{
"cells": [
{
"cell_type": "code",
"execution_count": 118,
"id": "fb106e20",
"metadata": {},
"outputs": [],
"source": [
"import json, pickle\n",
"import numpy as np\n",
"from keras.models import Model\n",
"from keras.layers import Input, Embedding, Bidirectional, LSTM, TimeDistributed, Dense\n",
"from keras.preprocessing.sequence import pad_sequences\n",
"from keras.utils import to_categorical\n",
"from seqeval.metrics import classification_report\n",
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 119,
"id": "00347a5f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total kalimat: 156\n",
"Total token: 1850\n"
]
}
],
"source": [
"#load the data\n",
"# with open(\"../dataset/dataset_ner_srl.json\", encoding=\"utf-8\") as f:\n",
"# data = json.load(f)\n",
"\n",
"# sentences = [[tok.lower() for tok in item[\"tokens\"]] for item in data]\n",
"# labels_ner = [item[\"labels_ner\"] for item in data]\n",
"# labels_srl = [item[\"labels_srl\"] for item in data]\n",
"\n",
"# for i, label_seq in enumerate(labels_ner):\n",
"# if \"V\" in label_seq:\n",
"# print(f\"Label 'V' ditemukan di index {i}: {label_seq}\")\n",
" \n",
" \n",
"data = []\n",
"\n",
"with open(\"../dataset/dataset_ner_srl.tsv\", encoding=\"utf-8\") as f:\n",
" tokens, ner_labels, srl_labels = [], [], []\n",
" \n",
" for line in f:\n",
" line = line.strip()\n",
" if not line:\n",
" # Jika baris kosong → akhir kalimat\n",
" if tokens:\n",
" data.append({\n",
" \"tokens\": tokens,\n",
" \"labels_ner\": ner_labels,\n",
" \"labels_srl\": srl_labels\n",
" })\n",
" tokens, ner_labels, srl_labels = [], [], []\n",
" else:\n",
" token, ner, srl = line.split(\"\\t\")\n",
" tokens.append(token)\n",
" ner_labels.append(ner)\n",
" srl_labels.append(srl)\n",
"\n",
"# Preprocessing sama seperti sebelumnya\n",
"sentences = [[tok.lower() for tok in item[\"tokens\"]] for item in data]\n",
"labels_ner = [item[\"labels_ner\"] for item in data]\n",
"labels_srl = [item[\"labels_srl\"] for item in data]\n",
"\n",
"total_kalimat = len(data)\n",
"total_token = sum(len(item[\"tokens\"]) for item in data)\n",
"\n",
"print(\"Total kalimat:\", total_kalimat)\n",
"print(\"Total token:\", total_token)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"id": "ac8eb374",
"metadata": {},
"outputs": [],
"source": [
"# tagging \n",
"words = sorted({w for s in sentences for w in s})\n",
"ner_tags = sorted({t for seq in labels_ner for t in seq})\n",
"srl_tags = sorted({t for seq in labels_srl for t in seq})\n",
"\n",
"word2idx = {w: i + 2 for i, w in enumerate(words)}\n",
"word2idx[\"PAD\"], word2idx[\"UNK\"] = 0, 1\n",
"\n",
"tag2idx_ner = {t: i for i, t in enumerate(ner_tags)}\n",
"tag2idx_srl = {t: i for i, t in enumerate(srl_tags)}\n",
"idx2tag_ner = {i: t for t, i in tag2idx_ner.items()}\n",
"idx2tag_srl = {i: t for t, i in tag2idx_srl.items()}"
]
},
{
"cell_type": "code",
"execution_count": 121,
"id": "80356f1f",
"metadata": {},
"outputs": [],
"source": [
"# encoding\n",
"\n",
"X = [[word2idx.get(w, word2idx[\"UNK\"]) for w in s] for s in sentences]\n",
"y_ner = [[tag2idx_ner[t] for t in seq] for seq in labels_ner]\n",
"y_srl = [[tag2idx_srl[t] for t in seq] for seq in labels_srl]\n",
"\n",
"maxlen = 50 \n",
"\n",
"X = pad_sequences(X, maxlen=maxlen, padding=\"post\", value=word2idx[\"PAD\"])\n",
"y_ner = pad_sequences(y_ner, maxlen=maxlen, padding=\"post\", value=tag2idx_ner[\"O\"])\n",
"y_srl = pad_sequences(y_srl, maxlen=maxlen, padding=\"post\", value=tag2idx_srl[\"O\"])\n",
"\n",
"y_ner = [to_categorical(seq, num_classes=len(tag2idx_ner)) for seq in y_ner]\n",
"y_srl = [to_categorical(seq, num_classes=len(tag2idx_srl)) for seq in y_srl]\n",
"\n",
"X = np.array(X)\n",
"y_ner = np.array(y_ner)\n",
"y_srl = np.array(y_srl)"
]
},
{
"cell_type": "code",
"execution_count": 122,
"id": "fe219c96",
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_ner_train, y_ner_test, y_srl_train, y_srl_test = train_test_split(\n",
" X, y_ner, y_srl, \n",
" test_size=0.20, \n",
" random_state=42, # supaya reproducible\n",
" shuffle=True # acak baris\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 123,
"id": "7a9636b6",
"metadata": {},
"outputs": [
{
"data": {
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"Model: \"functional_13\"\n",
"\n"
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"┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
"│ input_layer_13 │ (None, 50) │ 0 │ - │\n",
"│ (InputLayer) │ │ │ │\n",
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"│ embedding_13 │ (None, 50, 64) │ 44,544 │ input_layer_13[0… │\n",
"│ (Embedding) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ bidirectional_13 │ (None, 50, 128) │ 66,048 │ embedding_13[0][… │\n",
"│ (Bidirectional) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ ner_output │ (None, 50, 25) │ 3,225 │ bidirectional_13… │\n",
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"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ srl_output │ (None, 50, 20) │ 2,580 │ bidirectional_13… │\n",
"│ (TimeDistributed) │ │ │ │\n",
"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
"\n"
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"┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
"│ input_layer_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
"│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ embedding_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m44,544\u001b[0m │ input_layer_13[\u001b[38;5;34m0\u001b[0m… │\n",
"│ (\u001b[38;5;33mEmbedding\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ bidirectional_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m66,048\u001b[0m │ embedding_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
"│ (\u001b[38;5;33mBidirectional\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ ner_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m25\u001b[0m) │ \u001b[38;5;34m3,225\u001b[0m │ bidirectional_13… │\n",
"│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ │\n",
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
"│ srl_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m20\u001b[0m) │ \u001b[38;5;34m2,580\u001b[0m │ bidirectional_13… │\n",
"│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ │\n",
"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
]
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" Total params: 116,397 (454.68 KB)\n",
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" Trainable params: 116,397 (454.68 KB)\n",
"\n"
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"text": [
"Epoch 1/10\n",
"\u001b[1m62/62\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 19ms/step - loss: 3.3010 - ner_output_accuracy: 0.8807 - ner_output_loss: 1.5617 - srl_output_accuracy: 0.7456 - srl_output_loss: 1.7393 - val_loss: 0.7284 - val_ner_output_accuracy: 0.9519 - val_ner_output_loss: 0.2466 - val_srl_output_accuracy: 0.8300 - val_srl_output_loss: 0.4818\n",
"Epoch 2/10\n",
"\u001b[1m62/62\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.7355 - ner_output_accuracy: 0.9569 - ner_output_loss: 0.2279 - srl_output_accuracy: 0.8297 - srl_output_loss: 0.5076 - val_loss: 0.6655 - val_ner_output_accuracy: 0.9519 - val_ner_output_loss: 0.2323 - val_srl_output_accuracy: 0.8506 - val_srl_output_loss: 0.4332\n",
"Epoch 3/10\n",
"\u001b[1m62/62\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.7041 - ner_output_accuracy: 0.9522 - ner_output_loss: 0.2219 - srl_output_accuracy: 0.8488 - srl_output_loss: 0.4822 - val_loss: 0.6368 - val_ner_output_accuracy: 0.9519 - val_ner_output_loss: 0.2232 - val_srl_output_accuracy: 0.8744 - val_srl_output_loss: 0.4135\n",
"Epoch 4/10\n",
"\u001b[1m62/62\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.6864 - ner_output_accuracy: 0.9520 - ner_output_loss: 0.2184 - srl_output_accuracy: 0.8548 - srl_output_loss: 0.4680 - val_loss: 0.6078 - val_ner_output_accuracy: 0.9519 - val_ner_output_loss: 0.2193 - val_srl_output_accuracy: 0.8769 - val_srl_output_loss: 0.3885\n",
"Epoch 5/10\n",
"\u001b[1m62/62\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.6304 - ner_output_accuracy: 0.9545 - ner_output_loss: 0.2009 - srl_output_accuracy: 0.8675 - srl_output_loss: 0.4295 - val_loss: 0.5727 - val_ner_output_accuracy: 0.9519 - val_ner_output_loss: 0.2015 - val_srl_output_accuracy: 0.8812 - val_srl_output_loss: 0.3711\n",
"Epoch 6/10\n",
"\u001b[1m62/62\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 0.5679 - ner_output_accuracy: 0.9557 - ner_output_loss: 0.1749 - srl_output_accuracy: 0.8783 - srl_output_loss: 0.3930 - val_loss: 0.5471 - val_ner_output_accuracy: 0.9519 - val_ner_output_loss: 0.1956 - val_srl_output_accuracy: 0.8831 - val_srl_output_loss: 0.3515\n",
"Epoch 7/10\n",
"\u001b[1m62/62\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.5000 - ner_output_accuracy: 0.9587 - ner_output_loss: 0.1634 - srl_output_accuracy: 0.8917 - srl_output_loss: 0.3366 - val_loss: 0.5364 - val_ner_output_accuracy: 0.9513 - val_ner_output_loss: 0.1899 - val_srl_output_accuracy: 0.8850 - val_srl_output_loss: 0.3465\n",
"Epoch 8/10\n",
"\u001b[1m62/62\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.5526 - ner_output_accuracy: 0.9541 - ner_output_loss: 0.1791 - srl_output_accuracy: 0.8840 - srl_output_loss: 0.3735 - val_loss: 0.5054 - val_ner_output_accuracy: 0.9519 - val_ner_output_loss: 0.1799 - val_srl_output_accuracy: 0.8963 - val_srl_output_loss: 0.3256\n",
"Epoch 9/10\n",
"\u001b[1m62/62\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.5094 - ner_output_accuracy: 0.9561 - ner_output_loss: 0.1701 - srl_output_accuracy: 0.8915 - srl_output_loss: 0.3393 - val_loss: 0.4881 - val_ner_output_accuracy: 0.9512 - val_ner_output_loss: 0.1707 - val_srl_output_accuracy: 0.9013 - val_srl_output_loss: 0.3174\n",
"Epoch 10/10\n",
"\u001b[1m62/62\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.4633 - ner_output_accuracy: 0.9524 - ner_output_loss: 0.1675 - srl_output_accuracy: 0.9092 - srl_output_loss: 0.2959 - val_loss: 0.4804 - val_ner_output_accuracy: 0.9531 - val_ner_output_loss: 0.1597 - val_srl_output_accuracy: 0.9050 - val_srl_output_loss: 0.3206\n"
]
}
],
"source": [
"input_layer = Input(shape=(maxlen,))\n",
"embed = Embedding(len(word2idx), 64)(input_layer)\n",
"bilstm = Bidirectional(LSTM(64, return_sequences=True))(embed)\n",
"\n",
"ner_output = TimeDistributed(\n",
" Dense(len(tag2idx_ner), activation=\"softmax\"), name=\"ner_output\"\n",
")(bilstm)\n",
"srl_output = TimeDistributed(\n",
" Dense(len(tag2idx_srl), activation=\"softmax\"), name=\"srl_output\"\n",
")(bilstm)\n",
"\n",
"model = Model(inputs=input_layer, outputs=[ner_output, srl_output])\n",
"model.compile(\n",
" optimizer=\"adam\",\n",
" loss={\n",
" \"ner_output\": \"categorical_crossentropy\",\n",
" \"srl_output\": \"categorical_crossentropy\",\n",
" },\n",
" metrics={\"ner_output\": \"accuracy\", \"srl_output\": \"accuracy\"},\n",
")\n",
"model.summary()\n",
"model.fit(\n",
" X_train, {\"ner_output\": y_ner_train, \"srl_output\": y_srl_train}, \n",
" validation_data=(X_test, {\"ner_output\": y_ner_test, \"srl_output\": y_srl_test}),\n",
" batch_size=2,\n",
" epochs=10,\n",
" verbose=1\n",
")\n",
"\n",
"# ---------- 6. Simpan artefak ----------\n",
"model.save(\"multi_task_lstm_ner_srl_model.keras\")\n",
"with open(\"word2idx.pkl\", \"wb\") as f:\n",
" pickle.dump(word2idx, f)\n",
"with open(\"tag2idx_ner.pkl\", \"wb\") as f:\n",
" pickle.dump(tag2idx_ner, f)\n",
"with open(\"tag2idx_srl.pkl\", \"wb\") as f:\n",
" pickle.dump(tag2idx_srl, f)\n"
]
},
{
"cell_type": "code",
"execution_count": 124,
"id": "3a55990b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'loss': 0.48035523295402527, 'compile_metrics': 0.15973526239395142, 'ner_output_loss': 0.32061997056007385, 'srl_output_loss': 0.953125}\n",
"\n",
"📊 [NER] Classification Report (test set):\n",
" precision recall f1-score support\n",
"\n",
" DATE 0.25 0.12 0.17 8\n",
" EVENT 0.00 0.00 0.00 1\n",
" LOC 0.50 0.04 0.07 28\n",
" ORG 0.00 0.00 0.00 4\n",
" PER 0.00 0.00 0.00 2\n",
" TIME 0.20 0.10 0.13 10\n",
"\n",
" micro avg 0.27 0.06 0.09 53\n",
" macro avg 0.16 0.04 0.06 53\n",
"weighted avg 0.34 0.06 0.09 53\n",
"\n"
]
}
],
"source": [
"# evaluation\n",
"\n",
"results = model.evaluate(\n",
" X_test,\n",
" {\"ner_output\": y_ner_test, \"srl_output\": y_srl_test},\n",
" verbose=0\n",
")\n",
"\n",
"# `metrics_names` = [\"loss\",\n",
"# \"ner_output_loss\", \"srl_output_loss\",\n",
"# \"ner_output_accuracy\", \"srl_output_accuracy\"]\n",
"print(dict(zip(model.metrics_names, results)))\n",
"\n",
"def decode(pred, true, idx2tag):\n",
" out_true, out_pred = [], []\n",
" for p_seq, t_seq in zip(pred, true):\n",
" t_labels, p_labels = [], []\n",
" for p_tok, t_tok in zip(p_seq, t_seq):\n",
" if t_tok.sum() == 0: # token PAD → lewati\n",
" continue\n",
" t_labels.append(idx2tag[t_tok.argmax()])\n",
" p_labels.append(idx2tag[p_tok.argmax()])\n",
" out_true.append(t_labels)\n",
" out_pred.append(p_labels)\n",
" return out_true, out_pred\n",
"\n",
"# prediksi hanya pada test set\n",
"y_pred_ner, y_pred_srl = model.predict(X_test, verbose=0)\n",
"\n",
"true_ner, pred_ner = decode(y_pred_ner, y_ner_test, idx2tag_ner)\n",
"\n",
"print(\"\\n📊 [NER] Classification Report (test set):\")\n",
"print(classification_report(true_ner, pred_ner, digits=2))\n",
"\n",
"\n",
"\n",
"\n",
"# y_pred_ner, y_pred_srl = model.predict(X, verbose=0)\n",
"\n",
"\n",
"# def decode(pred, true, idx2tag):\n",
"# true_tags = [[idx2tag[np.argmax(tok)] for tok in seq] for seq in true]\n",
"# pred_tags = [[idx2tag[np.argmax(tok)] for tok in seq] for seq in pred]\n",
"# return true_tags, pred_tags\n",
"\n",
"\n",
"# true_ner, pred_ner = decode(y_pred_ner, y_ner, idx2tag_ner)\n",
"\n",
"# print(\"\\n📊 [NER] Classification Report:\")\n",
"# print(classification_report(true_ner, pred_ner))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 125,
"id": "547d1533",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{0: 'ARG0', 1: 'ARG1', 2: 'ARG2', 3: 'ARG3', 4: 'ARGM-BNF', 5: 'ARGM-CAU', 6: 'ARGM-COM', 7: 'ARGM-FRQ', 8: 'ARGM-LOC', 9: 'ARGM-MNR', 10: 'ARGM-MOD', 11: 'ARGM-NEG', 12: 'ARGM-PNC', 13: 'ARGM-PRD', 14: 'ARGM-PRP', 15: 'ARGM-SRC', 16: 'ARGM-TMP', 17: 'O', 18: 'R-ARG1', 19: 'V'}\n",
"\n",
"📊 [SRL] Classification Report (test set):\n",
" precision recall f1-score support\n",
"\n",
" CAU 0.00 0.00 0.00 1\n",
" FRQ 0.00 0.00 0.00 1\n",
" LOC 0.36 0.40 0.38 10\n",
" MNR 0.00 0.00 0.00 4\n",
" PNC 0.00 0.00 0.00 1\n",
" PRP 0.00 0.00 0.00 1\n",
" RG0 0.31 0.21 0.25 19\n",
" RG1 0.21 0.15 0.17 46\n",
" RG2 0.19 0.40 0.26 10\n",
" TMP 0.41 0.53 0.46 17\n",
" _ 0.10 0.06 0.07 33\n",
"\n",
" micro avg 0.25 0.21 0.23 143\n",
" macro avg 0.14 0.16 0.15 143\n",
"weighted avg 0.22 0.21 0.21 143\n",
"\n"
]
}
],
"source": [
"# true_srl, pred_srl = decode(y_pred_srl, y_srl, idx2tag_srl)\n",
"# print(\"\\n📊 [SRL] Classification Report:\")\n",
"# print(classification_report(true_srl, pred_srl))\n",
"\n",
"true_srl, pred_srl = decode(y_pred_srl, y_srl_test, idx2tag_srl)\n",
"print(idx2tag_srl)\n",
"print(\"\\n📊 [SRL] Classification Report (test set):\")\n",
"print(classification_report(true_srl, pred_srl, digits=2))"
]
}
],
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