1040 lines
365 KiB
Plaintext
1040 lines
365 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 86,
|
||
"id": "263af9e9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pickle, tensorflow as tf, numpy as np\n",
|
||
"from tensorflow.keras.models import Model\n",
|
||
"from tensorflow.keras.layers import (\n",
|
||
" Input,\n",
|
||
" Embedding,\n",
|
||
" SpatialDropout1D,\n",
|
||
" Bidirectional,\n",
|
||
" LSTM,\n",
|
||
" TimeDistributed,\n",
|
||
" Dense,\n",
|
||
")\n",
|
||
"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from collections import Counter\n",
|
||
"from itertools import zip_longest"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 87,
|
||
"id": "4fc87f1b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"total kalimat 1639\n",
|
||
"NER Label Count || SRL Label Count \n",
|
||
"-------------------------------------------------------\n",
|
||
"O 7468 || O 4182 \n",
|
||
"TIME 235 || ARGM-TMP 1292 \n",
|
||
"PER 1539 || ARG0 1965 \n",
|
||
"LOC 1586 || V 1738 \n",
|
||
"DATE 986 || ARG1 1308 \n",
|
||
"ETH 430 || ARGM-LOC 1503 \n",
|
||
"EVENT 125 || ARG2 293 \n",
|
||
"MISC 17 || ARGM-MOD 39 \n",
|
||
"ORG 39 || ARGM-MNR 37 \n",
|
||
" || ARGM-NEG 6 \n",
|
||
" || ARGM-DIR 41 \n",
|
||
" || ARGM-CAU 21 \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"data = []\n",
|
||
"with open(\"../dataset/ner_srl_without_bio.tsv\", encoding=\"utf-8\") as f:\n",
|
||
"# with open(\"../dataset/test_ns_dataset.tsv\", encoding=\"utf-8\") as f:\n",
|
||
" tok, ner, srl = [], [], []\n",
|
||
" for line in f:\n",
|
||
" line = line.strip()\n",
|
||
" if not line:\n",
|
||
" if tok:\n",
|
||
" data.append({\"tokens\": tok, \"labels_ner\": ner, \"labels_srl\": srl})\n",
|
||
" tok, ner, srl = [], [], []\n",
|
||
" else:\n",
|
||
" t, n, s = line.split(\"\\t\")\n",
|
||
" tok.append(t.lower())\n",
|
||
" ner.append(n.strip())\n",
|
||
" srl.append(s.strip())\n",
|
||
"\n",
|
||
"print(\"total kalimat \", len(data))\n",
|
||
"# ——————————————————\n",
|
||
"sentences = [d[\"tokens\"] for d in data]\n",
|
||
"labels_ner = [d[\"labels_ner\"] for d in data]\n",
|
||
"labels_srl = [d[\"labels_srl\"] for d in data]\n",
|
||
"\n",
|
||
"ner_counter = Counter(label for seq in labels_ner for label in seq)\n",
|
||
"\n",
|
||
"srl_counter = Counter(label for seq in labels_srl for label in seq)\n",
|
||
"\n",
|
||
"\n",
|
||
"print(f\"{'NER Label':<15} {'Count':<10} || {'SRL Label':<15} {'Count':<10}\")\n",
|
||
"print(\"-\" * 55)\n",
|
||
"\n",
|
||
"for (ner_label, ner_count), (srl_label, srl_count) in zip_longest(ner_counter.items(), srl_counter.items(), fillvalue=('', '')):\n",
|
||
" print(f\"{ner_label:<15} {ner_count:<10} || {srl_label:<15} {srl_count:<10}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 88,
|
||
"id": "8dda2d6c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"NER -> Total Labels: 12425, O Count: 7468, O Percentage: 60.10%\n",
|
||
"SRL -> Total Labels: 12425, O Count: 4182, O Percentage: 33.66%\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": 89,
|
||
"id": "48553e6b",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"PAD_TOKEN = \"<PAD>\"\n",
|
||
"words = sorted({w for s in sentences for w in s})\n",
|
||
"\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",
|
||
"ner_tags.insert(0, PAD_TOKEN)\n",
|
||
"srl_tags.insert(0, PAD_TOKEN)\n",
|
||
"\n",
|
||
"word2idx = {w: i + 2 for i, w in enumerate(words)}\n",
|
||
"word2idx[\"PAD\"] = 0\n",
|
||
"word2idx[\"UNK\"] = 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": 90,
|
||
"id": "096967e8",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"X = [[word2idx.get(w, 1) 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 = max(map(len, X))\n",
|
||
"pad_id = tag2idx_ner[PAD_TOKEN]\n",
|
||
"\n",
|
||
"X = pad_sequences(X, maxlen=maxlen, padding=\"post\", value=0)\n",
|
||
"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\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 91,
|
||
"id": "a26893cc",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"splits = train_test_split(\n",
|
||
" X, y_ner, y_srl, mask, test_size=0.2, random_state=42, shuffle=True\n",
|
||
")\n",
|
||
"X_tr, X_te, ner_tr, ner_te, srl_tr, srl_te, m_tr, m_te = splits"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 92,
|
||
"id": "1b4a1c61",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_5\"</span>\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\u001b[1mModel: \"functional_5\"\u001b[0m\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
|
||
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃<span style=\"font-weight: bold\"> Connected to </span>┃\n",
|
||
"┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
|
||
"│ tokens (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ - │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ embed (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">87,296</span> │ tokens[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ spatial_dropout1d_5 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ embed[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SpatialDropout1D</span>) │ │ │ │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ not_equal_5 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ tokens[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">NotEqual</span>) │ │ │ │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ bidirectional_10 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">66,048</span> │ spatial_dropout1… │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ │ │ not_equal_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ bidirectional_11 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</span> │ bidirectional_10… │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ │ │ not_equal_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ time_distributed_10 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ bidirectional_11… │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>) │ │ │ not_equal_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ time_distributed_11 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ bidirectional_11… │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>) │ │ │ not_equal_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ ner_output │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │ time_distributed… │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>) │ │ │ not_equal_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ srl_output │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">845</span> │ time_distributed… │\n",
|
||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>) │ │ │ not_equal_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
|
||
"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
|
||
"</pre>\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",
|
||
"│ 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;34m87,296\u001b[0m │ tokens[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ spatial_dropout1d_5 │ (\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_5 │ (\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_10 │ (\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_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ bidirectional_11 │ (\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_10… │\n",
|
||
"│ (\u001b[38;5;33mBidirectional\u001b[0m) │ │ │ not_equal_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ time_distributed_10 │ (\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_11… │\n",
|
||
"│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ time_distributed_11 │ (\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_11… │\n",
|
||
"│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal_5[\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;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │ time_distributed… │\n",
|
||
"│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal_5[\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_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">270,167</span> (1.03 MB)\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m270,167\u001b[0m (1.03 MB)\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">270,167</span> (1.03 MB)\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m270,167\u001b[0m (1.03 MB)\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"embed_dim = 64\n",
|
||
"lstm_units = 64\n",
|
||
"drop_embed = 0.2\n",
|
||
"drop_lstm = 0.2\n",
|
||
"\n",
|
||
"inp = Input(shape=(maxlen,), name=\"tokens\")\n",
|
||
"emb = Embedding(len(word2idx), embed_dim, mask_zero=True, name=\"embed\")(inp)\n",
|
||
"emb = SpatialDropout1D(drop_embed)(emb)\n",
|
||
"\n",
|
||
"x = Bidirectional(\n",
|
||
" LSTM(\n",
|
||
" lstm_units,\n",
|
||
" return_sequences=True,\n",
|
||
" dropout=drop_lstm,\n",
|
||
" recurrent_dropout=drop_lstm,\n",
|
||
" )\n",
|
||
")(emb)\n",
|
||
"x = Bidirectional(\n",
|
||
" LSTM(\n",
|
||
" lstm_units,\n",
|
||
" return_sequences=True,\n",
|
||
" dropout=drop_lstm,\n",
|
||
" recurrent_dropout=drop_lstm,\n",
|
||
" )\n",
|
||
")(x)\n",
|
||
"\n",
|
||
"ner_head = TimeDistributed(Dense(lstm_units, activation=\"relu\"))(x)\n",
|
||
"ner_out = TimeDistributed(\n",
|
||
" Dense(len(tag2idx_ner), activation=\"softmax\"), name=\"ner_output\"\n",
|
||
")(ner_head)\n",
|
||
"\n",
|
||
"srl_head = TimeDistributed(Dense(lstm_units, activation=\"relu\"))(x)\n",
|
||
"srl_out = TimeDistributed(\n",
|
||
" Dense(len(tag2idx_srl), activation=\"softmax\"), name=\"srl_output\"\n",
|
||
")(srl_head)\n",
|
||
"\n",
|
||
"model = Model(inp, [ner_out, srl_out])\n",
|
||
"\n",
|
||
"model.compile(\n",
|
||
" optimizer=tf.keras.optimizers.Adam(3e-4),\n",
|
||
" loss={\n",
|
||
" \"ner_output\": \"sparse_categorical_crossentropy\",\n",
|
||
" \"srl_output\": \"sparse_categorical_crossentropy\",\n",
|
||
" },\n",
|
||
" metrics={\n",
|
||
" \"ner_output\": [\"sparse_categorical_accuracy\"],\n",
|
||
" \"srl_output\": [\"sparse_categorical_accuracy\"],\n",
|
||
" },\n",
|
||
" # sample_weight_mode=\"temporal\"\n",
|
||
")\n",
|
||
"model.summary()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 93,
|
||
"id": "f41d6012",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 117ms/step - loss: 4.8459 - ner_output_loss: 2.2910 - ner_output_sparse_categorical_accuracy: 0.2072 - srl_output_loss: 2.5547 - srl_output_sparse_categorical_accuracy: 0.1950 - val_loss: 4.7202 - val_ner_output_loss: 2.2195 - val_ner_output_sparse_categorical_accuracy: 0.1365 - val_srl_output_loss: 2.4995 - val_srl_output_sparse_categorical_accuracy: 0.1022 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 2/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 4.5854 - ner_output_loss: 2.1443 - ner_output_sparse_categorical_accuracy: 0.1338 - srl_output_loss: 2.4403 - srl_output_sparse_categorical_accuracy: 0.1038 - val_loss: 3.7589 - val_ner_output_loss: 1.6920 - val_ner_output_sparse_categorical_accuracy: 0.1365 - val_srl_output_loss: 2.0515 - val_srl_output_sparse_categorical_accuracy: 0.1018 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 3/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 3.5163 - ner_output_loss: 1.5814 - ner_output_sparse_categorical_accuracy: 0.1362 - srl_output_loss: 1.9337 - srl_output_sparse_categorical_accuracy: 0.1038 - val_loss: 3.0063 - val_ner_output_loss: 1.3813 - val_ner_output_sparse_categorical_accuracy: 0.1365 - val_srl_output_loss: 1.6351 - val_srl_output_sparse_categorical_accuracy: 0.1005 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 4/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 2.8450 - ner_output_loss: 1.2706 - ner_output_sparse_categorical_accuracy: 0.1318 - srl_output_loss: 1.5737 - srl_output_sparse_categorical_accuracy: 0.1055 - val_loss: 2.5730 - val_ner_output_loss: 1.1236 - val_ner_output_sparse_categorical_accuracy: 0.1530 - val_srl_output_loss: 1.4498 - val_srl_output_sparse_categorical_accuracy: 0.1221 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 5/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - loss: 2.4606 - ner_output_loss: 1.0562 - ner_output_sparse_categorical_accuracy: 0.1529 - srl_output_loss: 1.4042 - srl_output_sparse_categorical_accuracy: 0.1295 - val_loss: 2.3535 - val_ner_output_loss: 1.0198 - val_ner_output_sparse_categorical_accuracy: 0.1537 - val_srl_output_loss: 1.3258 - val_srl_output_sparse_categorical_accuracy: 0.1332 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 6/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 61ms/step - loss: 2.2418 - ner_output_loss: 0.9661 - ner_output_sparse_categorical_accuracy: 0.1533 - srl_output_loss: 1.2767 - srl_output_sparse_categorical_accuracy: 0.1308 - val_loss: 2.1940 - val_ner_output_loss: 0.9440 - val_ner_output_sparse_categorical_accuracy: 0.1541 - val_srl_output_loss: 1.2342 - val_srl_output_sparse_categorical_accuracy: 0.1321 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 7/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 61ms/step - loss: 2.0980 - ner_output_loss: 0.9022 - ner_output_sparse_categorical_accuracy: 0.1548 - srl_output_loss: 1.1954 - srl_output_sparse_categorical_accuracy: 0.1307 - val_loss: 2.0748 - val_ner_output_loss: 0.8770 - val_ner_output_sparse_categorical_accuracy: 0.1525 - val_srl_output_loss: 1.1789 - val_srl_output_sparse_categorical_accuracy: 0.1280 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 8/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 63ms/step - loss: 1.9930 - ner_output_loss: 0.8414 - ner_output_sparse_categorical_accuracy: 0.1515 - srl_output_loss: 1.1518 - srl_output_sparse_categorical_accuracy: 0.1269 - val_loss: 1.9810 - val_ner_output_loss: 0.8291 - val_ner_output_sparse_categorical_accuracy: 0.1526 - val_srl_output_loss: 1.1352 - val_srl_output_sparse_categorical_accuracy: 0.1301 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 9/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 63ms/step - loss: 1.8461 - ner_output_loss: 0.7605 - ner_output_sparse_categorical_accuracy: 0.1606 - srl_output_loss: 1.0854 - srl_output_sparse_categorical_accuracy: 0.1314 - val_loss: 1.8764 - val_ner_output_loss: 0.7805 - val_ner_output_sparse_categorical_accuracy: 0.1697 - val_srl_output_loss: 1.0773 - val_srl_output_sparse_categorical_accuracy: 0.1538 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 10/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - loss: 1.7061 - ner_output_loss: 0.6997 - ner_output_sparse_categorical_accuracy: 0.1709 - srl_output_loss: 1.0061 - srl_output_sparse_categorical_accuracy: 0.1535 - val_loss: 1.7569 - val_ner_output_loss: 0.7278 - val_ner_output_sparse_categorical_accuracy: 0.1735 - val_srl_output_loss: 1.0110 - val_srl_output_sparse_categorical_accuracy: 0.1578 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 11/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - loss: 1.6203 - ner_output_loss: 0.6736 - ner_output_sparse_categorical_accuracy: 0.1751 - srl_output_loss: 0.9463 - srl_output_sparse_categorical_accuracy: 0.1598 - val_loss: 1.6111 - val_ner_output_loss: 0.6664 - val_ner_output_sparse_categorical_accuracy: 0.1803 - val_srl_output_loss: 0.9253 - val_srl_output_sparse_categorical_accuracy: 0.1653 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 12/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - loss: 1.4496 - ner_output_loss: 0.5973 - ner_output_sparse_categorical_accuracy: 0.1816 - srl_output_loss: 0.8514 - srl_output_sparse_categorical_accuracy: 0.1679 - val_loss: 1.4369 - val_ner_output_loss: 0.5879 - val_ner_output_sparse_categorical_accuracy: 0.1843 - val_srl_output_loss: 0.8279 - val_srl_output_sparse_categorical_accuracy: 0.1681 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 13/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - loss: 1.2751 - ner_output_loss: 0.5131 - ner_output_sparse_categorical_accuracy: 0.1851 - srl_output_loss: 0.7631 - srl_output_sparse_categorical_accuracy: 0.1701 - val_loss: 1.2719 - val_ner_output_loss: 0.5096 - val_ner_output_sparse_categorical_accuracy: 0.1931 - val_srl_output_loss: 0.7377 - val_srl_output_sparse_categorical_accuracy: 0.1765 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 14/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 50ms/step - loss: 1.1168 - ner_output_loss: 0.4512 - ner_output_sparse_categorical_accuracy: 0.1910 - srl_output_loss: 0.6648 - srl_output_sparse_categorical_accuracy: 0.1760 - val_loss: 1.1318 - val_ner_output_loss: 0.4365 - val_ner_output_sparse_categorical_accuracy: 0.2008 - val_srl_output_loss: 0.6629 - val_srl_output_sparse_categorical_accuracy: 0.1809 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 15/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - loss: 1.0122 - ner_output_loss: 0.3865 - ner_output_sparse_categorical_accuracy: 0.2025 - srl_output_loss: 0.6251 - srl_output_sparse_categorical_accuracy: 0.1847 - val_loss: 1.0384 - val_ner_output_loss: 0.3856 - val_ner_output_sparse_categorical_accuracy: 0.2036 - val_srl_output_loss: 0.6178 - val_srl_output_sparse_categorical_accuracy: 0.1809 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 16/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - loss: 0.9011 - ner_output_loss: 0.3445 - ner_output_sparse_categorical_accuracy: 0.1976 - srl_output_loss: 0.5563 - srl_output_sparse_categorical_accuracy: 0.1815 - val_loss: 0.9593 - val_ner_output_loss: 0.3423 - val_ner_output_sparse_categorical_accuracy: 0.2063 - val_srl_output_loss: 0.5780 - val_srl_output_sparse_categorical_accuracy: 0.1834 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 17/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 49ms/step - loss: 0.8421 - ner_output_loss: 0.3136 - ner_output_sparse_categorical_accuracy: 0.2026 - srl_output_loss: 0.5285 - srl_output_sparse_categorical_accuracy: 0.1858 - val_loss: 0.8992 - val_ner_output_loss: 0.3122 - val_ner_output_sparse_categorical_accuracy: 0.2074 - val_srl_output_loss: 0.5448 - val_srl_output_sparse_categorical_accuracy: 0.1862 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 18/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 50ms/step - loss: 0.8397 - ner_output_loss: 0.3007 - ner_output_sparse_categorical_accuracy: 0.1998 - srl_output_loss: 0.5386 - srl_output_sparse_categorical_accuracy: 0.1811 - val_loss: 0.8548 - val_ner_output_loss: 0.2913 - val_ner_output_sparse_categorical_accuracy: 0.2082 - val_srl_output_loss: 0.5180 - val_srl_output_sparse_categorical_accuracy: 0.1873 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 19/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 0.7490 - ner_output_loss: 0.2636 - ner_output_sparse_categorical_accuracy: 0.2070 - srl_output_loss: 0.4856 - srl_output_sparse_categorical_accuracy: 0.1884 - val_loss: 0.8179 - val_ner_output_loss: 0.2750 - val_ner_output_sparse_categorical_accuracy: 0.2094 - val_srl_output_loss: 0.4982 - val_srl_output_sparse_categorical_accuracy: 0.1891 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 20/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 46ms/step - loss: 0.7213 - ner_output_loss: 0.2332 - ner_output_sparse_categorical_accuracy: 0.2081 - srl_output_loss: 0.4868 - srl_output_sparse_categorical_accuracy: 0.1881 - val_loss: 0.7851 - val_ner_output_loss: 0.2585 - val_ner_output_sparse_categorical_accuracy: 0.2101 - val_srl_output_loss: 0.4775 - val_srl_output_sparse_categorical_accuracy: 0.1909 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 21/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - loss: 0.6443 - ner_output_loss: 0.2310 - ner_output_sparse_categorical_accuracy: 0.2064 - srl_output_loss: 0.4140 - srl_output_sparse_categorical_accuracy: 0.1924 - val_loss: 0.7585 - val_ner_output_loss: 0.2477 - val_ner_output_sparse_categorical_accuracy: 0.2110 - val_srl_output_loss: 0.4657 - val_srl_output_sparse_categorical_accuracy: 0.1923 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 22/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - loss: 0.6859 - ner_output_loss: 0.2301 - ner_output_sparse_categorical_accuracy: 0.2081 - srl_output_loss: 0.4560 - srl_output_sparse_categorical_accuracy: 0.1901 - val_loss: 0.7362 - val_ner_output_loss: 0.2395 - val_ner_output_sparse_categorical_accuracy: 0.2114 - val_srl_output_loss: 0.4505 - val_srl_output_sparse_categorical_accuracy: 0.1926 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 23/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 0.6254 - ner_output_loss: 0.2053 - ner_output_sparse_categorical_accuracy: 0.2158 - srl_output_loss: 0.4201 - srl_output_sparse_categorical_accuracy: 0.1990 - val_loss: 0.7182 - val_ner_output_loss: 0.2323 - val_ner_output_sparse_categorical_accuracy: 0.2120 - val_srl_output_loss: 0.4385 - val_srl_output_sparse_categorical_accuracy: 0.1937 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 24/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 49ms/step - loss: 0.6683 - ner_output_loss: 0.2242 - ner_output_sparse_categorical_accuracy: 0.2097 - srl_output_loss: 0.4438 - srl_output_sparse_categorical_accuracy: 0.1925 - val_loss: 0.7056 - val_ner_output_loss: 0.2291 - val_ner_output_sparse_categorical_accuracy: 0.2123 - val_srl_output_loss: 0.4295 - val_srl_output_sparse_categorical_accuracy: 0.1945 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 25/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.5698 - ner_output_loss: 0.1931 - ner_output_sparse_categorical_accuracy: 0.2121 - srl_output_loss: 0.3765 - srl_output_sparse_categorical_accuracy: 0.1977 - val_loss: 0.6894 - val_ner_output_loss: 0.2230 - val_ner_output_sparse_categorical_accuracy: 0.2123 - val_srl_output_loss: 0.4201 - val_srl_output_sparse_categorical_accuracy: 0.1949 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 26/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 57ms/step - loss: 0.5516 - ner_output_loss: 0.1684 - ner_output_sparse_categorical_accuracy: 0.2120 - srl_output_loss: 0.3825 - srl_output_sparse_categorical_accuracy: 0.1948 - val_loss: 0.6784 - val_ner_output_loss: 0.2192 - val_ner_output_sparse_categorical_accuracy: 0.2124 - val_srl_output_loss: 0.4127 - val_srl_output_sparse_categorical_accuracy: 0.1954 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 27/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 57ms/step - loss: 0.5239 - ner_output_loss: 0.1728 - ner_output_sparse_categorical_accuracy: 0.2128 - srl_output_loss: 0.3509 - srl_output_sparse_categorical_accuracy: 0.1991 - val_loss: 0.6638 - val_ner_output_loss: 0.2145 - val_ner_output_sparse_categorical_accuracy: 0.2131 - val_srl_output_loss: 0.4054 - val_srl_output_sparse_categorical_accuracy: 0.1954 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 28/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.4690 - ner_output_loss: 0.1533 - ner_output_sparse_categorical_accuracy: 0.2069 - srl_output_loss: 0.3151 - srl_output_sparse_categorical_accuracy: 0.1947 - val_loss: 0.6540 - val_ner_output_loss: 0.2132 - val_ner_output_sparse_categorical_accuracy: 0.2133 - val_srl_output_loss: 0.3967 - val_srl_output_sparse_categorical_accuracy: 0.1961 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 29/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 67ms/step - loss: 0.5244 - ner_output_loss: 0.1660 - ner_output_sparse_categorical_accuracy: 0.2142 - srl_output_loss: 0.3587 - srl_output_sparse_categorical_accuracy: 0.1986 - val_loss: 0.6477 - val_ner_output_loss: 0.2099 - val_ner_output_sparse_categorical_accuracy: 0.2134 - val_srl_output_loss: 0.3944 - val_srl_output_sparse_categorical_accuracy: 0.1962 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 30/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.4627 - ner_output_loss: 0.1465 - ner_output_sparse_categorical_accuracy: 0.2104 - srl_output_loss: 0.3153 - srl_output_sparse_categorical_accuracy: 0.1980 - val_loss: 0.6403 - val_ner_output_loss: 0.2087 - val_ner_output_sparse_categorical_accuracy: 0.2132 - val_srl_output_loss: 0.3895 - val_srl_output_sparse_categorical_accuracy: 0.1969 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 31/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - loss: 0.4648 - ner_output_loss: 0.1528 - ner_output_sparse_categorical_accuracy: 0.2080 - srl_output_loss: 0.3119 - srl_output_sparse_categorical_accuracy: 0.1966 - val_loss: 0.6317 - val_ner_output_loss: 0.2046 - val_ner_output_sparse_categorical_accuracy: 0.2140 - val_srl_output_loss: 0.3848 - val_srl_output_sparse_categorical_accuracy: 0.1966 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 32/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.4138 - ner_output_loss: 0.1280 - ner_output_sparse_categorical_accuracy: 0.2132 - srl_output_loss: 0.2859 - srl_output_sparse_categorical_accuracy: 0.2011 - val_loss: 0.6299 - val_ner_output_loss: 0.2061 - val_ner_output_sparse_categorical_accuracy: 0.2140 - val_srl_output_loss: 0.3815 - val_srl_output_sparse_categorical_accuracy: 0.1969 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 33/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.4210 - ner_output_loss: 0.1234 - ner_output_sparse_categorical_accuracy: 0.2122 - srl_output_loss: 0.2981 - srl_output_sparse_categorical_accuracy: 0.1995 - val_loss: 0.6221 - val_ner_output_loss: 0.2054 - val_ner_output_sparse_categorical_accuracy: 0.2143 - val_srl_output_loss: 0.3769 - val_srl_output_sparse_categorical_accuracy: 0.1973 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 34/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 57ms/step - loss: 0.4378 - ner_output_loss: 0.1216 - ner_output_sparse_categorical_accuracy: 0.2130 - srl_output_loss: 0.3170 - srl_output_sparse_categorical_accuracy: 0.1981 - val_loss: 0.6186 - val_ner_output_loss: 0.2045 - val_ner_output_sparse_categorical_accuracy: 0.2145 - val_srl_output_loss: 0.3730 - val_srl_output_sparse_categorical_accuracy: 0.1974 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 35/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.4272 - ner_output_loss: 0.1271 - ner_output_sparse_categorical_accuracy: 0.2143 - srl_output_loss: 0.3000 - srl_output_sparse_categorical_accuracy: 0.2012 - val_loss: 0.6103 - val_ner_output_loss: 0.2017 - val_ner_output_sparse_categorical_accuracy: 0.2148 - val_srl_output_loss: 0.3689 - val_srl_output_sparse_categorical_accuracy: 0.1978 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 36/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.3704 - ner_output_loss: 0.1129 - ner_output_sparse_categorical_accuracy: 0.2074 - srl_output_loss: 0.2577 - srl_output_sparse_categorical_accuracy: 0.1963 - val_loss: 0.6053 - val_ner_output_loss: 0.2009 - val_ner_output_sparse_categorical_accuracy: 0.2147 - val_srl_output_loss: 0.3655 - val_srl_output_sparse_categorical_accuracy: 0.1982 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 37/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 57ms/step - loss: 0.3921 - ner_output_loss: 0.1135 - ner_output_sparse_categorical_accuracy: 0.2075 - srl_output_loss: 0.2783 - srl_output_sparse_categorical_accuracy: 0.1952 - val_loss: 0.6011 - val_ner_output_loss: 0.2005 - val_ner_output_sparse_categorical_accuracy: 0.2147 - val_srl_output_loss: 0.3646 - val_srl_output_sparse_categorical_accuracy: 0.1987 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 38/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.4146 - ner_output_loss: 0.1258 - ner_output_sparse_categorical_accuracy: 0.2100 - srl_output_loss: 0.2892 - srl_output_sparse_categorical_accuracy: 0.1980 - val_loss: 0.5998 - val_ner_output_loss: 0.2024 - val_ner_output_sparse_categorical_accuracy: 0.2147 - val_srl_output_loss: 0.3608 - val_srl_output_sparse_categorical_accuracy: 0.1989 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 39/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 61ms/step - loss: 0.3869 - ner_output_loss: 0.1220 - ner_output_sparse_categorical_accuracy: 0.2129 - srl_output_loss: 0.2651 - srl_output_sparse_categorical_accuracy: 0.2028 - val_loss: 0.5957 - val_ner_output_loss: 0.1990 - val_ner_output_sparse_categorical_accuracy: 0.2148 - val_srl_output_loss: 0.3601 - val_srl_output_sparse_categorical_accuracy: 0.1989 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 40/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 62ms/step - loss: 0.3900 - ner_output_loss: 0.1127 - ner_output_sparse_categorical_accuracy: 0.2176 - srl_output_loss: 0.2772 - srl_output_sparse_categorical_accuracy: 0.2053 - val_loss: 0.5922 - val_ner_output_loss: 0.2016 - val_ner_output_sparse_categorical_accuracy: 0.2153 - val_srl_output_loss: 0.3550 - val_srl_output_sparse_categorical_accuracy: 0.1992 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 41/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 61ms/step - loss: 0.3872 - ner_output_loss: 0.1203 - ner_output_sparse_categorical_accuracy: 0.2168 - srl_output_loss: 0.2672 - srl_output_sparse_categorical_accuracy: 0.2061 - val_loss: 0.5881 - val_ner_output_loss: 0.1981 - val_ner_output_sparse_categorical_accuracy: 0.2147 - val_srl_output_loss: 0.3540 - val_srl_output_sparse_categorical_accuracy: 0.1996 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 42/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 62ms/step - loss: 0.3592 - ner_output_loss: 0.0983 - ner_output_sparse_categorical_accuracy: 0.2135 - srl_output_loss: 0.2606 - srl_output_sparse_categorical_accuracy: 0.2012 - val_loss: 0.5857 - val_ner_output_loss: 0.1976 - val_ner_output_sparse_categorical_accuracy: 0.2151 - val_srl_output_loss: 0.3504 - val_srl_output_sparse_categorical_accuracy: 0.1999 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 43/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 61ms/step - loss: 0.3662 - ner_output_loss: 0.1077 - ner_output_sparse_categorical_accuracy: 0.2175 - srl_output_loss: 0.2591 - srl_output_sparse_categorical_accuracy: 0.2057 - val_loss: 0.5860 - val_ner_output_loss: 0.2007 - val_ner_output_sparse_categorical_accuracy: 0.2151 - val_srl_output_loss: 0.3507 - val_srl_output_sparse_categorical_accuracy: 0.1995 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 44/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 62ms/step - loss: 0.3459 - ner_output_loss: 0.1004 - ner_output_sparse_categorical_accuracy: 0.2142 - srl_output_loss: 0.2449 - srl_output_sparse_categorical_accuracy: 0.2034 - val_loss: 0.5835 - val_ner_output_loss: 0.1974 - val_ner_output_sparse_categorical_accuracy: 0.2157 - val_srl_output_loss: 0.3529 - val_srl_output_sparse_categorical_accuracy: 0.1997 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 45/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 0.3159 - ner_output_loss: 0.0911 - ner_output_sparse_categorical_accuracy: 0.2134 - srl_output_loss: 0.2249 - srl_output_sparse_categorical_accuracy: 0.2037 - val_loss: 0.5785 - val_ner_output_loss: 0.1953 - val_ner_output_sparse_categorical_accuracy: 0.2147 - val_srl_output_loss: 0.3469 - val_srl_output_sparse_categorical_accuracy: 0.2003 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 46/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - loss: 0.3037 - ner_output_loss: 0.0897 - ner_output_sparse_categorical_accuracy: 0.2151 - srl_output_loss: 0.2139 - srl_output_sparse_categorical_accuracy: 0.2062 - val_loss: 0.5804 - val_ner_output_loss: 0.1998 - val_ner_output_sparse_categorical_accuracy: 0.2153 - val_srl_output_loss: 0.3477 - val_srl_output_sparse_categorical_accuracy: 0.2004 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 47/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 0.3003 - ner_output_loss: 0.0835 - ner_output_sparse_categorical_accuracy: 0.2179 - srl_output_loss: 0.2168 - srl_output_sparse_categorical_accuracy: 0.2086 - val_loss: 0.5815 - val_ner_output_loss: 0.2021 - val_ner_output_sparse_categorical_accuracy: 0.2155 - val_srl_output_loss: 0.3491 - val_srl_output_sparse_categorical_accuracy: 0.2006 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 48/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 0.3465 - ner_output_loss: 0.1017 - ner_output_sparse_categorical_accuracy: 0.2160 - srl_output_loss: 0.2446 - srl_output_sparse_categorical_accuracy: 0.2049 - val_loss: 0.5756 - val_ner_output_loss: 0.1990 - val_ner_output_sparse_categorical_accuracy: 0.2153 - val_srl_output_loss: 0.3467 - val_srl_output_sparse_categorical_accuracy: 0.2014 - learning_rate: 1.5000e-04\n",
|
||
"Epoch 49/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 0.3086 - ner_output_loss: 0.0847 - ner_output_sparse_categorical_accuracy: 0.2127 - srl_output_loss: 0.2245 - srl_output_sparse_categorical_accuracy: 0.2030 - val_loss: 0.5775 - val_ner_output_loss: 0.1999 - val_ner_output_sparse_categorical_accuracy: 0.2152 - val_srl_output_loss: 0.3486 - val_srl_output_sparse_categorical_accuracy: 0.2008 - learning_rate: 1.5000e-04\n",
|
||
"Epoch 50/50\n",
|
||
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 0.2874 - ner_output_loss: 0.0839 - ner_output_sparse_categorical_accuracy: 0.2122 - srl_output_loss: 0.2035 - srl_output_sparse_categorical_accuracy: 0.2042 - val_loss: 0.5741 - val_ner_output_loss: 0.1975 - val_ner_output_sparse_categorical_accuracy: 0.2153 - val_srl_output_loss: 0.3454 - val_srl_output_sparse_categorical_accuracy: 0.2010 - learning_rate: 1.5000e-04\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"callbacks = [\n",
|
||
" tf.keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True),\n",
|
||
" tf.keras.callbacks.ReduceLROnPlateau(patience=2, factor=0.5, min_lr=1e-5),\n",
|
||
"]\n",
|
||
"\n",
|
||
"history = model.fit(\n",
|
||
" X_tr,\n",
|
||
" [ner_tr, srl_tr], # y → LIST (pos 0 = ner_output, 1 = srl_output)\n",
|
||
" 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=64,\n",
|
||
" epochs=50,\n",
|
||
" callbacks=callbacks,\n",
|
||
" verbose=1,\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# 7. Save artefacts\n",
|
||
"# =========================\n",
|
||
"model.save(\"lstm_ner_srl_model.keras\")\n",
|
||
"for fname, obj in [\n",
|
||
" (\"word2idx.pkl\", word2idx),\n",
|
||
" (\"tag2idx_ner.pkl\", tag2idx_ner),\n",
|
||
" (\"tag2idx_srl.pkl\", tag2idx_srl),\n",
|
||
"]:\n",
|
||
" with open(fname, \"wb\") as f:\n",
|
||
" pickle.dump(obj, f)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 94,
|
||
"id": "430794b9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"def plot_training_history(history):\n",
|
||
" history_data = history.history\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",
|
||
" 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\")\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" # --- Plot Loss ---\n",
|
||
" plt.figure(figsize=(10, 6))\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(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\")\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
"plot_training_history(history)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 95,
|
||
"id": "e690a0e0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x1000 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x1000 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"==== Confusion Matrix Manual untuk Kelas: DATE ====\n",
|
||
" Prediksi Positif Prediksi Negatif\n",
|
||
"Aktual Positif 214 (TP) 7 (FN)\n",
|
||
"Aktual Negatif 5 (FP) 2319 (TN)\n",
|
||
"\n",
|
||
"==== Confusion Matrix Manual untuk Kelas: ETH ====\n",
|
||
" Prediksi Positif Prediksi Negatif\n",
|
||
"Aktual Positif 88 (TP) 3 (FN)\n",
|
||
"Aktual Negatif 4 (FP) 2450 (TN)\n",
|
||
"\n",
|
||
"==== Confusion Matrix Manual untuk Kelas: EVENT ====\n",
|
||
" Prediksi Positif Prediksi Negatif\n",
|
||
"Aktual Positif 2 (TP) 20 (FN)\n",
|
||
"Aktual Negatif 10 (FP) 2513 (TN)\n",
|
||
"\n",
|
||
"==== Confusion Matrix Manual untuk Kelas: LOC ====\n",
|
||
" Prediksi Positif Prediksi Negatif\n",
|
||
"Aktual Positif 288 (TP) 24 (FN)\n",
|
||
"Aktual Negatif 18 (FP) 2215 (TN)\n",
|
||
"\n",
|
||
"==== Confusion Matrix Manual untuk Kelas: MISC ====\n",
|
||
" Prediksi Positif Prediksi Negatif\n",
|
||
"Aktual Positif 0 (TP) 7 (FN)\n",
|
||
"Aktual Negatif 0 (FP) 2538 (TN)\n",
|
||
"\n",
|
||
"==== Confusion Matrix Manual untuk Kelas: O ====\n",
|
||
" Prediksi Positif Prediksi Negatif\n",
|
||
"Aktual Positif 1495 (TP) 27 (FN)\n",
|
||
"Aktual Negatif 77 (FP) 946 (TN)\n",
|
||
"\n",
|
||
"==== Confusion Matrix Manual untuk Kelas: ORG ====\n",
|
||
" Prediksi Positif Prediksi Negatif\n",
|
||
"Aktual Positif 0 (TP) 3 (FN)\n",
|
||
"Aktual Negatif 0 (FP) 2542 (TN)\n",
|
||
"\n",
|
||
"==== Confusion Matrix Manual untuk Kelas: PER ====\n",
|
||
" Prediksi Positif Prediksi Negatif\n",
|
||
"Aktual Positif 282 (TP) 17 (FN)\n",
|
||
"Aktual Negatif 19 (FP) 2227 (TN)\n",
|
||
"\n",
|
||
"==== Confusion Matrix Manual untuk Kelas: TIME ====\n",
|
||
" Prediksi Positif Prediksi Negatif\n",
|
||
"Aktual Positif 32 (TP) 36 (FN)\n",
|
||
"Aktual Negatif 11 (FP) 2466 (TN)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 1. Prediksi\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"pred_ner_prob, pred_srl_prob = model.predict(X_te, verbose=0)\n",
|
||
"\n",
|
||
"pred_ner = pred_ner_prob.argmax(-1)\n",
|
||
"pred_srl = pred_srl_prob.argmax(-1)\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 2. Siapkan masker PAD\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"pad_id = tag2idx_ner[\"<PAD>\"]\n",
|
||
"\n",
|
||
"mask_ner = ner_te != pad_id\n",
|
||
"mask_srl = srl_te != pad_id\n",
|
||
"\n",
|
||
"true_ner_flat = ner_te[mask_ner]\n",
|
||
"pred_ner_flat = pred_ner[mask_ner]\n",
|
||
"\n",
|
||
"true_srl_flat = srl_te[mask_srl]\n",
|
||
"pred_srl_flat = pred_srl[mask_srl]\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 3. Hitung confusion matrix TANPA PAD\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"labels_ner_no_pad = [i for i in range(len(tag2idx_ner)) if i != pad_id]\n",
|
||
"labels_srl_no_pad = [i for i in range(len(tag2idx_srl)) if i != pad_id]\n",
|
||
"\n",
|
||
"cm_ner = confusion_matrix(true_ner_flat, pred_ner_flat, labels=labels_ner_no_pad)\n",
|
||
"cm_srl = confusion_matrix(true_srl_flat, pred_srl_flat, labels=labels_srl_no_pad)\n",
|
||
"\n",
|
||
"display_labels_ner = [idx2tag_ner[i] for i in labels_ner_no_pad]\n",
|
||
"display_labels_srl = [idx2tag_srl[i] for i in labels_srl_no_pad]\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 4. Plot NER CM (tanpa PAD)\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"fig, ax = plt.subplots(figsize=(10, 10))\n",
|
||
"disp_ner = ConfusionMatrixDisplay(confusion_matrix=cm_ner, display_labels=display_labels_ner)\n",
|
||
"disp_ner.plot(include_values=True, values_format=\"d\", cmap=plt.cm.Blues, ax=ax, colorbar=False)\n",
|
||
"ax.set_title(\"NER Confusion Matrix\", fontsize=18)\n",
|
||
"plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 5. Plot SRL CM (tanpa PAD)\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"fig, ax = plt.subplots(figsize=(10, 10))\n",
|
||
"disp_srl = ConfusionMatrixDisplay(confusion_matrix=cm_srl, display_labels=display_labels_srl)\n",
|
||
"disp_srl.plot(include_values=True, values_format=\"d\", cmap=plt.cm.Blues, ax=ax, colorbar=False)\n",
|
||
"ax.set_title(\"SRL Confusion Matrix\", fontsize=18)\n",
|
||
"plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 6. Manual Confusion Table untuk Thesis (NER)\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"def manual_confusion_table(true_labels, pred_labels, labels, label_names):\n",
|
||
" true_labels = np.array(true_labels)\n",
|
||
" pred_labels = np.array(pred_labels)\n",
|
||
"\n",
|
||
" for i, label in enumerate(labels):\n",
|
||
" label_name = label_names[i]\n",
|
||
"\n",
|
||
" tp = np.sum((true_labels == label) & (pred_labels == label))\n",
|
||
" fn = np.sum((true_labels == label) & (pred_labels != label))\n",
|
||
" fp = np.sum((true_labels != label) & (pred_labels == label))\n",
|
||
" tn = np.sum((true_labels != label) & (pred_labels != label))\n",
|
||
"\n",
|
||
" table = pd.DataFrame({\n",
|
||
" 'Prediksi Positif': [f'{tp} (TP)', f'{fp} (FP)'],\n",
|
||
" 'Prediksi Negatif': [f'{fn} (FN)', f'{tn} (TN)']\n",
|
||
" }, index=['Aktual Positif', 'Aktual Negatif'])\n",
|
||
"\n",
|
||
" print(f\"\\n==== Confusion Matrix Manual untuk Kelas: {label_name} ====\")\n",
|
||
" print(table.to_string())\n",
|
||
"\n",
|
||
"# Jalankan untuk semua kelas NER\n",
|
||
"for label_id, label_name in zip(labels_ner_no_pad, display_labels_ner):\n",
|
||
" manual_confusion_table(true_ner_flat, pred_ner_flat, [label_id], [label_name])\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 96,
|
||
"id": "a49f1dfe",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"NER TAG accuracy : 94.34%\n",
|
||
"SRL TAG accuracy : 88.09%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.metrics import accuracy_score, classification_report\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 3b. Akurasi token‑level (tanpa PAD)\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"acc_ner = accuracy_score(true_ner_flat, pred_ner_flat)\n",
|
||
"acc_srl = accuracy_score(true_srl_flat, pred_srl_flat)\n",
|
||
"\n",
|
||
"print(f\"NER TAG accuracy : {acc_ner:.2%}\")\n",
|
||
"print(f\"SRL TAG accuracy : {acc_srl:.2%}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 97,
|
||
"id": "9adad755",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"[NER] Classification report:\n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" DATE 0.98 0.97 0.97 221\n",
|
||
" ETH 0.96 0.97 0.96 91\n",
|
||
" EVENT 0.17 0.09 0.12 22\n",
|
||
" LOC 0.94 0.92 0.93 312\n",
|
||
" MISC 0.00 0.00 0.00 7\n",
|
||
" O 0.95 0.98 0.97 1522\n",
|
||
" ORG 0.00 0.00 0.00 3\n",
|
||
" PER 0.94 0.94 0.94 299\n",
|
||
" TIME 0.74 0.47 0.58 68\n",
|
||
"\n",
|
||
" accuracy 0.94 2545\n",
|
||
" macro avg 0.63 0.59 0.61 2545\n",
|
||
"weighted avg 0.93 0.94 0.94 2545\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"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",
|
||
" _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"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# (Opsional) tampilkan ringkasan metrik per‑label\n",
|
||
"print(\"\\n[NER] Classification report:\")\n",
|
||
"print(\n",
|
||
" classification_report(\n",
|
||
" true_ner_flat,\n",
|
||
" pred_ner_flat,\n",
|
||
" labels=labels_ner_no_pad,\n",
|
||
" target_names=display_labels_ner,\n",
|
||
" digits=2,\n",
|
||
" )\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 98,
|
||
"id": "7cd28380",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"[SRL] Classification report:\n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" ARG0 0.92 0.91 0.91 396\n",
|
||
" ARG1 0.67 0.81 0.73 288\n",
|
||
" ARG2 0.54 0.70 0.61 54\n",
|
||
" ARGM-CAU 0.00 0.00 0.00 4\n",
|
||
" ARGM-DIR 0.50 0.18 0.27 11\n",
|
||
" ARGM-LOC 0.93 0.91 0.92 296\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 1\n",
|
||
" ARGM-TMP 0.92 0.86 0.89 302\n",
|
||
" O 0.94 0.95 0.95 836\n",
|
||
" V 0.91 0.83 0.87 342\n",
|
||
"\n",
|
||
" accuracy 0.88 2545\n",
|
||
" macro avg 0.53 0.51 0.51 2545\n",
|
||
"weighted avg 0.88 0.88 0.88 2545\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"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",
|
||
" _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"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"\\n[SRL] Classification report:\")\n",
|
||
"print(\n",
|
||
" classification_report(\n",
|
||
" true_srl_flat,\n",
|
||
" pred_srl_flat,\n",
|
||
" labels=labels_srl_no_pad,\n",
|
||
" target_names=display_labels_srl,\n",
|
||
" digits=2,\n",
|
||
" )\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 99,
|
||
"id": "333745fd",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# def plot_training_history(history):\n",
|
||
"# epochs = range(1, len(history['loss']) + 1)\n",
|
||
"\n",
|
||
"# plt.figure(figsize=(14, 6))\n",
|
||
"\n",
|
||
"# # Plot Loss\n",
|
||
"# plt.subplot(1, 2, 1)\n",
|
||
"# plt.plot(epochs, history['loss'], label='Training Loss')\n",
|
||
"# plt.plot(epochs, history['val_loss'], label='Validation Loss')\n",
|
||
"# plt.title('Loss During Training')\n",
|
||
"# plt.xlabel('Epochs')\n",
|
||
"# plt.ylabel('Loss')\n",
|
||
"# plt.legend()\n",
|
||
"\n",
|
||
"# # Plot Accuracy\n",
|
||
"# plt.subplot(1, 2, 2)\n",
|
||
"# plt.plot(epochs, history['ner_output_accuracy'], label='NER Train Acc')\n",
|
||
"# plt.plot(epochs, history['val_ner_output_accuracy'], label='NER Val Acc')\n",
|
||
"# plt.plot(epochs, history['srl_output_accuracy'], label='SRL Train Acc')\n",
|
||
"# plt.plot(epochs, history['val_srl_output_accuracy'], label='SRL Val Acc')\n",
|
||
"# plt.title('Accuracy During Training')\n",
|
||
"# plt.xlabel('Epochs')\n",
|
||
"# plt.ylabel('Accuracy')\n",
|
||
"# plt.legend()\n",
|
||
"\n",
|
||
"# plt.tight_layout()\n",
|
||
"# plt.show()\n",
|
||
"\n",
|
||
"# plot_training_history(history.history)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 100,
|
||
"id": "df36e200",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# def token_level_accuracy(y_true, y_pred):\n",
|
||
"# total, correct = 0, 0\n",
|
||
"# for true_seq, pred_seq in zip(y_true, y_pred):\n",
|
||
"# for t, p in zip(true_seq, pred_seq):\n",
|
||
"# if t.sum() == 0:\n",
|
||
"# continue\n",
|
||
"# total += 1\n",
|
||
"# if t.argmax() == p.argmax():\n",
|
||
"# correct += 1\n",
|
||
"# return correct / total\n",
|
||
"\n",
|
||
"# def decode_predictions(pred, true, idx2tag):\n",
|
||
"# true_out, pred_out = [], []\n",
|
||
"# for pred_seq, true_seq in zip(pred, true):\n",
|
||
"# t_labels, p_labels = [], []\n",
|
||
"# for p_tok, t_tok in zip(pred_seq, true_seq):\n",
|
||
"# if t_tok.sum() == 0:\n",
|
||
"# continue\n",
|
||
"# t_labels.append(idx2tag[t_tok.argmax()])\n",
|
||
"# p_labels.append(idx2tag[p_tok.argmax()])\n",
|
||
"# true_out.append(t_labels)\n",
|
||
"# pred_out.append(p_labels)\n",
|
||
"# return true_out, pred_out\n",
|
||
"\n",
|
||
"# results = model.evaluate(X_test, {\"ner_output\": y_ner_test, \"srl_output\": y_srl_test}, verbose=0)\n",
|
||
"# for name, value in zip(model.metrics_names, results):\n",
|
||
"# print(f\"{name}: {value}\")\n",
|
||
"\n",
|
||
"# y_pred_ner, y_pred_srl = model.predict(X_test, verbose=0)\n",
|
||
"\n",
|
||
"# true_ner, pred_ner = decode_predictions(y_pred_ner, y_ner_test, idx2tag_ner)\n",
|
||
"# true_srl, pred_srl = decode_predictions(y_pred_srl, y_srl_test, idx2tag_srl)\n",
|
||
"\n",
|
||
"# acc_ner = token_level_accuracy(y_ner_test, y_pred_ner)\n",
|
||
"# acc_srl = token_level_accuracy(y_srl_test, y_pred_srl)\n",
|
||
"\n",
|
||
"# print(f\"NER Token Accuracy {acc_ner:.2%}\")\n",
|
||
"# print(f\"SRL Token Accuracy {acc_srl:.2%}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 101,
|
||
"id": "9127cce0",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# print(\"[NER] Classification Report:\")\n",
|
||
"# print(classification_report(true_ner, pred_ner, digits=2))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 102,
|
||
"id": "300897b8",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# print(\"SRL Classification Resport:\")\n",
|
||
"# print(classification_report(true_srl, pred_srl, digits=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
|
||
}
|