TIF_E41211115_lstm-quiz-generator/NER_SRL/adjst_model_lstm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 224,
   "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 (Input, Embedding, SpatialDropout1D,\n",
    "                                     Bidirectional, LSTM,\n",
    "                                     TimeDistributed, Dense)\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.model_selection import train_test_split\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "4fc87f1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total per label NER:\n",
      "O: 2724\n",
      "B-TIME: 121\n",
      "B-PER: 281\n",
      "B-LOC: 443\n",
      "I-PER: 223\n",
      "B-DATE: 324\n",
      "I-DATE: 640\n",
      "B-ETH: 219\n",
      "I-ETH: 224\n",
      "B-EVENT: 37\n",
      "I-EVENT: 19\n",
      "I-LOC: 13\n",
      "I-TIME: 1\n",
      "B-ORG: 11\n",
      "I-ORG: 8\n",
      "\n",
      "Total per label SRL:\n",
      "O: 1489\n",
      "ARGM-TMP: 1096\n",
      "ARG0: 827\n",
      "V: 478\n",
      "ARG1: 929\n",
      "ARGM-LOC: 361\n",
      "ARG2: 98\n",
      "ARGM-MOD: 5\n",
      "ARGM-MNR: 5\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "with open(\"../dataset/new_ner_srl.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)\n",
    "            srl.append(s)\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",
    "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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "48553e6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.', '06:00', '06:15', '06:30', '06:45', '07:00', '07:15', '07:30', '08:15', '08:30', '08:45', '09:15', '09:30', '09:45', '1', '10', '10:00', '10:15', '10:30', '11', '11:00', '11:15', '11:45', '12', '12:15', '12:30', '12:45', '13', '13:15', '13:30', '13:45', '14', '14:15', '14:30', '14:45', '15', '15:00', '16', '1683', '16:00', '16:15', '16:30', '16:45', '17', '17:45', '18', '1825', '1879', '18:30', '19', '1902', '1928', '1945', '1948', '1949', '1959', '1965', '1970', '1999', '19:00', '19:15', '19:45', '2', '20', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022', '2023', '2024', '2025', '20:30', '21', '21:00', '21:15', '21:45', '22', '22:15', '23', '23:30', '24', '25', '26', '27', '28', '3', '4', '5', '6', '7', '73', '8', '9', 'a.h.', 'abad', 'abdul', 'abdurrahman', 'acara', 'aceh', 'adalah', 'adat', 'agenda', 'ageng', 'agung', 'agus', 'agustus', 'ahmad', 'ajeng', 'akan', 'amal', 'amanat', 'ambon', 'amir', 'andi', 'anggraini', 'antasari', 'april', 'aprilia', 'arif', 'arsitektur', 'as-shaleh', 'asing', 'asmat', \"asy'ari\", 'asyura', 'atau', 'awal', 'ayam', 'baharuddin', 'bajo', 'bali', 'balikpapan', 'bandung', 'bangsa', 'banjar', 'banjarmasin', 'banten', 'banyak', 'barat', 'barusan', 'batak', 'batam', 'batavia', 'bayu', 'belanda', 'benang', 'bentukan', 'berangkat', 'berasal', 'berbagai', 'bercita-cita', 'bergabung', 'bergulirnya', 'berhasil', 'berjuang', 'berpidato', 'bersama', 'berusia', 'besar', 'besok', 'betawi', 'betutu', 'bima', 'bonjol', 'bromo', 'budaya', 'budi', 'bugis', 'bung', 'buton', 'cakalele', 'cucunya', 'daerah', 'dagang', 'dalam', 'dan', 'dani', 'dari', 'dayak', 'debus', 'dekrit', 'demak', 'demi', 'demokrasi', 'dengan', 'denpasar', 'desember', 'dewantara', 'dewi', 'di', 'diangkat', 'digantikan', 'dilakukan', 'dilantik', 'dipakai', 'diperingati', 'diponegoro', 'diri', 'ditampilkan', 'ditangkap', 'ditarikan', 'ditawan', 'ditenun', 'diturunkan', 'drs.', 'dukungan', 'emas', 'ende', 'fajar', 'farah', 'favorit', 'februari', 'festival', 'fisik', 'gadang', 'gamal', 'gayo', 'gelar', 'gerakan', 'gerilya', 'gorontalo', 'gudeg', 'gugur', 'gunawan', 'gunung', 'gusti', 'habibie', 'habis-habisan', 'hajar', 'hamengkubuwana', 'hari', 'harus', 'hasyim', 'hatta', 'hendra', 'hidangan', 'hidayat', 'hingga', 'honai', 'i', 'iban', 'ii', 'iii', 'ikon', 'ilham', 'imam', 'india', 'indonesia', 'inggris', 'ini', 'intan', 'ir.', 'iskandar', 'islam', 'istana', 'itu', 'jadi', 'jadwal', 'jaipong', 'jakarta', 'jam', 'januari', 'jasanya', 'jawa', 'jawaharlal', 'jayapura', 'jenazah', 'jenderal', 'jengkal', 'jepang', 'jihad', 'joglo', 'joko', 'juga', 'juli', 'juni', 'kaili', 'kain', 'kalimantan', 'karo', 'kartini', 'kasada', 'ke', 'kecak', 'kedaulatan', 'kei', 'kekuasaan', 'kekuatan', 'kemaharajaan', 'kemakmuran', 'kemarin', 'kemerdekaan', 'kendali', 'kenyah', 'kerajaan', 'keraton', 'keris', 'kerja', 'kertanegara', 'kesultanan', 'ketahanan', 'kevin', 'kh', 'khas', 'ki', 'konferensi', 'koteka', 'kuliner', 'kupang', 'kurniawan', 'lahir', 'lainnya', 'lampung', 'letjen', 'letkol', 'lina', 'lio', 'lukman', 'madura', 'maengket', 'mahendra', 'makanan', 'makassar', 'malam', 'malik', 'maluku', 'manado', 'mandailing', 'mandau', 'manggarai', 'mansyur', 'maret', 'margarana', 'masa', 'masal', 'masih', 'masyarakat', 'mataram', 'maulid', 'maya', 'medan', 'mei', 'melapor', 'melawan', 'melayu', 'meluncurkan', 'membacakan', 'membawa', 'memberikan', 'membuka', 'memegang', 'memerintah', 'memimpin', 'memperingati', 'mempersatukan', 'mempertahankan', 'memproklamasikan', 'memulai', 'menandatangani', 'mencatat', 'mendapat', 'mendarat', 'menempatkan', 'menerima', 'menetapkan', 'mengadakan', 'menganggap', 'mengeluarkan', 'mengenai', 'menggelar', 'menghadapi', 'menghadiri', 'menghargai', 'menginaugurasi', 'mengirimkan', 'menguasai', 'mengumumkan', 'mengundurkan', 'mengunjungi', 'mengusir', 'mengusulkan', 'menjadi', 'menjadikan', 'menjaga', 'mentawai', 'menteri', 'menuju', 'menulis', 'menurut', 'menutup', 'menyadarkan', 'menyelenggarakan', 'menyenangkan', 'menyerang', 'merayakan', 'merdeka', 'meresmikan', 'merupakan', 'mesir', 'minahasa', 'minangkabau', 'mohammad', 'muhammad', 'muna', 'muslihat', 'nabi', 'nasional', 'nasser', 'nasution', 'nehru', 'ngaben', 'ngaju', 'ngurah', 'nias', 'nina', 'non-blok', 'november', 'nuku', 'nusantara', 'nyi', 'oktober', 'oleh', 'pada', 'padang', 'pagi', 'pahlawan', 'pajang', 'palembang', 'paling', 'pameran', 'pangeran', 'papak', 'papeda', 'papua', 'para', 'pariaman', 'pasai', 'pasir', 'pasukan', 'pejuang', 'pekanbaru', 'pelatihan', 'peluncuran', 'pemakaman', 'pembakaran', 'pembangunan', 'pembunuhan', 'pemerintahan', 'pemimpin', 'pempek', 'pemuda', 'pendidikan', 'pendiri', 'pengaruh', 'penyerangan', 'perang', 'percobaan', 'perdana', 'pergi', 'perintah', 'peristiwa', 'perjanjian', 'perlawanan', 'permata', 'pers', 'persembahan', 'pertama', 'pertempuran', 'pertunjukan', 'pesawat', 'pihak', 'piring', 'pm', 'pokok', 'pontianak', 'ppki', 'pratama', 'presiden', 'pria', 'produk', 'program', 'proklamasi', 'proyek', 'pukul', 'pulang', 'punan', 'puputan', 'putra', 'putri', 'r.a.', 'r.m.', 'raden', 'raffles', 'rahma', 'rahmatullah', 'rai', 'raja', 'raja-raja', 'rakyat', 'rakyatnya', 'ramadhan', 'rambu', 'rapat', 'ratna', 'reformasi', 'rejang', 'rencong', 'rendang', 'resmi', 'resolusi', 'rina', 'ritual', 'rizal', 'rote', 'rudi', 'rumah', 'saat', 'salah', 'sama', 'saman', 'sampai', 'samudra', 'sangat', 'sangir', 'saputra', 'sasak', 'satu', 'sebagai', 'sedang', 'sehingga', 'sekaten', 'sekitar', 'sekitarnya', 'selamat', 'selatan', 'seluruh', 'semarang', 'seminar', 'sendiri', 'seni', 'senjata', 'seperti', 'september', 'serang', 'serangan', 'serawai', 'serta', 'setelah', 'setiap', 'siak', 'siang', 'singhasari', 'siti', 'soedirman', 'soeharto', 'soekarno', 'solo', 'songket', 'sore', 'spiritual', 'sudah', 'suhu', 'suku', 'sulawesi', 'sultan', 'sumatera', 'sumpah', 'sunda', 'supersemar', 'surabaya', 'surakarta', 'surapati', 'surat', 'surya', 'susanto', 'sutan', 'sutomo', 'syafiudin', 'syah', 'syahrir', 'tabuik', 'tahta', 'tahun', 'talaud', 'tanah', 'tanggal', 'tari', 'tarian', 'tengah', 'tengger', 'terhadap', 'terkenal', 'termasuk', 'ternate', 'terus', 'tiba', 'tidore', 'tidung', 'tinggi', 'tipu', 'tirtayasa', 'tokoh', 'tolaki', 'tommo', 'tono', 'toraja', 'tradisi', 'tradisional', 'tuan', 'tumenggung', 'ulos', 'umum', 'untuk', 'upacara', 'upeti', 'utami', 'utara', 'voc', 'wafat', 'wahid', 'wahyuni', 'wakil', 'warisan', 'wijaya', 'wulan', 'yamin', 'yang', 'yani', 'yogyakarta', 'yusuf']\n"
     ]
    }
   ],
   "source": [
    "PAD_TOKEN = \"<PAD>\"\n",
    "words     = sorted({w for s in sentences for w in s})\n",
    "print(words)\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": 227,
   "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\")   # shape (N, L)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "a26893cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "splits = train_test_split(X, y_ner, y_srl, mask,\n",
    "                          test_size=0.2, random_state=42, shuffle=True)\n",
    "X_tr, X_te, ner_tr, ner_te, srl_tr, srl_te, m_tr, m_te = splits\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "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_19\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_19\"\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\">22</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\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">41,664</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_… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</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_18        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</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_36    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</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_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ bidirectional_37    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)   │     <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</span> │ bidirectional_36… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)     │                   │            │ not_equal_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ time_distributed_34 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ bidirectional_37… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │ not_equal_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ time_distributed_35 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ bidirectional_37… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │ not_equal_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ ner_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,040</span> │ time_distributed… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │ not_equal_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ srl_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</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_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</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;34m22\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;34m22\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │     \u001b[38;5;34m41,664\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;34m22\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_18        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\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_36    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\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_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ bidirectional_37    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m128\u001b[0m)   │     \u001b[38;5;34m98,816\u001b[0m │ bidirectional_36… │\n",
       "│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │ not_equal_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ time_distributed_34 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │      \u001b[38;5;34m8,256\u001b[0m │ bidirectional_37… │\n",
       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │ not_equal_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ time_distributed_35 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │      \u001b[38;5;34m8,256\u001b[0m │ bidirectional_37… │\n",
       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │ not_equal_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ ner_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m16\u001b[0m)    │      \u001b[38;5;34m1,040\u001b[0m │ time_distributed… │\n",
       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │ not_equal_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ srl_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\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_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\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\">224,730</span> (877.85 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m224,730\u001b[0m (877.85 KB)\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\">224,730</span> (877.85 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m224,730\u001b[0m (877.85 KB)\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 = 0.45\n",
    "drop_lstm  = 0.35\n",
    "\n",
    "inp  = Input(shape=(maxlen,), name=\"tokens\")\n",
    "emb  = Embedding(len(word2idx),\n",
    "                 embed_dim,\n",
    "                 mask_zero=True,\n",
    "                 name=\"embed\")(inp)\n",
    "emb  = SpatialDropout1D(drop_embed)(emb)\n",
    "\n",
    "x = Bidirectional(LSTM(lstm_units,\n",
    "                       return_sequences=True,\n",
    "                       dropout=drop_lstm,\n",
    "                       recurrent_dropout=drop_lstm))(emb)\n",
    "x = Bidirectional(LSTM(lstm_units,\n",
    "                       return_sequences=True,\n",
    "                       dropout=drop_lstm,\n",
    "                       recurrent_dropout=drop_lstm))(x)\n",
    "\n",
    "ner_head = TimeDistributed(Dense(lstm_units, activation=\"relu\"))(x)\n",
    "ner_out  = TimeDistributed(Dense(len(tag2idx_ner),\n",
    "                                 activation=\"softmax\"),\n",
    "                           name=\"ner_output\")(ner_head)\n",
    "\n",
    "srl_head = TimeDistributed(Dense(lstm_units, activation=\"relu\"))(x)\n",
    "srl_out  = TimeDistributed(Dense(len(tag2idx_srl),\n",
    "                                 activation=\"softmax\"),\n",
    "                           name=\"srl_output\")(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": 230,
   "id": "f41d6012",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 28ms/step - loss: 4.4003 - ner_output_loss: 2.3921 - ner_output_sparse_categorical_accuracy: 0.2545 - srl_output_loss: 2.0082 - srl_output_sparse_categorical_accuracy: 0.1825 - val_loss: 2.8942 - val_ner_output_loss: 1.5137 - val_ner_output_sparse_categorical_accuracy: 0.2742 - val_srl_output_loss: 1.3805 - val_srl_output_sparse_categorical_accuracy: 0.2340 - learning_rate: 3.0000e-04\n",
      "Epoch 2/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 23ms/step - loss: 2.7142 - ner_output_loss: 1.4036 - ner_output_sparse_categorical_accuracy: 0.2803 - srl_output_loss: 1.3106 - srl_output_sparse_categorical_accuracy: 0.2522 - val_loss: 2.7169 - val_ner_output_loss: 1.3986 - val_ner_output_sparse_categorical_accuracy: 0.2732 - val_srl_output_loss: 1.3184 - val_srl_output_sparse_categorical_accuracy: 0.2553 - learning_rate: 3.0000e-04\n",
      "Epoch 3/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 23ms/step - loss: 2.4396 - ner_output_loss: 1.2882 - ner_output_sparse_categorical_accuracy: 0.2876 - srl_output_loss: 1.1513 - srl_output_sparse_categorical_accuracy: 0.3009 - val_loss: 2.4399 - val_ner_output_loss: 1.2656 - val_ner_output_sparse_categorical_accuracy: 0.3129 - val_srl_output_loss: 1.1743 - val_srl_output_sparse_categorical_accuracy: 0.3027 - learning_rate: 3.0000e-04\n",
      "Epoch 4/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 22ms/step - loss: 2.1382 - ner_output_loss: 1.1303 - ner_output_sparse_categorical_accuracy: 0.3148 - srl_output_loss: 1.0078 - srl_output_sparse_categorical_accuracy: 0.3312 - val_loss: 2.0792 - val_ner_output_loss: 1.0723 - val_ner_output_sparse_categorical_accuracy: 0.3356 - val_srl_output_loss: 1.0069 - val_srl_output_sparse_categorical_accuracy: 0.3138 - learning_rate: 3.0000e-04\n",
      "Epoch 5/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 22ms/step - loss: 1.7690 - ner_output_loss: 0.9385 - ner_output_sparse_categorical_accuracy: 0.3687 - srl_output_loss: 0.8305 - srl_output_sparse_categorical_accuracy: 0.3661 - val_loss: 1.7089 - val_ner_output_loss: 0.8578 - val_ner_output_sparse_categorical_accuracy: 0.3907 - val_srl_output_loss: 0.8511 - val_srl_output_sparse_categorical_accuracy: 0.3583 - learning_rate: 3.0000e-04\n",
      "Epoch 6/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 22ms/step - loss: 1.3732 - ner_output_loss: 0.7002 - ner_output_sparse_categorical_accuracy: 0.4105 - srl_output_loss: 0.6729 - srl_output_sparse_categorical_accuracy: 0.3914 - val_loss: 1.5160 - val_ner_output_loss: 0.7476 - val_ner_output_sparse_categorical_accuracy: 0.3999 - val_srl_output_loss: 0.7684 - val_srl_output_sparse_categorical_accuracy: 0.3704 - learning_rate: 3.0000e-04\n",
      "Epoch 7/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 24ms/step - loss: 1.3125 - ner_output_loss: 0.6610 - ner_output_sparse_categorical_accuracy: 0.4190 - srl_output_loss: 0.6515 - srl_output_sparse_categorical_accuracy: 0.3948 - val_loss: 1.4319 - val_ner_output_loss: 0.6891 - val_ner_output_sparse_categorical_accuracy: 0.4105 - val_srl_output_loss: 0.7428 - val_srl_output_sparse_categorical_accuracy: 0.3752 - learning_rate: 3.0000e-04\n",
      "Epoch 8/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 23ms/step - loss: 1.1334 - ner_output_loss: 0.5667 - ner_output_sparse_categorical_accuracy: 0.4450 - srl_output_loss: 0.5666 - srl_output_sparse_categorical_accuracy: 0.4262 - val_loss: 1.3217 - val_ner_output_loss: 0.6339 - val_ner_output_sparse_categorical_accuracy: 0.4202 - val_srl_output_loss: 0.6878 - val_srl_output_sparse_categorical_accuracy: 0.3859 - learning_rate: 3.0000e-04\n",
      "Epoch 9/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 24ms/step - loss: 1.0389 - ner_output_loss: 0.5145 - ner_output_sparse_categorical_accuracy: 0.4476 - srl_output_loss: 0.5244 - srl_output_sparse_categorical_accuracy: 0.4295 - val_loss: 1.2370 - val_ner_output_loss: 0.5942 - val_ner_output_sparse_categorical_accuracy: 0.4202 - val_srl_output_loss: 0.6428 - val_srl_output_sparse_categorical_accuracy: 0.3965 - learning_rate: 3.0000e-04\n",
      "Epoch 10/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 23ms/step - loss: 0.9221 - ner_output_loss: 0.4444 - ner_output_sparse_categorical_accuracy: 0.4537 - srl_output_loss: 0.4778 - srl_output_sparse_categorical_accuracy: 0.4353 - val_loss: 1.2053 - val_ner_output_loss: 0.5670 - val_ner_output_sparse_categorical_accuracy: 0.4246 - val_srl_output_loss: 0.6383 - val_srl_output_sparse_categorical_accuracy: 0.3975 - learning_rate: 3.0000e-04\n",
      "Epoch 11/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 22ms/step - loss: 0.7102 - ner_output_loss: 0.3429 - ner_output_sparse_categorical_accuracy: 0.4708 - srl_output_loss: 0.3674 - srl_output_sparse_categorical_accuracy: 0.4610 - val_loss: 1.1245 - val_ner_output_loss: 0.5290 - val_ner_output_sparse_categorical_accuracy: 0.4275 - val_srl_output_loss: 0.5954 - val_srl_output_sparse_categorical_accuracy: 0.3994 - learning_rate: 3.0000e-04\n",
      "Epoch 12/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 23ms/step - loss: 0.7746 - ner_output_loss: 0.3707 - ner_output_sparse_categorical_accuracy: 0.4707 - srl_output_loss: 0.4039 - srl_output_sparse_categorical_accuracy: 0.4524 - val_loss: 1.0918 - val_ner_output_loss: 0.5028 - val_ner_output_sparse_categorical_accuracy: 0.4255 - val_srl_output_loss: 0.5889 - val_srl_output_sparse_categorical_accuracy: 0.3989 - learning_rate: 3.0000e-04\n",
      "Epoch 13/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 23ms/step - loss: 0.7520 - ner_output_loss: 0.3696 - ner_output_sparse_categorical_accuracy: 0.4590 - srl_output_loss: 0.3824 - srl_output_sparse_categorical_accuracy: 0.4434 - val_loss: 1.0326 - val_ner_output_loss: 0.4736 - val_ner_output_sparse_categorical_accuracy: 0.4309 - val_srl_output_loss: 0.5590 - val_srl_output_sparse_categorical_accuracy: 0.4052 - learning_rate: 3.0000e-04\n",
      "Epoch 14/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 23ms/step - loss: 0.6688 - ner_output_loss: 0.3218 - ner_output_sparse_categorical_accuracy: 0.4570 - srl_output_loss: 0.3470 - srl_output_sparse_categorical_accuracy: 0.4478 - val_loss: 0.9858 - val_ner_output_loss: 0.4498 - val_ner_output_sparse_categorical_accuracy: 0.4342 - val_srl_output_loss: 0.5360 - val_srl_output_sparse_categorical_accuracy: 0.4081 - learning_rate: 3.0000e-04\n",
      "Epoch 15/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 22ms/step - loss: 0.6571 - ner_output_loss: 0.3147 - ner_output_sparse_categorical_accuracy: 0.4686 - srl_output_loss: 0.3424 - srl_output_sparse_categorical_accuracy: 0.4534 - val_loss: 0.9533 - val_ner_output_loss: 0.4336 - val_ner_output_sparse_categorical_accuracy: 0.4391 - val_srl_output_loss: 0.5196 - val_srl_output_sparse_categorical_accuracy: 0.4120 - learning_rate: 3.0000e-04\n",
      "Epoch 16/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 23ms/step - loss: 0.4953 - ner_output_loss: 0.2380 - ner_output_sparse_categorical_accuracy: 0.4838 - srl_output_loss: 0.2573 - srl_output_sparse_categorical_accuracy: 0.4776 - val_loss: 0.8647 - val_ner_output_loss: 0.3953 - val_ner_output_sparse_categorical_accuracy: 0.4429 - val_srl_output_loss: 0.4694 - val_srl_output_sparse_categorical_accuracy: 0.4197 - learning_rate: 3.0000e-04\n",
      "Epoch 17/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 22ms/step - loss: 0.6268 - ner_output_loss: 0.3027 - ner_output_sparse_categorical_accuracy: 0.4726 - srl_output_loss: 0.3241 - srl_output_sparse_categorical_accuracy: 0.4585 - val_loss: 0.8576 - val_ner_output_loss: 0.3803 - val_ner_output_sparse_categorical_accuracy: 0.4492 - val_srl_output_loss: 0.4773 - val_srl_output_sparse_categorical_accuracy: 0.4207 - learning_rate: 3.0000e-04\n",
      "Epoch 18/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 22ms/step - loss: 0.5693 - ner_output_loss: 0.2790 - ner_output_sparse_categorical_accuracy: 0.4749 - srl_output_loss: 0.2903 - srl_output_sparse_categorical_accuracy: 0.4656 - val_loss: 0.7968 - val_ner_output_loss: 0.3606 - val_ner_output_sparse_categorical_accuracy: 0.4483 - val_srl_output_loss: 0.4362 - val_srl_output_sparse_categorical_accuracy: 0.4241 - learning_rate: 3.0000e-04\n",
      "Epoch 19/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 23ms/step - loss: 0.4516 - ner_output_loss: 0.2132 - ner_output_sparse_categorical_accuracy: 0.4864 - srl_output_loss: 0.2384 - srl_output_sparse_categorical_accuracy: 0.4796 - val_loss: 0.8022 - val_ner_output_loss: 0.3571 - val_ner_output_sparse_categorical_accuracy: 0.4473 - val_srl_output_loss: 0.4451 - val_srl_output_sparse_categorical_accuracy: 0.4246 - learning_rate: 3.0000e-04\n",
      "Epoch 20/20\n",
      "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 23ms/step - loss: 0.4330 - ner_output_loss: 0.2140 - ner_output_sparse_categorical_accuracy: 0.4825 - srl_output_loss: 0.2190 - srl_output_sparse_categorical_accuracy: 0.4754 - val_loss: 0.7122 - val_ner_output_loss: 0.3191 - val_ner_output_sparse_categorical_accuracy: 0.4536 - val_srl_output_loss: 0.3931 - val_srl_output_sparse_categorical_accuracy: 0.4381 - learning_rate: 3.0000e-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=(\n",
    "        X_te,\n",
    "        [ner_te, srl_te],\n",
    "        [m_te,  m_te]\n",
    "    ),\n",
    "    batch_size=2,\n",
    "    epochs=20,\n",
    "    callbacks=callbacks,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "# =========================\n",
    "# 7.  Save artefacts\n",
    "# =========================\n",
    "model.save(\"lstm_ner_srl_model.keras\")\n",
    "for fname, obj in [(\"word2idx.pkl\", word2idx),\n",
    "                   (\"tag2idx_ner.pkl\", tag2idx_ner),\n",
    "                   (\"tag2idx_srl.pkl\", tag2idx_srl)]:\n",
    "    with open(fname, \"wb\") as f:\n",
    "        pickle.dump(obj, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "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",
    "    # Extract data from history\n",
    "    history_data = history.history\n",
    "    epochs = range(1, len(history_data['ner_output_sparse_categorical_accuracy']) + 1)\n",
    "\n",
    "    # --- Plot Accuracy ---\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(epochs, history_data['ner_output_sparse_categorical_accuracy'], marker='o', label='NER Accuracy (Train)')\n",
    "    plt.plot(epochs, history_data['srl_output_sparse_categorical_accuracy'], marker='s', label='SRL Accuracy (Train)')\n",
    "\n",
    "    if 'val_ner_output_sparse_categorical_accuracy' in history_data:\n",
    "        plt.plot(epochs, history_data['val_ner_output_sparse_categorical_accuracy'], marker='o', linestyle='--', label='NER Accuracy (Val)')\n",
    "        plt.plot(epochs, history_data['val_srl_output_sparse_categorical_accuracy'], 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.show()\n",
    "\n",
    "    # --- Plot Loss ---\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(epochs, history_data['ner_output_loss'], marker='o', label='NER Loss (Train)')\n",
    "    plt.plot(epochs, history_data['srl_output_loss'], marker='s', label='SRL Loss (Train)')\n",
    "\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",
    "        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.show()\n",
    "\n",
    "plot_training_history(history)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "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"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\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",
    "# Buang ID PAD dari label list\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(\n",
    "    true_ner_flat, pred_ner_flat,\n",
    "    labels=labels_ner_no_pad\n",
    ")\n",
    "\n",
    "cm_srl = confusion_matrix(\n",
    "    true_srl_flat, pred_srl_flat,\n",
    "    labels=labels_srl_no_pad\n",
    ")\n",
    "\n",
    "# Siapkan label display TANPA PAD\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(\n",
    "    confusion_matrix=cm_ner,\n",
    "    display_labels=display_labels_ner\n",
    ")\n",
    "disp_ner.plot(\n",
    "    include_values=True,            # Tampilkan angka\n",
    "    values_format='d',              # Format integer\n",
    "    cmap=plt.cm.Blues,              # Biru-putih\n",
    "    ax=ax,\n",
    "    colorbar=False\n",
    ")\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(\n",
    "    confusion_matrix=cm_srl,\n",
    "    display_labels=display_labels_srl\n",
    ")\n",
    "disp_srl.plot(\n",
    "    include_values=True,\n",
    "    values_format='d',\n",
    "    cmap=plt.cm.Blues,\n",
    "    ax=ax,\n",
    "    colorbar=False\n",
    ")\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "a49f1dfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NER TAG accuracy : 91.51%\n",
      "SRL TAG accuracy : 88.39%\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%}\")\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "9adad755",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[NER] Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      B-DATE       1.00      0.96      0.98        56\n",
      "       B-ETH       1.00      0.76      0.87        38\n",
      "     B-EVENT       0.00      0.00      0.00        10\n",
      "       B-LOC       0.91      0.92      0.91        84\n",
      "       B-ORG       0.00      0.00      0.00         3\n",
      "       B-PER       0.90      0.88      0.89        60\n",
      "      B-TIME       0.86      0.55      0.67        33\n",
      "      I-DATE       0.99      0.99      0.99       110\n",
      "       I-ETH       1.00      0.89      0.94        36\n",
      "     I-EVENT       0.00      0.00      0.00         5\n",
      "       I-LOC       0.00      0.00      0.00         4\n",
      "       I-ORG       0.00      0.00      0.00         3\n",
      "       I-PER       0.89      0.80      0.84        50\n",
      "      I-TIME       0.00      0.00      0.00         0\n",
      "           O       0.89      0.99      0.94       533\n",
      "\n",
      "    accuracy                           0.92      1025\n",
      "   macro avg       0.56      0.52      0.54      1025\n",
      "weighted avg       0.89      0.92      0.90      1025\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: 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"
     ]
    }
   ],
   "source": [
    "# (Opsional) tampilkan ringkasan metrik per‑label\n",
    "print(\"\\n[NER] Classification report:\")\n",
    "print(classification_report(true_ner_flat, pred_ner_flat,\n",
    "                            labels=labels_ner_no_pad,\n",
    "                            target_names=display_labels_ner,\n",
    "                            digits=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "7cd28380",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[SRL] Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        ARG0       0.97      0.89      0.93       162\n",
      "        ARG1       0.75      0.91      0.82       186\n",
      "        ARG2       1.00      1.00      1.00        16\n",
      "    ARGM-LOC       0.84      0.80      0.82        66\n",
      "    ARGM-MNR       0.00      0.00      0.00         1\n",
      "    ARGM-MOD       0.00      0.00      0.00         1\n",
      "    ARGM-TMP       0.98      0.90      0.94       202\n",
      "           O       0.89      0.91      0.90       294\n",
      "           V       0.87      0.76      0.81        97\n",
      "\n",
      "    accuracy                           0.88      1025\n",
      "   macro avg       0.70      0.69      0.69      1025\n",
      "weighted avg       0.89      0.88      0.88      1025\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(classification_report(true_srl_flat, pred_srl_flat,\n",
    "                            labels=labels_srl_no_pad,\n",
    "                            target_names=display_labels_srl,\n",
    "                            digits=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "333745fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "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%}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "9127cce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"[NER] Classification Report:\")\n",
    "# print(classification_report(true_ner, pred_ner, digits=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "300897b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"SRL Classification Resport:\")\n",
    "# print(classification_report(true_srl, pred_srl, digits=2))"
   ]
  }
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