O", "O", "O", "O"],
+ "srl": ["ARG0", "ARG0", "ARG0", "V", "ARG1", "ARG1", "O"],
+ "question": [
+ "Sistem",
+ "angka",
+ "biner",
+ "menggunakan",
+ "basis",
+ "____",
+ "."
+ ],
+ "options": ["2", "8", "10", "16"],
+ "answer": ["2"],
+ "type": "opsi"
+ },
+ {
+ "tokens": [
+ "Batik",
+ "menjadi",
+ "warisan",
+ "budaya",
+ "dunia",
+ "yang",
+ "diakui",
+ "UNESCO",
+ "tahun",
+ "2009",
+ "."
+ ],
+ "ner": ["O", "O", "O", "O", "O", "O", "O", "B-ORG", "O", "B-DATE", "O"],
+ "srl": [
+ "ARG0",
+ "V",
+ "ARG1",
+ "I-ARG1",
+ "I-ARG1",
+ "O",
+ "V",
+ "ARGM-LOC",
+ "ARGM-TMP",
+ "ARGM-TMP",
+ "O"
+ ],
+ "question": [
+ "Batik",
+ "menjadi",
+ "warisan",
+ "budaya",
+ "dunia",
+ "yang",
+ "diakui",
+ "____",
+ "tahun",
+ "2009",
+ "."
+ ],
+ "options": ["FAO", "UNICEF", "UNESCO", "WHO"],
+ "answer": ["UNESCO"],
+ "type": "opsi"
+ }
+]
diff --git a/QC/qg_train.ipynb b/old/QC/qg_train.ipynb
similarity index 55%
rename from QC/qg_train.ipynb
rename to old/QC/qg_train.ipynb
index a6e0a46..70b8e6b 100644
--- a/QC/qg_train.ipynb
+++ b/old/QC/qg_train.ipynb
@@ -2,30 +2,10 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 51,
"id": "9bf2159a",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2025-05-02 15:16:40.916818: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
- "2025-05-02 15:16:40.923426: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
- "2025-05-02 15:16:40.983217: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
- "2025-05-02 15:16:41.024477: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
- "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
- "E0000 00:00:1746173801.069646 9825 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
- "E0000 00:00:1746173801.081087 9825 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
- "W0000 00:00:1746173801.169376 9825 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
- "W0000 00:00:1746173801.169393 9825 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
- "W0000 00:00:1746173801.169395 9825 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
- "W0000 00:00:1746173801.169396 9825 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
- "2025-05-02 15:16:41.179508: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
- "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import json\n",
"import numpy as np\n",
@@ -51,7 +31,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 52,
"id": "50118278",
"metadata": {},
"outputs": [
@@ -60,15 +40,15 @@
"output_type": "stream",
"text": [
"\n",
- " Jumlah data valid: 321 / 321\n",
+ " Jumlah data valid: 70 / 70\n",
" Jumlah data tidak valid: 0\n",
- "Counter({'ftb': 235, 'tof': 45, 'none': 41})\n"
+ "Counter({'tof': 30, 'isian': 30, 'opsi': 10})\n"
]
}
],
"source": [
"# Load raw data\n",
- "with open(\"normalize_dataset.json\", encoding=\"utf-8\") as f:\n",
+ "with open(\"qg_dataset.json\", encoding=\"utf-8\") as f:\n",
" raw_data = json.load(f)\n",
"\n",
"# Validasi lengkap\n",
@@ -123,7 +103,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 53,
"id": "4e3a0088",
"metadata": {},
"outputs": [],
@@ -149,7 +129,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 54,
"id": "555f9e22",
"metadata": {},
"outputs": [
@@ -157,7 +137,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "{'ftb', 'tof', 'none'}\n"
+ "{'isian', 'tof', 'opsi'}\n"
]
}
],
@@ -184,7 +164,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 55,
"id": "f530cfe7",
"metadata": {},
"outputs": [],
@@ -200,25 +180,18 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 56,
"id": "255e2a9a",
"metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2025-04-29 19:13:22.481835: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
- ]
- },
{
"data": {
"text/html": [
- "Model: \"functional\"\n",
+ "Model: \"functional_5\"\n",
"\n"
],
"text/plain": [
- "\u001b[1mModel: \"functional\"\u001b[0m\n"
+ "\u001b[1mModel: \"functional_5\"\u001b[0m\n"
]
},
"metadata": {},
@@ -239,30 +212,31 @@
"│ srl_input │ (None, None) │ 0 │ - │\n",
"│ (InputLayer) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ embedding │ (None, None, 128) │ 126,080 │ tok_input[0][0] │\n",
+ "│ embedding_15 │ (None, None, 128) │ 41,600 │ tok_input[0][0] │\n",
"│ (Embedding) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ embedding_1 │ (None, None, 16) │ 352 │ ner_input[0][0] │\n",
+ "│ embedding_16 │ (None, None, 16) │ 272 │ ner_input[0][0] │\n",
"│ (Embedding) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ embedding_2 │ (None, None, 16) │ 432 │ srl_input[0][0] │\n",
+ "│ embedding_17 │ (None, None, 16) │ 272 │ srl_input[0][0] │\n",
"│ (Embedding) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ concatenate │ (None, None, 160) │ 0 │ embedding[0][0], │\n",
- "│ (Concatenate) │ │ │ embedding_1[0][0… │\n",
- "│ │ │ │ embedding_2[0][0] │\n",
+ "│ concatenate_5 │ (None, None, 160) │ 0 │ embedding_15[0][… │\n",
+ "│ (Concatenate) │ │ │ embedding_16[0][… │\n",
+ "│ │ │ │ embedding_17[0][… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ lstm (LSTM) │ (None, None, 256) │ 427,008 │ concatenate[0][0] │\n",
+ "│ lstm_5 (LSTM) │ (None, None, 256) │ 427,008 │ concatenate_5[0]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ get_item (GetItem) │ (None, 256) │ 0 │ lstm[0][0] │\n",
+ "│ get_item_5 │ (None, 256) │ 0 │ lstm_5[0][0] │\n",
+ "│ (GetItem) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ question_output │ (None, None, 473) │ 121,561 │ lstm[0][0] │\n",
+ "│ question_output │ (None, None, 272) │ 69,904 │ lstm_5[0][0] │\n",
"│ (TimeDistributed) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ answer_output │ (None, None, 383) │ 98,431 │ lstm[0][0] │\n",
+ "│ answer_output │ (None, None, 60) │ 15,420 │ lstm_5[0][0] │\n",
"│ (TimeDistributed) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ type_output (Dense) │ (None, 3) │ 771 │ get_item[0][0] │\n",
+ "│ type_output (Dense) │ (None, 3) │ 771 │ get_item_5[0][0] │\n",
"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
"
\n"
],
@@ -279,30 +253,31 @@
"│ srl_input │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
"│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ embedding │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m126,080\u001b[0m │ tok_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ embedding_15 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m41,600\u001b[0m │ tok_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mEmbedding\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ embedding_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m352\u001b[0m │ ner_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ embedding_16 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m272\u001b[0m │ ner_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mEmbedding\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ embedding_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m432\u001b[0m │ srl_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ embedding_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m272\u001b[0m │ srl_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mEmbedding\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ concatenate │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ embedding[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
- "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ embedding_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
- "│ │ │ │ embedding_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ concatenate_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ embedding_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ embedding_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ │ │ │ embedding_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ lstm (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m427,008\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ lstm_5 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m427,008\u001b[0m │ concatenate_5[\u001b[38;5;34m0\u001b[0m]… │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ get_item (\u001b[38;5;33mGetItem\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ get_item_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ lstm_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mGetItem\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ question_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m473\u001b[0m) │ \u001b[38;5;34m121,561\u001b[0m │ lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ question_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m272\u001b[0m) │ \u001b[38;5;34m69,904\u001b[0m │ lstm_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ answer_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m383\u001b[0m) │ \u001b[38;5;34m98,431\u001b[0m │ lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ answer_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m) │ \u001b[38;5;34m15,420\u001b[0m │ lstm_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
- "│ type_output (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m771\u001b[0m │ get_item[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ type_output (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m771\u001b[0m │ get_item_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
]
},
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{
"data": {
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{
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"Epoch 1/30\n",
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+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step - answer_output_accuracy: 0.0030 - answer_output_loss: 4.1163 - loss: 10.8193 - question_output_accuracy: 0.0030 - question_output_loss: 5.6031 - type_output_accuracy: 0.2000 - type_output_loss: 1.0999 - val_answer_output_accuracy: 0.8833 - val_answer_output_loss: 4.0123 - val_loss: 10.6706 - val_question_output_accuracy: 0.6000 - val_question_output_loss: 5.5595 - val_type_output_accuracy: 0.1667 - val_type_output_loss: 1.0987\n",
"Epoch 2/30\n",
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+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - answer_output_accuracy: 0.8800 - answer_output_loss: 4.0174 - loss: 10.6778 - question_output_accuracy: 0.5640 - question_output_loss: 5.5631 - type_output_accuracy: 0.4200 - type_output_loss: 1.0973 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 3.8939 - val_loss: 10.4860 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 5.4945 - val_type_output_accuracy: 0.3333 - val_type_output_loss: 1.0976\n",
"Epoch 3/30\n",
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+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - answer_output_accuracy: 0.9370 - answer_output_loss: 3.9075 - loss: 10.5064 - question_output_accuracy: 0.5870 - question_output_loss: 5.5043 - type_output_accuracy: 0.6200 - type_output_loss: 1.0946 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 3.7157 - val_loss: 10.1938 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 5.3815 - val_type_output_accuracy: 0.3333 - val_type_output_loss: 1.0965\n",
"Epoch 4/30\n",
- "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - answer_output_accuracy: 0.8633 - answer_output_loss: 1.1478 - loss: 4.4374 - question_output_accuracy: 0.6639 - question_output_loss: 2.3671 - type_output_accuracy: 0.7490 - type_output_loss: 0.9088 - val_answer_output_accuracy: 0.9261 - val_answer_output_loss: 0.7059 - val_loss: 3.6255 - val_question_output_accuracy: 0.7500 - val_question_output_loss: 1.9356 - val_type_output_accuracy: 0.5652 - val_type_output_loss: 0.9840\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 3.7435 - loss: 10.2381 - question_output_accuracy: 0.5890 - question_output_loss: 5.4027 - type_output_accuracy: 0.6200 - type_output_loss: 1.0919 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 3.4257 - val_loss: 9.7085 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 5.1873 - val_type_output_accuracy: 0.3333 - val_type_output_loss: 1.0955\n",
"Epoch 5/30\n",
- "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - answer_output_accuracy: 0.8783 - answer_output_loss: 1.0187 - loss: 4.0230 - question_output_accuracy: 0.6760 - question_output_loss: 2.1959 - type_output_accuracy: 0.7563 - type_output_loss: 0.8131 - val_answer_output_accuracy: 0.9261 - val_answer_output_loss: 0.6848 - val_loss: 3.5743 - val_question_output_accuracy: 0.7500 - val_question_output_loss: 1.9039 - val_type_output_accuracy: 0.5652 - val_type_output_loss: 0.9857\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 3.4788 - loss: 9.7970 - question_output_accuracy: 0.5850 - question_output_loss: 5.2288 - type_output_accuracy: 0.6600 - type_output_loss: 1.0894 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 2.9617 - val_loss: 8.9146 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 4.8585 - val_type_output_accuracy: 0.3333 - val_type_output_loss: 1.0944\n",
"Epoch 6/30\n",
- "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - answer_output_accuracy: 0.8800 - answer_output_loss: 0.9845 - loss: 3.8171 - question_output_accuracy: 0.6878 - question_output_loss: 2.0357 - type_output_accuracy: 0.7328 - type_output_loss: 0.7942 - val_answer_output_accuracy: 0.9261 - val_answer_output_loss: 0.6742 - val_loss: 3.5592 - val_question_output_accuracy: 0.7500 - val_question_output_loss: 1.8777 - val_type_output_accuracy: 0.5652 - val_type_output_loss: 1.0074\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 3.0565 - loss: 9.0790 - question_output_accuracy: 0.5850 - question_output_loss: 4.9355 - type_output_accuracy: 0.6600 - type_output_loss: 1.0869 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 2.3649 - val_loss: 7.8024 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 4.3441 - val_type_output_accuracy: 0.3333 - val_type_output_loss: 1.0933\n",
"Epoch 7/30\n",
- "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - answer_output_accuracy: 0.8768 - answer_output_loss: 0.9756 - loss: 3.8569 - question_output_accuracy: 0.6743 - question_output_loss: 2.0795 - type_output_accuracy: 0.7030 - type_output_loss: 0.8039 - val_answer_output_accuracy: 0.9261 - val_answer_output_loss: 0.6769 - val_loss: 3.5671 - val_question_output_accuracy: 0.7500 - val_question_output_loss: 1.8631 - val_type_output_accuracy: 0.5652 - val_type_output_loss: 1.0272\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 2.5004 - loss: 8.0585 - question_output_accuracy: 0.5850 - question_output_loss: 4.4735 - type_output_accuracy: 0.6600 - type_output_loss: 1.0845 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 1.8898 - val_loss: 6.6823 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 3.7005 - val_type_output_accuracy: 0.3333 - val_type_output_loss: 1.0920\n",
"Epoch 8/30\n",
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+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 2.0239 - loss: 6.9823 - question_output_accuracy: 0.5850 - question_output_loss: 3.8764 - type_output_accuracy: 0.6600 - type_output_loss: 1.0821 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 1.5873 - val_loss: 5.7713 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 3.0934 - val_type_output_accuracy: 0.3333 - val_type_output_loss: 1.0906\n",
"Epoch 9/30\n",
- "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - answer_output_accuracy: 0.8703 - answer_output_loss: 0.9799 - loss: 3.6985 - question_output_accuracy: 0.6843 - question_output_loss: 1.9755 - type_output_accuracy: 0.7160 - type_output_loss: 0.7474 - val_answer_output_accuracy: 0.9261 - val_answer_output_loss: 0.6958 - val_loss: 3.5849 - val_question_output_accuracy: 0.7500 - val_question_output_loss: 1.8401 - val_type_output_accuracy: 0.5652 - val_type_output_loss: 1.0490\n"
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 1.6939 - loss: 6.0594 - question_output_accuracy: 0.5850 - question_output_loss: 3.2857 - type_output_accuracy: 0.6600 - type_output_loss: 1.0798 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 1.3585 - val_loss: 5.0778 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.6303 - val_type_output_accuracy: 0.3333 - val_type_output_loss: 1.0890\n",
+ "Epoch 10/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 1.4268 - loss: 5.3244 - question_output_accuracy: 0.5850 - question_output_loss: 2.8203 - type_output_accuracy: 0.6600 - type_output_loss: 1.0774 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 1.1559 - val_loss: 4.5630 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.3200 - val_type_output_accuracy: 0.3333 - val_type_output_loss: 1.0871\n",
+ "Epoch 11/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 1.1880 - loss: 4.7795 - question_output_accuracy: 0.5850 - question_output_loss: 2.5167 - type_output_accuracy: 0.6600 - type_output_loss: 1.0748 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.9716 - val_loss: 4.2001 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.1437 - val_type_output_accuracy: 0.3333 - val_type_output_loss: 1.0848\n",
+ "Epoch 12/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.9857 - loss: 4.4356 - question_output_accuracy: 0.5850 - question_output_loss: 2.3778 - type_output_accuracy: 0.6600 - type_output_loss: 1.0721 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.8171 - val_loss: 3.9799 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.0807 - val_type_output_accuracy: 0.3333 - val_type_output_loss: 1.0822\n",
+ "Epoch 13/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.8280 - loss: 4.2715 - question_output_accuracy: 0.5850 - question_output_loss: 2.3745 - type_output_accuracy: 0.6600 - type_output_loss: 1.0690 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.6995 - val_loss: 3.8760 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.0974 - val_type_output_accuracy: 0.3333 - val_type_output_loss: 1.0790\n",
+ "Epoch 14/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.7110 - loss: 4.2264 - question_output_accuracy: 0.5850 - question_output_loss: 2.4498 - type_output_accuracy: 0.6400 - type_output_loss: 1.0656 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.6143 - val_loss: 3.8415 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.1518 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0754\n",
+ "Epoch 15/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.6254 - loss: 4.2353 - question_output_accuracy: 0.5850 - question_output_loss: 2.5482 - type_output_accuracy: 0.6000 - type_output_loss: 1.0617 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.5529 - val_loss: 3.8335 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.2091 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0714\n",
+ "Epoch 16/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.5623 - loss: 4.2530 - question_output_accuracy: 0.5850 - question_output_loss: 2.6334 - type_output_accuracy: 0.6000 - type_output_loss: 1.0573 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.5083 - val_loss: 3.8255 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.2502 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0670\n",
+ "Epoch 17/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.5150 - loss: 4.2561 - question_output_accuracy: 0.5850 - question_output_loss: 2.6886 - type_output_accuracy: 0.6000 - type_output_loss: 1.0525 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.4752 - val_loss: 3.8053 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.2678 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0623\n",
+ "Epoch 18/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.4790 - loss: 4.2357 - question_output_accuracy: 0.5850 - question_output_loss: 2.7094 - type_output_accuracy: 0.6000 - type_output_loss: 1.0473 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.4503 - val_loss: 3.7689 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.2612 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0573\n",
+ "Epoch 19/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.4511 - loss: 4.1904 - question_output_accuracy: 0.5850 - question_output_loss: 2.6974 - type_output_accuracy: 0.5600 - type_output_loss: 1.0419 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.4313 - val_loss: 3.7162 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.2327 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0523\n",
+ "Epoch 20/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.4293 - loss: 4.1218 - question_output_accuracy: 0.5850 - question_output_loss: 2.6564 - type_output_accuracy: 0.5600 - type_output_loss: 1.0361 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.4164 - val_loss: 3.6494 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.1859 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0471\n",
+ "Epoch 21/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.4118 - loss: 4.0332 - question_output_accuracy: 0.5850 - question_output_loss: 2.5912 - type_output_accuracy: 0.5600 - type_output_loss: 1.0302 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.4046 - val_loss: 3.5722 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.1256 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0420\n",
+ "Epoch 22/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.3975 - loss: 3.9297 - question_output_accuracy: 0.5850 - question_output_loss: 2.5080 - type_output_accuracy: 0.5600 - type_output_loss: 1.0242 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.3951 - val_loss: 3.4909 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.0587 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0370\n",
+ "Epoch 23/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.3858 - loss: 3.8184 - question_output_accuracy: 0.5850 - question_output_loss: 2.4147 - type_output_accuracy: 0.5600 - type_output_loss: 1.0180 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.3875 - val_loss: 3.4143 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 1.9948 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0321\n",
+ "Epoch 24/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.3759 - loss: 3.7097 - question_output_accuracy: 0.5850 - question_output_loss: 2.3222 - type_output_accuracy: 0.5600 - type_output_loss: 1.0116 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.3812 - val_loss: 3.3557 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 1.9473 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0273\n",
+ "Epoch 25/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.3674 - loss: 3.6180 - question_output_accuracy: 0.5850 - question_output_loss: 2.2455 - type_output_accuracy: 0.5600 - type_output_loss: 1.0051 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.3759 - val_loss: 3.3316 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 1.9330 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0227\n",
+ "Epoch 26/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.3600 - loss: 3.5615 - question_output_accuracy: 0.5850 - question_output_loss: 2.2030 - type_output_accuracy: 0.5400 - type_output_loss: 0.9985 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.3715 - val_loss: 3.3519 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 1.9622 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0183\n",
+ "Epoch 27/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.3534 - loss: 3.5516 - question_output_accuracy: 0.5850 - question_output_loss: 2.2064 - type_output_accuracy: 0.5400 - type_output_loss: 0.9917 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.3677 - val_loss: 3.4014 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.0195 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0141\n",
+ "Epoch 28/30\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - answer_output_accuracy: 0.9380 - answer_output_loss: 0.3474 - loss: 3.5737 - question_output_accuracy: 0.5850 - question_output_loss: 2.2414 - type_output_accuracy: 0.5400 - type_output_loss: 0.9848 - val_answer_output_accuracy: 0.9250 - val_answer_output_loss: 0.3645 - val_loss: 3.4429 - val_question_output_accuracy: 0.6250 - val_question_output_loss: 2.0682 - val_type_output_accuracy: 0.5000 - val_type_output_loss: 1.0102\n"
]
}
],
@@ -426,7 +439,7 @@
" \"answer_output\": np.expand_dims(y_a_train, -1),\n",
" \"type_output\": y_type_train,\n",
" },\n",
- " batch_size=32,\n",
+ " batch_size=64,\n",
" epochs=30,\n",
" validation_split=0.1,\n",
" callbacks=[EarlyStopping(patience=3, restore_best_weights=True)],\n",
@@ -450,7 +463,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 57,
"id": "06fd86c7",
"metadata": {},
"outputs": [
@@ -458,12 +471,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 236ms/step\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step\n",
"\n",
"=== Akurasi Detail ===\n",
"Question Accuracy (Token-level): 0.0000\n",
"Answer Accuracy (Token-level) : 0.0000\n",
- "Type Accuracy (Class-level) : 0.68\n"
+ "Type Accuracy (Class-level) : 0.29\n"
]
}
],
@@ -506,7 +519,36 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 58,
+ "id": "b17b6470",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import sacrebleu\n",
+ "# from sacrebleu.metrics import BLEU # optional kalau mau smoothing/effective_order\n",
+ "\n",
+ "# idx2tok = {v:k for k,v in word2idx.items()}\n",
+ "# PAD_ID = word2idx[\"PAD\"]\n",
+ "# SOS_ID = word2idx.get(\"SOS\", None)\n",
+ "# EOS_ID = word2idx.get(\"EOS\", None)\n",
+ "\n",
+ "# def seq2str(seq):\n",
+ "# \"\"\"Konversi list index -> kalimat string, sambil buang token spesial.\"\"\"\n",
+ "# toks = [idx2tok[i] for i in seq\n",
+ "# if i not in {PAD_ID, SOS_ID, EOS_ID}]\n",
+ "# return \" \".join(toks).strip().lower()\n",
+ "\n",
+ "# bleu_metric = BLEU(effective_order=True) # lebih stabil utk kalimat pendek\n",
+ "\n",
+ "# def bleu_corpus(pred_seqs, true_seqs):\n",
+ "# preds = [seq2str(p) for p in pred_seqs]\n",
+ "# refs = [[seq2str(t)] for t in true_seqs] # list‑of‑list, satu ref/kalimat\n",
+ "# return bleu_metric.corpus_score(preds, refs).score\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
"id": "d5ed106c",
"metadata": {},
"outputs": [],
@@ -519,7 +561,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 60,
"id": "aa3860de",
"metadata": {},
"outputs": [],
diff --git a/QC/qg_v2_train.ipynb b/old/QC/qg_v2_train.ipynb
similarity index 100%
rename from QC/qg_v2_train.ipynb
rename to old/QC/qg_v2_train.ipynb
diff --git a/QC/question_generation_train.py b/old/QC/question_generation_train.py
similarity index 100%
rename from QC/question_generation_train.py
rename to old/QC/question_generation_train.py
diff --git a/QC/test_model_qc.py b/old/QC/test_model_qc.py
similarity index 78%
rename from QC/test_model_qc.py
rename to old/QC/test_model_qc.py
index beb9738..4af938b 100644
--- a/QC/test_model_qc.py
+++ b/old/QC/test_model_qc.py
@@ -62,45 +62,9 @@ if __name__ == "__main__":
# Example input
input_data = {
- "tokens": [
- "Ki",
- "Hajar",
- "Dewantara",
- "lahir",
- "pada",
- "2",
- "Mei",
- "1889",
- "di",
- "Yogyakarta",
- ".",
- ],
- "ner": [
- "B-PER",
- "I-PER",
- "I-PER",
- "O",
- "O",
- "B-DATE",
- "I-DATE",
- "I-DATE",
- "O",
- "B-LOC",
- "O",
- ],
- "srl": [
- "ARG0",
- "ARG0",
- "ARG0",
- "V",
- "O",
- "ARGM-TMP",
- "ARGM-TMP",
- "ARGM-TMP",
- "O",
- "ARGM-LOC",
- "O",
- ],
+ "tokens": ["Nama", "lengkap", "saya", "adalah", "Bayu", "Prabowo", "."],
+ "ner": ["O", "O", "O", "O", "B-PER", "I-PER", "O"],
+ "srl": ["ARG1", "ARG1", "ARG2", "V", "ARG0", "ARG0", "O"],
}
# input_data = {
diff --git a/QC/lstm_qg.keras b/question_generation/full_seq2seq.h5
similarity index 54%
rename from QC/lstm_qg.keras
rename to question_generation/full_seq2seq.h5
index 707bd8d..ce98fc4 100644
Binary files a/QC/lstm_qg.keras and b/question_generation/full_seq2seq.h5 differ
diff --git a/question_generation/qg.py b/question_generation/qg.py
new file mode 100644
index 0000000..107e728
--- /dev/null
+++ b/question_generation/qg.py
@@ -0,0 +1,308 @@
+#!/usr/bin/env python3
+# ===============================================================
+# Question‑Generation seq‑to‑seq (tokens + NER + SRL → Q/A/type)
+# – revised version 2025‑05‑11
+# ===============================================================
+
+import json, pickle, random
+from pathlib import Path
+from itertools import chain
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.layers import (
+ Input, Embedding, LSTM, Concatenate,
+ Dense, TimeDistributed
+)
+from tensorflow.keras.models import Model
+from nltk.translate.bleu_score import corpus_bleu, SmoothingFunction
+from rouge_score import rouge_scorer, scoring
+
+
+# -----------------------------------------------------------------
+# 0. LOAD & FLATTEN DATA
+# -----------------------------------------------------------------
+RAW = json.loads(Path("../dataset/dev_dataset_qg.json").read_text())
+
+samples = []
+for item in RAW:
+ for qp in item["quiz_posibility"]:
+ samples.append({
+ "tokens" : item["tokens"],
+ "ner" : item["ner"],
+ "srl" : item["srl"],
+ "q_type" : qp["type"], # isian / opsi / benar_salah
+ "q_toks" : qp["question"] + [""],
+ "a_toks" : (qp["answer"] if isinstance(qp["answer"], list)
+ else [qp["answer"]]) + [""]
+ })
+
+print("flattened samples :", len(samples))
+
+
+# -----------------------------------------------------------------
+# 1. VOCABULARIES
+# -----------------------------------------------------------------
+def build_vocab(seq_iter, reserved=("", "", "", "")):
+ vocab = {tok: idx for idx, tok in enumerate(reserved)}
+ for tok in chain.from_iterable(seq_iter):
+ vocab.setdefault(tok, len(vocab))
+ return vocab
+
+vocab_tok = build_vocab((s["tokens"] for s in samples))
+vocab_ner = build_vocab((s["ner"] for s in samples), reserved=("",""))
+vocab_srl = build_vocab((s["srl"] for s in samples), reserved=("",""))
+vocab_q = build_vocab((s["q_toks"] for s in samples))
+vocab_a = build_vocab((s["a_toks"] for s in samples))
+vocab_typ = {"isian":0, "opsi":1, "benar_salah":2}
+
+
+# -----------------------------------------------------------------
+# 2. ENCODING & PADDING
+# -----------------------------------------------------------------
+def enc(seq, v): return [v.get(t, v[""]) for t in seq]
+
+MAX_SENT = max(len(s["tokens"]) for s in samples)
+MAX_Q = max(len(s["q_toks"]) for s in samples)
+MAX_A = max(len(s["a_toks"]) for s in samples)
+
+def pad_batch(seqs, vmap, maxlen):
+ return tf.keras.preprocessing.sequence.pad_sequences(
+ [enc(s, vmap) for s in seqs], maxlen=maxlen, padding="post"
+ )
+
+X_tok = pad_batch((s["tokens"] for s in samples), vocab_tok, MAX_SENT)
+X_ner = pad_batch((s["ner"] for s in samples), vocab_ner, MAX_SENT)
+X_srl = pad_batch((s["srl"] for s in samples), vocab_srl, MAX_SENT)
+
+dec_q_in = pad_batch(
+ ([[""]+s["q_toks"][:-1] for s in samples]), vocab_q, MAX_Q)
+dec_q_out = pad_batch((s["q_toks"] for s in samples), vocab_q, MAX_Q)
+
+dec_a_in = pad_batch(
+ ([[""]+s["a_toks"][:-1] for s in samples]), vocab_a, MAX_A)
+dec_a_out = pad_batch((s["a_toks"] for s in samples), vocab_a, MAX_A)
+
+y_type = np.array([vocab_typ[s["q_type"]] for s in samples])
+
+
+# -----------------------------------------------------------------
+# 3. MODEL
+# -----------------------------------------------------------------
+d_tok, d_tag, units = 128, 32, 256
+pad_tok, pad_q, pad_a = vocab_tok[""], vocab_q[""], vocab_a[""]
+
+# ---- Encoder ----------------------------------------------------
+inp_tok = Input((MAX_SENT,), name="tok_in")
+inp_ner = Input((MAX_SENT,), name="ner_in")
+inp_srl = Input((MAX_SENT,), name="srl_in")
+
+emb_tok = Embedding(len(vocab_tok), d_tok, mask_zero=True, name="emb_tok")(inp_tok)
+emb_ner = Embedding(len(vocab_ner), d_tag, mask_zero=True, name="emb_ner")(inp_ner)
+emb_srl = Embedding(len(vocab_srl), d_tag, mask_zero=True, name="emb_srl")(inp_srl)
+
+enc_concat = Concatenate()([emb_tok, emb_ner, emb_srl])
+enc_out, state_h, state_c = LSTM(units, return_state=True, name="enc_lstm")(enc_concat)
+
+# ---- Decoder : Question ----------------------------------------
+dec_q_inp = Input((MAX_Q,), name="dec_q_in")
+dec_emb_q = Embedding(len(vocab_q), d_tok, mask_zero=True, name="emb_q")(dec_q_inp)
+dec_q_seq, _, _ = LSTM(units, return_sequences=True, return_state=True,
+ name="lstm_q")(dec_emb_q, initial_state=[state_h, state_c])
+q_out = TimeDistributed(Dense(len(vocab_q), activation="softmax"), name="q_out")(dec_q_seq)
+
+# ---- Decoder : Answer ------------------------------------------
+dec_a_inp = Input((MAX_A,), name="dec_a_in")
+dec_emb_a = Embedding(len(vocab_a), d_tok, mask_zero=True, name="emb_a")(dec_a_inp)
+dec_a_seq, _, _ = LSTM(units, return_sequences=True, return_state=True,
+ name="lstm_a")(dec_emb_a, initial_state=[state_h, state_c])
+a_out = TimeDistributed(Dense(len(vocab_a), activation="softmax"), name="a_out")(dec_a_seq)
+
+# ---- Classifier -------------------------------------------------
+type_out = Dense(len(vocab_typ), activation="softmax", name="type_out")(enc_out)
+
+model = Model(
+ [inp_tok, inp_ner, inp_srl, dec_q_inp, dec_a_inp],
+ [q_out, a_out, type_out]
+)
+
+# ---- Masked loss helpers ---------------------------------------
+scce = tf.keras.losses.SparseCategoricalCrossentropy(reduction="none")
+def masked_loss_factory(pad_id):
+ def loss(y_true, y_pred):
+ l = scce(y_true, y_pred)
+ mask = tf.cast(tf.not_equal(y_true, pad_id), tf.float32)
+ return tf.reduce_sum(l*mask) / tf.reduce_sum(mask)
+ return loss
+
+model.compile(
+ optimizer="adam",
+ loss = {"q_out":masked_loss_factory(pad_q),
+ "a_out":masked_loss_factory(pad_a),
+ "type_out":"sparse_categorical_crossentropy"},
+ loss_weights={"q_out":1.0, "a_out":1.0, "type_out":0.3},
+ metrics={"q_out":"sparse_categorical_accuracy",
+ "a_out":"sparse_categorical_accuracy",
+ "type_out":tf.keras.metrics.SparseCategoricalAccuracy(name="type_acc")}
+)
+model.summary()
+
+# -----------------------------------------------------------------
+# 4. TRAIN
+# -----------------------------------------------------------------
+history = model.fit(
+ [X_tok, X_ner, X_srl, dec_q_in, dec_a_in],
+ [dec_q_out, dec_a_out, y_type],
+ validation_split=0.1,
+ epochs=30,
+ batch_size=64,
+ callbacks=[tf.keras.callbacks.EarlyStopping(patience=4, restore_best_weights=True)],
+ verbose=2
+)
+model.save("full_seq2seq.keras")
+
+
+# -----------------------------------------------------------------
+# 5. SAVE VOCABS (.pkl keeps python dict intact)
+# -----------------------------------------------------------------
+def save_vocab(v, name): pickle.dump(v, open(name,"wb"))
+save_vocab(vocab_tok,"vocab_tok.pkl"); save_vocab(vocab_ner,"vocab_ner.pkl")
+save_vocab(vocab_srl,"vocab_srl.pkl"); save_vocab(vocab_q, "vocab_q.pkl")
+save_vocab(vocab_a, "vocab_a.pkl"); save_vocab(vocab_typ,"vocab_typ.pkl")
+
+
+# -----------------------------------------------------------------
+# 6. INFERENCE MODELS (encoder & decoders)
+# -----------------------------------------------------------------
+def build_inference_models(trained):
+ # encoder
+ t_in = Input((MAX_SENT,), name="t_in")
+ n_in = Input((MAX_SENT,), name="n_in")
+ s_in = Input((MAX_SENT,), name="s_in")
+ e_t = trained.get_layer("emb_tok")(t_in)
+ e_n = trained.get_layer("emb_ner")(n_in)
+ e_s = trained.get_layer("emb_srl")(s_in)
+ concat = Concatenate()([e_t,e_n,e_s])
+ _, h, c = trained.get_layer("enc_lstm")(concat)
+ enc_model = Model([t_in,n_in,s_in],[h,c])
+
+ # question‑decoder
+ dq_in = Input((1,), name="dq_tok")
+ dh = Input((units,), name="dh"); dc = Input((units,), name="dc")
+ dq_emb = trained.get_layer("emb_q")(dq_in)
+ dq_lstm, nh, nc = trained.get_layer("lstm_q")(dq_emb, initial_state=[dh,dc])
+ dq_out = trained.get_layer("q_out").layer(dq_lstm)
+ dec_q_model = Model([dq_in, dh, dc], [dq_out, nh, nc])
+
+ # answer‑decoder
+ da_in = Input((1,), name="da_tok")
+ ah = Input((units,), name="ah"); ac = Input((units,), name="ac")
+ da_emb = trained.get_layer("emb_a")(da_in)
+ da_lstm, nh2, nc2 = trained.get_layer("lstm_a")(da_emb, initial_state=[ah,ac])
+ da_out = trained.get_layer("a_out").layer(da_lstm)
+ dec_a_model = Model([da_in, ah, ac], [da_out, nh2, nc2])
+
+ # type classifier
+ type_dense = trained.get_layer("type_out")
+ type_model = Model([t_in,n_in,s_in], type_dense(_)) # use _ = enc_lstm output
+
+ return enc_model, dec_q_model, dec_a_model, type_model
+
+encoder_model, decoder_q, decoder_a, classifier_model = build_inference_models(model)
+
+inv_q = {v:k for k,v in vocab_q.items()}
+inv_a = {v:k for k,v in vocab_a.items()}
+
+def enc_pad(seq, vmap, maxlen):
+ x = [vmap.get(t, vmap[""]) for t in seq]
+ return x + [vmap[""]] * (maxlen-len(x))
+
+def greedy_decode(tokens, ner, srl, max_q=20, max_a=10):
+ et = np.array([enc_pad(tokens, vocab_tok, MAX_SENT)])
+ en = np.array([enc_pad(ner, vocab_ner, MAX_SENT)])
+ es = np.array([enc_pad(srl, vocab_srl, MAX_SENT)])
+
+ h,c = encoder_model.predict([et,en,es], verbose=0)
+
+ # --- question
+ q_ids = []
+ tgt = np.array([[vocab_q[""]]])
+ for _ in range(max_q):
+ logits,h,c = decoder_q.predict([tgt,h,c], verbose=0)
+ nxt = int(logits[0,-1].argmax())
+ if nxt==vocab_q[""]: break
+ q_ids.append(nxt)
+ tgt = np.array([[nxt]])
+
+ # --- answer (re‑use fresh h,c)
+ h,c = encoder_model.predict([et,en,es], verbose=0)
+ a_ids = []
+ tgt = np.array([[vocab_a[""]]])
+ for _ in range(max_a):
+ logits,h,c = decoder_a.predict([tgt,h,c], verbose=0)
+ nxt = int(logits[0,-1].argmax())
+ if nxt==vocab_a[""]: break
+ a_ids.append(nxt)
+ tgt = np.array([[nxt]])
+
+ # --- type
+ t_id = int(classifier_model.predict([et,en,es], verbose=0).argmax())
+
+ return [inv_q[i] for i in q_ids], [inv_a[i] for i in a_ids], \
+ [k for k,v in vocab_typ.items() if v==t_id][0]
+
+
+# -----------------------------------------------------------------
+# 7. QUICK DEMO
+# -----------------------------------------------------------------
+test_tokens = ["soekarno","membacakan","teks","proklamasi","pada",
+ "17","agustus","1945"]
+test_ner = ["B-PER","O","O","O","O","B-DATE","I-DATE","I-DATE"]
+test_srl = ["ARG0","V","ARG1","ARG1","O","ARGM-TMP","ARGM-TMP","ARGM-TMP"]
+
+q,a,t = greedy_decode(test_tokens,test_ner,test_srl,max_q=MAX_Q,max_a=MAX_A)
+print("\nDEMO\n----")
+print("Q :", " ".join(q))
+print("A :", " ".join(a))
+print("T :", t)
+
+
+# -----------------------------------------------------------------
+# 8. EVALUATION (corpus‑level BLEU + ROUGE‑1/‑L)
+# -----------------------------------------------------------------
+smooth = SmoothingFunction().method4
+r_scorer = rouge_scorer.RougeScorer(["rouge1","rougeL"], use_stemmer=True)
+
+def strip_special(seq, pad_id, eos_id):
+ return [x for x in seq if x not in (pad_id, eos_id)]
+
+def ids_to_text(ids, inv):
+ return " ".join(inv[i] for i in ids)
+
+def evaluate(n=200):
+ idxs = random.sample(range(len(samples)), n)
+ refs, hyps = [], []
+ agg = scoring.BootstrapAggregator()
+
+ for i in idxs:
+ gt_ids = strip_special(dec_q_out[i], pad_q, vocab_q[""])
+ ref = ids_to_text(gt_ids, inv_q)
+ pred = " ".join(greedy_decode(
+ samples[i]["tokens"],
+ samples[i]["ner"],
+ samples[i]["srl"]
+ )[0])
+ refs.append([ref.split()])
+ hyps.append(pred.split())
+ agg.add_scores(r_scorer.score(ref, pred))
+
+ bleu = corpus_bleu(refs, hyps, smoothing_function=smooth)
+ r1 = agg.aggregate()["rouge1"].mid
+ rL = agg.aggregate()["rougeL"].mid
+
+ print(f"\nEVAL (n={n})")
+ print(f"BLEU‑4 : {bleu:.4f}")
+ print(f"ROUGE‑1 : P={r1.precision:.3f} R={r1.recall:.3f} F1={r1.fmeasure:.3f}")
+ print(f"ROUGE‑L : P={rL.precision:.3f} R={rL.recall:.3f} F1={rL.fmeasure:.3f}")
+
+evaluate(2) # run on 150 random samples
diff --git a/question_generation/qg_lstm.ipynb b/question_generation/qg_lstm.ipynb
new file mode 100644
index 0000000..af6ef77
--- /dev/null
+++ b/question_generation/qg_lstm.ipynb
@@ -0,0 +1,798 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "id": "fb283f23",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total flattened samples: 342\n"
+ ]
+ }
+ ],
+ "source": [
+ "import json\n",
+ "from pathlib import Path\n",
+ "from itertools import chain\n",
+ "\n",
+ "RAW = json.loads(\n",
+ " Path(\"../dataset/dev_dataset_qg.json\").read_text()\n",
+ ") # ← file contoh Anda\n",
+ "\n",
+ "samples = []\n",
+ "for item in RAW:\n",
+ " for qp in item[\"quiz_posibility\"]:\n",
+ " samp = {\n",
+ " \"tokens\": [tok.lower() for tok in item[\"tokens\"]],\n",
+ " \"ner\": item[\"ner\"],\n",
+ " \"srl\": item[\"srl\"],\n",
+ " \"q_type\": qp[\"type\"], # isian / opsi / benar_salah\n",
+ " \"q_toks\": [tok.lower() for tok in qp[\"question\"]]\n",
+ " + [\"\"], # tambahkan \n",
+ " }\n",
+ " # Jawaban bisa multi token\n",
+ " if isinstance(qp[\"answer\"], list):\n",
+ " samp[\"a_toks\"] = [tok.lower() for tok in qp[\"answer\"]] + [\"\"]\n",
+ " else:\n",
+ " samp[\"a_toks\"] = [qp[\"answer\"].lower(), \"\"]\n",
+ " samples.append(samp)\n",
+ "\n",
+ "print(\"Total flattened samples:\", len(samples))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "id": "fa4f979d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'': 0, '': 1, '': 2, '': 3, 'jepara': 4, 'false': 5, 'trowulan': 6, '17': 7, 'agustus': 8, '1945': 9, 'soekarno': 10, 'mohammad hatta': 11, '365': 12, 'hari': 13, 'merkurius': 14, 'true': 15, 'mars': 16, 'jupiter': 17, 'saturnus': 18, 'uranus': 19, 'neptunus': 20, '5': 21, 'januari': 22, '2020': 23, '12': 24, 'februari': 25, '2019': 26, '23': 27, 'maret': 28, '2021': 29, '1': 30, 'april': 31, '2022': 32, '15': 33, 'mei': 34, '2023': 35, 'gunung': 36, 'everest': 37, 'amazon': 38, 'piramida': 39, 'giza': 40, 'benua': 41, 'asia': 42, 'colosseum': 43, 'taj': 44, 'mahal': 45, 'petra': 46, 'tembok': 47, 'cina': 48, 'chichen': 49, 'itza': 50, 'patung': 51, 'yesus': 52, 'penebus': 53, 'machu': 54, 'picchu': 55, 'stonehenge': 56, 'menara': 57, 'pisa': 58, 'angkot': 59, 'wat': 60, '8848': 61, 'meter': 62, '17 agustus 1945': 63, 'albert': 64, 'einstein': 65, 'jantung': 66, 'memompa darah': 67, 'tokyo': 68, '100': 69, 'derajat': 70, 'celsius': 71, 'thomas': 72, 'alva': 73, 'edison': 74, '1879': 75, 'ketiga': 76, 'leonardo': 77, 'da': 78, 'vinci': 79, 'leonardo da vinci': 80, '9,46': 81, 'triliun': 82, 'kilometer': 83, 'mahatma': 84, 'gandhi': 85, '1958': 86, 'kornea': 87, 'waterloo': 88, '1815': 89, 'indonesia': 90, 'marie': 91, 'curie': 92, 'fisika dan kimia': 93, 'inka': 94, 'oksigen': 95, 'karbon dioksida dan air': 96, 'vincent': 97, 'van': 98, 'gogh': 99, 'double': 100, 'helix': 101, 'double helix': 102, 'alexander': 103, 'fleming': 104, 'jeruk': 105, 'dan': 106, 'kiwi': 107, 'vitamin c': 108, 'nikola': 109, 'tesla': 110, 'sungai': 111, 'nil': 112, '6650 kilometer': 113, 'paus': 114, 'biru': 115, 'pankreas': 116, 'mengatur gula darah': 117, 'charles': 118, 'darwin': 119, 'shah': 120, 'jahan': 121, 'mumtaz mahal': 122, '44.58 juta km²': 123, '54': 124, 'di selatan laut mediterania': 125, 'eropa': 126, '10.18 juta km²': 127, 'atlantik': 128, 'pasifik': 129, 'hutan amazon': 130, 'australia': 131, 'belahan bumi selatan': 132, 'antartika': 133, 'kutub selatan': 134, '4.7 miliar': 135, 'kilimanjaro': 136, '5,895 meter': 137, 'sahara': 138, 'afrika': 139, 'alpen': 140, '8': 141, 'superior': 142, 'danau superior': 143, 'amerika selatan': 144, 'ali': 145, 'turnamen': 146, 'nina': 147, 'rapat': 148, 'farhan': 149, 'andi': 150, 'workshop': 151, 'lina': 152, 'pameran': 153, 'iqbal': 154, 'siti': 155, 'perlombaan': 156, 'konser': 157, 'fajar': 158, 'dina': 159, 'festival': 160, 'rian': 161, 'bazar': 162, 'tari': 163, 'seminar': 164, 'kompetisi': 165, 'rudi': 166, 'putri': 167, 'budi': 168, 'hana': 169, 'raka': 170, 'dewi': 171, 'surabaya': 172, 'yogyakarta': 173, 'kota': 174, 'jakarta': 175, 'bandung': 176, 'malang': 177, 'bali': 178, 'padang': 179, 'ibukota': 180, 'makassar': 181, 'medan': 182}\n"
+ ]
+ }
+ ],
+ "source": [
+ "def build_vocab(seq_iter, reserved=[\"\", \"\", \"\", \"\"]):\n",
+ " vocab = {tok: idx for idx, tok in enumerate(reserved)}\n",
+ " for tok in chain.from_iterable(seq_iter):\n",
+ " if tok not in vocab:\n",
+ " vocab[tok] = len(vocab)\n",
+ " return vocab\n",
+ "\n",
+ "\n",
+ "vocab_tok = build_vocab((s[\"tokens\"] for s in samples))\n",
+ "vocab_ner = build_vocab((s[\"ner\"] for s in samples), reserved=[\"\", \"\"])\n",
+ "vocab_srl = build_vocab((s[\"srl\"] for s in samples), reserved=[\"\", \"\"])\n",
+ "vocab_q = build_vocab((s[\"q_toks\"] for s in samples))\n",
+ "vocab_a = build_vocab((s[\"a_toks\"] for s in samples))\n",
+ "\n",
+ "vocab_typ = {\"isian\": 0, \"opsi\": 1, \"true_false\": 2}\n",
+ "\n",
+ "print(vocab_a)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "id": "d1a5b324",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
+ "\n",
+ "\n",
+ "def encode(seq, vmap): # token → id\n",
+ " return [vmap.get(t, vmap[\"\"]) for t in seq]\n",
+ "\n",
+ "\n",
+ "MAX_SENT = max(len(s[\"tokens\"]) for s in samples)\n",
+ "MAX_Q = max(len(s[\"q_toks\"]) for s in samples)\n",
+ "MAX_A = max(len(s[\"a_toks\"]) for s in samples)\n",
+ "\n",
+ "X_tok = pad_sequences(\n",
+ " [encode(s[\"tokens\"], vocab_tok) for s in samples], maxlen=MAX_SENT, padding=\"post\"\n",
+ ")\n",
+ "X_ner = pad_sequences(\n",
+ " [encode(s[\"ner\"], vocab_ner) for s in samples], maxlen=MAX_SENT, padding=\"post\"\n",
+ ")\n",
+ "X_srl = pad_sequences(\n",
+ " [encode(s[\"srl\"], vocab_srl) for s in samples], maxlen=MAX_SENT, padding=\"post\"\n",
+ ")\n",
+ "\n",
+ "# Decoder input = + target[:-1]\n",
+ "dec_q_in = pad_sequences(\n",
+ " [[vocab_q[\"\"], *encode(s[\"q_toks\"][:-1], vocab_q)] for s in samples],\n",
+ " maxlen=MAX_Q,\n",
+ " padding=\"post\",\n",
+ ")\n",
+ "dec_q_out = pad_sequences(\n",
+ " [encode(s[\"q_toks\"], vocab_q) for s in samples], maxlen=MAX_Q, padding=\"post\"\n",
+ ")\n",
+ "\n",
+ "dec_a_in = pad_sequences(\n",
+ " [[vocab_a[\"\"], *encode(s[\"a_toks\"][:-1], vocab_a)] for s in samples],\n",
+ " maxlen=MAX_A,\n",
+ " padding=\"post\",\n",
+ ")\n",
+ "dec_a_out = pad_sequences(\n",
+ " [encode(s[\"a_toks\"], vocab_a) for s in samples], maxlen=MAX_A, padding=\"post\"\n",
+ ")\n",
+ "\n",
+ "MAX_SENT = max(len(s[\"tokens\"]) for s in samples)\n",
+ "MAX_Q = max(len(s[\"q_toks\"]) for s in samples)\n",
+ "MAX_A = max(len(s[\"a_toks\"]) for s in samples)\n",
+ "y_type = np.array([vocab_typ[s[\"q_type\"]] for s in samples])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "id": "ff5bd85f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"functional_8\"\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"functional_8\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
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+ "│ │ (None, 256)] │ │ encoder_lstm[0][… │\n",
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+ "│ (NotEqual) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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+ "│ (LSTM) │ (None, 256), │ │ encoder_lstm[0][… │\n",
+ "│ │ (None, 256)] │ │ encoder_lstm[0][… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ not_equal_33 │ (None, 4) │ 0 │ dec_a_in[0][0] │\n",
+ "│ (NotEqual) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ q_output │ (None, 13, 407) │ 104,599 │ lstm_q_decoder[0… │\n",
+ "│ (TimeDistributed) │ │ │ not_equal_32[0][… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ a_output │ (None, 4, 183) │ 47,031 │ lstm_a_decoder[0… │\n",
+ "│ (TimeDistributed) │ │ │ not_equal_33[0][… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ type_output (Dense) │ (None, 3) │ 771 │ encoder_lstm[0][… │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ tok_in (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ ner_in (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ srl_in (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ embedding_tok │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m57,856\u001b[0m │ tok_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mEmbedding\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ embedding_ner │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m1,248\u001b[0m │ ner_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mEmbedding\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ embedding_srl │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m448\u001b[0m │ srl_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mEmbedding\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dec_q_in │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ concatenate_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ embedding_tok[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ embedding_ner[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ │ │ embedding_srl[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dec_a_in │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ embedding_q_decoder │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m52,096\u001b[0m │ dec_q_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mEmbedding\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ encoder_lstm (\u001b[38;5;33mLSTM\u001b[0m) │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ \u001b[38;5;34m459,776\u001b[0m │ concatenate_8[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ │ │\n",
+ "│ │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)] │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ embedding_a_decoder │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m23,424\u001b[0m │ dec_a_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mEmbedding\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ lstm_q_decoder │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ \u001b[38;5;34m394,240\u001b[0m │ embedding_q_deco… │\n",
+ "│ (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ │ encoder_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)] │ │ encoder_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ not_equal_32 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dec_q_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mNotEqual\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ lstm_a_decoder │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ \u001b[38;5;34m394,240\u001b[0m │ embedding_a_deco… │\n",
+ "│ (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ │ encoder_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "│ │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)] │ │ encoder_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ not_equal_33 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dec_a_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mNotEqual\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ q_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m407\u001b[0m) │ \u001b[38;5;34m104,599\u001b[0m │ lstm_q_decoder[\u001b[38;5;34m0\u001b[0m… │\n",
+ "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal_32[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ a_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m183\u001b[0m) │ \u001b[38;5;34m47,031\u001b[0m │ lstm_a_decoder[\u001b[38;5;34m0\u001b[0m… │\n",
+ "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ type_output (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m771\u001b[0m │ encoder_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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+ "metadata": {},
+ "output_type": "display_data"
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow.keras.layers import (\n",
+ " Input,\n",
+ " Embedding,\n",
+ " LSTM,\n",
+ " Concatenate,\n",
+ " Dense,\n",
+ " TimeDistributed,\n",
+ ")\n",
+ "from tensorflow.keras.models import Model\n",
+ "\n",
+ "# ---- constants ---------------------------------------------------\n",
+ "d_tok = 128 # token embedding dim\n",
+ "d_tag = 32 # NER / SRL embedding dim\n",
+ "units = 256\n",
+ "\n",
+ "# ---- encoder -----------------------------------------------------\n",
+ "inp_tok = Input((MAX_SENT,), name=\"tok_in\")\n",
+ "inp_ner = Input((MAX_SENT,), name=\"ner_in\")\n",
+ "inp_srl = Input((MAX_SENT,), name=\"srl_in\")\n",
+ "\n",
+ "# make ALL streams mask the same way (here: no masking,\n",
+ "# we'll just pad with 0s and let the LSTM ignore them)\n",
+ "emb_tok = Embedding(len(vocab_tok), d_tok, mask_zero=False, name=\"embedding_tok\")(\n",
+ " inp_tok\n",
+ ")\n",
+ "emb_ner = Embedding(len(vocab_ner), d_tag, mask_zero=False, name=\"embedding_ner\")(\n",
+ " inp_ner\n",
+ ")\n",
+ "emb_srl = Embedding(len(vocab_srl), d_tag, mask_zero=False, name=\"embedding_srl\")(\n",
+ " inp_srl\n",
+ ")\n",
+ "\n",
+ "enc_concat = Concatenate()([emb_tok, emb_ner, emb_srl])\n",
+ "enc_out, state_h, state_c = LSTM(units, return_state=True, name=\"encoder_lstm\")(\n",
+ " enc_concat\n",
+ ")\n",
+ "\n",
+ "\n",
+ "# ---------- DECODER : Question ----------\n",
+ "dec_q_inp = Input(shape=(MAX_Q,), name=\"dec_q_in\")\n",
+ "dec_emb_q = Embedding(len(vocab_q), d_tok, mask_zero=True, name=\"embedding_q_decoder\")(\n",
+ " dec_q_inp\n",
+ ")\n",
+ "dec_q, _, _ = LSTM(\n",
+ " units, return_state=True, return_sequences=True, name=\"lstm_q_decoder\"\n",
+ ")(dec_emb_q, initial_state=[state_h, state_c])\n",
+ "q_out = TimeDistributed(\n",
+ " Dense(len(vocab_q), activation=\"softmax\", name=\"dense_q_output\"), name=\"q_output\"\n",
+ ")(dec_q)\n",
+ "\n",
+ "# ---------- DECODER : Answer ----------\n",
+ "dec_a_inp = Input(shape=(MAX_A,), name=\"dec_a_in\")\n",
+ "dec_emb_a = Embedding(len(vocab_a), d_tok, mask_zero=True, name=\"embedding_a_decoder\")(\n",
+ " dec_a_inp\n",
+ ")\n",
+ "dec_a, _, _ = LSTM(\n",
+ " units, return_state=True, return_sequences=True, name=\"lstm_a_decoder\"\n",
+ ")(dec_emb_a, initial_state=[state_h, state_c])\n",
+ "a_out = TimeDistributed(\n",
+ " Dense(len(vocab_a), activation=\"softmax\", name=\"dense_a_output\"), name=\"a_output\"\n",
+ ")(dec_a)\n",
+ "\n",
+ "# ---------- CLASSIFIER : Question Type ----------\n",
+ "type_out = Dense(len(vocab_typ), activation=\"softmax\", name=\"type_output\")(enc_out)\n",
+ "\n",
+ "model = Model(\n",
+ " inputs=[inp_tok, inp_ner, inp_srl, dec_q_inp, dec_a_inp],\n",
+ " outputs=[q_out, a_out, type_out],\n",
+ ")\n",
+ "\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 97,
+ "id": "fece1ae9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/30\n",
+ "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 161ms/step - a_output_loss: 5.1540 - a_output_sparse_categorical_accuracy: 0.1507 - loss: 11.4761 - q_output_loss: 5.9970 - q_output_sparse_categorical_accuracy: 0.0600 - type_output_accuracy: 0.4506 - type_output_loss: 1.0728 - val_a_output_loss: 4.5900 - val_a_output_sparse_categorical_accuracy: 0.2500 - val_loss: 10.8292 - val_q_output_loss: 5.9316 - val_q_output_sparse_categorical_accuracy: 0.0769 - val_type_output_accuracy: 0.5143 - val_type_output_loss: 1.0253\n",
+ "Epoch 2/30\n",
+ "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - a_output_loss: 4.2365 - a_output_sparse_categorical_accuracy: 0.2500 - loss: 10.2493 - q_output_loss: 5.6397 - q_output_sparse_categorical_accuracy: 0.1183 - type_output_accuracy: 0.5209 - type_output_loss: 1.2188 - val_a_output_loss: 3.2588 - val_a_output_sparse_categorical_accuracy: 0.2500 - val_loss: 9.0808 - val_q_output_loss: 5.4082 - val_q_output_sparse_categorical_accuracy: 0.0923 - val_type_output_accuracy: 0.5143 - val_type_output_loss: 1.3791\n",
+ "Epoch 3/30\n",
+ "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - a_output_loss: 3.5259 - a_output_sparse_categorical_accuracy: 0.2500 - loss: 8.4974 - q_output_loss: 4.6444 - q_output_sparse_categorical_accuracy: 0.1174 - type_output_accuracy: 0.5233 - type_output_loss: 1.0788 - val_a_output_loss: 3.3879 - val_a_output_sparse_categorical_accuracy: 0.2500 - val_loss: 9.5209 - val_q_output_loss: 5.7546 - val_q_output_sparse_categorical_accuracy: 0.0769 - val_type_output_accuracy: 0.2000 - val_type_output_loss: 1.2615\n",
+ "Epoch 4/30\n",
+ "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - a_output_loss: 3.3147 - a_output_sparse_categorical_accuracy: 0.2500 - loss: 8.1027 - q_output_loss: 4.4209 - q_output_sparse_categorical_accuracy: 0.1099 - type_output_accuracy: 0.3256 - type_output_loss: 1.2069 - val_a_output_loss: 3.0792 - val_a_output_sparse_categorical_accuracy: 0.2500 - val_loss: 9.2232 - val_q_output_loss: 5.8382 - val_q_output_sparse_categorical_accuracy: 0.0769 - val_type_output_accuracy: 0.5143 - val_type_output_loss: 1.0193\n",
+ "Epoch 5/30\n",
+ "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - a_output_loss: 3.1559 - a_output_sparse_categorical_accuracy: 0.2500 - loss: 7.7733 - q_output_loss: 4.3048 - q_output_sparse_categorical_accuracy: 0.1120 - type_output_accuracy: 0.5160 - type_output_loss: 1.0414 - val_a_output_loss: 3.0450 - val_a_output_sparse_categorical_accuracy: 0.2500 - val_loss: 9.1657 - val_q_output_loss: 5.7943 - val_q_output_sparse_categorical_accuracy: 0.0923 - val_type_output_accuracy: 0.5143 - val_type_output_loss: 1.0881\n",
+ "Epoch 6/30\n",
+ "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - a_output_loss: 3.0962 - a_output_sparse_categorical_accuracy: 0.2569 - loss: 7.6096 - q_output_loss: 4.1973 - q_output_sparse_categorical_accuracy: 0.1121 - type_output_accuracy: 0.5318 - type_output_loss: 1.0492 - val_a_output_loss: 3.1428 - val_a_output_sparse_categorical_accuracy: 0.3214 - val_loss: 9.2982 - val_q_output_loss: 5.8475 - val_q_output_sparse_categorical_accuracy: 0.0769 - val_type_output_accuracy: 0.5143 - val_type_output_loss: 1.0265\n"
+ ]
+ }
+ ],
+ "source": [
+ "losses = {\n",
+ " \"q_output\": \"sparse_categorical_crossentropy\",\n",
+ " \"a_output\": \"sparse_categorical_crossentropy\",\n",
+ " \"type_output\": \"sparse_categorical_crossentropy\",\n",
+ "}\n",
+ "loss_weights = {\"q_output\": 1.0, \"a_output\": 1.0, \"type_output\": 0.3}\n",
+ "\n",
+ "model.compile(\n",
+ " optimizer=\"adam\",\n",
+ " loss=losses,\n",
+ " loss_weights=loss_weights,\n",
+ " metrics={\n",
+ " \"q_output\": \"sparse_categorical_accuracy\",\n",
+ " \"a_output\": \"sparse_categorical_accuracy\",\n",
+ " \"type_output\": \"accuracy\",\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "history = model.fit(\n",
+ " [X_tok, X_ner, X_srl, dec_q_in, dec_a_in],\n",
+ " [dec_q_out, dec_a_out, y_type],\n",
+ " validation_split=0.1,\n",
+ " epochs=30,\n",
+ " batch_size=64,\n",
+ " callbacks=[tf.keras.callbacks.EarlyStopping(patience=4, restore_best_weights=True)],\n",
+ " verbose=1,\n",
+ ")\n",
+ "\n",
+ "model.save(\"full_seq2seq.keras\")\n",
+ "\n",
+ "import json\n",
+ "import pickle\n",
+ "\n",
+ "# def save_vocab(vocab, path):\n",
+ "# with open(path, \"w\", encoding=\"utf-8\") as f:\n",
+ "# json.dump(vocab, f, ensure_ascii=False, indent=2)\n",
+ "\n",
+ "# # Simpan semua vocab\n",
+ "# save_vocab(vocab_tok, \"vocab_tok.json\")\n",
+ "# save_vocab(vocab_ner, \"vocab_ner.json\")\n",
+ "# save_vocab(vocab_srl, \"vocab_srl.json\")\n",
+ "# save_vocab(vocab_q, \"vocab_q.json\")\n",
+ "# save_vocab(vocab_a, \"vocab_a.json\")\n",
+ "# save_vocab(vocab_typ, \"vocab_typ.json\")\n",
+ "\n",
+ "\n",
+ "def save_vocab_pkl(vocab, path):\n",
+ " with open(path, \"wb\") as f:\n",
+ " pickle.dump(vocab, f)\n",
+ "\n",
+ "\n",
+ "# Simpan semua vocab\n",
+ "save_vocab_pkl(vocab_tok, \"vocab_tok.pkl\")\n",
+ "save_vocab_pkl(vocab_ner, \"vocab_ner.pkl\")\n",
+ "save_vocab_pkl(vocab_srl, \"vocab_srl.pkl\")\n",
+ "save_vocab_pkl(vocab_q, \"vocab_q.pkl\")\n",
+ "save_vocab_pkl(vocab_a, \"vocab_a.pkl\")\n",
+ "save_vocab_pkl(vocab_typ, \"vocab_typ.pkl\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "id": "3355c0c7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "from tensorflow.keras.models import load_model, Model\n",
+ "from tensorflow.keras.layers import Input, Concatenate\n",
+ "\n",
+ "# === Load Model Utama ===\n",
+ "model = load_model(\"full_seq2seq.keras\")\n",
+ "\n",
+ "\n",
+ "# === Load Vocabulary dari .pkl ===\n",
+ "def load_vocab(path):\n",
+ " with open(path, \"rb\") as f:\n",
+ " return pickle.load(f)\n",
+ "\n",
+ "\n",
+ "vocab_tok = load_vocab(\"vocab_tok.pkl\")\n",
+ "vocab_ner = load_vocab(\"vocab_ner.pkl\")\n",
+ "vocab_srl = load_vocab(\"vocab_srl.pkl\")\n",
+ "vocab_q = load_vocab(\"vocab_q.pkl\")\n",
+ "vocab_a = load_vocab(\"vocab_a.pkl\")\n",
+ "vocab_typ = load_vocab(\"vocab_typ.pkl\")\n",
+ "\n",
+ "inv_vocab_q = {v: k for k, v in vocab_q.items()}\n",
+ "inv_vocab_a = {v: k for k, v in vocab_a.items()}\n",
+ "\n",
+ "# === Build Encoder Model ===\n",
+ "MAX_SENT = model.input_shape[0][1] # Ambil shape dari model yang diload\n",
+ "MAX_Q = model.input_shape[3][1] # Max length for question\n",
+ "MAX_A = model.input_shape[4][1] # Max length for answer\n",
+ "\n",
+ "inp_tok_g = Input(shape=(MAX_SENT,), name=\"tok_in_g\")\n",
+ "inp_ner_g = Input(shape=(MAX_SENT,), name=\"ner_in_g\")\n",
+ "inp_srl_g = Input(shape=(MAX_SENT,), name=\"srl_in_g\")\n",
+ "\n",
+ "emb_tok = model.get_layer(\"embedding_tok\").call(inp_tok_g)\n",
+ "emb_ner = model.get_layer(\"embedding_ner\").call(inp_ner_g)\n",
+ "emb_srl = model.get_layer(\"embedding_srl\").call(inp_srl_g)\n",
+ "\n",
+ "enc_concat = Concatenate(name=\"concat_encoder\")([emb_tok, emb_ner, emb_srl])\n",
+ "\n",
+ "encoder_lstm = model.get_layer(\"encoder_lstm\")\n",
+ "enc_out, state_h, state_c = encoder_lstm(enc_concat)\n",
+ "\n",
+ "# Create encoder model with full output including enc_out\n",
+ "encoder_model = Model(\n",
+ " inputs=[inp_tok_g, inp_ner_g, inp_srl_g],\n",
+ " outputs=[enc_out, state_h, state_c],\n",
+ " name=\"encoder_model\",\n",
+ ")\n",
+ "\n",
+ "# === Build Decoder for Question ===\n",
+ "dec_q_inp = Input(shape=(1,), name=\"dec_q_in\")\n",
+ "dec_emb_q = model.get_layer(\"embedding_q_decoder\").call(dec_q_inp)\n",
+ "\n",
+ "state_h_dec = Input(shape=(256,), name=\"state_h_dec\")\n",
+ "state_c_dec = Input(shape=(256,), name=\"state_c_dec\")\n",
+ "\n",
+ "lstm_decoder_q = model.get_layer(\"lstm_q_decoder\")\n",
+ "\n",
+ "dec_out_q, state_h_q, state_c_q = lstm_decoder_q(\n",
+ " dec_emb_q, initial_state=[state_h_dec, state_c_dec]\n",
+ ")\n",
+ "\n",
+ "q_time_dist_layer = model.get_layer(\"q_output\")\n",
+ "dense_q = q_time_dist_layer.layer\n",
+ "q_output = dense_q(dec_out_q)\n",
+ "\n",
+ "decoder_q = Model(\n",
+ " inputs=[dec_q_inp, state_h_dec, state_c_dec],\n",
+ " outputs=[q_output, state_h_q, state_c_q],\n",
+ " name=\"decoder_question_model\",\n",
+ ")\n",
+ "\n",
+ "# === Build Decoder for Answer ===\n",
+ "dec_a_inp = Input(shape=(1,), name=\"dec_a_in\")\n",
+ "dec_emb_a = model.get_layer(\"embedding_a_decoder\").call(dec_a_inp)\n",
+ "\n",
+ "state_h_a = Input(shape=(256,), name=\"state_h_a\")\n",
+ "state_c_a = Input(shape=(256,), name=\"state_c_a\")\n",
+ "\n",
+ "lstm_decoder_a = model.get_layer(\"lstm_a_decoder\")\n",
+ "\n",
+ "dec_out_a, state_h_a_out, state_c_a_out = lstm_decoder_a(\n",
+ " dec_emb_a, initial_state=[state_h_a, state_c_a]\n",
+ ")\n",
+ "\n",
+ "a_time_dist_layer = model.get_layer(\"a_output\")\n",
+ "dense_a = a_time_dist_layer.layer\n",
+ "a_output = dense_a(dec_out_a)\n",
+ "\n",
+ "decoder_a = Model(\n",
+ " inputs=[dec_a_inp, state_h_a, state_c_a],\n",
+ " outputs=[a_output, state_h_a_out, state_c_a_out],\n",
+ " name=\"decoder_answer_model\",\n",
+ ")\n",
+ "\n",
+ "# === Build Classifier for Question Type ===\n",
+ "type_dense = model.get_layer(\"type_output\")\n",
+ "type_out = type_dense(enc_out)\n",
+ "\n",
+ "classifier_model = Model(\n",
+ " inputs=[inp_tok_g, inp_ner_g, inp_srl_g], outputs=type_out, name=\"classifier_model\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "id": "d406e6ff",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generated Question: menghadiri menghadiri ___ ___\n",
+ "Generated Answer : \n",
+ "Question Type : isian\n"
+ ]
+ }
+ ],
+ "source": [
+ "def encode(seq, vmap):\n",
+ " return [vmap.get(tok, vmap[\"\"]) for tok in seq]\n",
+ "\n",
+ "\n",
+ "def encode_and_pad(seq, vmap, max_len=MAX_SENT):\n",
+ " encoded = [vmap.get(tok, vmap[\"\"]) for tok in seq]\n",
+ " # Pad with vocab[\"\"] to the right if sequence is shorter than max_len\n",
+ " padded = encoded + [vmap[\"\"]] * (max_len - len(encoded))\n",
+ " return padded[:max_len] # Ensure it doesn't exceed max_len\n",
+ "\n",
+ "\n",
+ "def greedy_decode(tokens, ner, srl, max_q=20, max_a=10):\n",
+ " # --- encode encoder inputs -------------------------------------------\n",
+ " if isinstance(tokens, np.ndarray):\n",
+ " enc_tok = tokens\n",
+ " enc_ner = ner\n",
+ " enc_srl = srl\n",
+ " else:\n",
+ " enc_tok = np.array([encode_and_pad(tokens, vocab_tok, MAX_SENT)])\n",
+ " enc_ner = np.array([encode_and_pad(ner, vocab_ner, MAX_SENT)])\n",
+ " enc_srl = np.array([encode_and_pad(srl, vocab_srl, MAX_SENT)])\n",
+ "\n",
+ " # --- Get encoder outputs ---\n",
+ " enc_out, h, c = encoder_model.predict([enc_tok, enc_ner, enc_srl], verbose=0)\n",
+ "\n",
+ " # QUESTION Decoding\n",
+ " tgt = np.array([[vocab_q[\"\"]]])\n",
+ " question_ids = []\n",
+ " for _ in range(max_q):\n",
+ " logits, h, c = decoder_q.predict([tgt, h, c], verbose=0)\n",
+ " next_id = int(logits[0, 0].argmax()) # Get the predicted token ID\n",
+ " if next_id == vocab_q[\"\"]:\n",
+ " break\n",
+ " question_ids.append(next_id)\n",
+ " tgt = np.array([[next_id]]) # Feed the predicted token back as input\n",
+ "\n",
+ " # ANSWER Decoding - use encoder outputs again for fresh state\n",
+ " _, h, c = encoder_model.predict([enc_tok, enc_ner, enc_srl], verbose=0)\n",
+ " tgt = np.array([[vocab_a[\"\"]]])\n",
+ " answer_ids = []\n",
+ " for _ in range(max_a):\n",
+ " logits, h, c = decoder_a.predict([tgt, h, c], verbose=0)\n",
+ " next_id = int(logits[0, 0].argmax())\n",
+ " if next_id == vocab_a[\"\"]:\n",
+ " break\n",
+ " answer_ids.append(next_id)\n",
+ " tgt = np.array([[next_id]])\n",
+ "\n",
+ " # Question Type\n",
+ " qtype_logits = classifier_model.predict([enc_tok, enc_ner, enc_srl], verbose=0)\n",
+ " qtype_id = int(qtype_logits.argmax())\n",
+ "\n",
+ " # Final output\n",
+ " question = [inv_vocab_q.get(i, \"\") for i in question_ids]\n",
+ " answer = [inv_vocab_a.get(i, \"\") for i in answer_ids]\n",
+ " q_type = [k for k, v in vocab_typ.items() if v == qtype_id][0]\n",
+ "\n",
+ " return question, answer, q_type\n",
+ "\n",
+ "\n",
+ "def test_model():\n",
+ " test_data = {\n",
+ " \"tokens\": [\"nama\", \"lengkap\", \"saya\", \"Maya\", \"Maya\"],\n",
+ " \"ner\": [\"O\", \"O\", \"O\", \"B-PER\", \"B-PER\"],\n",
+ " \"srl\": [\"O\", \"O\", \"ARG0\", \"ARG0\", \"ARG0\"],\n",
+ " }\n",
+ " # tokens = [\n",
+ " # \"soekarno\",\n",
+ " # \"membacakan\",\n",
+ " # \"teks\",\n",
+ " # \"proklamasi\",\n",
+ " # \"pada\",\n",
+ " # \"17\",\n",
+ " # \"agustus\",\n",
+ " # \"1945\",\n",
+ " # ]\n",
+ " # ner_tags = [\"B-PER\", \"O\", \"O\", \"O\", \"O\", \"B-DATE\", \"I-DATE\", \"I-DATE\"]\n",
+ " # srl_tags = [\"ARG0\", \"V\", \"ARG1\", \"ARG1\", \"O\", \"ARGM-TMP\", \"ARGM-TMP\", \"ARGM-TMP\"]\n",
+ "\n",
+ " question, answer, q_type = greedy_decode(\n",
+ " test_data[\"tokens\"], test_data[\"ner\"], test_data[\"srl\"]\n",
+ " )\n",
+ " print(f\"Generated Question: {' '.join(question)}\")\n",
+ " print(f\"Generated Answer : {' '.join(answer)}\")\n",
+ " print(f\"Question Type : {q_type}\")\n",
+ "\n",
+ "\n",
+ "test_model()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "id": "5adde3c3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "BLEU : 0.0385\n",
+ "ROUGE1: 0.1052 | ROUGE-L: 0.1052\n"
+ ]
+ }
+ ],
+ "source": [
+ "from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction\n",
+ "from rouge_score import rouge_scorer\n",
+ "\n",
+ "smoothie = SmoothingFunction().method4\n",
+ "scorer = rouge_scorer.RougeScorer([\"rouge1\", \"rougeL\"], use_stemmer=True)\n",
+ "\n",
+ "\n",
+ "# Helper to strip special ids\n",
+ "def strip_special(ids, vocab):\n",
+ " pad = vocab[\"\"] if \"\" in vocab else None\n",
+ " eos = vocab[\"\"]\n",
+ " return [i for i in ids if i not in (pad, eos)]\n",
+ "\n",
+ "\n",
+ "def ids_to_text(ids, inv_vocab):\n",
+ " return \" \".join(inv_vocab[i] for i in ids)\n",
+ "\n",
+ "\n",
+ "# ---- evaluation over a set of indices ----\n",
+ "import random\n",
+ "\n",
+ "\n",
+ "def evaluate(indices=None):\n",
+ " if indices is None:\n",
+ " indices = random.sample(range(len(X_tok)), k=min(100, len(X_tok)))\n",
+ "\n",
+ " bleu_scores, rou1, rouL = [], [], []\n",
+ " for idx in indices:\n",
+ " # Ground truth\n",
+ " gt_q = strip_special(dec_q_out[idx], vocab_q)\n",
+ " gt_a = strip_special(dec_a_out[idx], vocab_a)\n",
+ " # Prediction\n",
+ " q_pred, a_pred, _ = greedy_decode(\n",
+ " X_tok[idx : idx + 1], X_ner[idx : idx + 1], X_srl[idx : idx + 1]\n",
+ " )\n",
+ "\n",
+ " # BLEU on question tokens\n",
+ " bleu_scores.append(\n",
+ " sentence_bleu(\n",
+ " [[inv_vocab_q[i] for i in gt_q]], q_pred, smoothing_function=smoothie\n",
+ " )\n",
+ " )\n",
+ " # ROUGE on question strings\n",
+ " r = scorer.score(ids_to_text(gt_q, inv_vocab_q), \" \".join(q_pred))\n",
+ " rou1.append(r[\"rouge1\"].fmeasure)\n",
+ " rouL.append(r[\"rougeL\"].fmeasure)\n",
+ "\n",
+ " print(f\"BLEU : {np.mean(bleu_scores):.4f}\")\n",
+ " print(f\"ROUGE1: {np.mean(rou1):.4f} | ROUGE-L: {np.mean(rouL):.4f}\")\n",
+ "\n",
+ "\n",
+ "evaluate()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "myenv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/question_generation/qg_lstm_v2.ipynb b/question_generation/qg_lstm_v2.ipynb
new file mode 100644
index 0000000..41bab86
--- /dev/null
+++ b/question_generation/qg_lstm_v2.ipynb
@@ -0,0 +1,615 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "58e41ccb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json, pickle, random\n",
+ "from pathlib import Path\n",
+ "from itertools import chain\n",
+ "\n",
+ "import numpy as np\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.keras.layers import (\n",
+ " Input, Embedding, LSTM, Concatenate,\n",
+ " Dense, TimeDistributed\n",
+ ")\n",
+ "from tensorflow.keras.models import Model\n",
+ "from nltk.translate.bleu_score import corpus_bleu, SmoothingFunction\n",
+ "from rouge_score import rouge_scorer, scoring\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "a94dd46a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "flattened samples : 8\n"
+ ]
+ }
+ ],
+ "source": [
+ "RAW = json.loads(Path(\"../dataset/dev_dataset_qg.json\").read_text())\n",
+ "\n",
+ "samples = []\n",
+ "for item in RAW:\n",
+ " for qp in item[\"quiz_posibility\"]:\n",
+ " samples.append({\n",
+ " \"tokens\" : item[\"tokens\"],\n",
+ " \"ner\" : item[\"ner\"],\n",
+ " \"srl\" : item[\"srl\"],\n",
+ " \"q_type\" : qp[\"type\"], # isian / opsi / benar_salah\n",
+ " \"q_toks\" : qp[\"question\"] + [\"\"],\n",
+ " \"a_toks\" : (qp[\"answer\"] if isinstance(qp[\"answer\"], list)\n",
+ " else [qp[\"answer\"]]) + [\"\"]\n",
+ " })\n",
+ "\n",
+ "print(\"flattened samples :\", len(samples))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "852fb9a8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def build_vocab(seq_iter, reserved=(\"\", \"\", \"\", \"\")):\n",
+ " vocab = {tok: idx for idx, tok in enumerate(reserved)}\n",
+ " for tok in chain.from_iterable(seq_iter):\n",
+ " vocab.setdefault(tok, len(vocab))\n",
+ " return vocab\n",
+ "\n",
+ "vocab_tok = build_vocab((s[\"tokens\"] for s in samples))\n",
+ "vocab_ner = build_vocab((s[\"ner\"] for s in samples), reserved=(\"\",\"\"))\n",
+ "vocab_srl = build_vocab((s[\"srl\"] for s in samples), reserved=(\"\",\"\"))\n",
+ "vocab_q = build_vocab((s[\"q_toks\"] for s in samples))\n",
+ "vocab_a = build_vocab((s[\"a_toks\"] for s in samples))\n",
+ "vocab_typ = {\"isian\":0, \"opsi\":1, \"benar_salah\":2}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "fdf696cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def enc(seq, v): return [v.get(t, v[\"\"]) for t in seq]\n",
+ "\n",
+ "MAX_SENT = max(len(s[\"tokens\"]) for s in samples)\n",
+ "MAX_Q = max(len(s[\"q_toks\"]) for s in samples)\n",
+ "MAX_A = max(len(s[\"a_toks\"]) for s in samples)\n",
+ "\n",
+ "def pad_batch(seqs, vmap, maxlen):\n",
+ " return tf.keras.preprocessing.sequence.pad_sequences(\n",
+ " [enc(s, vmap) for s in seqs], maxlen=maxlen, padding=\"post\"\n",
+ " )\n",
+ "\n",
+ "X_tok = pad_batch((s[\"tokens\"] for s in samples), vocab_tok, MAX_SENT)\n",
+ "X_ner = pad_batch((s[\"ner\"] for s in samples), vocab_ner, MAX_SENT)\n",
+ "X_srl = pad_batch((s[\"srl\"] for s in samples), vocab_srl, MAX_SENT)\n",
+ "\n",
+ "dec_q_in = pad_batch(\n",
+ " ([[\"\"]+s[\"q_toks\"][:-1] for s in samples]), vocab_q, MAX_Q)\n",
+ "dec_q_out = pad_batch((s[\"q_toks\"] for s in samples), vocab_q, MAX_Q)\n",
+ "\n",
+ "dec_a_in = pad_batch(\n",
+ " ([[\"\"]+s[\"a_toks\"][:-1] for s in samples]), vocab_a, MAX_A)\n",
+ "dec_a_out = pad_batch((s[\"a_toks\"] for s in samples), vocab_a, MAX_A)\n",
+ "\n",
+ "y_type = np.array([vocab_typ[s[\"q_type\"]] for s in samples])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "33074619",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"functional_2\"\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"functional_2\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ tok_in (InputLayer) │ (None, 11) │ 0 │ - │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ ner_in (InputLayer) │ (None, 11) │ 0 │ - │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ srl_in (InputLayer) │ (None, 11) │ 0 │ - │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ not_equal_8 │ (None, 11) │ 0 │ tok_in[0][0] │\n",
+ "│ (NotEqual) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ emb_ner (Embedding) │ (None, 11, 32) │ 352 │ ner_in[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ emb_srl (Embedding) │ (None, 11, 32) │ 288 │ srl_in[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ expand_dims_4 │ (None, 11, 1) │ 0 │ not_equal_8[0][0] │\n",
+ "│ (ExpandDims) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ broadcast_to_4 │ (None, 11, 128) │ 0 │ expand_dims_4[0]… │\n",
+ "│ (BroadcastTo) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ ones_like_2 │ (None, 11, 32) │ 0 │ emb_ner[0][0] │\n",
+ "│ (OnesLike) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ ones_like_3 │ (None, 11, 32) │ 0 │ emb_srl[0][0] │\n",
+ "│ (OnesLike) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ emb_tok (Embedding) │ (None, 11, 128) │ 4,992 │ tok_in[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ concatenate_5 │ (None, 11, 192) │ 0 │ broadcast_to_4[0… │\n",
+ "│ (Concatenate) │ │ │ ones_like_2[0][0… │\n",
+ "│ │ │ │ ones_like_3[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dec_q_in │ (None, 9) │ 0 │ - │\n",
+ "│ (InputLayer) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ concatenate_4 │ (None, 11, 192) │ 0 │ emb_tok[0][0], │\n",
+ "│ (Concatenate) │ │ │ emb_ner[0][0], │\n",
+ "│ │ │ │ emb_srl[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ any_2 (Any) │ (None, 11) │ 0 │ concatenate_5[0]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dec_a_in │ (None, 4) │ 0 │ - │\n",
+ "│ (InputLayer) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ emb_q (Embedding) │ (None, 9, 128) │ 3,968 │ dec_q_in[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ enc_lstm (LSTM) │ [(None, 256), │ 459,776 │ concatenate_4[0]… │\n",
+ "│ │ (None, 256), │ │ any_2[0][0] │\n",
+ "│ │ (None, 256)] │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ emb_a (Embedding) │ (None, 4, 128) │ 1,792 │ dec_a_in[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ lstm_q (LSTM) │ [(None, 9, 256), │ 394,240 │ emb_q[0][0], │\n",
+ "│ │ (None, 256), │ │ enc_lstm[0][1], │\n",
+ "│ │ (None, 256)] │ │ enc_lstm[0][2] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ not_equal_9 │ (None, 9) │ 0 │ dec_q_in[0][0] │\n",
+ "│ (NotEqual) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ lstm_a (LSTM) │ [(None, 4, 256), │ 394,240 │ emb_a[0][0], │\n",
+ "│ │ (None, 256), │ │ enc_lstm[0][1], │\n",
+ "│ │ (None, 256)] │ │ enc_lstm[0][2] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ not_equal_10 │ (None, 4) │ 0 │ dec_a_in[0][0] │\n",
+ "│ (NotEqual) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ q_out │ (None, 9, 31) │ 7,967 │ lstm_q[0][0], │\n",
+ "│ (TimeDistributed) │ │ │ not_equal_9[0][0] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ a_out │ (None, 4, 14) │ 3,598 │ lstm_a[0][0], │\n",
+ "│ (TimeDistributed) │ │ │ not_equal_10[0][… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ type_out (Dense) │ (None, 3) │ 771 │ enc_lstm[0][0] │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ tok_in (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ ner_in (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ srl_in (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ not_equal_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ tok_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mNotEqual\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ emb_ner (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m352\u001b[0m │ ner_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ emb_srl (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m288\u001b[0m │ srl_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ expand_dims_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ not_equal_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mExpandDims\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ broadcast_to_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ expand_dims_4[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ (\u001b[38;5;33mBroadcastTo\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ ones_like_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ emb_ner[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mOnesLike\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ ones_like_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ emb_srl[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mOnesLike\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ emb_tok (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m4,992\u001b[0m │ tok_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ concatenate_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ broadcast_to_4[\u001b[38;5;34m0\u001b[0m… │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ ones_like_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
+ "│ │ │ │ ones_like_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dec_q_in │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ concatenate_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ emb_tok[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
+ "│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ emb_ner[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
+ "│ │ │ │ emb_srl[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ any_2 (\u001b[38;5;33mAny\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ concatenate_5[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ dec_a_in │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
+ "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ emb_q (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m3,968\u001b[0m │ dec_q_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ enc_lstm (\u001b[38;5;33mLSTM\u001b[0m) │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ \u001b[38;5;34m459,776\u001b[0m │ concatenate_4[\u001b[38;5;34m0\u001b[0m]… │\n",
+ "│ │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ │ any_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)] │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ emb_a (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m1,792\u001b[0m │ dec_a_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ lstm_q (\u001b[38;5;33mLSTM\u001b[0m) │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ \u001b[38;5;34m394,240\u001b[0m │ emb_q[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
+ "│ │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ │ enc_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m], │\n",
+ "│ │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)] │ │ enc_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m2\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ not_equal_9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dec_q_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mNotEqual\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ lstm_a (\u001b[38;5;33mLSTM\u001b[0m) │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ \u001b[38;5;34m394,240\u001b[0m │ emb_a[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
+ "│ │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m), │ │ enc_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m], │\n",
+ "│ │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)] │ │ enc_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m2\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ not_equal_10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dec_a_in[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "│ (\u001b[38;5;33mNotEqual\u001b[0m) │ │ │ │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ q_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m31\u001b[0m) │ \u001b[38;5;34m7,967\u001b[0m │ lstm_q[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
+ "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ a_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m14\u001b[0m) │ \u001b[38;5;34m3,598\u001b[0m │ lstm_a[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
+ "│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
+ "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+ "│ type_out (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m771\u001b[0m │ enc_lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
+ "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
+ ]
+ },
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+ "output_type": "display_data"
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+ "source": [
+ "d_tok, d_tag, units = 128, 32, 256\n",
+ "pad_tok, pad_q, pad_a = vocab_tok[\"\"], vocab_q[\"\"], vocab_a[\"\"]\n",
+ "\n",
+ "# ---- Encoder ----------------------------------------------------\n",
+ "inp_tok = Input((MAX_SENT,), name=\"tok_in\")\n",
+ "inp_ner = Input((MAX_SENT,), name=\"ner_in\")\n",
+ "inp_srl = Input((MAX_SENT,), name=\"srl_in\")\n",
+ "\n",
+ "emb_tok = Embedding(len(vocab_tok), d_tok, mask_zero=True, name=\"emb_tok\")(inp_tok)\n",
+ "emb_ner = Embedding(len(vocab_ner), d_tag, mask_zero=False, name=\"emb_ner\")(inp_ner)\n",
+ "emb_srl = Embedding(len(vocab_srl), d_tag, mask_zero=False, name=\"emb_srl\")(inp_srl)\n",
+ "\n",
+ "enc_concat = Concatenate()([emb_tok, emb_ner, emb_srl])\n",
+ "enc_out, state_h, state_c = LSTM(units, return_state=True, name=\"enc_lstm\")(enc_concat)\n",
+ "\n",
+ "# ---- Decoder : Question ----------------------------------------\n",
+ "dec_q_inp = Input((MAX_Q,), name=\"dec_q_in\")\n",
+ "dec_emb_q = Embedding(len(vocab_q), d_tok, mask_zero=True, name=\"emb_q\")(dec_q_inp)\n",
+ "dec_q_seq, _, _ = LSTM(units, return_sequences=True, return_state=True,\n",
+ " name=\"lstm_q\")(dec_emb_q, initial_state=[state_h, state_c])\n",
+ "q_out = TimeDistributed(Dense(len(vocab_q), activation=\"softmax\"), name=\"q_out\")(dec_q_seq)\n",
+ "\n",
+ "# ---- Decoder : Answer ------------------------------------------\n",
+ "dec_a_inp = Input((MAX_A,), name=\"dec_a_in\")\n",
+ "dec_emb_a = Embedding(len(vocab_a), d_tok, mask_zero=True, name=\"emb_a\")(dec_a_inp)\n",
+ "dec_a_seq, _, _ = LSTM(units, return_sequences=True, return_state=True,\n",
+ " name=\"lstm_a\")(dec_emb_a, initial_state=[state_h, state_c])\n",
+ "a_out = TimeDistributed(Dense(len(vocab_a), activation=\"softmax\"), name=\"a_out\")(dec_a_seq)\n",
+ "\n",
+ "# ---- Classifier -------------------------------------------------\n",
+ "type_out = Dense(len(vocab_typ), activation=\"softmax\", name=\"type_out\")(enc_out)\n",
+ "\n",
+ "model = Model(\n",
+ " [inp_tok, inp_ner, inp_srl, dec_q_inp, dec_a_inp],\n",
+ " [q_out, a_out, type_out]\n",
+ ")\n",
+ "\n",
+ "# ---- Masked loss helpers ---------------------------------------\n",
+ "scce = tf.keras.losses.SparseCategoricalCrossentropy(reduction=\"none\")\n",
+ "def masked_loss_factory(pad_id):\n",
+ " def loss(y_true, y_pred):\n",
+ " l = scce(y_true, y_pred)\n",
+ " mask = tf.cast(tf.not_equal(y_true, pad_id), tf.float32)\n",
+ " return tf.reduce_sum(l*mask) / tf.reduce_sum(mask)\n",
+ " return loss\n",
+ "\n",
+ "model.compile(\n",
+ " optimizer=\"adam\",\n",
+ " loss = {\"q_out\":masked_loss_factory(pad_q),\n",
+ " \"a_out\":masked_loss_factory(pad_a),\n",
+ " \"type_out\":\"sparse_categorical_crossentropy\"},\n",
+ " loss_weights={\"q_out\":1.0, \"a_out\":1.0, \"type_out\":0.3},\n",
+ " metrics={\"q_out\":\"sparse_categorical_accuracy\",\n",
+ " \"a_out\":\"sparse_categorical_accuracy\",\n",
+ " \"type_out\":tf.keras.metrics.SparseCategoricalAccuracy(name=\"type_acc\")}\n",
+ ")\n",
+ "model.summary()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "44d36899",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/30\n"
+ ]
+ },
+ {
+ "ename": "TypeError",
+ "evalue": "Exception encountered when calling BroadcastTo.call().\n\n\u001b[1mFailed to convert elements of (None, 11, 128) to Tensor. Consider casting elements to a supported type. See https://www.tensorflow.org/api_docs/python/tf/dtypes for supported TF dtypes.\u001b[0m\n\nArguments received by BroadcastTo.call():\n • x=tf.Tensor(shape=(None, 11, 1), dtype=bool)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mX_tok\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_ner\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_srl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdec_q_in\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdec_a_in\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mdec_q_out\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdec_a_out\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_type\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_split\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m30\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m64\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkeras\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEarlyStopping\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrestore_best_weights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\n\u001b[1;32m 9\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m model\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfull_seq2seq.keras\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# -----------------------------------------------------------------\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# 5. SAVE VOCABS (.pkl keeps python dict intact)\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# -----------------------------------------------------------------\u001b[39;00m\n",
+ "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/keras/src/utils/traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m 120\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+ "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/keras/src/utils/traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m 120\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+ "\u001b[0;31mTypeError\u001b[0m: Exception encountered when calling BroadcastTo.call().\n\n\u001b[1mFailed to convert elements of (None, 11, 128) to Tensor. Consider casting elements to a supported type. See https://www.tensorflow.org/api_docs/python/tf/dtypes for supported TF dtypes.\u001b[0m\n\nArguments received by BroadcastTo.call():\n • x=tf.Tensor(shape=(None, 11, 1), dtype=bool)"
+ ]
+ }
+ ],
+ "source": [
+ "history = model.fit(\n",
+ " [X_tok, X_ner, X_srl, dec_q_in, dec_a_in],\n",
+ " [dec_q_out, dec_a_out, y_type],\n",
+ " validation_split=0.1,\n",
+ " epochs=30,\n",
+ " batch_size=64,\n",
+ " callbacks=[tf.keras.callbacks.EarlyStopping(patience=4, restore_best_weights=True)],\n",
+ " verbose=2\n",
+ ")\n",
+ "model.save(\"full_seq2seq.keras\")\n",
+ "\n",
+ "\n",
+ "# -----------------------------------------------------------------\n",
+ "# 5. SAVE VOCABS (.pkl keeps python dict intact)\n",
+ "# -----------------------------------------------------------------\n",
+ "def save_vocab(v, name): pickle.dump(v, open(name,\"wb\"))\n",
+ "save_vocab(vocab_tok,\"vocab_tok.pkl\"); save_vocab(vocab_ner,\"vocab_ner.pkl\")\n",
+ "save_vocab(vocab_srl,\"vocab_srl.pkl\"); save_vocab(vocab_q, \"vocab_q.pkl\")\n",
+ "save_vocab(vocab_a, \"vocab_a.pkl\"); save_vocab(vocab_typ,\"vocab_typ.pkl\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "61003de5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def build_inference_models(trained):\n",
+ " # encoder\n",
+ " t_in = Input((MAX_SENT,), name=\"t_in\")\n",
+ " n_in = Input((MAX_SENT,), name=\"n_in\")\n",
+ " s_in = Input((MAX_SENT,), name=\"s_in\")\n",
+ " e_t = trained.get_layer(\"emb_tok\")(t_in)\n",
+ " e_n = trained.get_layer(\"emb_ner\")(n_in)\n",
+ " e_s = trained.get_layer(\"emb_srl\")(s_in)\n",
+ " concat = Concatenate()([e_t,e_n,e_s])\n",
+ " _, h, c = trained.get_layer(\"enc_lstm\")(concat)\n",
+ " enc_model = Model([t_in,n_in,s_in],[h,c])\n",
+ "\n",
+ " # question‑decoder\n",
+ " dq_in = Input((1,), name=\"dq_tok\")\n",
+ " dh = Input((units,), name=\"dh\"); dc = Input((units,), name=\"dc\")\n",
+ " dq_emb = trained.get_layer(\"emb_q\")(dq_in)\n",
+ " dq_lstm, nh, nc = trained.get_layer(\"lstm_q\")(dq_emb, initial_state=[dh,dc])\n",
+ " dq_out = trained.get_layer(\"q_out\").layer(dq_lstm)\n",
+ " dec_q_model = Model([dq_in, dh, dc], [dq_out, nh, nc])\n",
+ "\n",
+ " # answer‑decoder\n",
+ " da_in = Input((1,), name=\"da_tok\")\n",
+ " ah = Input((units,), name=\"ah\"); ac = Input((units,), name=\"ac\")\n",
+ " da_emb = trained.get_layer(\"emb_a\")(da_in)\n",
+ " da_lstm, nh2, nc2 = trained.get_layer(\"lstm_a\")(da_emb, initial_state=[ah,ac])\n",
+ " da_out = trained.get_layer(\"a_out\").layer(da_lstm)\n",
+ " dec_a_model = Model([da_in, ah, ac], [da_out, nh2, nc2])\n",
+ "\n",
+ " # type classifier\n",
+ " type_dense = trained.get_layer(\"type_out\")\n",
+ " type_model = Model([t_in,n_in,s_in], type_dense(_)) # use _ = enc_lstm output\n",
+ "\n",
+ " return enc_model, dec_q_model, dec_a_model, type_model\n",
+ "\n",
+ "encoder_model, decoder_q, decoder_a, classifier_model = build_inference_models(model)\n",
+ "\n",
+ "inv_q = {v:k for k,v in vocab_q.items()}\n",
+ "inv_a = {v:k for k,v in vocab_a.items()}\n",
+ "\n",
+ "def enc_pad(seq, vmap, maxlen):\n",
+ " x = [vmap.get(t, vmap[\"\"]) for t in seq]\n",
+ " return x + [vmap[\"\"]] * (maxlen-len(x))\n",
+ "\n",
+ "def greedy_decode(tokens, ner, srl, max_q=20, max_a=10):\n",
+ " et = np.array([enc_pad(tokens, vocab_tok, MAX_SENT)])\n",
+ " en = np.array([enc_pad(ner, vocab_ner, MAX_SENT)])\n",
+ " es = np.array([enc_pad(srl, vocab_srl, MAX_SENT)])\n",
+ "\n",
+ " h,c = encoder_model.predict([et,en,es], verbose=0)\n",
+ "\n",
+ " # --- question\n",
+ " q_ids = []\n",
+ " tgt = np.array([[vocab_q[\"\"]]])\n",
+ " for _ in range(max_q):\n",
+ " logits,h,c = decoder_q.predict([tgt,h,c], verbose=0)\n",
+ " nxt = int(logits[0,-1].argmax())\n",
+ " if nxt==vocab_q[\"\"]: break\n",
+ " q_ids.append(nxt)\n",
+ " tgt = np.array([[nxt]])\n",
+ "\n",
+ " # --- answer (re‑use fresh h,c)\n",
+ " h,c = encoder_model.predict([et,en,es], verbose=0)\n",
+ " a_ids = []\n",
+ " tgt = np.array([[vocab_a[\"\"]]])\n",
+ " for _ in range(max_a):\n",
+ " logits,h,c = decoder_a.predict([tgt,h,c], verbose=0)\n",
+ " nxt = int(logits[0,-1].argmax())\n",
+ " if nxt==vocab_a[\"\"]: break\n",
+ " a_ids.append(nxt)\n",
+ " tgt = np.array([[nxt]])\n",
+ "\n",
+ " # --- type\n",
+ " t_id = int(classifier_model.predict([et,en,es], verbose=0).argmax())\n",
+ "\n",
+ " return [inv_q[i] for i in q_ids], [inv_a[i] for i in a_ids], \\\n",
+ " [k for k,v in vocab_typ.items() if v==t_id][0]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5279b631",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_tokens = [\"soekarno\",\"membacakan\",\"teks\",\"proklamasi\",\"pada\",\n",
+ " \"17\",\"agustus\",\"1945\"]\n",
+ "test_ner = [\"B-PER\",\"O\",\"O\",\"O\",\"O\",\"B-DATE\",\"I-DATE\",\"I-DATE\"]\n",
+ "test_srl = [\"ARG0\",\"V\",\"ARG1\",\"ARG1\",\"O\",\"ARGM-TMP\",\"ARGM-TMP\",\"ARGM-TMP\"]\n",
+ "\n",
+ "q,a,t = greedy_decode(test_tokens,test_ner,test_srl,max_q=MAX_Q,max_a=MAX_A)\n",
+ "print(\"\\nDEMO\\n----\")\n",
+ "print(\"Q :\", \" \".join(q))\n",
+ "print(\"A :\", \" \".join(a))\n",
+ "print(\"T :\", t)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "850d4905",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "smooth = SmoothingFunction().method4\n",
+ "r_scorer = rouge_scorer.RougeScorer([\"rouge1\",\"rougeL\"], use_stemmer=True)\n",
+ "\n",
+ "def strip_special(seq, pad_id, eos_id):\n",
+ " return [x for x in seq if x not in (pad_id, eos_id)]\n",
+ "\n",
+ "def ids_to_text(ids, inv):\n",
+ " return \" \".join(inv[i] for i in ids)\n",
+ "\n",
+ "def evaluate(n=200):\n",
+ " idxs = random.sample(range(len(samples)), n)\n",
+ " refs, hyps = [], []\n",
+ " agg = scoring.BootstrapAggregator()\n",
+ "\n",
+ " for i in idxs:\n",
+ " gt_ids = strip_special(dec_q_out[i], pad_q, vocab_q[\"\"])\n",
+ " ref = ids_to_text(gt_ids, inv_q)\n",
+ " pred = \" \".join(greedy_decode(\n",
+ " samples[i][\"tokens\"],\n",
+ " samples[i][\"ner\"],\n",
+ " samples[i][\"srl\"]\n",
+ " )[0])\n",
+ " refs.append([ref.split()])\n",
+ " hyps.append(pred.split())\n",
+ " agg.add_scores(r_scorer.score(ref, pred))\n",
+ "\n",
+ " bleu = corpus_bleu(refs, hyps, smoothing_function=smooth)\n",
+ " r1 = agg.aggregate()[\"rouge1\"].mid\n",
+ " rL = agg.aggregate()[\"rougeL\"].mid\n",
+ "\n",
+ " print(f\"\\nEVAL (n={n})\")\n",
+ " print(f\"BLEU‑4 : {bleu:.4f}\")\n",
+ " print(f\"ROUGE‑1 : P={r1.precision:.3f} R={r1.recall:.3f} F1={r1.fmeasure:.3f}\")\n",
+ " print(f\"ROUGE‑L : P={rL.precision:.3f} R={rL.recall:.3f} F1={rL.fmeasure:.3f}\")\n",
+ "\n",
+ "evaluate(2) "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "myenv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}