diff --git a/NER_SRL/accuracy_plot.png b/NER_SRL/accuracy_plot.png
new file mode 100644
index 0000000..7a71b9d
Binary files /dev/null and b/NER_SRL/accuracy_plot.png differ
diff --git a/NER_SRL/adjst_model_lstm.ipynb b/NER_SRL/adjst_model_lstm.ipynb
index 35253da..79cc041 100644
--- a/NER_SRL/adjst_model_lstm.ipynb
+++ b/NER_SRL/adjst_model_lstm.ipynb
@@ -2,106 +2,175 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 224,
    "id": "263af9e9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "import pickle\n",
-    "import tensorflow as tf\n",
+    "import pickle, tensorflow as tf, numpy as np\n",
     "from tensorflow.keras.models import Model\n",
-    "from tensorflow.keras.layers import Input, Embedding, Bidirectional, LSTM, TimeDistributed, Dense\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 tensorflow.keras.utils import to_categorical\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from seqeval.metrics import classification_report\n",
-    "import matplotlib.pyplot as plt"
+    "from collections import Counter"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 225,
    "id": "4fc87f1b",
    "metadata": {},
-   "outputs": [],
+   "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/dataset_ner_srl.tsv\", encoding=\"utf-8\") as f:\n",
-    "    tokens, ner_labels, srl_labels = [], [], []\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 tokens:\n",
-    "                data.append({\n",
-    "                    \"tokens\": tokens,\n",
-    "                    \"labels_ner\": ner_labels,\n",
-    "                    \"labels_srl\": srl_labels\n",
-    "                })\n",
-    "                tokens, ner_labels, srl_labels = [], [], []\n",
+    "            if tok:\n",
+    "                data.append({\"tokens\": tok, \"labels_ner\": ner, \"labels_srl\": srl})\n",
+    "                tok, ner, srl = [], [], []\n",
     "        else:\n",
-    "            token, ner, srl = line.split(\"\\t\")\n",
-    "            tokens.append(token)\n",
-    "            ner_labels.append(ner)\n",
-    "            srl_labels.append(srl)"
+    "            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": 21,
+   "execution_count": 226,
    "id": "48553e6b",
    "metadata": {},
-   "outputs": [],
+   "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",
-    "# 2. Preprocessing\n",
-    "sentences = [[tok.lower() for tok in item[\"tokens\"]] for item in data]\n",
-    "labels_ner = [item[\"labels_ner\"] for item in data]\n",
-    "labels_srl = [item[\"labels_srl\"] for item in data]\n",
+    "ner_tags.insert(0, PAD_TOKEN)\n",
+    "srl_tags.insert(0, PAD_TOKEN)\n",
     "\n",
-    "words = sorted({w for s in sentences for w in s})\n",
-    "ner_tags = sorted({t for seq in labels_ner for t in seq})\n",
-    "srl_tags = sorted({t for seq in labels_srl for t in seq})\n",
-    "\n",
-    "word2idx = {w: i + 2 for i, w in enumerate(words)}\n",
-    "word2idx[\"PAD\"], word2idx[\"UNK\"] = 0, 1\n",
+    "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()}\n",
-    "\n",
-    "X = [[word2idx.get(w, word2idx[\"UNK\"]) for w in s] for s in sentences]\n",
-    "y_ner = [[tag2idx_ner[t] for t in seq] for seq in labels_ner]\n",
-    "y_srl = [[tag2idx_srl[t] for t in seq] for seq in labels_srl]\n",
-    "\n",
-    "maxlen = 50\n",
-    "X = pad_sequences(X, maxlen=maxlen, padding=\"post\", value=word2idx[\"PAD\"])\n",
-    "y_ner = pad_sequences(y_ner, maxlen=maxlen, padding=\"post\", value=tag2idx_ner[\"O\"])\n",
-    "y_srl = pad_sequences(y_srl, maxlen=maxlen, padding=\"post\", value=tag2idx_srl[\"O\"])\n",
-    "\n",
-    "y_ner = to_categorical(y_ner, num_classes=len(tag2idx_ner))\n",
-    "y_srl = to_categorical(y_srl, num_classes=len(tag2idx_srl))\n",
-    "\n",
-    "X_train, X_test, y_ner_train, y_ner_test, y_srl_train, y_srl_test = train_test_split(\n",
-    "    X, y_ner, y_srl, test_size=0.2, random_state=42, shuffle=True\n",
-    ")"
+    "idx2tag_srl = {i: t for t, i in tag2idx_srl.items()}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "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_2\"</span>\n",
+       "<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_2\"\u001b[0m\n"
+       "\u001b[1mModel: \"functional_19\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -113,20 +182,33 @@
        "<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",
-       "│ input_layer_2       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\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",
-       "│ embedding_2         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">92,800</span> │ input_layer_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │                   │            │                   │\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",
-       "│ bidirectional_2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)   │     <span style=\"color: #00af00; text-decoration-color: #00af00\">66,048</span> │ embedding_2[<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\">Bidirectional</span>)     │                   │            │                   │\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",
-       "│ ner_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,225</span> │ bidirectional_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │                   │\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",
-       "│ srl_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,999</span> │ bidirectional_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │                   │\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"
       ],
@@ -134,20 +216,33 @@
        "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\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",
-       "│ input_layer_2       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
-       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
+       "│ 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",
-       "│ embedding_2         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │     \u001b[38;5;34m92,800\u001b[0m │ input_layer_2[\u001b[38;5;34m0\u001b[0m]… │\n",
-       "│ (\u001b[38;5;33mEmbedding\u001b[0m)         │                   │            │                   │\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",
-       "│ bidirectional_2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)   │     \u001b[38;5;34m66,048\u001b[0m │ embedding_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
-       "│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │                   │\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",
-       "│ ner_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m25\u001b[0m)    │      \u001b[38;5;34m3,225\u001b[0m │ bidirectional_2[\u001b[38;5;34m…\u001b[0m │\n",
-       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │                   │\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",
-       "│ srl_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m31\u001b[0m)    │      \u001b[38;5;34m3,999\u001b[0m │ bidirectional_2[\u001b[38;5;34m…\u001b[0m │\n",
-       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │                   │\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"
       ]
      },
@@ -157,11 +252,11 @@
     {
      "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\">166,072</span> (648.72 KB)\n",
+       "<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;34m166,072\u001b[0m (648.72 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m224,730\u001b[0m (877.85 KB)\n"
       ]
      },
      "metadata": {},
@@ -170,11 +265,11 @@
     {
      "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\">166,072</span> (648.72 KB)\n",
+       "<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;34m166,072\u001b[0m (648.72 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m224,730\u001b[0m (877.85 KB)\n"
       ]
      },
      "metadata": {},
@@ -195,34 +290,57 @@
     }
    ],
    "source": [
+    "embed_dim  = 64\n",
+    "lstm_units = 64\n",
+    "drop_embed = 0.45\n",
+    "drop_lstm  = 0.35\n",
     "\n",
-    "# 3. Model\n",
-    "input_layer = Input(shape=(maxlen,))\n",
-    "embedding_layer = Embedding(input_dim=len(word2idx), output_dim=64)(input_layer)\n",
-    "bilstm_layer = Bidirectional(LSTM(units=64, return_sequences=True))(embedding_layer)\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",
-    "ner_output = TimeDistributed(Dense(len(tag2idx_ner), activation=\"softmax\"), name=\"ner_output\")(bilstm_layer)\n",
-    "srl_output = TimeDistributed(Dense(len(tag2idx_srl), activation=\"softmax\"), name=\"srl_output\")(bilstm_layer)\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 = Model(inputs=input_layer, outputs=[ner_output, srl_output])\n",
     "model.compile(\n",
-    "    optimizer=\"adam\",\n",
+    "    optimizer=tf.keras.optimizers.Adam(3e-4),\n",
     "    loss={\n",
-    "        \"ner_output\": \"categorical_crossentropy\",\n",
-    "        \"srl_output\": \"categorical_crossentropy\",\n",
+    "        \"ner_output\": \"sparse_categorical_crossentropy\",\n",
+    "        \"srl_output\": \"sparse_categorical_crossentropy\",\n",
     "    },\n",
     "    metrics={\n",
-    "        \"ner_output\": [tf.keras.metrics.CategoricalAccuracy(name=\"accuracy\")],\n",
-    "        \"srl_output\": [tf.keras.metrics.CategoricalAccuracy(name=\"accuracy\")],\n",
-    "    }\n",
+    "        \"ner_output\": [\"sparse_categorical_accuracy\"],\n",
+    "        \"srl_output\": [\"sparse_categorical_accuracy\"],\n",
+    "    },\n",
+    "    # sample_weight_mode=\"temporal\"\n",
     ")\n",
-    "\n",
     "model.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 230,
    "id": "f41d6012",
    "metadata": {},
    "outputs": [
@@ -230,60 +348,176 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10\n",
-      "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 14ms/step - loss: 2.2486 - ner_output_accuracy: 0.9123 - ner_output_loss: 0.9804 - srl_output_accuracy: 0.7926 - srl_output_loss: 1.2682 - val_loss: 0.7742 - val_ner_output_accuracy: 0.9400 - val_ner_output_loss: 0.2603 - val_srl_output_accuracy: 0.8402 - val_srl_output_loss: 0.5139\n",
-      "Epoch 2/10\n",
-      "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.6923 - ner_output_accuracy: 0.9402 - ner_output_loss: 0.2531 - srl_output_accuracy: 0.8617 - srl_output_loss: 0.4393 - val_loss: 0.7104 - val_ner_output_accuracy: 0.9400 - val_ner_output_loss: 0.2412 - val_srl_output_accuracy: 0.8593 - val_srl_output_loss: 0.4692\n",
-      "Epoch 3/10\n",
-      "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.5879 - ner_output_accuracy: 0.9419 - ner_output_loss: 0.2117 - srl_output_accuracy: 0.8888 - srl_output_loss: 0.3762 - val_loss: 0.6122 - val_ner_output_accuracy: 0.9423 - val_ner_output_loss: 0.2058 - val_srl_output_accuracy: 0.8839 - val_srl_output_loss: 0.4064\n",
-      "Epoch 4/10\n",
-      "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.4602 - ner_output_accuracy: 0.9512 - ner_output_loss: 0.1677 - srl_output_accuracy: 0.9173 - srl_output_loss: 0.2925 - val_loss: 0.5394 - val_ner_output_accuracy: 0.9552 - val_ner_output_loss: 0.1733 - val_srl_output_accuracy: 0.8986 - val_srl_output_loss: 0.3661\n",
-      "Epoch 5/10\n",
-      "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.3767 - ner_output_accuracy: 0.9629 - ner_output_loss: 0.1242 - srl_output_accuracy: 0.9273 - srl_output_loss: 0.2525 - val_loss: 0.4978 - val_ner_output_accuracy: 0.9625 - val_ner_output_loss: 0.1567 - val_srl_output_accuracy: 0.9052 - val_srl_output_loss: 0.3411\n",
-      "Epoch 6/10\n",
-      "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.3493 - ner_output_accuracy: 0.9664 - ner_output_loss: 0.1120 - srl_output_accuracy: 0.9309 - srl_output_loss: 0.2373 - val_loss: 0.4831 - val_ner_output_accuracy: 0.9655 - val_ner_output_loss: 0.1478 - val_srl_output_accuracy: 0.9052 - val_srl_output_loss: 0.3353\n",
-      "Epoch 7/10\n",
-      "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.2634 - ner_output_accuracy: 0.9743 - ner_output_loss: 0.0914 - srl_output_accuracy: 0.9533 - srl_output_loss: 0.1721 - val_loss: 0.4774 - val_ner_output_accuracy: 0.9659 - val_ner_output_loss: 0.1442 - val_srl_output_accuracy: 0.9123 - val_srl_output_loss: 0.3332\n",
-      "Epoch 8/10\n",
-      "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.2110 - ner_output_accuracy: 0.9801 - ner_output_loss: 0.0732 - srl_output_accuracy: 0.9630 - srl_output_loss: 0.1378 - val_loss: 0.4799 - val_ner_output_accuracy: 0.9670 - val_ner_output_loss: 0.1466 - val_srl_output_accuracy: 0.9134 - val_srl_output_loss: 0.3333\n",
-      "Epoch 9/10\n",
-      "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.1970 - ner_output_accuracy: 0.9848 - ner_output_loss: 0.0605 - srl_output_accuracy: 0.9630 - srl_output_loss: 0.1366 - val_loss: 0.4818 - val_ner_output_accuracy: 0.9684 - val_ner_output_loss: 0.1470 - val_srl_output_accuracy: 0.9150 - val_srl_output_loss: 0.3348\n",
-      "Epoch 10/10\n",
-      "\u001b[1m176/176\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.1772 - ner_output_accuracy: 0.9852 - ner_output_loss: 0.0620 - srl_output_accuracy: 0.9705 - srl_output_loss: 0.1152 - val_loss: 0.4976 - val_ner_output_accuracy: 0.9686 - val_ner_output_loss: 0.1515 - val_srl_output_accuracy: 0.9120 - val_srl_output_loss: 0.3461\n"
+      "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_train,\n",
-    "    {\"ner_output\": y_ner_train, \"srl_output\": y_srl_train},\n",
-    "    validation_data=(X_test, {\"ner_output\": y_ner_test, \"srl_output\": y_srl_test}),\n",
+    "    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=10,\n",
+    "    epochs=20,\n",
+    "    callbacks=callbacks,\n",
     "    verbose=1\n",
     ")\n",
     "\n",
-    "# 5. Save artifacts\n",
-    "model.save(\"multi_task_lstm_ner_srl_model_tf.keras\")\n",
-    "with open(\"word2idx.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(word2idx, f)\n",
-    "with open(\"tag2idx_ner.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(tag2idx_ner, f)\n",
-    "with open(\"tag2idx_srl.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(tag2idx_srl, f)"
+    "\n",
+    "\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": 24,
-   "id": "333745fd",
+   "execution_count": 231,
+   "id": "430794b9",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1400x600 with 2 Axes>"
+       "<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": {},
@@ -292,179 +526,353 @@
    ],
    "source": [
     "\n",
-    "def plot_training_history(history):\n",
-    "    epochs = range(1, len(history['loss']) + 1)\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
     "\n",
-    "    plt.figure(figsize=(14, 6))\n",
+    "# ------------------------------------------------------------------\n",
+    "# 1.  Prediksi\n",
+    "# ------------------------------------------------------------------\n",
+    "pred_ner_prob, pred_srl_prob = model.predict(X_te, verbose=0)\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",
+    "pred_ner = pred_ner_prob.argmax(-1)\n",
+    "pred_srl = pred_srl_prob.argmax(-1)\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",
+    "# 2.  Siapkan masker PAD\n",
+    "# ------------------------------------------------------------------\n",
+    "pad_id = tag2idx_ner[\"<PAD>\"]\n",
     "\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "    \n",
-    "plot_training_history(history.history)\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": 25,
-   "id": "df36e200",
+   "execution_count": 233,
+   "id": "a49f1dfe",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loss: 0.49759986996650696\n",
-      "compile_metrics: 0.15222841501235962\n",
-      "ner_output_loss: 0.3517407178878784\n",
-      "srl_output_loss: 0.9686364531517029\n",
-      "WARNING:tensorflow:5 out of the last 7 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7f1c38cdba30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
-      "NER Token Accuracy 96.86%\n",
-      "SRL Token Accuracy 91.20%\n"
+      "NER TAG accuracy : 91.51%\n",
+      "SRL TAG accuracy : 88.39%\n"
      ]
     }
    ],
    "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",
+    "from sklearn.metrics import accuracy_score, classification_report\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",
+    "# 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",
-    "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",
+    "print(f\"NER TAG accuracy : {acc_ner:.2%}\")\n",
+    "print(f\"SRL TAG accuracy : {acc_srl:.2%}\")\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": 26,
-   "id": "9127cce0",
+   "execution_count": 234,
+   "id": "9adad755",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[NER] Classification Report:\n",
+      "\n",
+      "[NER] Classification report:\n",
       "              precision    recall  f1-score   support\n",
       "\n",
-      "        DATE       0.57      0.24      0.33        17\n",
-      "       EVENT       0.33      0.60      0.43         5\n",
-      "         LOC       0.73      0.66      0.69       108\n",
-      "         MIN       0.00      0.00      0.00         6\n",
-      "         ORG       1.00      0.18      0.31        11\n",
-      "         PER       0.40      0.15      0.22        13\n",
-      "       QUANT       0.00      0.00      0.00         1\n",
-      "         RES       0.00      0.00      0.00         3\n",
-      "        TIME       0.22      0.17      0.19        12\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",
-      "   micro avg       0.65      0.48      0.55       176\n",
-      "   macro avg       0.36      0.22      0.24       176\n",
-      "weighted avg       0.62      0.48      0.52       176\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": [
-    "print(\"[NER] Classification Report:\")\n",
-    "print(classification_report(true_ner, pred_ner, digits=2))"
+    "# (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": 27,
-   "id": "300897b8",
+   "execution_count": 235,
+   "id": "7cd28380",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "SRL Classification Resport:\n",
+      "\n",
+      "[SRL] Classification report:\n",
       "              precision    recall  f1-score   support\n",
       "\n",
-      "         ADV       0.00      0.00      0.00         6\n",
-      "        ARG1       0.00      0.00      0.00         7\n",
-      "        ARG2       0.00      0.00      0.00         2\n",
-      "         CAU       0.07      0.12      0.09         8\n",
-      "         COM       0.00      0.00      0.00         1\n",
-      "         DIS       0.00      0.00      0.00         2\n",
-      "         FRQ       0.00      0.00      0.00         1\n",
-      "         LOC       0.58      0.67      0.62        57\n",
-      "         MNR       0.07      0.33      0.12        12\n",
-      "         MOD       1.00      0.17      0.29         6\n",
-      "         NEG       0.00      0.00      0.00         3\n",
-      "         ORD       0.00      0.00      0.00         1\n",
-      "         PRD       0.00      0.00      0.00         1\n",
-      "         PRP       0.00      0.00      0.00         3\n",
-      "         RG0       0.29      0.28      0.29        32\n",
-      "         RG1       0.39      0.56      0.46       140\n",
-      "         RG2       0.04      0.03      0.03        32\n",
-      "         RG3       0.00      0.00      0.00         6\n",
-      "         SRC       0.00      0.00      0.00         1\n",
-      "         TMP       0.48      0.44      0.46        34\n",
-      "           _       0.72      0.56      0.63       103\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",
-      "   micro avg       0.40      0.45      0.43       458\n",
-      "   macro avg       0.17      0.15      0.14       458\n",
-      "weighted avg       0.43      0.45      0.43       458\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(\"SRL Classification Resport:\")\n",
-    "print(classification_report(true_srl, pred_srl, digits=2))"
+    "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))"
    ]
   }
  ],
diff --git a/NER_SRL/loss_plot.png b/NER_SRL/loss_plot.png
new file mode 100644
index 0000000..657162f
Binary files /dev/null and b/NER_SRL/loss_plot.png differ
diff --git a/NER_SRL/lses.py b/NER_SRL/lses.py
deleted file mode 100644
index 8e032b9..0000000
--- a/NER_SRL/lses.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# ner_srl_multitask.py
-# ----------------------------------------------------------
-# Train a multi‑task (Bi)LSTM that predicts NER + SRL tags
-# ----------------------------------------------------------
-import json, numpy as np, tensorflow as tf
-from tensorflow.keras.layers import (Input, Embedding, LSTM, Bidirectional,
-                                     TimeDistributed, Dense)
-from tensorflow.keras.models import Model
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.utils import to_categorical
-from sklearn.model_selection import train_test_split
-from seqeval.metrics import classification_report
-# ----------------------------------------------------------
-# 1. Load and prepare data
-# ----------------------------------------------------------
-DATA = json.load(open("../dataset/dataset_ner_srl.json", "r", encoding="utf8"))
-
-# --- token vocabulary -------------------------------------------------
-vocab = {"PAD": 0, "UNK": 1}
-for sample in DATA:
-    for tok in sample["tokens"]:
-        vocab.setdefault(tok.lower(), len(vocab))
-
-# --- label maps -------------------------------------------------------
-def build_label_map(key):
-    tags = {"PAD": 0}                          # keep 0 for padding
-    for s in DATA:
-        for t in s[key]:
-            tags.setdefault(t, len(tags))
-    return tags
-
-ner2idx = build_label_map("labels_ner")
-srl2idx = build_label_map("labels_srl")
-idx2ner = {i: t for t, i in ner2idx.items()}
-idx2srl = {i: t for t, i in srl2idx.items()}
-
-# --- sequences --------------------------------------------------------
-MAXLEN = max(len(x["tokens"]) for x in DATA)
-
-X      = [[vocab.get(tok.lower(), vocab["UNK"])     for tok in s["tokens"]]
-          for s in DATA]
-y_ner  = [[ner2idx[t]                                for t   in s["labels_ner"]]
-          for s in DATA]
-y_srl  = [[srl2idx[t]                                for t   in s["labels_srl"]]
-          for s in DATA]
-
-X      = pad_sequences(X,     maxlen=MAXLEN, padding="post", value=vocab["PAD"])
-y_ner  = pad_sequences(y_ner, maxlen=MAXLEN, padding="post", value=ner2idx["PAD"])
-y_srl  = pad_sequences(y_srl, maxlen=MAXLEN, padding="post", value=srl2idx["PAD"])
-
-# --- one‑hot for softmax ---------------------------------------------
-y_ner  = to_categorical(y_ner, num_classes=len(ner2idx))
-y_srl  = to_categorical(y_srl, num_classes=len(srl2idx))
-
-# ----------------------------------------------------------
-# 2. Train / validation split
-# ----------------------------------------------------------
-# *All* arrays must be passed to train_test_split in one call so they
-# stay aligned. Order‑of‑return = train,test for each array.
-X_tr, X_val, y_tr_ner, y_val_ner, y_tr_srl, y_val_srl = train_test_split(
-    X, y_ner, y_srl, test_size=0.15, random_state=42
-)
-
-# ----------------------------------------------------------
-# 3. Model definition
-# ----------------------------------------------------------
-EMB_DIM    = 128
-LSTM_UNITS = 128
-
-inp   = Input(shape=(MAXLEN,))
-emb   = Embedding(len(vocab), EMB_DIM, mask_zero=True)(inp)
-bilstm= Bidirectional(LSTM(LSTM_UNITS, return_sequences=True))(emb)
-
-ner_out = TimeDistributed(
-            Dense(len(ner2idx), activation="softmax"), name="ner")(bilstm)
-srl_out = TimeDistributed(
-            Dense(len(srl2idx), activation="softmax"), name="srl")(bilstm)
-
-model = Model(inp, [ner_out, srl_out])
-model.compile(
-    optimizer="adam",
-    loss   ={"ner": "categorical_crossentropy",
-             "srl": "categorical_crossentropy"},
-    metrics={"ner": "accuracy",
-             "srl": "accuracy"}
-)
-model.summary()
-
-# ----------------------------------------------------------
-# 4. Train
-# ----------------------------------------------------------
-history = model.fit(
-    X_tr,
-    {"ner": y_tr_ner, "srl": y_tr_srl},
-    validation_data=(X_val, {"ner": y_val_ner, "srl": y_val_srl}),
-    epochs=15,
-    batch_size=32,
-    verbose=2,
-)
-
-# ----------------------------------------------------------
-# 5. Helper: decode with a mask (so lens always match)
-# ----------------------------------------------------------
-def decode(pred, idx2tag, mask):
-    """
-    pred : [n, MAXLEN, n_tags] (one‑hot or probabilities)
-    mask : [n, MAXLEN]         (True for real tokens, False for PAD)
-    """
-    out = []
-    for seq, m in zip(pred, mask):
-        tags = [idx2tag[np.argmax(tok)] for tok, keep in zip(seq, m) if keep]
-        out.append(tags)
-    return out
-
-# ----------------------------------------------------------
-# 6. Evaluation
-# ----------------------------------------------------------
-y_pred_ner, y_pred_srl = model.predict(X_val, verbose=0)
-
-mask_val = (X_val != vocab["PAD"])                 # True for real tokens
-
-true_ner = decode(y_val_ner , idx2ner, mask_val)
-pred_ner = decode(y_pred_ner, idx2ner, mask_val)
-true_srl = decode(y_val_srl , idx2srl, mask_val)
-pred_srl = decode(y_pred_srl, idx2srl, mask_val)
-
-print("\n📊 NER report")
-print(classification_report(true_ner, pred_ner))
-
-print("\n📊 SRL report")
-print(classification_report(true_srl, pred_srl))
-
-# # ----------------------------------------------------------
-# # 7. Quick inference function
-# # ----------------------------------------------------------
-# def predict_sentence(sentence: str):
-#     tokens = sentence.strip().split()
-#     ids    = [vocab.get(w.lower(), vocab["UNK"]) for w in tokens]
-#     ids    = pad_sequences([ids], maxlen=MAXLEN, padding="post",
-#                            value=vocab["PAD"])
-#     mask   = (ids != vocab["PAD"])
-#     p_ner, p_srl = model.predict(ids, verbose=0)
-#     ner_tags  = decode(p_ner , idx2ner , mask)[0]
-#     srl_tags  = decode(p_srl , idx2srl , mask)[0]
-#     return list(zip(tokens, ner_tags, srl_tags))
-
-# # ---- demo ------------------------------------------------
-# if __name__ == "__main__":
-#     print("\n🔍 Demo:")
-#     for tok, ner, srl in predict_sentence(
-#             "Keanekaragaman hayati Indonesia sangat dipengaruhi faktor iklim."):
-#         print(f"{tok:15}  {ner:10}  {srl}")
diff --git a/NER_SRL/lstm_ner_srl.ipynb b/NER_SRL/lstm_ner_srl.ipynb
deleted file mode 100644
index fa96eaf..0000000
--- a/NER_SRL/lstm_ner_srl.ipynb
+++ /dev/null
@@ -1,2791 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "fcdce269",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-04-29 19:42:49.399316: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
-      "2025-04-29 19:42:49.399795: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
-      "2025-04-29 19:42:49.402037: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
-      "2025-04-29 19:42:49.408084: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1745930569.418345  277850 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1745930569.421510  277850 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "W0000 00:00:1745930569.429407  277850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
-      "W0000 00:00:1745930569.429422  277850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
-      "W0000 00:00:1745930569.429424  277850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
-      "W0000 00:00:1745930569.429425  277850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
-      "2025-04-29 19:42:49.432428: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from keras.models import Model\n",
-    "from keras.layers import Input, Embedding, Bidirectional, LSTM, TimeDistributed, Dense\n",
-    "from keras.utils import to_categorical\n",
-    "from keras.preprocessing.sequence import pad_sequences\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from seqeval.metrics import classification_report\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import nltk\n",
-    "from nltk.corpus import stopwords\n",
-    "from nltk.tokenize import word_tokenize\n",
-    "\n",
-    "from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
-    "\n",
-    "from collections import Counter\n",
-    "import re\n",
-    "import string\n",
-    "import pickle\n",
-    "import json\n",
-    "import numpy as np\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "92b6b57f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package stopwords to /home/akeon/nltk_data...\n",
-      "[nltk_data]   Package stopwords is already up-to-date!\n",
-      "[nltk_data] Downloading package punkt to /home/akeon/nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n",
-      "[nltk_data] Downloading package punkt_tab to /home/akeon/nltk_data...\n",
-      "[nltk_data]   Package punkt_tab is already up-to-date!\n",
-      "[nltk_data] Downloading package wordnet to /home/akeon/nltk_data...\n",
-      "[nltk_data]   Package wordnet is already up-to-date!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nltk.download(\"stopwords\")\n",
-    "nltk.download(\"punkt\")\n",
-    "nltk.download(\"punkt_tab\")\n",
-    "nltk.download(\"wordnet\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d568e8f2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "158 sentences\n",
-      "=== NER LABEL COUNTS ===\n",
-      "O -> 1495 labels\n",
-      "B-LOC -> 100 labels\n",
-      "B-MISC -> 6 labels\n",
-      "B-TIME -> 46 labels\n",
-      "I-TIME -> 37 labels\n",
-      "I-LOC -> 19 labels\n",
-      "B-QUANT -> 4 labels\n",
-      "I-QUANT -> 5 labels\n",
-      "B-DATE -> 42 labels\n",
-      "B-REL -> 2 labels\n",
-      "B-ETH -> 2 labels\n",
-      "I-ETH -> 2 labels\n",
-      "B-ORG -> 9 labels\n",
-      "I-ORG -> 5 labels\n",
-      "B-MIN -> 6 labels\n",
-      "B-TERM -> 2 labels\n",
-      "I-TERM -> 3 labels\n",
-      "B-RES -> 8 labels\n",
-      "I-RES -> 2 labels\n",
-      "B-PER -> 13 labels\n",
-      "I-PER -> 16 labels\n",
-      "I-DATE -> 34 labels\n",
-      "I-MISC -> 4 labels\n",
-      "B-EVENT -> 4 labels\n",
-      "I-EVENT -> 4 labels\n",
-      "\n",
-      "=== SRL LABEL COUNTS ===\n",
-      "ARG1 -> 421 labels\n",
-      "ARGM-LOC -> 65 labels\n",
-      "AM-NEG -> 2 labels\n",
-      "V -> 196 labels\n",
-      "ARGM-SRC -> 13 labels\n",
-      "O -> 320 labels\n",
-      "AM-QUE -> 5 labels\n",
-      "ARGM-BNF -> 6 labels\n",
-      "ARG2 -> 184 labels\n",
-      "ARGM-MNR -> 9 labels\n",
-      "ARG0 -> 129 labels\n",
-      "AM-TMP -> 279 labels\n",
-      "AM-PRP -> 1 labels\n",
-      "AM-MOD -> 5 labels\n",
-      "AM-ADV -> 1 labels\n",
-      "AM-CAU -> 14 labels\n",
-      "AM-EXT -> 6 labels\n",
-      "AM-MNR -> 22 labels\n",
-      "AM-DIS -> 2 labels\n",
-      "AM-FRQ -> 2 labels\n",
-      "ARGM-PNC -> 4 labels\n",
-      "R-ARG1 -> 3 labels\n",
-      "AM-LOC -> 78 labels\n",
-      "AM-DIR -> 4 labels\n",
-      "ARGM-CAU -> 17 labels\n",
-      "ARGM-MOD -> 11 labels\n",
-      "ARGM-EXT -> 2 labels\n",
-      "ARGM-TMP -> 12 labels\n",
-      "ARGM-DIS -> 9 labels\n",
-      "ARG3 -> 12 labels\n",
-      "ARGM-NEG -> 2 labels\n",
-      "ARGM-COM -> 3 labels\n",
-      "ARGM-PRP -> 10 labels\n",
-      "ARGM-EX -> 4 labels\n",
-      "ARGM-PRD -> 4 labels\n",
-      "AM-COM -> 9 labels\n",
-      "I-AM-LOC -> 1 labels\n",
-      "AM-PNC -> 5 labels\n"
-     ]
-    }
-   ],
-   "source": [
-    "# === LOAD DATA ===\n",
-    "with open(\"../dataset/dataset_ner_srl.json\", \"r\", encoding=\"utf-8\") as f:\n",
-    "    data = json.load(f)\n",
-    "\n",
-    "sentences = [[token.lower() for token in item[\"tokens\"]] for item in data]\n",
-    "ner_labels = [item[\"labels_ner\"] for item in data]\n",
-    "srl_labels = [item[\"labels_srl\"] for item in data]\n",
-    "\n",
-    "print(len(sentences), \"sentences\")\n",
-    "\n",
-    "# === COUNTERS ===\n",
-    "ner_counter = Counter()\n",
-    "srl_counter = Counter()\n",
-    "\n",
-    "for ner_seq in ner_labels:\n",
-    "    ner_counter.update(ner_seq)\n",
-    "\n",
-    "for srl_seq in srl_labels:\n",
-    "    srl_counter.update(srl_seq)\n",
-    "\n",
-    "# === PRINT RESULT ===\n",
-    "print(\"=== NER LABEL COUNTS ===\")\n",
-    "for label, count in ner_counter.items():\n",
-    "    print(f\"{label} -> {count} labels\")\n",
-    "\n",
-    "print(\"\\n=== SRL LABEL COUNTS ===\")\n",
-    "for label, count in srl_counter.items():\n",
-    "    print(f\"{label} -> {count} labels\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "95f16969",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "old [['keberagaman', 'potensi', 'sumber', 'daya', 'alam', 'indonesia', 'tidak', 'lepas', 'dari', 'proses', 'geografis', 'yang', 'terjadi', '.'], ['bagaimana', 'proses', 'geografis', 'di', 'indonesia', '?'], ['bagaimana', 'pengaruh', 'proses', 'geografis', 'bagi', 'keragaman', 'alam', 'dan', 'keragaman', 'sosial', 'masyarakat', 'indonesia', '?'], ['bagaimana', 'mengoptimalkan', 'peranan', 'sumber', 'daya', 'manusia', 'dalam', 'mengelola', 'sumber', 'daya', 'alam', 'indonesia', '?'], ['apakah', 'sumber', 'daya', 'manusia', 'di', 'indonesia', 'sudah', 'memenuhi', 'syarat', 'untuk', 'mengolah', 'pariwisata', 'yang', 'dimilikinya', '?'], ['bagaimana', 'lembaga', 'sosial', 'yang', 'akan', 'mewadahi', 'untuk', 'mengolah', 'sumber', 'daya', 'alam', 'dan', 'sumber', 'daya', 'manusianya', '?'], ['kalian', 'juga', 'perlu', 'memahami', ',', 'bahwa', 'keragaman', 'sosial', 'dan', 'budaya', 'telah', 'menarik', 'kedatangan', 'bangsa-bangsa', 'asing', 'sejak', 'ribuan', 'tahun', 'yang', 'lalu', '.'], ['perkembangan', 'hindu-buddha', 'di', 'indonesia', 'tidak', 'lepas', 'dari', 'perkembangan', 'perdagangan', 'dan', 'pelayaran', 'pada', 'awal', 'abad', 'masehi', '.'], ['bangsa', 'indonesia', 'patut', 'bersyukur', 'karena', 'proses', 'geografis', 'dan', 'keragaman', 'alam', 'yang', 'dimiliki', '.'], ['indonesia', 'merupakan', 'negara', 'terluas', 'di', 'asia', 'tenggara', '.'], ['luas', 'daratan', 'indonesia', 'sebesar', '1.910.932,37', 'km2', '.'], ['dan', 'lautan', 'indonesia', 'mencapai', '5,8', 'juta', 'km2', '.'], ['letak', 'indonesia', 'sangat', 'menguntungkan', 'bagi', 'kehidupan', 'masyarakat', '.'], ['selain', 'memiliki', 'letak', 'geografis', 'yang', 'sangat', 'menguntungkan', ',', 'indonesia', 'juga', 'memiliki', 'letak', 'geologis', ',', 'iklim', ',', 'dan', 'cuaca', 'yang', 'sangat', 'menguntungkan', '.'], ['kalian', 'tentu', 'sering', 'membincangkan', 'tentang', 'musim', 'dan', 'hubungannya', 'dengan', 'aktivitas', 'sehari-hari', '.'], ['masyarakat', 'memiliki', 'kebiasaan', 'di', 'musim', 'hujan', 'dan', 'musim', 'kemarau', 'baik', 'berhubungan', 'dengan', 'mata', 'pencaharian', 'dan', 'kesenangan', '(', 'hobi', ')', '.'], ['kalian', 'juga', 'sering', 'memperhatikan', 'prakiraan', 'cuaca', 'untuk', 'merancang', 'kegiatan', 'harian', '.'], ['cuaca', 'dan', 'iklim', 'inilah', 'bagian', 'penting', 'yang', 'memengaruhi', 'aktivitas', 'masyarakat', 'indonesia', '.'], ['cuaca', 'adalah', 'kondisi', 'rata-rata', 'udara', 'pada', 'saat', 'tertentu', 'di', 'suatu', 'wilayah', 'yang', 'relatif', 'sempit', 'dan', 'dalam', 'waktu', 'yang', 'singkat', '.'], ['iklim', 'merupakan', 'kondisi', 'cuaca', 'rata-rata', 'tahunan', 'pada', 'suatu', 'wilayah', 'yang', 'luas', '.'], ['indonesia', 'memiliki', 'iklim', 'tropis', 'yang', 'memiliki', 'dua', 'musim', 'yaitu', 'musim', 'hujan', 'dan', 'musim', 'kemarau', '.'], ['musim', 'hujan', 'terjadi', 'pada', 'bulan', 'oktober', '-', 'maret', ',', 'sedangkan', 'musim', 'kemarau', 'terjadi', 'pada', 'bulan', 'april', '-', 'september', '.'], ['semakin', 'ke', 'timur', 'curah', 'hujan', 'semakin', 'sedikit', '.'], ['hal', 'ini', 'karena', 'hujan', 'telah', 'banyak', 'jatuh', 'dan', 'menguap', 'di', 'bagian', 'barat', '.'], ['keadaan', 'iklim', 'dapat', 'diamati', 'dengan', 'memperhatikan', 'unsur-unsur', 'cuaca', 'dan', 'iklim', '.'], ['unsur-unsur', 'tersebut', 'antara', 'lain', ',', 'penyinaran', 'matahari', ',', 'suhu', 'udara', ',', 'kelembaban', 'udara', ',', 'angin', ',', 'dan', 'hujan', '.'], ['tanaman', 'tropis', 'memiliki', 'banyak', 'varietas', 'yang', 'kaya', 'akan', 'hidrat', 'arang', 'terutama', 'tanaman', 'bahan', 'makanan', 'pokok', '.'], ['berikut', 'pengaruh', 'unsur-unsur', 'iklim', 'terhadap', 'tanaman'], ['penyinaran', 'matahari', 'memengaruhi', 'fotosintesis', 'tanaman', ',', 'dapat', 'meningkatkan', 'suhu', 'udara', '.'], ['suhu', 'mengurangi', 'kadar', 'air', 'sehingga', 'cenderung', 'menjadi', 'kering', '.'], ['kelembaban', 'membatasi', 'hilangnya', 'air', '.'], ['angin', 'membantu', 'proses', 'penyerbukan', 'secara', 'alami', ',', 'mengurangi', 'kadar', 'air', '.'], ['hujan', 'meningkatkan', 'kadar', 'air', ',', 'mengikis', 'tanah', '.'], ['kalian', 'menemukan', 'berbagai', 'perbedaan', 'sosial', 'budaya', 'masyarakat', 'di', 'sekitar', 'tempat', 'tinggalmu', '.'], ['apabila', 'kalian', 'tinggal', 'di', 'perkotaan', ',', 'perbedaan', 'sosial', 'budaya', 'akan', 'semakin', 'banyak', '.'], ['perbedaan', 'sosial', 'budaya', 'meliputi', 'perbedaan', 'nilai-nilai', ',', 'norma', ',', 'dan', 'karakteristik', 'dari', 'suatu', 'kelompok', '.'], ['keragaman', 'sosial', 'budaya', 'di', 'masyarakat', 'dapat', 'terjadi', 'saat', 'berbagai', 'jenis', 'suku', 'dan', 'agama', 'yang', 'ada', 'di', 'suatu', 'ruang', 'bertemu', 'dan', 'berinteraksi', 'setiap', 'harinya', '.'], ['ruang', 'tersebut', 'adalah', 'ruang', 'yang', 'ada', 'pada', 'masyarakat', '.'], ['budaya', 'dapat', 'berupa', 'cara', 'hidup', 'masyarakat', ',', 'cara', 'berpakaian', ',', 'adat', 'istiadat', ',', 'jenis', 'mata', 'pecaharian', ',', 'dan', 'tata', 'upacara', 'keagamaan', '.'], ['keragaman', 'budaya', 'juga', 'mencakup', 'barang-barang', 'yang', 'dihasilkan', 'oleh', 'masyarakat', ',', 'seperti', 'senjata', ',', 'alat', 'bajak', 'sawah', ',', 'kitab', 'hukum', 'adat', ',', 'dan', 'tempat', 'tinggal', '.'], ['budaya', 'dapat', 'dianggap', 'sebagai', 'serangkaian', 'rancangan', 'untuk', 'bertahan', 'hidup', 'atau', 'alat', 'dari', 'praktik', ',', 'pengetahuan', ',', 'dan', 'simbol', 'yang', 'diperoleh', 'melalui', 'pembelajaran', ',', 'bukan', 'oleh', 'naluri', ',', 'yang', 'memungkinkan', 'orang', 'untuk', 'hidup', 'dalam', 'masyarakat', '.'], ['masyarakat', 'terdiri', 'dari', 'orang-orang', 'yang', 'berinteraksi', 'dan', 'berbagi', 'budaya', 'yang', 'sama', '.'], ['perbedaan', 'budaya', 'dapat', 'disebabkan', 'oleh', 'berbagai', 'hal', 'seperti', 'sejarah', ',', 'keturunan', ',', 'keyakinan', ',', 'dan', 'faktor', 'geografis', '.'], ['salah', 'satu', 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'memiliki', 'kebiasaan', 'di', 'musim', 'hujan', 'dan', 'musim', 'kemarau', 'baik', 'berhubungan', 'dengan', 'mata', 'pencaharian', 'dan', 'kesenangan', '(', 'hobi', ')', '.'], ['kalian', 'juga', 'sering', 'memperhatikan', 'prakiraan', 'cuaca', 'untuk', 'merancang', 'kegiatan', 'harian', '.'], ['cuaca', 'dan', 'iklim', 'inilah', 'bagian', 'penting', 'yang', 'memengaruhi', 'aktivitas', 'masyarakat', 'indonesia', '.'], ['cuaca', 'adalah', 'kondisi', 'rata-rata', 'udara', 'pada', 'saat', 'tertentu', 'di', 'suatu', 'wilayah', 'yang', 'relatif', 'sempit', 'dan', 'dalam', 'waktu', 'yang', 'singkat', '.'], ['iklim', 'merupakan', 'kondisi', 'cuaca', 'rata-rata', 'tahunan', 'pada', 'suatu', 'wilayah', 'yang', 'luas', '.'], ['indonesia', 'memiliki', 'iklim', 'tropis', 'yang', 'memiliki', 'dua', 'musim', 'yaitu', 'musim', 'hujan', 'dan', 'musim', 'kemarau', '.'], ['musim', 'hujan', 'terjadi', 'pada', 'bulan', 'oktober', '-', 'maret', ',', 'sedangkan', 'musim', 'kemarau', 'terjadi', 'pada', 'bulan', 'april', '-', 'september', '.'], ['semakin', 'ke', 'timur', 'curah', 'hujan', 'semakin', 'sedikit', '.'], ['hal', 'ini', 'karena', 'hujan', 'telah', 'banyak', 'jatuh', 'dan', 'menguap', 'di', 'bagian', 'barat', '.'], ['keadaan', 'iklim', 'dapat', 'diamati', 'dengan', 'memperhatikan', 'unsur-unsur', 'cuaca', 'dan', 'iklim', '.'], ['unsur-unsur', 'tersebut', 'antara', 'lain', ',', 'penyinaran', 'matahari', ',', 'suhu', 'udara', ',', 'kelembaban', 'udara', ',', 'angin', ',', 'dan', 'hujan', '.'], ['tanaman', 'tropis', 'memiliki', 'banyak', 'varietas', 'yang', 'kaya', 'akan', 'hidrat', 'arang', 'terutama', 'tanaman', 'bahan', 'makanan', 'pokok', '.'], ['berikut', 'pengaruh', 'unsur-unsur', 'iklim', 'terhadap', 'tanaman'], ['penyinaran', 'matahari', 'memengaruhi', 'fotosintesis', 'tanaman', ',', 'dapat', 'meningkatkan', 'suhu', 'udara', '.'], ['suhu', 'mengurangi', 'kadar', 'air', 'sehingga', 'cenderung', 'menjadi', 'kering', '.'], ['kelembaban', 'membatasi', 'hilangnya', 'air', '.'], ['angin', 'membantu', 'proses', 'penyerbukan', 'secara', 'alami', ',', 'mengurangi', 'kadar', 'air', '.'], ['hujan', 'meningkatkan', 'kadar', 'air', ',', 'mengikis', 'tanah', '.'], ['kalian', 'menemukan', 'berbagai', 'perbedaan', 'sosial', 'budaya', 'masyarakat', 'di', 'sekitar', 'tempat', 'tinggalmu', '.'], ['apabila', 'kalian', 'tinggal', 'di', 'perkotaan', ',', 'perbedaan', 'sosial', 'budaya', 'akan', 'semakin', 'banyak', '.'], ['perbedaan', 'sosial', 'budaya', 'meliputi', 'perbedaan', 'nilai-nilai', ',', 'norma', ',', 'dan', 'karakteristik', 'dari', 'suatu', 'kelompok', '.'], ['keragaman', 'sosial', 'budaya', 'di', 'masyarakat', 'dapat', 'terjadi', 'saat', 'berbagai', 'jenis', 'suku', 'dan', 'agama', 'yang', 'ada', 'di', 'suatu', 'ruang', 'bertemu', 'dan', 'berinteraksi', 'setiap', 'harinya', '.'], ['ruang', 'tersebut', 'adalah', 'ruang', 'yang', 'ada', 'pada', 'masyarakat', '.'], ['budaya', 'dapat', 'berupa', 'cara', 'hidup', 'masyarakat', ',', 'cara', 'berpakaian', ',', 'adat', 'istiadat', ',', 'jenis', 'mata', 'pecaharian', ',', 'dan', 'tata', 'upacara', 'keagamaan', '.'], ['keragaman', 'budaya', 'juga', 'mencakup', 'barang-barang', 'yang', 'dihasilkan', 'oleh', 'masyarakat', ',', 'seperti', 'senjata', ',', 'alat', 'bajak', 'sawah', ',', 'kitab', 'hukum', 'adat', ',', 'dan', 'tempat', 'tinggal', '.'], ['budaya', 'dapat', 'dianggap', 'sebagai', 'serangkaian', 'rancangan', 'untuk', 'bertahan', 'hidup', 'atau', 'alat', 'dari', 'praktik', ',', 'pengetahuan', ',', 'dan', 'simbol', 'yang', 'diperoleh', 'melalui', 'pembelajaran', ',', 'bukan', 'oleh', 'naluri', ',', 'yang', 'memungkinkan', 'orang', 'untuk', 'hidup', 'dalam', 'masyarakat', '.'], ['masyarakat', 'terdiri', 'dari', 'orang-orang', 'yang', 'berinteraksi', 'dan', 'berbagi', 'budaya', 'yang', 'sama', '.'], ['perbedaan', 'budaya', 'dapat', 'disebabkan', 'oleh', 'berbagai', 'hal', 'seperti', 'sejarah', ',', 'keturunan', ',', 'keyakinan', ',', 'dan', 'faktor', 'geografis', '.'], ['salah', 'satu', 'penyebab', 'perbedaan', 'budaya', 'adalah', 'faktor', 'geografis', '.'], ['faktor', 'geografis', 'yang', 'memengaruhi', 'keragaman', 'budaya', 'yang', 'akan', 'dibahas', 'berikut', 'ini'], ['dari', 'teks', 'tersebut', 'dapat', 'kita', 'pelajari', 'bahwa', 'budaya', 'yang', 'ada', 'di', 'masyarakat', 'dapat', 'dipengaruhi', 'oleh', 'lingkungan', 'yang', 'ada', 'di', 'sekitarnya', ','], ['misalnya', 'suku', 'lawu', 'dan', 'suku', 'bugis', 'yang', 'bermata', 'pencaharian', 'sebagai', 'nelayan', 'dengan', 'kapal', 'pinisinya', ','], ['sehingga', 'menjadi', 'sebuah', 'simbol', 'bahwa', 'indonesia', 'merupakan', 'negara', 'maritim', 'yang', 'kuat', 'dan', 'disegani', 'di', 'lautan', '.'], ['keragaman', 'budaya', 'dipengaruhi', 'oleh', 'lingkungan', 'fisik', '.'], ['manusia', 'sebagai', 'individu', 'adalah', 'kesatuan', 'jiwa', ',', 'raga', 'dan', 'kegiatan', 'atau', 'perilaku', 'pribadi', 'itu', 'sendiri', '.'], ['sebagai', 'individu', ',', 'dalam', 'pribadi', 'manusia', 'terdapat', 'tiga', 'unsur', ',', 'yaitu', 'nafsu', ',', 'semangat', ',', 'dan', 'intelegensi', '.'], ['kombinasi', 'dari', 'unsur', 'tersebut', 'menghasilkan', 'tingkah', 'laku', 'seseorang', 'yang', 'mencerminkan', 'karakter', 'atau', 'budayaanya', '.'], ['kesatuan', 'dari', 'kepribadian-kepribadian', 'seseorang', 'pada', 'suatu', 'daerah', 'yang', 'mempunyai', 'pola', 'yang', 'sama', 'dapat', 'membentuk', 'budaya', 'daerah', 'tersebut', 'yang', 'membedakan', 'dengan', 'tempat', 'lain', '.'], ['indonesia', 'memiliki', 'kebudayaan', 'yang', 'beragam', '.'], ['indonesia', 'memiliki', 'kekayaan', 'yang', 'begitu', 'besar', '.'], ['bukan', 'hanya', 'pemandangan', 'alam', 'budaya', ',', 'jauh', 'di', 'kedalaman', 'tanahnya', 'begitu', 'banyak', 'kandungan', 'mineral', 'berharga', '.'], ['selama', 'puluhan', 'tahun', ',', 'freeport', 'mengelola', 'tambang', 'mineral', 'di', 'tanah', 'papua', ',', 'indonesia', '.'], ['berdasarkan', 'laporan', 'keuangan', 'freeport', 'mcmorran', 'inc', 'periode', '2017', ',', 'freeport', 'indonesia', 'di', 'papua', 'tercatat', 'memiliki', '6', 'tambang', ',', 'yakni', 'grasberg', 'block', 'cave', ',', 'dmlz', ',', 'tambang', 'kucing', 'liar', ',', 'doz', ',', 'big', 'gossan', ',', 'dan', 'grasberg', 'open', 'pit', '.'], ['tambang', 'freeport', 'memiliki', 'beberapa', 'kandungan', 'cadangan', 'mineral', ',', 'yaitu', 'tembaga', ',', 'emas', ',', 'dan', 'perak', '.'], ['sumber', 'daya', 'alam', 'yang', 'terdapat', 'pada', 'pertambangan', 'freeport', 'di', 'atas', 'merupakan', 'salah', 'satu', 'contoh', 'dari', 'berbagai', 'sumber', 'daya', 'yang', 'ada', 'di', 'indonesia', 'yang', 'memiliki', 'beberapa', 'kandungan', 'cadangan', 'mineral', ',', 'seperti', 'tembaga', ',', 'emas', ',', 'dan', 'perak', '.'], ['kemudian', 'apa', 'sih', 'sumber', 'daya', 'alam', 'itu', '?'], ['apakah', 'ada', 'manfaatnya', 'untuk', 'kita', '?'], ['yuk', 'silahkan', 'simak', 'penjelasan', 'di', 'bawah', 'ini', '.'], ['sumber', 'daya', 'alam', 'merupakan', 'segala', 'sesuatu', 'yang', 'ada', 'di', 'permukaan', 'bumi', 'dan', 'dapat', 'dimanfaatkan', 'untuk', 'memenuhi', 'kebutuhan', 'manusia', '.'], ['potensi', 'sumber', 'daya', 'ini', 'mencakup', 'hal', 'yang', 'ada', 'di', 'udara', ',', 'daratan', ',', 'dan', 'perairan', '.'], ['berdasarkan', 'kelestariannya', ',', 'sumber', 'daya', 'alam', 'dapat', 'dibedakan', 'menjadi', 'dua', 'yaitu', 'sumber', 'daya', 'alam', 'yang', 'dapat', 'diperbarui', '(', 'renewable', 'resources', ')', 'dan', 'tidak', 'dapat', 'diperbarui', '(', 'non', 'renewable', 'resource', ')', '.'], ['contoh', 'sumber', 'daya', 'alam', 'yang', 'dapat', 'diperbarui', 'yaitu', 'seperti', 'air', ',', 'tanah', ',', 'dan', 'hutan', '.'], ['sedangkan', 'sumber', 'daya', 'alam', 'yang', 'tidak', 'dapat', 'diperbarui', 'seperti', 'minyak', 'bumi', 'dan', 'batu', 'bara', '.'], ['berikut', 'ini', 'merupakan', 'potensi', 'sumber', 'daya', 'alam', 'di', 'indonesia', 'yang', 'dirinci', 'menjadi', 'tiga', 'yaitu', 'sumber', 'daya', 'alam', 'hutan', ',', 'sumber', 'daya', 'alam', 'tambang', ',', 'dan', 'sumber', 'daya', 'alam', 'kemaritiman', '.'], ['indonesia', 'termasuk', 'negara', 'yang', 'memiliki', 'kekayaan', 'alam', 'yang', 'berlimpah', 'dibandingkan', 'negara-negara', 'yang', 'lain', '.'], ['potensi', 'sumber', 'daya', 'alam', 'indonesia', 'sangat', 'beraneka', 'ragam', '.'], ['bangsa', 'indonesia', 'memiliki', 'modal', 'penting', 'dalam', 'pembangunan', '.'], ['jumlah', 'penduduk', 'indonesia', 'yang', 'lebih', 'dari', '270', 'juta', 'merupakan', 'potensi', 'penting', 'dalam', 'pembangunan', '.'], ['pada', 'tahun', '2016', 'badan', 'pusat', 'statistik', 'mencatat', 'bahwa', 'di', 'indonesia', 'terdapat', 'angkatan', 'kerja', '127,67', 'juta', 'jiwa', '.'], ['di', 'antara', 'negara', 'asean', ',', 'kualitas', 'sdm', 'dan', 'ketenagakerjaan', 'indonesia', 'masih', 'berada', 'di', 'peringkat', 'bawah', '.'], ['kualitas', 'sdm', 'dan', 'ketenagakerjaan', 'indonesia', 'menempati', 'urutan', 'kelima', '.'], ['peringkat', 'ini', 'masih', 'kalah', 'jika', 'dibandingkan', 'singapura', ',', 'brunei', 'darussalam', ',', 'malaysia', ',', 'dan', 'thailand', '.'], ['kualitas', 'sumber', 'daya', 'manusia', 'di', 'indonesia', 'memengaruhi', 'terhadap', 'kemajuan', 'sebuah', 'bangsa', '.'], ['peristiwa', 'itu', 'dilatarbelakangi', 'oleh', 'peristiwa', 'yang', 'jauh', 'dari', 'indonesia', ',', 'misalnya', 'peristiwa', 'jatuhnya', 'konstantinopel', 'di', 'kawasan', 'laut', 'tengah', 'pada', 'tahun', '1453', '.'], ['kehidupan', 'global', 'semakin', 'berkembang', 'dengan', 'maraknya', 'penjelajahan', 'samudera', 'orang-orang', 'eropa', 'ke', 'dunia', 'timur', '.'], ['begitu', 'juga', 'peristiwa', 'kedatangan', 'bangsa', 'eropa', 'ke', 'indonesia', ',', 'telah', 'ikut', 'meningkatkan', 'kehidupan', 'global', '.'], ['pada', 'tahun', '1488', 'karena', 'serangan', 'ombak', 'besar', 'terpaksa', 'bartholomeus', 'diaz', 'mendarat', 'di', 'suatu', 'ujung', 'selatan', 'benua', 'afrika', '.'], ['pada', 'juli', '1497', 'vasco', 'da', 'gama', 'berangkat', 'dari', 'pelabuhan', 'lisabon', 'untuk', 'memulai', 'penjelajahan', 'samudra', '.'], ['berdasarkan', 'pengalaman', 'bartholomeus', 'diaz', 'tersebut', ',', 'vasco', 'da', 'gama', 'juga', 'berlayar', 'mengambil', 'rute', 'yang', 'pernah', 'dilayari', 'bartholomeus', 'diaz', '.'], ['rombongan', 'vasco', 'da', 'gama', 'juga', 'singgah', 'di', 'tanjung', 'harapan', '.'], ['atas', 'petunjuk', 'dari', 'pelaut', 'bangsa', 'moor', 'yang', 'telah', 'disewanya', ',', 'rombongan', 'vasco', 'da', 'gama', 'melanjutkan', 'penjelajahan', ',', 'berlayar', 'menelusuri', 'pantai', 'timur', 'afrika', 'kemudian', 'berbelok', 'ke', 'kanan', 'untuk', 'mengarungi', 'lautan', 'hindia', '(', 'samudra', 'indonesia', ')', '.'], ['pada', 'tahun', '1498', 'rombongan', 'vasco', 'da', 'gama', 'mendarat', 'sampai', 'di', 'kalikut', 'dan', 'juga', 'goa', 'di', 'pantai', 'barat', 'india', '.'], ['pada', 'tahun', '1511', 'armada', 'portugis', 'berhasil', 'menguasai', 'malaka', '.'], ['proklamasi', 'kemerdekaan', 'indonesia', 'terjadi', 'pada', '17', 'agustus', '1945', '.'], ['barack', 'obama', 'lahir', 'pada', '4', 'agustus', '1961', 'di', 'hawaii', '.'], ['reformasi', 'indonesia', 'dimulai', 'tahun', '1998', 'setelah', 'soeharto', 'mundur', '.'], ['perang', 'dunia', 'ii', 'berakhir', 'pada', '2', 'september', '1945', '.'], ['indonesia', 'menjadi', 'anggota', 'pbb', 'sejak', '28', 'september', '1950', '.'], ['banjir', 'bandang', 'terjadi', 'pada', '5', 'januari', '2021', 'di', 'bandung', '.'], ['hari', 'pahlawan', 'diperingati', 'setiap', '10', 'november', '.'], ['pada', 'tahun', '1511', 'portugis', 'menguasai', 'malaka', '.'], ['konferensi', 'asia-afrika', 'diselenggarakan', 'tahun', '1955', 'di', 'bandung', '.'], ['musim', 'kemarau', 'diperkirakan', 'mulai', 'april', '2025', '.'], ['rapat', 'dimulai', 'pukul', '09.00', 'pagi', '.'], ['kereta', 'akan', 'tiba', 'sekitar', 'jam', '3', 'sore', '.'], ['pertandingan', 'akan', 'dimulai', 'pada', 'pukul', '19.30', '.'], ['matahari', 'terbit', 'sekitar', '05.45', 'pagi', 'di', 'jakarta', '.'], ['makan', 'siang', 'biasanya', 'dilakukan', 'sekitar', 'jam', '12', 'siang', '.'], ['penerbangan', 'dijadwalkan', 'lepas', 'landas', 'pukul', '23.15', '.'], ['film', 'tayang', 'mulai', 'jam', '8', 'malam', 'nanti', '.'], ['pesawat', 'mendarat', 'tepat', 'pada', '00.30', 'dinihari', '.'], ['siaran', 'langsung', 'dimulai', 'pukul', '18.00', '.'], ['jam', 'kerja', 'dimulai', 'pukul', '08.00', 'dan', 'berakhir', 'pukul', '17.00', '.'], ['alarm', 'berbunyi', 'pada', 'pukul', '06.00', 'pagi', '.'], ['saya', 'bangun', 'sekitar', 'jam', '5', 'pagi', 'setiap', 'hari', '.'], ['konser', 'dimulai', 'sekitar', '20.00', 'malam', 'di', 'stadion', '.'], ['wawancara', 'dijadwalkan', 'pada', 'jam', '11', 'pagi', '.'], ['kami', 'tiba', 'di', 'bandara', 'sekitar', 'jam', '2', 'dinihari', '.'], ['dia', 'mengajar', 'kelas', 'pada', 'pukul', '13.00', '.'], ['peserta', 'diminta', 'hadir', 'sebelum', 'jam', '7', 'pagi', '.'], ['televisi', 'menayangkan', 'berita', 'malam', 'pada', '22.00', '.'], ['kami', 'akan', 'bertemu', 'jam', '10', 'malam', 'di', 'kafe', '.'], ['toko', 'buka', 'hingga', 'pukul', '21.00', '.'], ['dia', 'biasanya', 'berolahraga', 'pada', 'pagi', 'hari', '.'], ['kami', 'bertemu', 'lagi', 'pada', 'malam', 'hari', 'itu', '.'], ['upacara', 'dilaksanakan', 'pada', 'sore', 'hari', 'di', 'lapangan', '.'], ['ia', 'pulang', 'setiap', 'malam', 'sekitar', 'jam', '9', '.'], ['kami', 'berangkat', 'di', 'pagi', 'hari', 'menggunakan', 'mobil', '.'], ['acara', 'berlangsung', 'hingga', 'malam', 'hari', '.'], ['kami', 'tiba', 'di', 'bandara', 'pada', 'dinihari', '.'], ['pintu', 'gerbang', 'dibuka', 'setiap', 'pagi', '.'], ['ia', 'selalu', 'belajar', 'di', 'malam', '.'], ['waktu', 'bermain', 'dimulai', 'sore', 'hari', '.'], ['pelajaran', 'kedua', 'dimulai', 'sekitar', 'jam', 'tujuh', 'lebih', 'sepuluh', 'menit', '.'], ['bus', 'berangkat', 'kurang', 'lebih', 'jam', 'delapan', 'malam', '.'], ['pertemuan', 'terakhir', 'dilaksanakan', 'sebelum', 'matahari', 'terbenam', '.'], ['kereta', 'berangkat', 'sekitar', 'tengah', 'malam', 'dari', 'stasiun', 'gambir', '.'], ['jadwal', 'sholat', 'dimulai', 'pukul', 'empat', 'lebih', 'lima', 'menit', '.'], ['pemadaman', 'listrik', 'akan', 'dimulai', 'menjelang', 'malam', '.'], ['layanan', 'pelanggan', 'dibuka', 'setiap', 'hari', 'kerja', 'jam', 'sembilan', '.'], ['ia', 'terjaga', 'di', 'tengah', 'malam', 'karena', 'petir', '.'], ['kelas', 'selesai', 'sekitar', 'jam', 'dua', 'kurang', 'seperempat', '.'], ['waktu', 'sarapan', 'dimulai', 'pukul', '6.30', 'hingga', '7.30', '.'], ['proklamasi', 'kemerdekaan', 'terjadi', 'pada', '17', 'agustus', '1945', '.'], ['indonesia', 'merdeka', 'pada', 'tahun', '1945', '.'], ['pemilu', 'diadakan', 'pada', '14', 'februari', '2024', '.'], ['tanggal', '1', 'januari', '2023', 'merupakan', 'hari', 'libur', '.'], ['barack', 'obama', 'lahir', 'pada', '4', 'agustus', '1961', '.'], ['hari', 'bumi', 'diperingati', 'setiap', '22', 'april', '.'], ['musim', 'kemarau', 'terjadi', 'antara', 'bulan', 'april', 'hingga', 'oktober', '.'], ['reformasi', '1998', 'mengubah', 'sistem', 'politik', 'indonesia', '.'], ['konferensi', 'asia-afrika', 'digelar', 'pada', 'tahun', '1955', 'di', 'bandung', '.'], ['perang', 'dunia', 'kedua', 'berakhir', 'tahun', '1945', '.'], ['sumpah', 'pemuda', 'diperingati', 'setiap', '28', 'oktober', '.'], ['habibie', 'dilantik', 'menjadi', 'presiden', 'pada', '21', 'mei', '1998', '.'], ['hari', 'kemerdekaan', 'indonesia', 'dirayakan', 'setiap', '17', 'agustus', '.'], ['pada', 'tahun', '1949', ',', 'belanda', 'mengakui', 'kemerdekaan', 'indonesia', '.'], ['tsunami', 'aceh', 'terjadi', 'pada', '26', 'desember', '2004', '.'], ['bung', 'karno', 'meninggal', 'pada', '21', 'juni', '1970', '.'], ['jakarta', 'ditetapkan', 'sebagai', 'ibu', 'kota', 'negara', 'pada', 'tahun', '1961', '.'], ['pada', '1955', ',', 'indonesia', 'menjadi', 'tuan', 'rumah', 'konferensi', 'asia-afrika', '.'], ['pemerintah', 'mengumumkan', 'kebijakan', 'psbb', 'pada', 'april', '2020', 'di', 'jakarta', '.'], ['undang-undang', 'dasar', '1945', 'disahkan', 'pada', 'tanggal', '18', 'agustus', '1945', '.']] \n",
-      " 158\n"
-     ]
-    }
-   ],
-   "source": [
-    "# text preprocessing\n",
-    "stop_words = set(stopwords.words(\"indonesian\"))                                                                                                                                                                                                                                                                             \n",
-    "factory = StemmerFactory()\n",
-    "stemmer = factory.create_stemmer()\n",
-    "\n",
-    "with open(\"../normalize_text/normalize.json\", \"r\", encoding=\"utf-8\") as file:\n",
-    "    normalization_dict = json.load(file)\n",
-    "    \n",
-    "def text_preprocessing(text):\n",
-    "    \n",
-    "    # if(text == \"?\" or text == \".\" or text == \"!\"): return text\n",
-    "    # lowercase\n",
-    "    text = text.lower()\n",
-    "    \n",
-    "    # remove punctuation\n",
-    "    # text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n",
-    "    \n",
-    "    # remove extra spaces\n",
-    "    text = re.sub(r\"\\s+\", \" \", text).strip()\n",
-    "    \n",
-    "    # tokenize\n",
-    "    # tokens = word_tokenize(text)\n",
-    "    \n",
-    "    # normalization\n",
-    "    # tokens = normalization_dict.get(text, text)  \n",
-    "  \n",
-    "    \n",
-    "    # stemming\n",
-    "    # tokens = stemmer.stem(tokens)\n",
-    "    \n",
-    "    \n",
-    "    # remove stopwords\n",
-    "    # tokens = [word for word in tokens if word not in stop_words]\n",
-    "    \n",
-    "    # print(f\"Original: {text}\")\n",
-    "    # print(f\"Normalized: {tokens}\")\n",
-    "    \n",
-    "    return text\n",
-    "\n",
-    "# sentences = [text_preprocessing(\" \".join(sentence)) for sentence in sentences]\n",
-    "print(\"old\", sentences)\n",
-    "preprocessing_sentences = []\n",
-    "\n",
-    "for text in sentences:\n",
-    "    result = []\n",
-    "    for i in range(len(text)):\n",
-    "        text[i] = text_preprocessing(text[i])\n",
-    "        result.append(text[i])\n",
-    "    preprocessing_sentences.append(result)\n",
-    "\n",
-    "print(\"new\", preprocessing_sentences, \"\\n\", len(preprocessing_sentences))\n",
-    "\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e9653d99",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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'nafsu': 515, 'kelompok': 516, '22': 517, 'perak': 518, 'berolahraga': 519, 'pokok': 520, 'aceh': 521, '1498': 522, 'mengolah': 523, 'bandara': 524, 'berlangsung': 525, '5': 526, 'nelayan': 527, 'kerja': 528, 'afrika': 529, 'keturunan': 530, '18': 531, 'syarat': 532, 'senjata': 533, 'dimilikinya': 534, 'mewadahi': 535, 'mencatat': 536, 'mengelola': 537, 'listrik': 538, 'agama': 539, 'salah': 540, 'jiwa': 541, 'diminta': 542, 'sekitar': 543, 'selatan': 544, 'raga': 545, 'individu': 546, '(': 547, '09.00': 548, 'kaya': 549, 'dimulai': 550, 'dimiliki': 551, 'pelajaran': 552, 'selain': 553, 'kita': 554, 'pemandangan': 555, 'diaz': 556, 'tentang': 557, 'sama': 558, 'sosial': 559, 'lawu': 560, 'mengumumkan': 561, ',': 562, 'bertahan': 563, 'anggota': 564, 'habibie': 565, 'sehari-hari': 566, 'sampai': 567, 'berhasil': 568, 'siaran': 569, 'obama': 570, 'mempunyai': 571, 'dapat': 572, 'berinteraksi': 573, 'rute': 574, 'non': 575, 'dari': 576, 'sering': 577, 'hadir': 578, 'pariwisata': 579, 'bara': 580, 'kemarau': 581, 'brunei': 582, 'film': 583, '2021': 584, 'samudra': 585, 'terutama': 586, 'big': 587, '11': 588, 'mineral': 589, 'bahan': 590, 'pelajari': 591, 'goa': 592, '8': 593, '1998': 594, 'tentu': 595, 'berbagi': 596, 'jakarta': 597, 'dibandingkan': 598, 'menarik': 599, 'oktober': 600, 'tanah': 601, 'jumlah': 602, 'sudah': 603, 'berupa': 604, 'menjelang': 605, 'pintu': 606, 'berharga': 607, 'apa': 608, 'lembaga': 609, 'periode': 610, 'sedikit': 611, 'yang': 612, 'maraknya': 613, 'dilantik': 614, 'negara': 615, 'begitu': 616, 'petunjuk': 617, 'lisabon': 618, 'mengikis': 619, 'malaysia': 620, 'terjadi': 621, '1961': 622, 'keragaman': 623, 'berakhir': 624, 'kota': 625, 'kurang': 626, 'sholat': 627, 'lahir': 628, 'undang-undang': 629, 'ribuan': 630, 'tinggalmu': 631, 'pada': 632, 'air': 633, 'kemajuan': 634, 'kalah': 635, 'sarapan': 636, 'dibahas': 637, 'daya': 638, 'pelaut': 639, 'tiba': 640, 'dilakukan': 641, '2004': 642, 'pulang': 643, 'yuk': 644, 'dipengaruhi': 645, 'tercatat': 646, 'banyak': 647, 'dunia': 648, 'atas': 649, 'disebabkan': 650, 'kelembaban': 651, 'pembangunan': 652, 'norma': 653, 'inilah': 654, 'baik': 655, 'menguap': 656, 'januari': 657, 'singkat': 658, 'luas': 659, 'curah': 660, 'ada': 661, 'open': 662, 'tembaga': 663, 'mencapai': 664, 'yakni': 665, 'berlimpah': 666, '270': 667, 'masyarakat': 668, 'hubungannya': 669, 'waktu': 670, 'menguasai': 671, 'menghasilkan': 672, 'disegani': 673, 'kereta': 674, 'abad': 675, 'layanan': 676, 'bartholomeus': 677, 'pemilu': 678, 'saat': 679, 'karena': 680, 'geografis': 681, 'matahari': 682, 'wawancara': 683, 'sistem': 684, 'pemadaman': 685, 'harinya': 686, 'psbb': 687, 'awal': 688, 'mundur': 689, 'mengurangi': 690, 'tata': 691, 'diperkirakan': 692, 'nanti': 693, '1949': 694, '4': 695, 'dmlz': 696, 'cenderung': 697, 'mulai': 698, 'mencerminkan': 699, 'terakhir': 700, 'pola': 701, 'keagamaan': 702, 'ombak': 703, 'timur': 704, 'terluas': 705, 'PAD': 0, 'UNK': 1}\n",
-      "['B-DATE', 'B-ETH', 'B-EVENT', 'B-LOC', 'B-MIN', 'B-MISC', 'B-ORG', 'B-PER', 'B-QUANT', 'B-REL', 'B-RES', 'B-TERM', 'B-TIME', 'I-DATE', 'I-ETH', 'I-EVENT', 'I-LOC', 'I-MISC', 'I-ORG', 'I-PER', 'I-QUANT', 'I-RES', 'I-TERM', 'I-TIME', 'O']\n",
-      "['AM-ADV', 'AM-CAU', 'AM-COM', 'AM-DIR', 'AM-DIS', 'AM-EXT', 'AM-FRQ', 'AM-LOC', 'AM-MNR', 'AM-MOD', 'AM-NEG', 'AM-PNC', 'AM-PRP', 'AM-QUE', 'AM-TMP', 'ARG0', 'ARG1', 'ARG2', 'ARG3', 'ARGM-BNF', 'ARGM-CAU', 'ARGM-COM', 'ARGM-DIS', 'ARGM-EX', 'ARGM-EXT', 'ARGM-LOC', 'ARGM-MNR', 'ARGM-MOD', 'ARGM-NEG', 'ARGM-PNC', 'ARGM-PRD', 'ARGM-PRP', 'ARGM-SRC', 'ARGM-TMP', 'I-AM-LOC', 'O', 'R-ARG1', 'V']\n",
-      "{'B-DATE': 0, 'B-ETH': 1, 'B-EVENT': 2, 'B-LOC': 3, 'B-MIN': 4, 'B-MISC': 5, 'B-ORG': 6, 'B-PER': 7, 'B-QUANT': 8, 'B-REL': 9, 'B-RES': 10, 'B-TERM': 11, 'B-TIME': 12, 'I-DATE': 13, 'I-ETH': 14, 'I-EVENT': 15, 'I-LOC': 16, 'I-MISC': 17, 'I-ORG': 18, 'I-PER': 19, 'I-QUANT': 20, 'I-RES': 21, 'I-TERM': 22, 'I-TIME': 23, 'O': 24}\n",
-      "{'AM-ADV': 0, 'AM-CAU': 1, 'AM-COM': 2, 'AM-DIR': 3, 'AM-DIS': 4, 'AM-EXT': 5, 'AM-FRQ': 6, 'AM-LOC': 7, 'AM-MNR': 8, 'AM-MOD': 9, 'AM-NEG': 10, 'AM-PNC': 11, 'AM-PRP': 12, 'AM-QUE': 13, 'AM-TMP': 14, 'ARG0': 15, 'ARG1': 16, 'ARG2': 17, 'ARG3': 18, 'ARGM-BNF': 19, 'ARGM-CAU': 20, 'ARGM-COM': 21, 'ARGM-DIS': 22, 'ARGM-EX': 23, 'ARGM-EXT': 24, 'ARGM-LOC': 25, 'ARGM-MNR': 26, 'ARGM-MOD': 27, 'ARGM-NEG': 28, 'ARGM-PNC': 29, 'ARGM-PRD': 30, 'ARGM-PRP': 31, 'ARGM-SRC': 32, 'ARGM-TMP': 33, 'I-AM-LOC': 34, 'O': 35, 'R-ARG1': 36, 'V': 37}\n",
-      "{0: 'B-DATE', 1: 'B-ETH', 2: 'B-EVENT', 3: 'B-LOC', 4: 'B-MIN', 5: 'B-MISC', 6: 'B-ORG', 7: 'B-PER', 8: 'B-QUANT', 9: 'B-REL', 10: 'B-RES', 11: 'B-TERM', 12: 'B-TIME', 13: 'I-DATE', 14: 'I-ETH', 15: 'I-EVENT', 16: 'I-LOC', 17: 'I-MISC', 18: 'I-ORG', 19: 'I-PER', 20: 'I-QUANT', 21: 'I-RES', 22: 'I-TERM', 23: 'I-TIME', 24: 'O'}\n",
-      "{0: 'AM-ADV', 1: 'AM-CAU', 2: 'AM-COM', 3: 'AM-DIR', 4: 'AM-DIS', 5: 'AM-EXT', 6: 'AM-FRQ', 7: 'AM-LOC', 8: 'AM-MNR', 9: 'AM-MOD', 10: 'AM-NEG', 11: 'AM-PNC', 12: 'AM-PRP', 13: 'AM-QUE', 14: 'AM-TMP', 15: 'ARG0', 16: 'ARG1', 17: 'ARG2', 18: 'ARG3', 19: 'ARGM-BNF', 20: 'ARGM-CAU', 21: 'ARGM-COM', 22: 'ARGM-DIS', 23: 'ARGM-EX', 24: 'ARGM-EXT', 25: 'ARGM-LOC', 26: 'ARGM-MNR', 27: 'ARGM-MOD', 28: 'ARGM-NEG', 29: 'ARGM-PNC', 30: 'ARGM-PRD', 31: 'ARGM-PRP', 32: 'ARGM-SRC', 33: 'ARGM-TMP', 34: 'I-AM-LOC', 35: 'O', 36: 'R-ARG1', 37: 'V'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "words = list(set(word for sentence in preprocessing_sentences for word in sentence))\n",
-    "word2idx = {word: idx + 2 for idx, word in enumerate(words)}\n",
-    "word2idx[\"PAD\"] = 0\n",
-    "word2idx[\"UNK\"] = 1\n",
-    "\n",
-    "all_ner_tags = sorted(set(tag for seq in ner_labels for tag in seq))\n",
-    "all_srl_tags = sorted(set(tag for seq in srl_labels for tag in seq))\n",
-    "tag2idx_ner = {tag: idx for idx, tag in enumerate(all_ner_tags)}\n",
-    "tag2idx_srl = {tag: idx for idx, tag in enumerate(all_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()}\n",
-    "\n",
-    "print(words)\n",
-    "print(word2idx)\n",
-    "print(all_ner_tags)\n",
-    "print(all_srl_tags)\n",
-    "print(tag2idx_ner)\n",
-    "print(tag2idx_srl)\n",
-    "print(idx2tag_ner)\n",
-    "print(idx2tag_srl)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "9d3a37b3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[328 174 380 ...   0   0   0]\n",
-      " [513 457 681 ...   0   0   0]\n",
-      " [513 329 457 ...   0   0   0]\n",
-      " ...\n",
-      " [632 402 562 ...   0   0   0]\n",
-      " [168 561 162 ...   0   0   0]\n",
-      " [629  93 109 ...   0   0   0]]\n",
-      "y_ner \n",
-      " \n",
-      "[[24 24 24 ... 24 24 24]\n",
-      " [24 24 24 ... 24 24 24]\n",
-      " [24 24 24 ... 24 24 24]\n",
-      " ...\n",
-      " [24  0 24 ... 24 24 24]\n",
-      " [24 24 24 ... 24 24 24]\n",
-      " [24 24  0 ... 24 24 24]]\n",
-      "y_srl \n",
-      " \n",
-      "[[16 16 16 ... 35 35 35]\n",
-      " [13 16 16 ... 35 35 35]\n",
-      " [13 16 16 ... 35 35 35]\n",
-      " ...\n",
-      " [14 14 35 ... 35 35 35]\n",
-      " [15 37 16 ... 35 35 35]\n",
-      " [16 16 14 ... 35 35 35]]\n",
-      "y_ner cat \n",
-      " \n",
-      "[array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 0.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 0.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 0.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 0.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 0.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
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-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
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-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
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-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
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-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
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-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 1., ..., 0., 0., 0.],\n",
-      "       [0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [1., 0., 0., ..., 0., 0., 0.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [1., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       ...,\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]]), array([[0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 0., 0., 1.],\n",
-      "       [0., 0., 0., ..., 0., 0., 1.]])]\n",
-      "y_srl cat \n",
-      " \n",
-      "[array([[0., 0., 0., ..., 0., 0., 0.],\n",
-      "       [0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       ...,\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 1., 0., 0.],\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "# === ENCODING ===\n",
-    "X = [[word2idx.get(w, word2idx[\"UNK\"]) for w in s] for s in sentences]\n",
-    "y_ner = [[tag2idx_ner[t] for t in ts] for ts in ner_labels]\n",
-    "y_srl = [[tag2idx_srl[t] for t in ts] for ts in srl_labels]\n",
-    "\n",
-    "maxlen = 50\n",
-    "\n",
-    "X = pad_sequences(X, maxlen=maxlen, padding=\"post\", value=word2idx[\"PAD\"])\n",
-    "y_ner = pad_sequences(y_ner, maxlen=maxlen, padding=\"post\", value=tag2idx_ner[\"O\"])\n",
-    "y_srl = pad_sequences(y_srl, maxlen=maxlen, padding=\"post\", value=tag2idx_srl[\"O\"])\n",
-    "\n",
-    "y_ner_cat = [to_categorical(seq, num_classes=len(tag2idx_ner)) for seq in y_ner]\n",
-    "y_srl_cat = [to_categorical(seq, num_classes=len(tag2idx_srl)) for seq in y_srl]\n",
-    "\n",
-    "print(X)\n",
-    "print(\"y_ner \\n \")\n",
-    "print(y_ner)\n",
-    "print(\"y_srl \\n \")\n",
-    "print(y_srl)\n",
-    "print(\"y_ner cat \\n \")\n",
-    "print(y_ner_cat)\n",
-    "print(\"y_srl cat \\n \")\n",
-    "print(y_srl_cat)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "a5c264df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# split dataset \n",
-    "X_temp, X_test, y_ner_temp, y_ner_test, y_srl_temp, y_srl_test = train_test_split(\n",
-    "    X, y_ner_cat, y_srl_cat, test_size=0.1, random_state=42\n",
-    ")\n",
-    "X_train, X_val, y_ner_train, y_ner_val, y_srl_train, y_srl_val = train_test_split(\n",
-    "    X_temp, y_ner_temp, y_srl_temp, test_size=0.1111, random_state=42  # ~10% of total\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "712c1789",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-04-29 19:42:52.271455: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<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\"</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"functional\"\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",
-       "│ input_layer         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ embedding           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">45,184</span> │ input_layer[<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\">Embedding</span>)         │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bidirectional       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)   │     <span style=\"color: #00af00; text-decoration-color: #00af00\">66,048</span> │ embedding[<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\">Bidirectional</span>)     │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ ner_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,225</span> │ bidirectional[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ srl_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">38</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,902</span> │ bidirectional[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</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",
-       "│ input_layer         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
-       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ embedding           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │     \u001b[38;5;34m45,184\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
-       "│ (\u001b[38;5;33mEmbedding\u001b[0m)         │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bidirectional       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)   │     \u001b[38;5;34m66,048\u001b[0m │ embedding[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
-       "│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ ner_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m25\u001b[0m)    │      \u001b[38;5;34m3,225\u001b[0m │ bidirectional[\u001b[38;5;34m0\u001b[0m]… │\n",
-       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ srl_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m38\u001b[0m)    │      \u001b[38;5;34m4,902\u001b[0m │ bidirectional[\u001b[38;5;34m0\u001b[0m]… │\n",
-       "│ (\u001b[38;5;33mTimeDistributed\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\">119,359</span> (466.25 KB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m119,359\u001b[0m (466.25 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\">119,359</span> (466.25 KB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m119,359\u001b[0m (466.25 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": [
-    "#training model\n",
-    "input_layer = Input(shape=(maxlen,))\n",
-    "embedding = Embedding(input_dim=len(word2idx), output_dim=64)(input_layer)\n",
-    "bilstm = Bidirectional(LSTM(units=64, return_sequences=True))(embedding)\n",
-    "out_ner = TimeDistributed(Dense(len(tag2idx_ner), activation=\"softmax\"), name=\"ner_output\")(bilstm)\n",
-    "out_srl = TimeDistributed(Dense(len(tag2idx_srl), activation=\"softmax\"), name=\"srl_output\")(bilstm)\n",
-    "\n",
-    "model = Model(inputs=input_layer, outputs=[out_ner, out_srl])\n",
-    "model.compile(\n",
-    "    optimizer=\"adam\",\n",
-    "    loss={\"ner_output\": \"categorical_crossentropy\", \"srl_output\": \"categorical_crossentropy\"},\n",
-    "    metrics={\"ner_output\": \"accuracy\", \"srl_output\": \"accuracy\"}\n",
-    ")\n",
-    "\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "98feee87",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/10\n",
-      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 19ms/step - loss: 3.7132 - ner_output_accuracy: 0.8752 - ner_output_loss: 1.6339 - srl_output_accuracy: 0.7399 - srl_output_loss: 2.0793 - val_loss: 0.7544 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2714 - val_srl_output_accuracy: 0.8450 - val_srl_output_loss: 0.4830\n",
-      "Epoch 2/10\n",
-      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.7800 - ner_output_accuracy: 0.9586 - ner_output_loss: 0.2194 - srl_output_accuracy: 0.8145 - srl_output_loss: 0.5605 - val_loss: 0.6925 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2589 - val_srl_output_accuracy: 0.8563 - val_srl_output_loss: 0.4336\n",
-      "Epoch 3/10\n",
-      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.7723 - ner_output_accuracy: 0.9535 - ner_output_loss: 0.2264 - srl_output_accuracy: 0.8309 - srl_output_loss: 0.5460 - val_loss: 0.6375 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2429 - val_srl_output_accuracy: 0.8825 - val_srl_output_loss: 0.3945\n",
-      "Epoch 4/10\n",
-      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.7463 - ner_output_accuracy: 0.9521 - ner_output_loss: 0.2214 - srl_output_accuracy: 0.8501 - srl_output_loss: 0.5249 - val_loss: 0.5878 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2284 - val_srl_output_accuracy: 0.8950 - val_srl_output_loss: 0.3594\n",
-      "Epoch 5/10\n",
-      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.7682 - ner_output_accuracy: 0.9441 - ner_output_loss: 0.2412 - srl_output_accuracy: 0.8410 - srl_output_loss: 0.5270 - val_loss: 0.5590 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2182 - val_srl_output_accuracy: 0.9037 - val_srl_output_loss: 0.3408\n",
-      "Epoch 6/10\n",
-      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.6489 - ner_output_accuracy: 0.9487 - ner_output_loss: 0.2089 - srl_output_accuracy: 0.8736 - srl_output_loss: 0.4399 - val_loss: 0.5293 - val_ner_output_accuracy: 0.9463 - val_ner_output_loss: 0.2094 - val_srl_output_accuracy: 0.9012 - val_srl_output_loss: 0.3199\n",
-      "Epoch 7/10\n",
-      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.6142 - ner_output_accuracy: 0.9540 - ner_output_loss: 0.1842 - srl_output_accuracy: 0.8802 - srl_output_loss: 0.4300 - val_loss: 0.5180 - val_ner_output_accuracy: 0.9475 - val_ner_output_loss: 0.2047 - val_srl_output_accuracy: 0.9025 - val_srl_output_loss: 0.3134\n",
-      "Epoch 8/10\n",
-      "\u001b[1m13/63\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.5499 - ner_output_accuracy: 0.9632 - ner_output_loss: 0.1377 - srl_output_accuracy: 0.8832 - srl_output_loss: 0.4122"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[9], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# === TRAINING ===\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mner_output\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_ner_train\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msrl_output\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_srl_train\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_val\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mner_output\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_ner_val\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msrl_output\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_srl_val\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\n\u001b[1;32m      8\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# === SAVE ===\u001b[39;00m\n\u001b[1;32m     11\u001b[0m model\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulti_task_bilstm_model.keras\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
-      "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py:371\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator:\n\u001b[1;32m    370\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 371\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    372\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n\u001b[1;32m    373\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
-      "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py:219\u001b[0m, in \u001b[0;36mTensorFlowTrainer._make_function.<locals>.function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfunction\u001b[39m(iterator):\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[1;32m    217\u001b[0m         iterator, (tf\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mIterator, tf\u001b[38;5;241m.\u001b[39mdistribute\u001b[38;5;241m.\u001b[39mDistributedIterator)\n\u001b[1;32m    218\u001b[0m     ):\n\u001b[0;32m--> 219\u001b[0m         opt_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmulti_step_on_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    220\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m opt_outputs\u001b[38;5;241m.\u001b[39mhas_value():\n\u001b[1;32m    221\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m\n",
-      "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
-      "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
-      "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[1;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1324\u001b[0m     args,\n\u001b[1;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1326\u001b[0m     executing_eagerly)\n\u001b[1;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
-      "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
-      "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[1;32m    261\u001b[0m     )\n",
-      "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/tensorflow/python/eager/context.py:1688\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1686\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1687\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1688\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1689\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1690\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1691\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1692\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1693\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1694\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1695\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1696\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1697\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1698\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1702\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1703\u001b[0m   )\n",
-      "File \u001b[0;32m/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "# === TRAINING ===\n",
-    "history = model.fit(\n",
-    "    X_train,\n",
-    "    {\"ner_output\": np.array(y_ner_train), \"srl_output\": np.array(y_srl_train)},\n",
-    "    validation_data=(X_val, {\"ner_output\": np.array(y_ner_val), \"srl_output\": np.array(y_srl_val)}),\n",
-    "    batch_size=2,\n",
-    "    epochs=10\n",
-    ")\n",
-    "\n",
-    "# === SAVE ===\n",
-    "model.save(\"multi_task_bilstm_model.keras\")\n",
-    "with open(\"word2idx.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(word2idx, f)\n",
-    "with open(\"tag2idx_ner.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(tag2idx_ner, f)\n",
-    "with open(\"tag2idx_srl.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(tag2idx_srl, f)\n",
-    "    \n",
-    "    \n",
-    "history_dict = history.history\n",
-    "\n",
-    "# === LOSS ===\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "\n",
-    "plt.plot(history_dict[\"loss\"], label=\"Total Loss (train)\")\n",
-    "plt.plot(history_dict[\"val_loss\"], label=\"Total Loss (val)\")\n",
-    "plt.plot(history_dict[\"ner_output_loss\"], label=\"NER Loss (train)\")\n",
-    "plt.plot(history_dict[\"val_ner_output_loss\"], label=\"NER Loss (val)\")\n",
-    "plt.plot(history_dict[\"srl_output_loss\"], label=\"SRL Loss (train)\")\n",
-    "plt.plot(history_dict[\"val_srl_output_loss\"], label=\"SRL Loss (val)\")\n",
-    "\n",
-    "plt.title(\"Model Loss per Epoch\")\n",
-    "plt.xlabel(\"Epoch\")\n",
-    "plt.ylabel(\"Loss\")\n",
-    "plt.legend()\n",
-    "plt.grid(True)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "# === ACCURACY ===\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "\n",
-    "plt.plot(history_dict[\"ner_output_accuracy\"], label=\"NER Accuracy (train)\")\n",
-    "plt.plot(history_dict[\"val_ner_output_accuracy\"], label=\"NER Accuracy (val)\")\n",
-    "plt.plot(history_dict[\"srl_output_accuracy\"], label=\"SRL Accuracy (train)\")\n",
-    "plt.plot(history_dict[\"val_srl_output_accuracy\"], label=\"SRL Accuracy (val)\")\n",
-    "\n",
-    "plt.title(\"Model Accuracy per Epoch\")\n",
-    "plt.xlabel(\"Epoch\")\n",
-    "plt.ylabel(\"Accuracy\")\n",
-    "plt.legend()\n",
-    "plt.grid(True)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aeef32c1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7f8ec792f520> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 475ms/step\n",
-      "\n",
-      "📊 [NER] Test Set Classification Report:\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "         LOC       0.00      0.00      0.00         6\n",
-      "       QUANT       0.00      0.00      0.00         1\n",
-      "\n",
-      "   micro avg       0.00      0.00      0.00         7\n",
-      "   macro avg       0.00      0.00      0.00         7\n",
-      "weighted avg       0.00      0.00      0.00         7\n",
-      "\n",
-      "\n",
-      "📊 [SRL] Test Set Classification Report:\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "         BNF       0.00      0.00      0.00         1\n",
-      "         EXT       0.00      0.00      0.00         2\n",
-      "         LOC       0.00      0.00      0.00         4\n",
-      "         MNR       0.00      0.00      0.00         1\n",
-      "         MOD       0.00      0.00      0.00         1\n",
-      "         NEG       0.00      0.00      0.00         1\n",
-      "         PRP       0.00      0.00      0.00         1\n",
-      "         QUE       0.00      0.00      0.00         1\n",
-      "         RG0       0.00      0.00      0.00         5\n",
-      "         RG1       0.00      0.00      0.00         8\n",
-      "         RG2       0.00      0.00      0.00         2\n",
-      "         SRC       0.00      0.00      0.00         1\n",
-      "         TMP       0.00      0.00      0.00         1\n",
-      "           _       0.00      0.00      0.00         6\n",
-      "\n",
-      "   micro avg       0.00      0.00      0.00        35\n",
-      "   macro avg       0.00      0.00      0.00        35\n",
-      "weighted avg       0.00      0.00      0.00        35\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG0 seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG1 seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-LOC seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-NEG seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-SRC seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-MOD seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-EXT seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-MNR seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-BNF seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-EXT seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-QUE seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-TMP seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-PRP seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG2 seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n"
-     ]
-    }
-   ],
-   "source": [
-    "# evaluation\n",
-    "y_pred_ner, y_pred_srl = model.predict(X_test)\n",
-    "\n",
-    "y_true_ner = [[idx2tag_ner[np.argmax(tok)] for tok in seq] for seq in y_ner_test]\n",
-    "y_pred_ner = [[idx2tag_ner[np.argmax(tok)] for tok in seq] for seq in y_pred_ner]\n",
-    "\n",
-    "y_true_srl = [[idx2tag_srl[np.argmax(tok)] for tok in seq] for seq in y_srl_test]\n",
-    "y_pred_srl = [[idx2tag_srl[np.argmax(tok)] for tok in seq] for seq in y_pred_srl]\n",
-    "\n",
-    "print(\"\\n📊 [NER] Test Set Classification Report:\")\n",
-    "print(classification_report(y_true_ner, y_pred_ner))\n",
-    "\n",
-    "print(\"\\n📊 [SRL] Test Set Classification Report:\")\n",
-    "print(classification_report(y_true_srl, y_pred_srl))\n",
-    "\n",
-    "\n",
-    "# import numpy as np\n",
-    "\n",
-    "# # Prediksi model (output = probabilitas)\n",
-    "# y_pred_ner = model.predict(X_test)[0]\n",
-    "# y_pred_ner_idx = np.argmax(y_pred_ner, axis=-1)\n",
-    "# y_true_ner_idx = np.argmax(y_ner_test, axis=-1)\n",
-    "\n",
-    "# # Mapping ke string\n",
-    "# y_pred_ner_str = []\n",
-    "# y_true_ner_str = []\n",
-    "\n",
-    "# for y_true_seq, y_pred_seq in zip(y_true_ner_idx, y_pred_ner_idx):\n",
-    "#     true_seq = []\n",
-    "#     pred_seq = []\n",
-    "#     for t, p in zip(y_true_seq, y_pred_seq):\n",
-    "#         if idx2tag_ner[t] != \"PAD\":\n",
-    "#             true_seq.append(idx2tag_ner[t])\n",
-    "#             pred_seq.append(idx2tag_ner[p])\n",
-    "#     y_true_ner_str.append(true_seq)\n",
-    "#     y_pred_ner_str.append(pred_seq)\n",
-    "\n",
-    "# from seqeval.metrics import classification_report\n",
-    "# print(\"\\n📊 [NER] Test Set Classification Report:\")\n",
-    "# print(classification_report(y_true_ner_str, y_pred_ner_str))\n",
-    "\n",
-    "\n",
-    "# from collections import Counter\n",
-    "\n",
-    "# flat_preds = [tag for seq in y_pred_ner_str for tag in seq]\n",
-    "# print(Counter(flat_preds))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a18da05",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "def plot_confusion_matrix(y_true_flat, y_pred_flat, labels, title=\"Confusion Matrix\"):\n",
-    "    cm = confusion_matrix(y_true_flat, y_pred_flat, labels=labels)\n",
-    "    plt.figure(figsize=(10, 8))\n",
-    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
-    "                xticklabels=labels, yticklabels=labels)\n",
-    "    plt.title(title)\n",
-    "    plt.xlabel(\"Predicted\")\n",
-    "    plt.ylabel(\"Actual\")\n",
-    "    plt.xticks(rotation=45)\n",
-    "    plt.yticks(rotation=0)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cee30988",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "# Flatten label\n",
-    "y_true_flat_ner = [tag for seq in y_true_ner for tag in seq]\n",
-    "y_pred_flat_ner = [tag for seq in y_pred_ner for tag in seq]\n",
-    "\n",
-    "# Buat plot\n",
-    "plot_confusion_matrix(\n",
-    "    y_true_flat_ner, \n",
-    "    y_pred_flat_ner, \n",
-    "    labels=list(tag2idx_ner.keys()), \n",
-    "    title=\"NER Confusion Matrix\"\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4ba2b85c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_true_flat_srl = [tag for seq in y_true_srl for tag in seq]\n",
-    "y_pred_flat_srl = [tag for seq in y_pred_srl for tag in seq]\n",
-    "\n",
-    "plot_confusion_matrix(\n",
-    "    y_true_flat_srl, \n",
-    "    y_pred_flat_srl, \n",
-    "    labels=list(tag2idx_srl.keys()), \n",
-    "    title=\"SRL Confusion Matrix\"\n",
-    ")\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "myenv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/NER_SRL/multi_task_bilstm_model.keras b/NER_SRL/multi_task_bilstm_model.keras
deleted file mode 100644
index 27cc11e..0000000
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diff --git a/NER_SRL/multi_task_lstm_ner_srl_model.keras b/NER_SRL/multi_task_lstm_ner_srl_model.keras
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index 613a674..0000000
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diff --git a/NER_SRL/multi_task_lstm_ner_srl_model_tf.keras b/NER_SRL/multi_task_lstm_ner_srl_model_tf.keras
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diff --git a/NER_SRL/new_lstm_ner_srl.ipynb b/NER_SRL/new_lstm_ner_srl.ipynb
deleted file mode 100644
index ac14a4e..0000000
--- a/NER_SRL/new_lstm_ner_srl.ipynb
+++ /dev/null
@@ -1,558 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "fb106e20",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json, pickle\n",
-    "import numpy as np\n",
-    "from keras.models import Model\n",
-    "from keras.layers import Input, Embedding, Bidirectional, LSTM, TimeDistributed, Dense\n",
-    "from keras.preprocessing.sequence import pad_sequences\n",
-    "from keras.utils import to_categorical\n",
-    "from seqeval.metrics import classification_report\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from tensorflow.keras.metrics import CategoricalAccuracy\n",
-    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
-    "import matplotlib.pyplot as plt\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "00347a5f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total kalimat: 159\n",
-      "Total token: 1891\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "data = []\n",
-    "\n",
-    "with open(\"../dataset/dataset_ner_srl.tsv\", encoding=\"utf-8\") as f:\n",
-    "    tokens, ner_labels, srl_labels = [], [], []\n",
-    "    \n",
-    "    for line in f:\n",
-    "        line = line.strip()\n",
-    "        if not line:\n",
-    "            if tokens:\n",
-    "                data.append({\n",
-    "                    \"tokens\": tokens,\n",
-    "                    \"labels_ner\": ner_labels,\n",
-    "                    \"labels_srl\": srl_labels\n",
-    "                })\n",
-    "                tokens, ner_labels, srl_labels = [], [], []\n",
-    "        else:\n",
-    "            token, ner, srl = line.split(\"\\t\")\n",
-    "            tokens.append(token)\n",
-    "            ner_labels.append(ner)\n",
-    "            srl_labels.append(srl)\n",
-    "\n",
-    "# Preprocessing sama seperti sebelumnya\n",
-    "sentences = [[tok.lower() for tok in item[\"tokens\"]] for item in data]\n",
-    "labels_ner = [item[\"labels_ner\"] for item in data]\n",
-    "labels_srl = [item[\"labels_srl\"] for item in data]\n",
-    "\n",
-    "total_kalimat = len(data)\n",
-    "total_token = sum(len(item[\"tokens\"]) for item in data)\n",
-    "\n",
-    "print(\"Total kalimat:\", total_kalimat)\n",
-    "print(\"Total token:\", total_token)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3793950a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "ac8eb374",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# tagging \n",
-    "words = sorted({w for s in sentences for w in s})\n",
-    "ner_tags = sorted({t for seq in labels_ner for t in seq})\n",
-    "srl_tags = sorted({t for seq in labels_srl for t in seq})\n",
-    "\n",
-    "word2idx = {w: i + 2 for i, w in enumerate(words)}\n",
-    "word2idx[\"PAD\"], word2idx[\"UNK\"] = 0, 1\n",
-    "\n",
-    "tag2idx_ner = {t: i for i, t in enumerate(ner_tags)}\n",
-    "tag2idx_srl = {t: i for i, t in enumerate(srl_tags)}\n",
-    "idx2tag_ner = {i: t for t, i in tag2idx_ner.items()}\n",
-    "idx2tag_srl = {i: t for t, i in tag2idx_srl.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "80356f1f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# encoding\n",
-    "\n",
-    "X = [[word2idx.get(w, word2idx[\"UNK\"]) for w in s] for s in sentences]\n",
-    "y_ner = [[tag2idx_ner[t] for t in seq] for seq in labels_ner]\n",
-    "y_srl = [[tag2idx_srl[t] for t in seq] for seq in labels_srl]\n",
-    "\n",
-    "maxlen = 50 \n",
-    "\n",
-    "X = pad_sequences(X, maxlen=maxlen, padding=\"post\", value=word2idx[\"PAD\"])\n",
-    "y_ner = pad_sequences(y_ner, maxlen=maxlen, padding=\"post\", value=tag2idx_ner[\"O\"])\n",
-    "y_srl = pad_sequences(y_srl, maxlen=maxlen, padding=\"post\", value=tag2idx_srl[\"O\"])\n",
-    "\n",
-    "y_ner = [to_categorical(seq, num_classes=len(tag2idx_ner)) for seq in y_ner]\n",
-    "y_srl = [to_categorical(seq, num_classes=len(tag2idx_srl)) for seq in y_srl]\n",
-    "\n",
-    "X = np.array(X)\n",
-    "y_ner = np.array(y_ner)\n",
-    "y_srl = np.array(y_srl)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "fe219c96",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_ner_train, y_ner_test, y_srl_train, y_srl_test = train_test_split(\n",
-    "    X, y_ner, y_srl,            \n",
-    "    test_size=0.20,             \n",
-    "    random_state=42,\n",
-    "    shuffle=True            \n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "7a9636b6",
-   "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_4\"</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"functional_4\"\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",
-       "│ input_layer_4       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ embedding_4         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">46,016</span> │ input_layer_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bidirectional_4     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)   │     <span style=\"color: #00af00; text-decoration-color: #00af00\">66,048</span> │ embedding_4[<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\">Bidirectional</span>)     │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ ner_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,225</span> │ bidirectional_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ srl_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,322</span> │ bidirectional_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
-       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</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",
-       "│ input_layer_4       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
-       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ embedding_4         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │     \u001b[38;5;34m46,016\u001b[0m │ input_layer_4[\u001b[38;5;34m0\u001b[0m]… │\n",
-       "│ (\u001b[38;5;33mEmbedding\u001b[0m)         │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ bidirectional_4     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)   │     \u001b[38;5;34m66,048\u001b[0m │ embedding_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
-       "│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ ner_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m25\u001b[0m)    │      \u001b[38;5;34m3,225\u001b[0m │ bidirectional_4[\u001b[38;5;34m…\u001b[0m │\n",
-       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │                   │\n",
-       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
-       "│ srl_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m18\u001b[0m)    │      \u001b[38;5;34m2,322\u001b[0m │ bidirectional_4[\u001b[38;5;34m…\u001b[0m │\n",
-       "│ (\u001b[38;5;33mTimeDistributed\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\">117,611</span> (459.42 KB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m117,611\u001b[0m (459.42 KB)\n"
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-       "<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\">117,611</span> (459.42 KB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m117,611\u001b[0m (459.42 KB)\n"
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-     "output_type": "display_data"
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-       "<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"
-      ]
-     },
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/10\n",
-      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 31ms/step - loss: 3.2183 - ner_output_accuracy: 0.8700 - ner_output_loss: 1.5503 - srl_output_accuracy: 0.7278 - srl_output_loss: 1.6679 - val_loss: 0.7435 - val_ner_output_accuracy: 0.9488 - val_ner_output_loss: 0.2671 - val_srl_output_accuracy: 0.8413 - val_srl_output_loss: 0.4764\n",
-      "Epoch 2/10\n",
-      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.7484 - ner_output_accuracy: 0.9476 - ner_output_loss: 0.2574 - srl_output_accuracy: 0.8292 - srl_output_loss: 0.4911 - val_loss: 0.6928 - val_ner_output_accuracy: 0.9488 - val_ner_output_loss: 0.2566 - val_srl_output_accuracy: 0.8538 - val_srl_output_loss: 0.4362\n",
-      "Epoch 3/10\n",
-      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.7115 - ner_output_accuracy: 0.9501 - ner_output_loss: 0.2363 - srl_output_accuracy: 0.8392 - srl_output_loss: 0.4751 - val_loss: 0.6501 - val_ner_output_accuracy: 0.9488 - val_ner_output_loss: 0.2476 - val_srl_output_accuracy: 0.8556 - val_srl_output_loss: 0.4026\n",
-      "Epoch 4/10\n",
-      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.6596 - ner_output_accuracy: 0.9499 - ner_output_loss: 0.2149 - srl_output_accuracy: 0.8501 - srl_output_loss: 0.4448 - val_loss: 0.6068 - val_ner_output_accuracy: 0.9488 - val_ner_output_loss: 0.2321 - val_srl_output_accuracy: 0.8888 - val_srl_output_loss: 0.3746\n",
-      "Epoch 5/10\n",
-      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.6708 - ner_output_accuracy: 0.9500 - ner_output_loss: 0.2116 - srl_output_accuracy: 0.8565 - srl_output_loss: 0.4591 - val_loss: 0.5745 - val_ner_output_accuracy: 0.9488 - val_ner_output_loss: 0.2257 - val_srl_output_accuracy: 0.8969 - val_srl_output_loss: 0.3489\n",
-      "Epoch 6/10\n",
-      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.5632 - ner_output_accuracy: 0.9518 - ner_output_loss: 0.1934 - srl_output_accuracy: 0.8837 - srl_output_loss: 0.3702 - val_loss: 0.5507 - val_ner_output_accuracy: 0.9488 - val_ner_output_loss: 0.2125 - val_srl_output_accuracy: 0.8981 - val_srl_output_loss: 0.3382\n",
-      "Epoch 7/10\n",
-      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.5163 - ner_output_accuracy: 0.9542 - ner_output_loss: 0.1730 - srl_output_accuracy: 0.8909 - srl_output_loss: 0.3432 - val_loss: 0.5265 - val_ner_output_accuracy: 0.9494 - val_ner_output_loss: 0.2101 - val_srl_output_accuracy: 0.9031 - val_srl_output_loss: 0.3165\n",
-      "Epoch 8/10\n",
-      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.4597 - ner_output_accuracy: 0.9560 - ner_output_loss: 0.1609 - srl_output_accuracy: 0.9077 - srl_output_loss: 0.2989 - val_loss: 0.5069 - val_ner_output_accuracy: 0.9506 - val_ner_output_loss: 0.1971 - val_srl_output_accuracy: 0.9063 - val_srl_output_loss: 0.3097\n",
-      "Epoch 9/10\n",
-      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.4826 - ner_output_accuracy: 0.9573 - ner_output_loss: 0.1635 - srl_output_accuracy: 0.9079 - srl_output_loss: 0.3192 - val_loss: 0.4902 - val_ner_output_accuracy: 0.9506 - val_ner_output_loss: 0.1912 - val_srl_output_accuracy: 0.9125 - val_srl_output_loss: 0.2990\n",
-      "Epoch 10/10\n",
-      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.4266 - ner_output_accuracy: 0.9513 - ner_output_loss: 0.1734 - srl_output_accuracy: 0.9242 - srl_output_loss: 0.2531 - val_loss: 0.4749 - val_ner_output_accuracy: 0.9550 - val_ner_output_loss: 0.1855 - val_srl_output_accuracy: 0.9138 - val_srl_output_loss: 0.2895\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_layer = Input(shape=(maxlen,))\n",
-    "embed = Embedding(len(word2idx), 64)(input_layer)\n",
-    "bilstm = Bidirectional(LSTM(64, return_sequences=True))(embed)\n",
-    "\n",
-    "ner_output = TimeDistributed(\n",
-    "    Dense(len(tag2idx_ner), activation=\"softmax\"), name=\"ner_output\"\n",
-    ")(bilstm)\n",
-    "srl_output = TimeDistributed(\n",
-    "    Dense(len(tag2idx_srl), activation=\"softmax\"), name=\"srl_output\"\n",
-    ")(bilstm)\n",
-    "\n",
-    "model = Model(inputs=input_layer, outputs=[ner_output, srl_output])\n",
-    "model.compile(\n",
-    "    optimizer=\"adam\",\n",
-    "    loss={\n",
-    "        \"ner_output\": \"categorical_crossentropy\",\n",
-    "        \"srl_output\": \"categorical_crossentropy\",\n",
-    "    },\n",
-    "    metrics={\n",
-    "        \"ner_output\": [CategoricalAccuracy(name=\"accuracy\")],\n",
-    "        \"srl_output\": [CategoricalAccuracy(name=\"accuracy\")],\n",
-    "    },\n",
-    ")\n",
-    "\n",
-    "model.summary()\n",
-    "model.fit(\n",
-    "    X_train, {\"ner_output\": y_ner_train, \"srl_output\": y_srl_train}, \n",
-    "    validation_data=(X_test, {\"ner_output\": y_ner_test, \"srl_output\": y_srl_test}),\n",
-    "    batch_size=2,\n",
-    "    epochs=10,\n",
-    "    verbose=1\n",
-    ")\n",
-    "\n",
-    "# ---------- 6. Simpan artefak ----------\n",
-    "model.save(\"multi_task_lstm_ner_srl_model.keras\")\n",
-    "with open(\"word2idx.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(word2idx, f)\n",
-    "with open(\"tag2idx_ner.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(tag2idx_ner, f)\n",
-    "with open(\"tag2idx_srl.pkl\", \"wb\") as f:\n",
-    "    pickle.dump(tag2idx_srl, f)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "3a55990b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metrics names: ['loss', 'compile_metrics', 'ner_output_loss', 'srl_output_loss']\n",
-      "loss: 0.47491562366485596\n",
-      "compile_metrics: 0.18545177578926086\n",
-      "ner_output_loss: 0.2894638478755951\n",
-      "srl_output_loss: 0.9550000429153442\n",
-      "WARNING:tensorflow:5 out of the last 8 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7f1ed50939a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
-      "[NER] Token-level Accuracy: 1.0\n",
-      "[SRL] Token-level Accuracy: 0.9\n",
-      "[NER] Classification Report (test set):\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "        DATE       0.33      0.09      0.14        11\n",
-      "       EVENT       0.00      0.00      0.00         1\n",
-      "         LOC       1.00      0.19      0.32        21\n",
-      "         MIN       0.00      0.00      0.00         3\n",
-      "        MISC       0.00      0.00      0.00         1\n",
-      "         ORG       0.00      0.00      0.00         3\n",
-      "         PER       0.00      0.00      0.00         2\n",
-      "         RES       0.00      0.00      0.00         2\n",
-      "        TIME       0.00      0.00      0.00         8\n",
-      "\n",
-      "   micro avg       0.42      0.10      0.16        52\n",
-      "   macro avg       0.15      0.03      0.05        52\n",
-      "weighted avg       0.47      0.10      0.16        52\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# evaluation\n",
-    "\n",
-    "print(\"Metrics names:\", model.metrics_names)\n",
-    "\n",
-    "\n",
-    "results = model.evaluate(\n",
-    "    X_test,\n",
-    "    {\"ner_output\": y_ner_test, \"srl_output\": y_srl_test},\n",
-    "    verbose=0\n",
-    ")\n",
-    "for name, value in zip(model.metrics_names, results):\n",
-    "    print(f\"{name}: {value}\")\n",
-    "    \n",
-    "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:  # skip PAD\n",
-    "                continue\n",
-    "            total += 1\n",
-    "            if t.argmax() == p.argmax():\n",
-    "                correct += 1\n",
-    "    return correct / total\n",
-    "\n",
-    "\n",
-    "def decode(pred, true, idx2tag):\n",
-    "    out_true, out_pred = [], []\n",
-    "    for p_seq, t_seq in zip(pred, true):\n",
-    "        t_labels, p_labels = [], []\n",
-    "        for p_tok, t_tok in zip(p_seq, t_seq):\n",
-    "            if t_tok.sum() == 0:          # token PAD → lewati\n",
-    "                continue\n",
-    "            t_labels.append(idx2tag[t_tok.argmax()])\n",
-    "            p_labels.append(idx2tag[p_tok.argmax()])\n",
-    "        out_true.append(t_labels)\n",
-    "        out_pred.append(p_labels)\n",
-    "    return out_true, out_pred\n",
-    "\n",
-    "# prediksi hanya pada test set\n",
-    "y_pred_ner, y_pred_srl = model.predict(X_test, verbose=0)\n",
-    "\n",
-    "true_ner, pred_ner = decode(y_pred_ner, y_ner_test, idx2tag_ner)\n",
-    "\n",
-    "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-level Accuracy: {acc_ner:.1f}\")\n",
-    "print(f\"[SRL] Token-level Accuracy: {acc_srl:.1f}\")\n",
-    "# print(idx2tag_ner)\n",
-    "print(\"[NER] Classification Report (test set):\")\n",
-    "print(classification_report(true_ner, pred_ner, digits=2))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "547d1533",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[SRL] Classification Report (test set):\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "         FRQ       0.00      0.00      0.00         1\n",
-      "         LOC       0.21      0.43      0.29         7\n",
-      "         MNR       0.00      0.00      0.00         3\n",
-      "         PRP       0.00      0.00      0.00         1\n",
-      "         RG0       0.20      0.24      0.22        17\n",
-      "         RG1       0.20      0.19      0.19        47\n",
-      "         RG2       0.25      0.45      0.32        11\n",
-      "         RG3       0.00      0.00      0.00         3\n",
-      "         TMP       0.59      0.72      0.65        18\n",
-      "           _       0.50      0.12      0.20        33\n",
-      "\n",
-      "   micro avg       0.29      0.27      0.28       141\n",
-      "   macro avg       0.20      0.22      0.19       141\n",
-      "weighted avg       0.31      0.27      0.26       141\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG1 seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG0 seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-TMP seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-PRP seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-LOC seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG2 seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-MNR seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-FRQ seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
-      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG3 seems not to be NE tag.\n",
-      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n"
-     ]
-    }
-   ],
-   "source": [
-    "true_srl, pred_srl = decode(y_pred_srl, y_srl_test, idx2tag_srl)\n",
-    "# print(idx2tag_srl)\n",
-    "print(\"[SRL] Classification Report (test set):\")\n",
-    "print(classification_report(true_srl, pred_srl, digits=2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "8d44d51e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "flat_true_ner = [t for seq in true_ner for t in seq]\n",
-    "flat_pred_ner = [p for seq in pred_ner for p in seq]\n",
-    "\n",
-    "labels_ner = sorted(list(set(flat_true_ner + flat_pred_ner)))\n",
-    "cm_ner = confusion_matrix(flat_true_ner, flat_pred_ner, labels=labels_ner)\n",
-    "\n",
-    "disp = ConfusionMatrixDisplay(confusion_matrix=cm_ner, display_labels=labels_ner)\n",
-    "disp.plot(xticks_rotation=45, cmap=plt.cm.Blues)\n",
-    "plt.title(\"Confusion Matrix NER\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "918dbefb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "flat_true_srl = [t for seq in true_srl for t in seq]\n",
-    "flat_pred_srl = [p for seq in pred_srl for p in seq]\n",
-    "\n",
-    "labels_srl = sorted(list(set(flat_true_srl + flat_pred_srl)))\n",
-    "cm_srl = confusion_matrix(flat_true_srl, flat_pred_srl, labels=labels_srl)\n",
-    "\n",
-    "disp = ConfusionMatrixDisplay(confusion_matrix=cm_srl, display_labels=labels_srl)\n",
-    "disp.plot(xticks_rotation=45, cmap=plt.cm.Blues)\n",
-    "plt.title(\"Confusion Matrix srl\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "myenv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/NER_SRL/tag2idx_ner.pkl b/NER_SRL/tag2idx_ner.pkl
index 0981659..e7c4d20 100644
Binary files a/NER_SRL/tag2idx_ner.pkl and b/NER_SRL/tag2idx_ner.pkl differ
diff --git a/NER_SRL/tag2idx_srl.pkl b/NER_SRL/tag2idx_srl.pkl
index 92bf6be..39c90f9 100644
Binary files a/NER_SRL/tag2idx_srl.pkl and b/NER_SRL/tag2idx_srl.pkl differ
diff --git a/NER_SRL/test_model.py b/NER_SRL/test_model.py
index b31dd28..7d6ffa8 100644
--- a/NER_SRL/test_model.py
+++ b/NER_SRL/test_model.py
@@ -1,57 +1,52 @@
 import json
 import numpy as np
 import pickle
+from tensorflow.keras.models import load_model # type: ignore
+from tensorflow.keras.preprocessing.sequence import pad_sequences # type: ignore
 
-from keras.models import load_model
-from keras.preprocessing.sequence import pad_sequences
-
-
-model = load_model("multi_task_lstm_ner_srl_model_tf.keras")
+# -----------------------------
+# 1.  Load artefak
+# -----------------------------
+MODEL_PATH = "lstm_ner_srl_model.keras"  # ← nama file baru
+model = load_model(MODEL_PATH)
 
 with open("word2idx.pkl", "rb") as f:
     word2idx = pickle.load(f)
-
 with open("tag2idx_ner.pkl", "rb") as f:
     tag2idx_ner = pickle.load(f)
-
 with open("tag2idx_srl.pkl", "rb") as f:
     tag2idx_srl = pickle.load(f)
 
-
 idx2tag_ner = {i: t for t, i in tag2idx_ner.items()}
 idx2tag_srl = {i: t for t, i in tag2idx_srl.items()}
 
-
-max = 50
+PAD_WORD_ID = word2idx["PAD"]  # 0
+MAXLEN = model.input_shape[1]  # ambil langsung dari model
 
 
-def predict_sentence(sentence):
+# -----------------------------
+# 2.  Fungsi prediksi
+# -----------------------------
+def predict_sentence(sentence: str) -> dict:
     tokens = sentence.strip().lower().split()
-    print(tokens)
+    seq = [word2idx.get(tok, word2idx["UNK"]) for tok in tokens]
+    seq = pad_sequences([seq], maxlen=MAXLEN, padding="post", value=PAD_WORD_ID)
 
-    x = [word2idx.get(w.lower(), word2idx["UNK"]) for w in tokens]
-    x = pad_sequences([x], maxlen=50, padding="post", value=word2idx["PAD"])
+    pred_ner_prob, pred_srl_prob = model.predict(seq, verbose=0)
+    pred_ner = pred_ner_prob.argmax(-1)[0][: len(tokens)]
+    pred_srl = pred_srl_prob.argmax(-1)[0][: len(tokens)]
 
-    preds = model.predict(x)
-    pred_labels_ner = np.argmax(preds[0], axis=-1)[0]
-    pred_labels_srl = np.argmax(preds[1], axis=-1)[0]
-
-    result = {
+    return {
         "tokens": tokens,
-        "labels_ner": [
-            idx2tag_ner[int(label)] for label in pred_labels_ner[: len(tokens)]
-        ],
-        "labels_srl": [
-            idx2tag_srl[int(label)] for label in pred_labels_srl[: len(tokens)]
-        ],
+        "labels_ner": [idx2tag_ner[int(i)] for i in pred_ner],
+        "labels_srl": [idx2tag_srl[int(i)] for i in pred_srl],
     }
 
-    return result
-
 
+# -----------------------------
+# 3.  Demo
+# -----------------------------
 if __name__ == "__main__":
-    try:
-        sentence = "sore ini aku pergi ke indonesia"
-        print(predict_sentence(sentence))
-    except KeyboardInterrupt:
-        print("\n\nSelesai.")
+    sample = "Suku Karo merayakan upacara pada juni"
+    result = predict_sentence(sample)
+    print(json.dumps(result, ensure_ascii=False, indent=2))
diff --git a/NER_SRL/train.py b/NER_SRL/train.py
deleted file mode 100644
index f139e8d..0000000
--- a/NER_SRL/train.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import json, pickle
-import numpy as np
-from keras.models import Model
-from keras.layers import Input, Embedding, Bidirectional, LSTM, TimeDistributed, Dense
-from keras.preprocessing.sequence import pad_sequences
-from keras.utils import to_categorical
-from seqeval.metrics import classification_report
-
-# ---------- 1. Muat data ----------
-with open("dataset/dataset_ner_srl.json", encoding="utf-8") as f:
-    data = json.load(f)
-
-sentences = [[tok.lower() for tok in item["tokens"]] for item in data]
-labels_ner = [item["labels_ner"] for item in data]
-labels_srl = [item["labels_srl"] for item in data]
-
-for i, label_seq in enumerate(labels_ner):
-    if "V" in label_seq:
-        print(f"Label 'V' ditemukan di index {i}: {label_seq}")
-
-# ---------- 2. Bangun vocab & label map ----------
-words = sorted({w for s in sentences for w in s})
-ner_tags = sorted({t for seq in labels_ner for t in seq})
-srl_tags = sorted({t for seq in labels_srl for t in seq})
-
-word2idx = {w: i + 2 for i, w in enumerate(words)}
-word2idx["PAD"], word2idx["UNK"] = 0, 1
-
-tag2idx_ner = {t: i for i, t in enumerate(ner_tags)}
-tag2idx_srl = {t: i for i, t in enumerate(srl_tags)}
-idx2tag_ner = {i: t for t, i in tag2idx_ner.items()}
-idx2tag_srl = {i: t for t, i in tag2idx_srl.items()}
-
-# ---------- 3. Encoding token & label ----------
-X = [[word2idx.get(w, word2idx["UNK"]) for w in s] for s in sentences]
-y_ner = [[tag2idx_ner[t] for t in seq] for seq in labels_ner]
-y_srl = [[tag2idx_srl[t] for t in seq] for seq in labels_srl]
-
-maxlen = max(len(seq) for seq in X)
-
-X = pad_sequences(X, maxlen=maxlen, padding="post", value=word2idx["PAD"])
-y_ner = pad_sequences(y_ner, maxlen=maxlen, padding="post", value=tag2idx_ner["O"])
-y_srl = pad_sequences(y_srl, maxlen=maxlen, padding="post", value=tag2idx_srl["O"])
-
-y_ner = [to_categorical(seq, num_classes=len(tag2idx_ner)) for seq in y_ner]
-y_srl = [to_categorical(seq, num_classes=len(tag2idx_srl)) for seq in y_srl]
-
-# cast ke np.array biar Keras happy
-X = np.array(X)
-y_ner = np.array(y_ner)
-y_srl = np.array(y_srl)
-
-# ---------- 4. Arsitektur BiLSTM multi‑task ----------
-input_layer = Input(shape=(maxlen,))
-embed = Embedding(len(word2idx), 64)(input_layer)
-bilstm = Bidirectional(LSTM(64, return_sequences=True))(embed)
-
-ner_output = TimeDistributed(
-    Dense(len(tag2idx_ner), activation="softmax"), name="ner_output"
-)(bilstm)
-srl_output = TimeDistributed(
-    Dense(len(tag2idx_srl), activation="softmax"), name="srl_output"
-)(bilstm)
-
-model = Model(inputs=input_layer, outputs=[ner_output, srl_output])
-model.compile(
-    optimizer="adam",
-    loss={
-        "ner_output": "categorical_crossentropy",
-        "srl_output": "categorical_crossentropy",
-    },
-    metrics={"ner_output": "accuracy", "srl_output": "accuracy"},
-)
-model.summary()
-
-# ---------- 5. Training ----------
-model.fit(
-    X, {"ner_output": y_ner, "srl_output": y_srl}, batch_size=2, epochs=10, verbose=1
-)
-
-# ---------- 6. Simpan artefak ----------
-model.save("NER_SRL/multi_task_bilstm_model.keras")
-with open("NER_SRL/word2idx.pkl", "wb") as f:
-    pickle.dump(word2idx, f)
-with open("NER_SRL/tag2idx_ner.pkl", "wb") as f:
-    pickle.dump(tag2idx_ner, f)
-with open("NER_SRL/tag2idx_srl.pkl", "wb") as f:
-    pickle.dump(tag2idx_srl, f)
-
-# ---------- 7. Evaluasi ----------
-y_pred_ner, y_pred_srl = model.predict(X, verbose=0)
-
-
-def decode(pred, true, idx2tag):
-    true_tags = [[idx2tag[np.argmax(tok)] for tok in seq] for seq in true]
-    pred_tags = [[idx2tag[np.argmax(tok)] for tok in seq] for seq in pred]
-    return true_tags, pred_tags
-
-
-true_ner, pred_ner = decode(y_pred_ner, y_ner, idx2tag_ner)
-true_srl, pred_srl = decode(y_pred_srl, y_srl, idx2tag_srl)
-
-print("\n📊 [NER] Classification Report:")
-print(classification_report(true_ner, pred_ner))
-
-print("\n📊 [SRL] Classification Report:")
-print(classification_report(true_srl, pred_srl))
diff --git a/NER_SRL/word2idx.pkl b/NER_SRL/word2idx.pkl
index 17ea8e6..733bc86 100644
Binary files a/NER_SRL/word2idx.pkl and b/NER_SRL/word2idx.pkl differ