From 1743cc1e18d23b8bce625603661ea391e8d9250d Mon Sep 17 00:00:00 2001
From: akhdanre <danakhdan12@gmail.com>
Date: Mon, 21 Apr 2025 17:46:30 +0700
Subject: [PATCH] feat: adding dataset and fix the code ner srl with lstm

---
 NER/lstm_ner_qc.py                    |     6 +-
 NER/ner_bilstm_model.keras            |   Bin 1111645 -> 1192405 bytes
 NER/tag2idx.pkl                       |   Bin 116 -> 218 bytes
 NER/test_ner.py                       |     2 +-
 NER/word2idx.pkl                      |   Bin 4101 -> 5286 bytes
 NER_SRL/lses.py                       |   152 +
 NER_SRL/lstm_ner_srl.ipynb            | 10368 +++++++++++++++++++-----
 NER_SRL/multi_task_bilstm_model.keras |   Bin 1034314 -> 1312600 bytes
 NER_SRL/tag2idx_ner.pkl               |   Bin 116 -> 243 bytes
 NER_SRL/tag2idx_srl.pkl               |   Bin 287 -> 447 bytes
 NER_SRL/test_model.py                 |    52 +
 NER_SRL/train.py                      |   107 +
 NER_SRL/word2idx.pkl                  |   Bin 2085 -> 6103 bytes
 dataset/dataset_ner_srl.json          |  3069 ++++++-
 14 files changed, 11740 insertions(+), 2016 deletions(-)
 create mode 100644 NER_SRL/lses.py
 create mode 100644 NER_SRL/test_model.py
 create mode 100644 NER_SRL/train.py

diff --git a/NER/lstm_ner_qc.py b/NER/lstm_ner_qc.py
index 50be877..bcf1af3 100644
--- a/NER/lstm_ner_qc.py
+++ b/NER/lstm_ner_qc.py
@@ -16,7 +16,7 @@ from seqeval.metrics import classification_report
 import pickle
 
 
-with open("dataset/lstm_ner_dataset.json", "r", encoding="utf-8") as f:
+with open("dataset/dataset_ner_srl.json", "r", encoding="utf-8") as f:
     data = json.load(f)
 
 
@@ -26,7 +26,7 @@ total_b_per = 0
 total_i_per = 0
 
 for idx, block in enumerate(data, start=1):
-    for token in block["labels"]:
+    for token in block["labels_ner"]:
         if token == "B-LOC":
             total_bLoc += 1
         elif token == "O":
@@ -43,7 +43,7 @@ print("Total I-PER:", total_i_per)
 print("Total B-PER + I-PER:", total_b_per + total_i_per)
 
 sentences = [[token.lower() for token in item["tokens"]] for item in data]
-labels = [item["labels"] for item in data]
+labels = [item["labels_ner"] for item in data]
 
 
 words = list(set(word for sentence in sentences for word in sentence))
diff --git a/NER/ner_bilstm_model.keras b/NER/ner_bilstm_model.keras
index 5932c291b84bb63e327ec242f711d94a9dde9471..9bcaf2a5e90aaca5b23d3404194898e871b867fa 100644
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diff --git a/NER/tag2idx.pkl b/NER/tag2idx.pkl
index 978ff9d6834e38e0281a5b8f099193cee06babf6..0bba3d3016e22163a9a8f25cbe9a09b245a29594 100644
GIT binary patch
literal 218
zcmXv|Ne;p=3~Zoj*w;^R34drIg2+uP(!ftRBJltp&Aa##wJUrYk7j%4pKmtYiw>wO
z+r|q)#wfauKrsrwAsC_dwr)=ZV>I60?q!n*a%RP&v8ZfvF%T3CgYSB+!NS|*gCm%5
zlmtxCRR4e(YtLos1@ob`CRj+8xRy-1bR}4Em7q1M2f>Da8M#H(66~Z(2JD~5ADopu
A`~Uy|

literal 116
zcmZo*nVQJ}0ku;!df1$FeLaJnr+72;usZ4b2f0u2X6#}0)b;TPiZS)Dc|xU`dl>zv
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diff --git a/NER/test_ner.py b/NER/test_ner.py
index a7f3dd8..58a589f 100644
--- a/NER/test_ner.py
+++ b/NER/test_ner.py
@@ -35,7 +35,7 @@ def predict_sentence(sentence):
 
 if __name__ == "__main__":
     try:
-        sentence = "korea adalah tempat lahir jun"
+        sentence = "saat ini indonesia adalah negara yang sangat indah"
         predict_sentence(sentence)
     except KeyboardInterrupt:
         print("\n\nSelesai.")
diff --git a/NER/word2idx.pkl b/NER/word2idx.pkl
index fc7e43ef8c77073cb2080b5bca998e703a330067..87c06847f11ee9dd9dae3826890adbfee394642f 100644
GIT binary patch
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diff --git a/NER_SRL/lses.py b/NER_SRL/lses.py
new file mode 100644
index 0000000..8e032b9
--- /dev/null
+++ b/NER_SRL/lses.py
@@ -0,0 +1,152 @@
+# 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
index 2f958dd..42c2e7f 100644
--- a/NER_SRL/lstm_ner_srl.ipynb
+++ b/NER_SRL/lstm_ner_srl.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 55,
    "id": "fcdce269",
    "metadata": {},
    "outputs": [],
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 56,
    "id": "92b6b57f",
    "metadata": {},
    "outputs": [
@@ -60,7 +60,7 @@
        "True"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 57,
    "id": "d568e8f2",
    "metadata": {},
    "outputs": [
@@ -82,45 +82,66 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "28 sentences\n",
+      "78 sentences\n",
       "=== NER LABEL COUNTS ===\n",
-      "O -> 338 labels\n",
-      "B-LOC -> 17 labels\n",
+      "O -> 1003 labels\n",
+      "B-LOC -> 59 labels\n",
       "V -> 2 labels\n",
-      "B-MISC -> 1 labels\n",
-      "B-TIME -> 1 labels\n",
-      "I-TIME -> 2 labels\n",
-      "I-LOC -> 1 labels\n",
-      "B-QUANT -> 2 labels\n",
-      "I-QUANT -> 3 labels\n",
-      "B-DATE -> 2 labels\n",
+      "B-MISC -> 3 labels\n",
+      "B-TIME -> 3 labels\n",
+      "I-TIME -> 4 labels\n",
+      "I-LOC -> 12 labels\n",
+      "B-QUANT -> 4 labels\n",
+      "I-QUANT -> 5 labels\n",
+      "B-DATE -> 4 labels\n",
+      "B-REL -> 2 labels\n",
+      "B-ETH -> 2 labels\n",
+      "I-ETH -> 2 labels\n",
+      "B-ORG -> 6 labels\n",
+      "I-ORG -> 4 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",
       "\n",
       "=== SRL LABEL COUNTS ===\n",
-      "ARG1 -> 116 labels\n",
-      "ARGM-LOC -> 13 labels\n",
+      "ARG1 -> 311 labels\n",
+      "ARGM-LOC -> 65 labels\n",
       "AM-NEG -> 2 labels\n",
-      "V -> 34 labels\n",
-      "ARGM-SRC -> 9 labels\n",
-      "O -> 69 labels\n",
+      "V -> 105 labels\n",
+      "ARGM-SRC -> 13 labels\n",
+      "O -> 194 labels\n",
       "AM-QUE -> 5 labels\n",
-      "ARGM-BNF -> 4 labels\n",
-      "ARG2 -> 24 labels\n",
-      "ARGM-MNR -> 1 labels\n",
-      "ARG0 -> 16 labels\n",
-      "AM-TMP -> 25 labels\n",
+      "ARGM-BNF -> 6 labels\n",
+      "ARG2 -> 178 labels\n",
+      "ARGM-MNR -> 7 labels\n",
+      "ARG0 -> 68 labels\n",
+      "AM-TMP -> 28 labels\n",
       "AM-PRP -> 1 labels\n",
       "AM-MOD -> 5 labels\n",
       "AM-ADV -> 1 labels\n",
       "AM-CAU -> 1 labels\n",
       "AM-EXT -> 6 labels\n",
-      "AM-MNR -> 9 labels\n",
+      "AM-MNR -> 13 labels\n",
       "AM-DIS -> 2 labels\n",
       "AM-FRQ -> 2 labels\n",
-      "ARGM-PNC -> 1 labels\n",
+      "ARGM-PNC -> 4 labels\n",
       "R-ARG1 -> 3 labels\n",
-      "AM-LOC -> 14 labels\n",
+      "AM-LOC -> 25 labels\n",
       "AM-DIR -> 4 labels\n",
-      "ARGM-CAU -> 3 labels\n"
+      "ARGM-CAU -> 13 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"
      ]
     }
    ],
@@ -157,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 58,
    "id": "95f16969",
    "metadata": {},
    "outputs": [
@@ -165,9 +186,9 @@
      "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', ':']]\n",
-      "new [['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', ':']] \n",
-      " 28\n"
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',', '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', 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'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', '.']]\n",
+      "new [['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', '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', '.']] \n",
+      " 78\n"
      ]
     }
    ],
@@ -229,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 59,
    "id": "e9653d99",
    "metadata": {},
    "outputs": [
@@ -237,14 +258,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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-      "{'mengelola': 2, 'memengaruhi': 3, 'mata': 4, 'yaitu': 5, 'april-september': 6, 'kehidupan': 7, 'matahari': 8, ')': 9, '.': 10, 'pencaharian': 11, 'cuaca': 12, 'bagaimana': 13, 'bangsa-bangsa': 14, 'selain': 15, 'kalian': 16, 'singkat': 17, 'tropis': 18, 'dapat': 19, '(': 20, 'kebiasaan': 21, 'barat': 22, 'lembaga': 23, 'lain': 24, 'masyarakat': 25, 'letak': 26, 'waktu': 27, 'sehari-hari': 28, 'dimiliki': 29, 'dengan': 30, 'hobi': 31, 'ke': 32, 'sumber': 33, 'hal': 34, 'karena': 35, 'mengoptimalkan': 36, 'relatif': 37, 'prakiraan': 38, 'bahan': 39, 'sedikit': 40, 'kedatangan': 41, 'dan': 42, 'musim': 43, 'kondisi': 44, 'kesenangan': 45, 'bagi': 46, 'peranan': 47, 'semakin': 48, 'makanan': 49, 'manusianya': 50, 'akan': 51, 'tentang': 52, 'juga': 53, 'sosial': 54, 'asia': 55, 'pada': 56, 'terluas': 57, 'wilayah': 58, 'negara': 59, 'sedangkan': 60, 'lautan': 61, 'oktober-maret': 62, 'pengaruh': 63, 'awal': 64, '1.910.932,37': 65, 'bersyukur': 66, 'iklim': 67, 'tersebut': 68, 'merupakan': 69, 'rata-rata': 70, 'timur': 71, 'kelembaban': 72, 'angin': 73, 'memperhatikan': 74, 'curah': 75, 'berikut': 76, 'jatuh': 77, 'perkembangan': 78, 'suhu': 79, 'tidak': 80, 'patut': 81, '5,8': 82, ',': 83, 'abad': 84, 'daya': 85, 'merancang': 86, 'juta': 87, 'udara': 88, 'adalah': 89, 'penting': 90, 'dalam': 91, 'bagian': 92, 'syarat': 93, 'mencapai': 94, 'ini': 95, 'indonesia': 96, 'unsur-unsur': 97, 'hidrat': 98, 'hujan': 99, 'keragaman': 100, 'diamati': 101, 'tanaman': 102, 'sempit': 103, 'bahwa': 104, 'berhubungan': 105, 'memenuhi': 106, 'keadaan': 107, 'menguap': 108, 'kaya': 109, 'pariwisata': 110, 'membincangkan': 111, 'di': 112, '?': 113, 'suatu': 114, 'asing': 115, 'tahunan': 116, 'memahami': 117, 'tenggara': 118, 'dua': 119, 'telah': 120, 'lalu': 121, 'sering': 122, 'menguntungkan': 123, 'proses': 124, 'inilah': 125, 'antara': 126, 'bangsa': 127, ':': 128, 'hindu-buddha': 129, 'budaya': 130, 'harian': 131, 'keberagaman': 132, 'arang': 133, 'daratan': 134, 'terhadap': 135, 'tentu': 136, 'kemarau': 137, 'sudah': 138, 'potensi': 139, 'apakah': 140, 'menarik': 141, 'lepas': 142, 'yang': 143, 'geologis': 144, 'hubungannya': 145, 'dimilikinya': 146, 'sangat': 147, 'sebesar': 148, 'baik': 149, 'penyinaran': 150, 'pokok': 151, 'sejak': 152, 'varietas': 153, 'saat': 154, 'mengolah': 155, 'bulan': 156, 'perlu': 157, 'manusia': 158, 'masehi': 159, 'luas': 160, 'pelayaran': 161, 'banyak': 162, 'dari': 163, 'alam': 164, 'ribuan': 165, 'terjadi': 166, 'memiliki': 167, 'tertentu': 168, 'mewadahi': 169, 'terutama': 170, 'km2': 171, 'tahun': 172, 'kegiatan': 173, 'geografis': 174, 'untuk': 175, 'perdagangan': 176, 'aktivitas': 177, 'PAD': 0, 'UNK': 1}\n",
-      "['B-DATE', 'B-LOC', 'B-MISC', 'B-QUANT', 'B-TIME', 'I-LOC', 'I-QUANT', 'I-TIME', 'O', 'V']\n",
-      "['AM-ADV', 'AM-CAU', 'AM-DIR', 'AM-DIS', 'AM-EXT', 'AM-FRQ', 'AM-LOC', 'AM-MNR', 'AM-MOD', 'AM-NEG', 'AM-PRP', 'AM-QUE', 'AM-TMP', 'ARG0', 'ARG1', 'ARG2', 'ARGM-BNF', 'ARGM-CAU', 'ARGM-LOC', 'ARGM-MNR', 'ARGM-PNC', 'ARGM-SRC', 'O', 'R-ARG1', 'V']\n",
-      "{'B-DATE': 0, 'B-LOC': 1, 'B-MISC': 2, 'B-QUANT': 3, 'B-TIME': 4, 'I-LOC': 5, 'I-QUANT': 6, 'I-TIME': 7, 'O': 8, 'V': 9}\n",
-      "{'AM-ADV': 0, 'AM-CAU': 1, 'AM-DIR': 2, 'AM-DIS': 3, 'AM-EXT': 4, 'AM-FRQ': 5, 'AM-LOC': 6, 'AM-MNR': 7, 'AM-MOD': 8, 'AM-NEG': 9, 'AM-PRP': 10, 'AM-QUE': 11, 'AM-TMP': 12, 'ARG0': 13, 'ARG1': 14, 'ARG2': 15, 'ARGM-BNF': 16, 'ARGM-CAU': 17, 'ARGM-LOC': 18, 'ARGM-MNR': 19, 'ARGM-PNC': 20, 'ARGM-SRC': 21, 'O': 22, 'R-ARG1': 23, 'V': 24}\n",
-      "{0: 'B-DATE', 1: 'B-LOC', 2: 'B-MISC', 3: 'B-QUANT', 4: 'B-TIME', 5: 'I-LOC', 6: 'I-QUANT', 7: 'I-TIME', 8: 'O', 9: 'V'}\n",
-      "{0: 'AM-ADV', 1: 'AM-CAU', 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-PRP', 11: 'AM-QUE', 12: 'AM-TMP', 13: 'ARG0', 14: 'ARG1', 15: 'ARG2', 16: 'ARGM-BNF', 17: 'ARGM-CAU', 18: 'ARGM-LOC', 19: 'ARGM-MNR', 20: 'ARGM-PNC', 21: 'ARGM-SRC', 22: 'O', 23: 'R-ARG1', 24: 'V'}\n"
+      "['baik', 'daratan', 'suatu', 'suhu', 'bumi', 'menemukan', 'memengaruhi', 'sosial', 'letak', 'hidrat', 'perairan', 'geologis', 'puluhan', 'lain', 'kalah', 'penyebab', 'emas', 'tambang', 'hindu-buddha', 'simak', 'pemandangan', 'sesuatu', 'makanan', 'sedikit', 'intelegensi', 'wilayah', 'kitab', 'tertentu', 'varietas', 'atau', 'kemudian', 'kapal', 'mata', 'juta', 'darussalam', 'tanahnya', 'sangat', 'kelima', 'kegiatan', 'liar', 'hilangnya', 'april-september', 'pelayaran', 'pencaharian', 'sih', 'udara', 'merancang', 'pelajari', 'negara-negara', 'lawu', 'keagamaan', 'kualitas', 'iklim', 'asia', 'alat', 'laporan', 'cave', 'upacara', 'urutan', 'kesenangan', 'bahan', 'mengelola', 'mengurangi', 'orang', 'mencakup', 'bersyukur', 'tanah', 'banyak', 'diamati', 'mencerminkan', 'raga', 'rata-rata', 'jatuh', 'kemarau', 'bahwa', 'masyarakat', 'budaya', 'meningkatkan', 'antara', 'peranan', 'memungkinkan', 'maritim', 'apabila', 'apakah', '.', 'tanaman', 'yang', 'ke', 'tenggara', 'cadangan', 'setiap', 'penjelasan', 'sendiri', 'kepribadian-kepribadian', 'harinya', '270', 'individu', 'pecaharian', 'yuk', '2016', 'potensi', 'pada', 'kehidupan', 'bagaimana', 'dimanfaatkan', '1.910.932,37', 'berada', 'luas', 'terhadap', 'membentuk', 'hobi', 'sawah', 'alami', 'bagian', 'dapat', 'tidak', 'grasberg', 'kelestariannya', 'patut', 'menarik', 'kesatuan', 'penduduk', 'sejarah', 'periode', ',', 'karakteristik', 'asing', 'ragam', 'menjadi', 'istiadat', 'oleh', 'syarat', 'kelembaban', 'daya', 'untuk', 'contoh', 'semakin', 'perak', 'pinisinya', 'mencapai', 'ini', 'berbagi', 'selama', 'tata', 'yakni', 'mcmorran', 'kemaritiman', 'perdagangan', '(', 'termasuk', 'awal', 'tembaga', 'hutan', 'pit', 'suku', 'pusat', 'tahun', 'diperbarui', 'renewable', 'jika', 'melalui', 'dibedakan', 'budayaanya', 'membedakan', 'secara', 'manfaatnya', 'merupakan', 'apa', 'keragaman', 'tinggalmu', 'bukan', 'tempat', 'mengolah', 'open', 'dmlz', 'peringkat', 'atas', 'ketenagakerjaan', 'proses', 'daerah', 'badan', 'tentang', 'block', 'terjadi', 'jiwa', '?', 'bangsa-bangsa', 'perkembangan', 'jauh', 'doz', 'keberagaman', 'adat', 'sehari-hari', 'air', 'dirinci', 'dibandingkan', 'km2', '2017', 'tercatat', 'dengan', 'terdiri', 'salah', 'pariwisata', 'praktik', 'lingkungan', 'asean', 'keturunan', 'menguap', 'membatasi', 'fisik', 'semangat', 'unsur', 'bugis', 'memiliki', 'singkat', 'kering', 'lautan', 'sama', 'non', 'inilah', 'kaya', 'thailand', 'dibahas', 'serangkaian', 'cenderung', 'nilai-nilai', 'statistik', 'beragam', 'sering', 'berikut', 'membantu', 'kedalaman', 'begitu', 'manusia', 'berharga', 'kebutuhan', 'sekitarnya', 'pokok', 'besar', 'tropis', 'telah', 'saat', 'tiga', 'hukum', ')', 'sebesar', 'penyerbukan', 'penting', 'cuaca', 'lembaga', 'perbedaan', 'bulan', 'batu', 'fotosintesis', 'bagi', 'kerja', 'pola', 'kandungan', 'cara', 'resources', 'aktivitas', 'perlu', 'selain', 'segala', 'papua', 'modal', 'juga', 'sumber', 'tentu', 'simbol', 'kelompok', 'disebabkan', 'sejak', 'barang-barang', 'waktu', 'menghasilkan', 'barat', 'jumlah', 'angin', 'lepas', 'sekitar', 'mineral', 'ribuan', 'berupa', 'masehi', 'gossan', 'nelayan', 'beberapa', 'kedatangan', 'agama', 'misalnya', 'tahunan', 'manusianya', 'relatif', 'arang', 'lebih', 'resource', 'kebudayaan', 'faktor', 'bangsa', '5,8', 'teks', 'kalian', 'sedangkan', 'dihasilkan', 'mempunyai', '127,67', 'pertambangan', 'berbagai', 'memperhatikan', 'tingkah', '6', 'memenuhi', 'sebuah', 'malaysia', 'berhubungan', 'oktober-maret', 'di', 'disegani', 'sehingga', 'abad', 'mencatat', 'timur', 'berpakaian', 'mengikis', 'meliputi', 'masih', 'dipengaruhi', 'inc', 'jenis', 'hanya', 'negara', 'kondisi', 'dimilikinya', 'keuangan', 'norma', 'dua', 'hujan', 'keadaan', 'hidup', 'bara', 'bertahan', 'pengaruh', 'kucing', 'akan', 'ruang', 'hal', 'harian', 'geografis', 'sudah', 'minyak', 'berlimpah', 'adalah', 'dalam', 'naluri', 'karena', 'orang-orang', 'tersebut', 'alam', 'seperti', 'kita', 'perilaku', 'kekayaan', 'bertemu', 'berinteraksi', 'seseorang', 'prakiraan', 'kadar', 'beraneka', 'berdasarkan', 'musim', 'memahami', 'ada', 'dianggap', 'unsur-unsur', 'kebiasaan', 'pribadi', 'kemajuan', 'dan', 'senjata', 'sebagai', 'terdapat', 'indonesia', 'dari', 'sempit', 'lalu', 'dimiliki', 'angkatan', 'tinggal', 'bermata', 'curah', 'pembangunan', 'diperoleh', 'keyakinan', 'hubungannya', 'itu', 'mewadahi', 'mengoptimalkan', 'freeport', 'big', 'membincangkan', 'sdm', 'brunei', 'nafsu', 'satu', 'matahari', 'penyinaran', 'pengetahuan', 'perkotaan', 'silahkan', 'kuat', 'laku', 'bawah', 'terluas', 'karakter', 'yaitu', 'pembelajaran', 'bajak', 'rancangan', 'menempati', 'singapura', 'kombinasi', 'menguntungkan', 'terutama', 'permukaan']\n",
+      "{'baik': 2, 'daratan': 3, 'suatu': 4, 'suhu': 5, 'bumi': 6, 'menemukan': 7, 'memengaruhi': 8, 'sosial': 9, 'letak': 10, 'hidrat': 11, 'perairan': 12, 'geologis': 13, 'puluhan': 14, 'lain': 15, 'kalah': 16, 'penyebab': 17, 'emas': 18, 'tambang': 19, 'hindu-buddha': 20, 'simak': 21, 'pemandangan': 22, 'sesuatu': 23, 'makanan': 24, 'sedikit': 25, 'intelegensi': 26, 'wilayah': 27, 'kitab': 28, 'tertentu': 29, 'varietas': 30, 'atau': 31, 'kemudian': 32, 'kapal': 33, 'mata': 34, 'juta': 35, 'darussalam': 36, 'tanahnya': 37, 'sangat': 38, 'kelima': 39, 'kegiatan': 40, 'liar': 41, 'hilangnya': 42, 'april-september': 43, 'pelayaran': 44, 'pencaharian': 45, 'sih': 46, 'udara': 47, 'merancang': 48, 'pelajari': 49, 'negara-negara': 50, 'lawu': 51, 'keagamaan': 52, 'kualitas': 53, 'iklim': 54, 'asia': 55, 'alat': 56, 'laporan': 57, 'cave': 58, 'upacara': 59, 'urutan': 60, 'kesenangan': 61, 'bahan': 62, 'mengelola': 63, 'mengurangi': 64, 'orang': 65, 'mencakup': 66, 'bersyukur': 67, 'tanah': 68, 'banyak': 69, 'diamati': 70, 'mencerminkan': 71, 'raga': 72, 'rata-rata': 73, 'jatuh': 74, 'kemarau': 75, 'bahwa': 76, 'masyarakat': 77, 'budaya': 78, 'meningkatkan': 79, 'antara': 80, 'peranan': 81, 'memungkinkan': 82, 'maritim': 83, 'apabila': 84, 'apakah': 85, '.': 86, 'tanaman': 87, 'yang': 88, 'ke': 89, 'tenggara': 90, 'cadangan': 91, 'setiap': 92, 'penjelasan': 93, 'sendiri': 94, 'kepribadian-kepribadian': 95, 'harinya': 96, '270': 97, 'individu': 98, 'pecaharian': 99, 'yuk': 100, '2016': 101, 'potensi': 102, 'pada': 103, 'kehidupan': 104, 'bagaimana': 105, 'dimanfaatkan': 106, '1.910.932,37': 107, 'berada': 108, 'luas': 109, 'terhadap': 110, 'membentuk': 111, 'hobi': 112, 'sawah': 113, 'alami': 114, 'bagian': 115, 'dapat': 116, 'tidak': 117, 'grasberg': 118, 'kelestariannya': 119, 'patut': 120, 'menarik': 121, 'kesatuan': 122, 'penduduk': 123, 'sejarah': 124, 'periode': 125, ',': 126, 'karakteristik': 127, 'asing': 128, 'ragam': 129, 'menjadi': 130, 'istiadat': 131, 'oleh': 132, 'syarat': 133, 'kelembaban': 134, 'daya': 135, 'untuk': 136, 'contoh': 137, 'semakin': 138, 'perak': 139, 'pinisinya': 140, 'mencapai': 141, 'ini': 142, 'berbagi': 143, 'selama': 144, 'tata': 145, 'yakni': 146, 'mcmorran': 147, 'kemaritiman': 148, 'perdagangan': 149, '(': 150, 'termasuk': 151, 'awal': 152, 'tembaga': 153, 'hutan': 154, 'pit': 155, 'suku': 156, 'pusat': 157, 'tahun': 158, 'diperbarui': 159, 'renewable': 160, 'jika': 161, 'melalui': 162, 'dibedakan': 163, 'budayaanya': 164, 'membedakan': 165, 'secara': 166, 'manfaatnya': 167, 'merupakan': 168, 'apa': 169, 'keragaman': 170, 'tinggalmu': 171, 'bukan': 172, 'tempat': 173, 'mengolah': 174, 'open': 175, 'dmlz': 176, 'peringkat': 177, 'atas': 178, 'ketenagakerjaan': 179, 'proses': 180, 'daerah': 181, 'badan': 182, 'tentang': 183, 'block': 184, 'terjadi': 185, 'jiwa': 186, '?': 187, 'bangsa-bangsa': 188, 'perkembangan': 189, 'jauh': 190, 'doz': 191, 'keberagaman': 192, 'adat': 193, 'sehari-hari': 194, 'air': 195, 'dirinci': 196, 'dibandingkan': 197, 'km2': 198, '2017': 199, 'tercatat': 200, 'dengan': 201, 'terdiri': 202, 'salah': 203, 'pariwisata': 204, 'praktik': 205, 'lingkungan': 206, 'asean': 207, 'keturunan': 208, 'menguap': 209, 'membatasi': 210, 'fisik': 211, 'semangat': 212, 'unsur': 213, 'bugis': 214, 'memiliki': 215, 'singkat': 216, 'kering': 217, 'lautan': 218, 'sama': 219, 'non': 220, 'inilah': 221, 'kaya': 222, 'thailand': 223, 'dibahas': 224, 'serangkaian': 225, 'cenderung': 226, 'nilai-nilai': 227, 'statistik': 228, 'beragam': 229, 'sering': 230, 'berikut': 231, 'membantu': 232, 'kedalaman': 233, 'begitu': 234, 'manusia': 235, 'berharga': 236, 'kebutuhan': 237, 'sekitarnya': 238, 'pokok': 239, 'besar': 240, 'tropis': 241, 'telah': 242, 'saat': 243, 'tiga': 244, 'hukum': 245, ')': 246, 'sebesar': 247, 'penyerbukan': 248, 'penting': 249, 'cuaca': 250, 'lembaga': 251, 'perbedaan': 252, 'bulan': 253, 'batu': 254, 'fotosintesis': 255, 'bagi': 256, 'kerja': 257, 'pola': 258, 'kandungan': 259, 'cara': 260, 'resources': 261, 'aktivitas': 262, 'perlu': 263, 'selain': 264, 'segala': 265, 'papua': 266, 'modal': 267, 'juga': 268, 'sumber': 269, 'tentu': 270, 'simbol': 271, 'kelompok': 272, 'disebabkan': 273, 'sejak': 274, 'barang-barang': 275, 'waktu': 276, 'menghasilkan': 277, 'barat': 278, 'jumlah': 279, 'angin': 280, 'lepas': 281, 'sekitar': 282, 'mineral': 283, 'ribuan': 284, 'berupa': 285, 'masehi': 286, 'gossan': 287, 'nelayan': 288, 'beberapa': 289, 'kedatangan': 290, 'agama': 291, 'misalnya': 292, 'tahunan': 293, 'manusianya': 294, 'relatif': 295, 'arang': 296, 'lebih': 297, 'resource': 298, 'kebudayaan': 299, 'faktor': 300, 'bangsa': 301, '5,8': 302, 'teks': 303, 'kalian': 304, 'sedangkan': 305, 'dihasilkan': 306, 'mempunyai': 307, '127,67': 308, 'pertambangan': 309, 'berbagai': 310, 'memperhatikan': 311, 'tingkah': 312, '6': 313, 'memenuhi': 314, 'sebuah': 315, 'malaysia': 316, 'berhubungan': 317, 'oktober-maret': 318, 'di': 319, 'disegani': 320, 'sehingga': 321, 'abad': 322, 'mencatat': 323, 'timur': 324, 'berpakaian': 325, 'mengikis': 326, 'meliputi': 327, 'masih': 328, 'dipengaruhi': 329, 'inc': 330, 'jenis': 331, 'hanya': 332, 'negara': 333, 'kondisi': 334, 'dimilikinya': 335, 'keuangan': 336, 'norma': 337, 'dua': 338, 'hujan': 339, 'keadaan': 340, 'hidup': 341, 'bara': 342, 'bertahan': 343, 'pengaruh': 344, 'kucing': 345, 'akan': 346, 'ruang': 347, 'hal': 348, 'harian': 349, 'geografis': 350, 'sudah': 351, 'minyak': 352, 'berlimpah': 353, 'adalah': 354, 'dalam': 355, 'naluri': 356, 'karena': 357, 'orang-orang': 358, 'tersebut': 359, 'alam': 360, 'seperti': 361, 'kita': 362, 'perilaku': 363, 'kekayaan': 364, 'bertemu': 365, 'berinteraksi': 366, 'seseorang': 367, 'prakiraan': 368, 'kadar': 369, 'beraneka': 370, 'berdasarkan': 371, 'musim': 372, 'memahami': 373, 'ada': 374, 'dianggap': 375, 'unsur-unsur': 376, 'kebiasaan': 377, 'pribadi': 378, 'kemajuan': 379, 'dan': 380, 'senjata': 381, 'sebagai': 382, 'terdapat': 383, 'indonesia': 384, 'dari': 385, 'sempit': 386, 'lalu': 387, 'dimiliki': 388, 'angkatan': 389, 'tinggal': 390, 'bermata': 391, 'curah': 392, 'pembangunan': 393, 'diperoleh': 394, 'keyakinan': 395, 'hubungannya': 396, 'itu': 397, 'mewadahi': 398, 'mengoptimalkan': 399, 'freeport': 400, 'big': 401, 'membincangkan': 402, 'sdm': 403, 'brunei': 404, 'nafsu': 405, 'satu': 406, 'matahari': 407, 'penyinaran': 408, 'pengetahuan': 409, 'perkotaan': 410, 'silahkan': 411, 'kuat': 412, 'laku': 413, 'bawah': 414, 'terluas': 415, 'karakter': 416, 'yaitu': 417, 'pembelajaran': 418, 'bajak': 419, 'rancangan': 420, 'menempati': 421, 'singapura': 422, 'kombinasi': 423, 'menguntungkan': 424, 'terutama': 425, 'permukaan': 426, 'PAD': 0, 'UNK': 1}\n",
+      "['B-DATE', 'B-ETH', 'B-LOC', 'B-MIN', 'B-MISC', 'B-ORG', 'B-QUANT', 'B-REL', 'B-RES', 'B-TERM', 'B-TIME', 'I-ETH', 'I-LOC', 'I-ORG', 'I-QUANT', 'I-RES', 'I-TERM', 'I-TIME', 'O', 'V']\n",
+      "['AM-ADV', 'AM-CAU', 'AM-COM', 'AM-DIR', 'AM-DIS', 'AM-EXT', 'AM-FRQ', 'AM-LOC', 'AM-MNR', 'AM-MOD', 'AM-NEG', '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', 'O', 'R-ARG1', 'V']\n",
+      "{'B-DATE': 0, 'B-ETH': 1, 'B-LOC': 2, 'B-MIN': 3, 'B-MISC': 4, 'B-ORG': 5, 'B-QUANT': 6, 'B-REL': 7, 'B-RES': 8, 'B-TERM': 9, 'B-TIME': 10, 'I-ETH': 11, 'I-LOC': 12, 'I-ORG': 13, 'I-QUANT': 14, 'I-RES': 15, 'I-TERM': 16, 'I-TIME': 17, 'O': 18, 'V': 19}\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-PRP': 11, 'AM-QUE': 12, 'AM-TMP': 13, 'ARG0': 14, 'ARG1': 15, 'ARG2': 16, 'ARG3': 17, 'ARGM-BNF': 18, 'ARGM-CAU': 19, 'ARGM-COM': 20, 'ARGM-DIS': 21, 'ARGM-EX': 22, 'ARGM-EXT': 23, 'ARGM-LOC': 24, 'ARGM-MNR': 25, 'ARGM-MOD': 26, 'ARGM-NEG': 27, 'ARGM-PNC': 28, 'ARGM-PRD': 29, 'ARGM-PRP': 30, 'ARGM-SRC': 31, 'ARGM-TMP': 32, 'O': 33, 'R-ARG1': 34, 'V': 35}\n",
+      "{0: 'B-DATE', 1: 'B-ETH', 2: 'B-LOC', 3: 'B-MIN', 4: 'B-MISC', 5: 'B-ORG', 6: 'B-QUANT', 7: 'B-REL', 8: 'B-RES', 9: 'B-TERM', 10: 'B-TIME', 11: 'I-ETH', 12: 'I-LOC', 13: 'I-ORG', 14: 'I-QUANT', 15: 'I-RES', 16: 'I-TERM', 17: 'I-TIME', 18: 'O', 19: 'V'}\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-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: 'O', 34: 'R-ARG1', 35: 'V'}\n"
      ]
     }
    ],
@@ -273,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 60,
    "id": "9d3a37b3",
    "metadata": {},
    "outputs": [
@@ -281,1920 +302,8227 @@
      "name": "stdout",
      "output_type": "stream",
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       "y_ner \n",
       " \n",
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-      " [8 8 8 8 1 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8]\n",
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-      " [8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8]]\n",
+      "[[18 18 18 ... 18 18 18]\n",
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+      " [18 18 18 ... 18 18 18]\n",
+      " ...\n",
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       "y_srl \n",
       " \n",
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-      " [12 14 14 14  2 15 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22]]\n",
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+      " ...\n",
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       "y_ner cat \n",
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+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 1.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 1.],\n",
+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
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+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
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+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]]), array([[0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       [0., 0., 0., ..., 0., 0., 0.],\n",
+      "       ...,\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.],\n",
+      "       [0., 0., 0., ..., 1., 0., 0.]])]\n"
      ]
     }
    ],
@@ -2205,10 +8533,12 @@
     "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 = max(len(x) for x in X)\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",
@@ -2225,7 +8555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 61,
    "id": "a5c264df",
    "metadata": {},
    "outputs": [],
@@ -2241,7 +8571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 62,
    "id": "712c1789",
    "metadata": {},
    "outputs": [
@@ -2264,19 +8594,19 @@
        "<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\">22</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\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\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">11,392</span> │ input_layer_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\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\">27,328</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\">22</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",
+       "│ 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\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │ bidirectional_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\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\">20</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,580</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\">22</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",
+       "│ 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\">36</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,644</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"
@@ -2285,19 +8615,19 @@
        "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\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;34m22\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ -                 │\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;34m22\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │     \u001b[38;5;34m11,392\u001b[0m │ input_layer_4[\u001b[38;5;34m0\u001b[0m]… │\n",
+       "│ embedding_4         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │     \u001b[38;5;34m27,328\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;34m22\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",
+       "│ 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;34m22\u001b[0m, \u001b[38;5;34m10\u001b[0m)    │      \u001b[38;5;34m1,290\u001b[0m │ bidirectional_4[\u001b[38;5;34m…\u001b[0m │\n",
+       "│ ner_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m20\u001b[0m)    │      \u001b[38;5;34m2,580\u001b[0m │ bidirectional_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;34m22\u001b[0m, \u001b[38;5;34m25\u001b[0m)    │      \u001b[38;5;34m3,225\u001b[0m │ bidirectional_4[\u001b[38;5;34m…\u001b[0m │\n",
+       "│ srl_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m36\u001b[0m)    │      \u001b[38;5;34m4,644\u001b[0m │ bidirectional_4[\u001b[38;5;34m…\u001b[0m │\n",
        "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │                   │\n",
        "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
       ]
@@ -2308,11 +8638,11 @@
     {
      "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\">81,955</span> (320.14 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\">100,600</span> (392.97 KB)\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m81,955\u001b[0m (320.14 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m100,600\u001b[0m (392.97 KB)\n"
       ]
      },
      "metadata": {},
@@ -2321,11 +8651,11 @@
     {
      "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\">81,955</span> (320.14 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\">100,600</span> (392.97 KB)\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m81,955\u001b[0m (320.14 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m100,600\u001b[0m (392.97 KB)\n"
       ]
      },
      "metadata": {},
@@ -2365,7 +8695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 63,
    "id": "98feee87",
    "metadata": {},
    "outputs": [
@@ -2374,30 +8704,30 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/10\n",
-      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 5.4074 - ner_output_accuracy: 0.7423 - ner_output_loss: 2.2128 - srl_output_accuracy: 0.2337 - srl_output_loss: 3.1946 - val_loss: 4.5710 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 1.6993 - val_srl_output_accuracy: 0.6061 - val_srl_output_loss: 2.9475\n",
+      "\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 30ms/step - loss: 4.8129 - ner_output_accuracy: 0.9343 - ner_output_loss: 1.9817 - srl_output_accuracy: 0.6363 - srl_output_loss: 2.8312 - val_loss: 1.0304 - val_ner_output_accuracy: 0.9500 - val_ner_output_loss: 0.3266 - val_srl_output_accuracy: 0.7825 - val_srl_output_loss: 0.7039\n",
       "Epoch 2/10\n",
-      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.1426 - ner_output_accuracy: 0.9434 - ner_output_loss: 1.3429 - srl_output_accuracy: 0.5027 - srl_output_loss: 2.7998 - val_loss: 1.8463 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3212 - val_srl_output_accuracy: 0.6061 - val_srl_output_loss: 1.6901\n",
+      "\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.2668 - ner_output_accuracy: 0.9576 - ner_output_loss: 0.3078 - srl_output_accuracy: 0.7510 - srl_output_loss: 0.9590 - val_loss: 0.8654 - val_ner_output_accuracy: 0.9500 - val_ner_output_loss: 0.2764 - val_srl_output_accuracy: 0.8550 - val_srl_output_loss: 0.5891\n",
       "Epoch 3/10\n",
-      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.1865 - ner_output_accuracy: 0.9540 - ner_output_loss: 0.3207 - srl_output_accuracy: 0.5062 - srl_output_loss: 1.8658 - val_loss: 1.5277 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3891 - val_srl_output_accuracy: 0.6061 - val_srl_output_loss: 1.3498\n",
+      "\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.0249 - ner_output_accuracy: 0.9636 - ner_output_loss: 0.2245 - srl_output_accuracy: 0.7679 - srl_output_loss: 0.8004 - val_loss: 0.8157 - val_ner_output_accuracy: 0.9500 - val_ner_output_loss: 0.2696 - val_srl_output_accuracy: 0.8600 - val_srl_output_loss: 0.5461\n",
       "Epoch 4/10\n",
-      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.8984 - ner_output_accuracy: 0.9315 - ner_output_loss: 0.4461 - srl_output_accuracy: 0.5915 - srl_output_loss: 1.4523 - val_loss: 1.4642 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3424 - val_srl_output_accuracy: 0.6818 - val_srl_output_loss: 1.2855\n",
+      "\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.9711 - ner_output_accuracy: 0.9491 - ner_output_loss: 0.2695 - srl_output_accuracy: 0.7957 - srl_output_loss: 0.7016 - val_loss: 0.8024 - val_ner_output_accuracy: 0.9500 - val_ner_output_loss: 0.2703 - val_srl_output_accuracy: 0.8600 - val_srl_output_loss: 0.5321\n",
       "Epoch 5/10\n",
-      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.8848 - ner_output_accuracy: 0.9281 - ner_output_loss: 0.3844 - srl_output_accuracy: 0.6055 - srl_output_loss: 1.5004 - val_loss: 1.3921 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3394 - val_srl_output_accuracy: 0.7121 - val_srl_output_loss: 1.2169\n",
+      "\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.8603 - ner_output_accuracy: 0.9654 - ner_output_loss: 0.1976 - srl_output_accuracy: 0.8146 - srl_output_loss: 0.6628 - val_loss: 0.7739 - val_ner_output_accuracy: 0.9500 - val_ner_output_loss: 0.2624 - val_srl_output_accuracy: 0.8700 - val_srl_output_loss: 0.5115\n",
       "Epoch 6/10\n",
-      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.7063 - ner_output_accuracy: 0.9498 - ner_output_loss: 0.2498 - srl_output_accuracy: 0.5942 - srl_output_loss: 1.4566 - val_loss: 1.3408 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3570 - val_srl_output_accuracy: 0.7121 - val_srl_output_loss: 1.1573\n",
+      "\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.9369 - ner_output_accuracy: 0.9689 - ner_output_loss: 0.1662 - srl_output_accuracy: 0.7877 - srl_output_loss: 0.7708 - val_loss: 0.7699 - val_ner_output_accuracy: 0.9500 - val_ner_output_loss: 0.2638 - val_srl_output_accuracy: 0.8700 - val_srl_output_loss: 0.5061\n",
       "Epoch 7/10\n",
-      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.7423 - ner_output_accuracy: 0.9510 - ner_output_loss: 0.2533 - srl_output_accuracy: 0.5673 - srl_output_loss: 1.4890 - val_loss: 1.3257 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3620 - val_srl_output_accuracy: 0.7121 - val_srl_output_loss: 1.1422\n",
+      "\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.8213 - ner_output_accuracy: 0.9669 - ner_output_loss: 0.1638 - srl_output_accuracy: 0.8177 - srl_output_loss: 0.6575 - val_loss: 0.7435 - val_ner_output_accuracy: 0.9500 - val_ner_output_loss: 0.2585 - val_srl_output_accuracy: 0.8725 - val_srl_output_loss: 0.4851\n",
       "Epoch 8/10\n",
-      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.5830 - ner_output_accuracy: 0.9456 - ner_output_loss: 0.2462 - srl_output_accuracy: 0.6336 - srl_output_loss: 1.3368 - val_loss: 1.3145 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3698 - val_srl_output_accuracy: 0.6970 - val_srl_output_loss: 1.1328\n",
+      "\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.7950 - ner_output_accuracy: 0.9632 - ner_output_loss: 0.1834 - srl_output_accuracy: 0.8328 - srl_output_loss: 0.6116 - val_loss: 0.7397 - val_ner_output_accuracy: 0.9500 - val_ner_output_loss: 0.2531 - val_srl_output_accuracy: 0.8750 - val_srl_output_loss: 0.4866\n",
       "Epoch 9/10\n",
-      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.4591 - ner_output_accuracy: 0.9449 - ner_output_loss: 0.2502 - srl_output_accuracy: 0.6599 - srl_output_loss: 1.2089 - val_loss: 1.2947 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3815 - val_srl_output_accuracy: 0.6970 - val_srl_output_loss: 1.1036\n",
+      "\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.7290 - ner_output_accuracy: 0.9734 - ner_output_loss: 0.1391 - srl_output_accuracy: 0.8389 - srl_output_loss: 0.5899 - val_loss: 0.7254 - val_ner_output_accuracy: 0.9500 - val_ner_output_loss: 0.2459 - val_srl_output_accuracy: 0.8800 - val_srl_output_loss: 0.4794\n",
       "Epoch 10/10\n",
-      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5367 - ner_output_accuracy: 0.9705 - ner_output_loss: 0.1523 - srl_output_accuracy: 0.5899 - srl_output_loss: 1.3844 - val_loss: 1.2802 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3883 - val_srl_output_accuracy: 0.7273 - val_srl_output_loss: 1.0798\n"
+      "\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.6823 - ner_output_accuracy: 0.9706 - ner_output_loss: 0.1407 - srl_output_accuracy: 0.8542 - srl_output_loss: 0.5415 - val_loss: 0.7060 - val_ner_output_accuracy: 0.9500 - val_ner_output_loss: 0.2435 - val_srl_output_accuracy: 0.8800 - val_srl_output_loss: 0.4625\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -2407,7 +8737,7 @@
     },
     {
      "data": {
-      "image/png": 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SXl6qcuedarBgvkJHjiCUAgAAAEqpQo+YWrlypRYvXqygoCBZrVZZrVZdccUVGjdunIYNG6a///67eCoFAOAsGQcOKP6995Twvx8lt1uyWBTQ60YFDx0q7zp1zC4PAAAAwHkUOphyuVyqXLmyJCkoKEiHDx9WkyZNFBERoW3bthVHjQAA5OCMiVH8Bx/o5NyvpcxMSVLlrl0U9Nhj8mnc2OzyAAAAABRQoYOpyMhI/fPPP6pXr546duyoCRMmyNvbWx9++KHq169fPFUCACAp8/hxHfvwI5344gsZ6emSJP8rrlDw44/LNyrS7PIAAAAAFFKhg6lRo0YpOTlZkvTKK6/oxhtv1JVXXqnq1atrzpw5xVEjAKCCcyUl6fiMGTo+81O5U1IkSb7t2inkicfld8klZpcHAAAA4AIVOpjq3r2753nDhg21detWHT9+XFWrVvXcmQ8AgKLgTknR8c9n6dj06XInJEiSfFq0UPATj8v/iiv4uQMAAACUcYUKppxOp3x9fbV+/XpFRv47ZaJatWrFURsAoIJyZ2To5JdzFP/hh3LFx0uSvBs2UPCwYarctSuBFAAAAFBOFCqYstvtqlOnjlwuV/FVBACosIzMTJ389lvFv/+BMo8ckSTZw8MV/NhQBdxwgyw2m9klAgAAAChChZ7K98ILL+j555/XZ599xkgpAECRMNxuJc77WXHvTpFz335JkldoqIIeeURVbu0ti91udokAAAAAikGhg6n33ntPO3fuVK1atRQRESF/f/8cx9etW1eU9QEAyjHDMHRq8WLFTZ6i9O3bJUm2atVU/YEhqtq3r6wOh9klAgAAAChGhQ6mbrnlluKpBABQYRiGoeQVKxQ3eYrSNmyQJFkrV1b1wfepWv/+sp71jx4AAAAAyqdCB1OjR48unkoAABVCyrq/FTdpklLWrJEkWXx9Va1/f1UffJ9sgYFmlwcAAACgBBU6mAIA4EKkbd6s2MmTlfz7UkmSxW5Xlb59FPTAA/IKCjK7PAAAAAAmKHQwZbVaz3mbbu7YBwA4U/quXYqb8q6S5s/P2mGzqcqttyrokYdlr1nT7PIAAAAAmKjQwdS3336bY9vpdOrvv//Wp59+qjFjxhRlbQCAMizj4EHFvzdVCT/8ILndksWigBtuUPDQR+Vdt67Z5QEAAAAoBQodTN1888259t1+++1q0aKF5syZo8GDBxdVbQCAMsgZE6v4aR/o5NyvJadTklSpy3UKfmyYfJo0Nrs8AAAAAKVIka0x1alTJz3wwANF9XIAgDIm88QJHfvoY52YNUtGerokyf/yyxX8xOPyjYoyuzwAAAAApVCRBFOpqamaMmWKwsLCiuLlAABliCspScdnzNTxTz+VOzlZkuTbtq2Cn3hc/h06mF0eAAAAgFKs0MFU1apVcyx+bhiGkpKS5Ofnp88//7yo6wMAlFLulBQdnzVLxz6eLndCgiTJp3nzrEDqyivPeaMMAAAAANCFBFPvvPNOjl82rFargoOD1bFjR1WtWrWo6wMAlDLujAydnPNfxf/nP3LFx0uSvBs0UPCwYarcrSuBFAAAAIACK3Qwde+99xZPJQCAUs3IzFTC998rbupUZR4+Ikmy166toKGPKrBXL1lsNrNLBAAAAFDGFDqYmjFjhipVqqQ77rgjx/6vvvpKKSkpGjhwYFHWBwAwmeF2K/HnnxX/7nvK2LtXkuQVEqKgRx5WlVtvlcXb2+wSAQAAAJRR1sKeMG7cOAUFBeXaHxISorFjxxZVXQAAkxmGoaTFv2lP71t1+KmnlbF3r2xVqyrkuefUYMF8Ve3Th1AKAAAAwEUp9Iip/fv3q169ern2R0REaP/+/UVVFwDARMkrVyp20iSl/bNBkmStVEnV7hukagMGylbJ3+zyAAAAAJQThQ6mQkJCtGHDBtWtWzfH/n/++UfVq1cvytoAACUs5e+/FTdpslJWr5YkWXx9Ve2ee1R98H2yValidnkAAAAAyplCB1N9+/bVsGHDVLlyZXXu3FmS9Pvvv+vxxx9Xnz59iqNGAEAxS9uyRXGTp+jUkiWSJIvdrip9+ijogSHyCg42uzwAAAAA5VShg6lXX31Ve/fu1XXXXScvr6zT3W63BgwYwBpTAFDGpO/erbh331XSz79k7bDZVOXW3gp6+GHZa9UyuzwAAAAA5Vyhgylvb2/NmTNHr732mtavXy9fX19FRUUpIiKieCoEABS5jIOHFD91qhK+/15yuyWLRQE9eyr4saHyPmuqNgAAAAAUl0IHU9kaNWqkRo0aFW01AIBi5YyN1bFp03Tiq7mS0ylJqnTddQoeNkw+TRqbXR4AAACACqbQwdRtt92mDh066Lnnnsuxf8KECfrzzz/11VdfFWV9AIAikHnihI59/LFOzJotIy1NkuR/2aUKfvxx+bZqZXZ5AAAAACqoQgdTS5cu1csvv5xrf48ePfT2228XVV0AgCLgOnVKx2fM1PGZM+VOTpYk+bZpo+AnnpB/xw5mlwcAAACggit0MHXq1Cl5e3vn2m+325WYmFhUdQEALoI7NVUnZs/WsQ8/kishQZLkaNZMIU88Lv/OnWWxWMwuEQAAAAAKH0xFRUVpzpw5eumll3Ls//LLL9W8efOirA0AUEhGRoZOfPWV4qdNkysuXpLkXb++goc9psrduslitZpdIgAAAAB4FDqYevHFF3Xrrbdq165duvbaayVJixYt0uzZszV37tziqBEAcB5GZqYSvv9B8VOnynn4sCTJHhamoKFDFdjrRlm8LvheFwAAAABQbAr9m0qvXr303XffaezYsZo7d658fX3VqlUrLV68WNWqVSueKgEAeTLcbiXNn6+4Ke8qY88eSZJXcLCCHnlYVW67TZY8pl4DAAAAQGlxQf+EfsMNN+iGG26QJCUmJuqLL77Q008/rbVr18rlchV1jQCAsxiGoVNLlihu8hSlb90qSbJVqaLqDzygqv36yurjY3aJAAAAAHBeFzy3Y+nSpZo+fbq+/vpr1apVS7feequmTp1atNUBAHJJXrVKce9MUuo//0iSrJUqqdqge1Vt4EDZKlUyuzwAAAAAKLBCBVNHjx7VzJkzNX36dCUmJurOO+9Uenq6vvvuOxY+B4Bilrp+vWInTVbKqlWSJIuPj6r1v0fV7rtPXlWrml0eAAAAABRagYOpXr16aenSpbrhhhs0adIkXX/99bLZbJo2bVrxVggAFVza1q2KmzxFp377LWuH3a6qd96p6g8+IHtIiNnlAQAAAMAFK3Aw9fPPP2vYsGF6+OGH1ahRo+KtCgCg9N17FP/eu0qc93PWDqtVgb1vUfAjj8geFmZ2eQAAAABw0QocTC1fvlzTp09Xu3bt1KxZM/Xv3199+vQp3uoAoAJyHjqkuKnvK+G77yS3W5IU0LOngh4bKke9emaXBwAAAABFxlrQhp06ddJHH32kI0eO6MEHH9SXX36pWrVqye12a+HChUpKSireSgGgnMuMi9PRV1/Tzut7KOGbbyS3W5WuuUb1vvtWYRPfJpQCAAAAUO4UOJjK5u/vr/vuu0/Lly9XdHS0nnrqKY0fP14hISG66aabiqdKACjHMk+cUOxbb2ln1246MWuW5HTK79JOqvvlFwr/4H35NG1qdokAAAAAUCwKHUydqUmTJpowYYIOHjyoL774ouiqAoAKwHXqlOKmTtWurt107OPpMtLS5NuqlerMnKGIGTPk27q12SUCAAAAQLEq8BpT52Kz2XTLLbfolltuKYqXA4ByzZ2WphOzZuvYRx/JdfKkJMnRtKmCHx+mSldfLYvFYnaJAAAAAFAiiiSYAgCcn5GRoRNz5+rYB9OUGRcnSfKuV0/Bwx5T5e7dZbFe1CBWAAAAAChzCKYAoJgZmZlK+OF/ip86Vc5DhyRJ9lq1FDR0qAJv6iWLF/8rBgAAAFAx8dsQABQTw+1W0oIFipvyrjJ275Yk2YKDFPTQQ6pyxx2yenubXSIAAAAAmIpgCgCKmGEYOvX774qbPEXpW7ZIkmyBgar+wBBV7ddPVl9fs0sEAAAAgFKBYAoAilDy6jWKmzRJqX//LUmy+vur2qBBqnbvQNkqVTK7PAAAAAAoVQimAKAIpEVH68h77yl5xUpJksXHR1Xv7qfq998vr6pVzS4PAAAAQAkwDEOZRqacLqfSXenKcGUow53x77Y7I2tf9sOd+/lNDW5SVZ+K8zsEwVQZlvTLfFVet06JLpe8bDazywEqpEynU7Vmz9bBzVlT9mS3q+odd6j6Qw/KHhJidnkAAABAuWcYhpxupzJcGUp3pXueZ7gylO5Ol9Pl9AQ/6a7T26dDoDO3z3UsV5B0joDJkHFR19OxZkeCKZQN8ePGqubxE4qd81+zSwEqtEqSZLUq8OabFfToo/KuHWZ2SQAAAECxchvu8wY0F3LM6c450ujMNmceOzuIKq28LF6y2+zytnnLYXV4nntbveWwnd62emftO/3wt/ubXXaJMj2Ymjp1qt58800dPXpUrVq10rvvvqsOHTrk2/7kyZN64YUX9M033+j48eOKiIjQpEmT1LNnzxKtuzTwvaSDYvbsUXBwkCwWq9nlABWSYbh1JCNDUaNelH+TxmaXAwAAgArAMAylu9KVmpmq1MxUpWWmKSktSXsy92jVkVVyW9w5RwCdDnCyp5RlH8sV8OQ3OiiP0UKZ7kyzP4Z82a3/hj92m10Om8MT/py5neuY9fT26XbZ4VH2MW9b3tue9zt72+otm5XZTedjajA1Z84cDR8+XNOmTVPHjh01adIkde/eXdu2bVNIHlNgMjIy1LVrV4WEhGju3LkKCwvTvn37VKVKFVPqN1uNt97Uunnz1KpnT9ntdrPLASokp9Op9fPmqV39emaXAgAAgFLE5XZ5gqNCP5ypSnXlsS87iHKlyW2483zf6b9NL/FrlZQj7MkroMlvdFCO7Qs5dtZ+u9UuKwM3yhRTg6mJEydqyJAhGjRokCRp2rRp+umnn/TJJ59oxIgRudp/8sknOn78uFasWOEJYurWrVvidQMAAAAAyjbDMJThzsgR+KRmpiolMyXHSKRzBUgpmSme89NcaTlCpAx3Rolch7fVW752X/nafJWZlqmqAVVzjPrJd3TQhY4WOit88rZ6y8vqJYvFUiLXi/LHtGAqIyNDa9eu1ciRIz37rFarunTpopUrV+Z5zg8//KBLL71Ujz76qL7//nsFBwerX79+eu6552TLZ/Hv9PR0paene7YTExOl06McnM7SOw+1ILLrL+vXAZRl9EPAfPRDwHz0QxQXl9ulVFfugChHCJTH/jwDpTNeJy0zTamu1HxHHRUliyzy9fL1PHy8fORrO+N59jHbWdtnnmPzyXn+Gfu9rFm/1judTi1cuFBdu3Yt2Rk1bpXqaX0wR2F+HpgWTMXHx8vlcik0NDTH/tDQUG3dujXPc3bv3q3Fixfr7rvv1rx587Rz50498sgjcjqdGj16dJ7njBs3TmPGjMm1f8GCBfLz8yuiqzHXwoULzS4BqPDoh4D56IeA+eiHFY9hGMpUppyGUxnKUIaR4XnuNJzKMDJyPjcy5JTz/O1OP89UyQQeNtnkbfGWXXZ5W7zzfW632OUt77yfW7zlraznZx7zUh6jiVynH+n5VZTFkKGU0/8VFP0QpUFKSsG/Z01f/Lww3G63QkJC9OGHH8pms6ldu3Y6dOiQ3nzzzXyDqZEjR2r48OGe7cTERIWHh6tbt24KCAgoweqLnmmJOAAP+iFgPvohYD76YemRPT3tzMWqcyxofdadzzzH3BlKz0zPsa7RuUYpeY6ZMOooe/RQnqOLzhh15Dluy2MEUj6jjsoy+iFKk+zZagVhWu8LCgqSzWZTTExMjv0xMTGqUaNGnufUrFlTdrs9x7S9Zs2a6ejRo8rIyJC3t3eucxwOhxwOR679dru93HTW8nQtQFlFPwTMRz8EzFdR+6HL7coR+HjugHaO29vn2s4OkLLvjpbXsbPuspZj+4z3N4vdas9zGlqeD3sB251+OGwO1jAqoIraD1G6FOZ70LRgytvbW+3atdOiRYt0yy23SKdHRC1atEhDhw7N85zLL79cs2fPltvtltWatcr+9u3bVbNmzTxDKQAAAADlU6Y7M89b2Oe4tf25jrkL8PyMEUfZAVGG6/S2O93zPNMovevrnHnHshyLW595q/szFrouSIjk5+WX52il8jDqCEDJM/X/HMOHD9fAgQPVvn17dejQQZMmTVJycrLnLn0DBgxQWFiYxo0bJ0l6+OGH9d577+nxxx/XY489ph07dmjs2LEaNmyYmZcBAAAAlHuGYWSFQafDmTNH6qSkp2h/5n79FfOX3BZ31nF3ep4jgAoy6qcg4VFJTB+7EBZZCnd7+zPvllaAY3neWc3qyPN97FY7o4wAlHqmBlN33XWX4uLi9NJLL+no0aNq3bq1fvnlF8+C6Pv37/eMjJKk8PBwzZ8/X08++aRatmypsLAwPf7443ruuedMvAoAAACgeORaL+hCRgBdyIigM6efnREYGTLOWe+Hiz4ssc/mTFaLNWvkT34jgk4/z7V9VnCU77H8AqY8jnlZ8ljoGgCQL9PHWg4dOjTfqXtLlizJte/SSy/VqlWrSqAyAABQmhiGoWRnsmJTYxWfEq+41DjFpcQpJjlGW1O2av2a9bJ72WWz2LIe1rO+XszzfI5bLVZ5Wb3ybZ/XcavFWoCrhZnOXi8oO6CpaOsFnY+XxSvHFLHM9ExVqVRFDi/HOUf9nBkYZR/LnkbmGQF0jmM5tk+/FlPIAKDs4v/gAADAVIZhKMmZpPiUeMWmxiouJc4TOsWnxis2JVbxqVlBVGpmar6v89fOv0q07gtlkSXPsMtqscrL4vXv89OBVvbzXMfPem61WGWz2rKeW62yWWye8858n1xt83h+vrZ5hXSe9zrf8bPrOt3GkHHOgCbP7eypYYVcL+hcz0v7ekF5jQI6e0TQuaaMFXTUT0GOnRmyOp1OzZs3Tz179mTRZQBAoRBMAQCAYmEYhhIzEhWXEpc1yik1PkfodGb4lOZKK/DrVrJXUrBfsIJ9gxXkG6TqPtV1eM9hNWrUSIbFkMtwZT3cZ30tguduw61Md2aB2uY35cmQoUwjU5mu0ht+4F+e9YLOCmfOnDJ29ro/dlv+I4LOdexcI4KywyemiAEAyhuCKQAAUCiGYSghPcEzpS47dPKMbDojdMpwZxT4dSt7V1awb7AndPJ8PeN5kG+Q/Ox+Oc5zOp2ad2SeekaVrpEabsOdM9AyMvMNy848nqvtWSHbOY8X5HkB2uYbwOXxGme2Pd/z7M/kfM5cL+h8awJd0KifgixIfXrby8p6QQAAFCeCKQAAIJ0OUk6mn8xzVFNcas4RToVZ9ybQEegJlUL8QvL8GuwbLB8vn2K9vpJmtVhltVhlt5aesKw0MAwjz+BKZ0xTY70gAAAqDn7qAwBQzrkNt46nHc8xqinX6KbUOMWnxBdqbZ0qjiq5RjVlh01nbjtsjmK9PpQtFotFXpasv4J627zNLgcAAJiMYAoAgDLK5XbpeNrxPEc1nfn1WOqxAk2fylbNp1rWKKazQqcQ3xAF+QV5Rj8RKgAAAOBiEUwBAFDKZLozswKn08FSrlFOp0OnY2nHPFOgzscii6r5VMuxflOQb5AnbArxDVGwX7Cq+1SX3cbUMwAAAJQMgikAAEqI0+3UsdRjnsApr6l1sSmxOp52PN87up3NarFmBU7nWTS8mm811joCAABAqUMwBQDARXK6nDlGMuUY5XT6znVxqXE6kXaiwIGTzWJTdZ/q/96Nzi/n6Kbsr1V9qrJQNAAAAMos/iYLAEA+MlwZudZsyjG66XTodCL9RIFf08vipeq+1fMe4XTG16qOqrJZbcV6fQAAAIDZCKYAABXazhM7tfro6lwLhselxikhPaHAr+Nl9fJMnztz4fAQvxDPneqCfINU1aeqrBZrsV4TAAAAUFYQTAEAKqSkjCRNXT9VX2z94pwLiNut9jxHNZ0ZNoX4hSjQEUjgBAAAABQSwRQAoEIxDEM/7/lZb/71puJT4yVJl9a8VPUC6+W5eHigI1AWi8XssgEAAIByiWAKAFBh7EnYo9dXv67VR1ZLkiICIvR8x+d1Wa3LzC4NAAAAqJAIpgAA5V5qZqo+2vCRZmyaoUx3phw2h4ZEDdGgyEHytnmbXR4AAABQYRFMAQDKtd8P/K5xa8bp0KlDkqQrw67UyI4jFV453OzSAAAAgAqPYAoAUC4dPnVYb6x5Q4sPLJYk1fCvoRGXjNC1da5lzSgAAACglCCYAgCUK06XU/+3+f/0nw3/UWpmqrwsXurfor8eavmQ/Ox+ZpcHAAAA4AwEUwCAcuPPo3/qtVWvaXfCbklSu9B2GtVxlBpWbWh2aQAAAADyQDAFACjz4lPj9fZfb+vH3T9Kkqr5VNPT7Z/WjfVvZNoeAAAAUIoRTAEAyiyX26Wvtn+lKeumKMmZJIssurPJnXqszWMKdASaXR4AAACA8yCYAgCUSRvjN+rVVa9q87HNkqTm1ZvrxU4vKjIo0uzSAAAAABQQwRQAoExJSE/Qu3+/q/9u+68MGapsr6zH2j6mOxvfKZvVZnZ5AAAAAAqBYAoAUCYYhqH/7f6f3v7rbR1POy5JurH+jXqq/VMK8g0yuzwAAAAAF4BgCgBQ6u08sVOvrX5Na2PWSpLqB9bXqE6jdEmNS8wuDQAAAMBFIJgCAJRaKc4UTdswTZ9t+kyZRqZ8bD56qNVDGtB8gOw2u9nlAQAAALhIBFMAgFLHMAwtPrBY49eM19Hko5Kka8Kv0YgOI1SrUi2zywMAAABQRAimAAClyoGkAxq/ZryWHlwqSQqrFKYRHUbo6vCrzS4NAAAAQBEjmAIAlAoZrgzN2DhDH0V/pHRXurysXhrUYpCGtBwiXy9fs8sDAAAAUAwIpgAAplt5eKXGrh6rvYl7JUkda3TU852eV/3A+maXBgAAAKAYEUwBAEwTmxKrN/98U7/s/UWSFOQbpGfaP6Me9XrIYrGYXR4AAACAYkYwBQAocZnuTH259Uu9t/49JTuTZbVY1bdpXz3a+lFV9q5sdnkAAAAASgjBFACgRK2PXa/XVr2mbSe2SZJaBrXUqE6j1Kx6M7NLAwAAAFDCCKYAACXiZNpJTVo3SV/v+FqSFOAdoCfaPaHbGt0mq8VqdnkAAAAATEAwBQAoVm7Dre93fq+JayfqZPpJSdItDW/Rk+2eVDWfamaXBwAAAMBEBFMAgGKz7fg2vbbqNa2PWy9JaliloV7s9KLahrY1uzQAAAAApQDBFACgyCU7kzV1/VTN3jJbLsMlXy9fPdr6UfVr1k92q93s8gAAAACUEgRTAIAiYxiGFuxboAlrJig2NVaS1DWiq5695FnV8K9hdnkAAAAAShmCKQBAkdiXuE9jV4/VisMrJEnhlcP1fMfndUXYFWaXBgAAAKCUIpgCAFyUtMw0Td84XdOjp8vpdsrb6q3BUYM1OGqwHDaH2eUBAAAAKMUIpgAAF2zZwWUat2acDiQdkCRdXutyjew4UhEBEWaXBgAAAKAMIJgCABTa0eSjmvDnBC3ct1CSFOIXoucueU5dI7rKYrGYXR4AAACAMoJgCgBQYE63U7M2z9L7/7yv1MxU2Sw23dPsHj3c+mH52/3NLg8AAABAGUMwBQAokLUxa/Xaqte08+ROSVKbkDYa1WmUGldtbHZpAAAAAMoogikAwDkdTzuut/96Wz/s+kGSVMVRRcPbDdfNDW+W1WI1uzwAAAAAZRjBFAAgT27Drbnb52ryuslKzEiUJN3e+HY93uZxVfGpYnZ5AAAAAMoBgikAQC6bj23Wa6teU3R8tCSpabWmGtVplFoFtzK7NAAAAADlCMEUAMAjKSNJ7/39nr7c9qXchlv+dn891uYx3dXkLnlZ+ZEBAAAAoGjxWwYAQIZhaN6eeXrzzzd1LO2YJKlHvR56pv0zCvYLNrs8AAAAAOUUwRQAVHC7E3br9VWva83RNZKkugF19UKnF9SpZiezSwMAAABQzhFMAUAFlZqZqo82fKQZm2Yo050ph82hB1o+oHtb3Ctvm7fZ5QEAAACoAAimAKACWnJgicatHqfDyYclSZ1rd9bIDiNVu3Jts0sDAAAAUIEQTAFABXLo1CGNXzNeSw4skSTV9K+pER1G6Jrwa2SxWMwuDwAAAEAFQzAFABWA0+XUp5s/1X/++Y/SXGnysnhpYIuBeqDlA/Kz+5ldHgAAAIAKimAKAMq5NUfW6LXVr2lPwh5JUvvQ9hrVaZQaVGlgdmkAAAAAKjiCKQAop+JT4/XWX2/pp90/SZKq+VTT0+2f1o31b2TaHgAAAIBSgWAKAMoZl9ulOdvm6N2/39Up5ylZZNFdTe7SY20fU4B3gNnlAQAAAIAHwRQAlCPRcdF6ddWr2nJ8iySpRfUWerHTi2oR1MLs0gAAAAAgF4IpACgHEtITNGXdFH21/SsZMlTZXlmPt31ctze+XTarzezyAAAAACBPBFMAUIYZhqEfdv2giWsn6njacUnSTQ1u0pPtnlSQb5DZ5QEAAADAORFMAUAZtePEDr226jWti10nSWoQ2EAvdHpBl9S4xOzSAAAAAKBACKYAoIxJcaZo2j/T9Nnmz5RpZMrXy1cPtXpI/Zv1l91mN7s8AAAAACgwgikAKCMMw9Ci/Ys0fs14xaTESJKuq3OdnrvkOdWsVNPs8gAAAACg0AimAKAMOJB4QGPXjNXyQ8slSWGVwvR8x+fVuXZns0sDAAAAgAtGMAUApViGK0OfbPxEH0d/rHRXuuxWuwZFDtL9UffL18vX7PIAAAAA4KIQTAFAKbXi8AqNXT1W+xL3SZI61uyoFzq+oHqB9cwuDQAAAACKhNXsAiRp6tSpqlu3rnx8fNSxY0etWbMm37YzZ86UxWLJ8fDx8SnRegGgOMUkx+jp35/Wgwsf1L7EfQr2Ddabnd/UR10/IpQCAAAAUK6YPmJqzpw5Gj58uKZNm6aOHTtq0qRJ6t69u7Zt26aQkJA8zwkICNC2bds82xaLpQQrBoDikenO1OwtszV1/VSlZKbIarGqX9N+erT1o6rkXcns8gAAAACgyJkeTE2cOFFDhgzRoEGDJEnTpk3TTz/9pE8++UQjRozI8xyLxaIaNWqUcKUAUHzWx67Xq6te1fYT2yVJLYNb6sVOL6pptaZmlwYAAAAAxcbUYCojI0Nr167VyJEjPfusVqu6dOmilStX5nveqVOnFBERIbfbrbZt22rs2LFq0aJFCVUNAEXnRNoJTVo3Sd/s+EaSFOgI1JNtn1TvRr1ltZSK2dYAAAAAUGxMDabi4+PlcrkUGhqaY39oaKi2bt2a5zlNmjTRJ598opYtWyohIUFvvfWWLrvsMm3atEm1a9fO1T49PV3p6eme7cTEREmS0+mU0+ks8msqSdn1l/XrAMqyC+2HbsOt73d9rynrpyghI0GSdHP9mzWs9TBV9akqV6ZLLrmKpWagvOHnIWA++iFgPvohSpPCfB9aDMMwirWaczh8+LDCwsK0YsUKXXrppZ79zz77rH7//XetXr36vK/hdDrVrFkz9e3bV6+++mqu4y+//LLGjBmTa//s2bPl5+dXBFcBAIVzxHVEP6T8oAOuA5KkUGuobvK7SRFeEWaXBgAAAAAXLSUlRf369VNCQoICAgLO2dbUEVNBQUGy2WyKiYnJsT8mJqbAa0jZ7Xa1adNGO3fuzPP4yJEjNXz4cM92YmKiwsPD1a1bt/N+OKWd0+nUwoUL1bVrV9ntdrPLASqkwvTDU85TmrZhmr7c/qXchlt+Xn56qOVD6tO4j7yspi/5B5RZ/DwEzEc/BMxHP0Rpkj1brSBM/U3I29tb7dq106JFi3TLLbdIktxutxYtWqShQ4cW6DVcLpeio6PVs2fPPI87HA45HI5c++12e7nprOXpWoCy6lz90DAMzd83X2+ueVOxqbGSpG4R3fTsJc8q1D80z3MAFB4/DwHz0Q8B89EPURoU5nvQ9H+iHz58uAYOHKj27durQ4cOmjRpkpKTkz136RswYIDCwsI0btw4SdIrr7yiTp06qWHDhjp58qTefPNN7du3T/fff7/JVwIAue1N2Kuxq8dq5ZGsGzrUqVxHz3d8XpeHXW52aQAAAABgOtODqbvuuktxcXF66aWXdPToUbVu3Vq//PKLZ0H0/fv3y2r9985UJ06c0JAhQ3T06FFVrVpV7dq104oVK9S8eXMTrwIAckrLTNPH0R/rk42fyOl2ytvqrftb3q/7Iu+Tw5Z7FCcAAAAAVESmB1OSNHTo0Hyn7i1ZsiTH9jvvvKN33nmnhCoDgMJbenCpxq4eq0OnDkmSLg+7XC90eEHhAeFmlwYAAAAApUqpCKYAoDw4mnxU49eM16L9iyRJoX6hGtFhhK6rc50sFovZ5QEAAABAqUMwBQAXyWW49OnmT/Xhxg+Vmpkqm8Wm/s376+FWD8vP7md2eQAAAABQahFMASbadnybVh9ZbXYZuAhOl1Ozk2Yrdn3W3fbahrTVC51eUOOqjc0uDQAAAABKPYIpwCTf7PhGr658VZlGptmloAhUcVTR0+2f1k0NbmLaHgAAAAAUEMEUUMLchluT1k3SjI0zJEntQ9sr1D/U7LJwgdxut1KOpOjlG19WUKUgs8sBAAAAgDKFYAooQamZqXp+2fP6df+vkqSHWj2kR1o9wgibMszpdGrevHkKdASaXQoAAAAAlDkEU0AJiUuJ02OLH9OmY5tkt9o15rIx6tWgl9llAQAAAABgGoIpoARsO75NQxcP1dHko6riqKLJ10xW29C2ZpcFAAAAAICpCKaAYrb04FI98/szSslMUd2Aunr/uvcVHhBudlkAAAAAAJiOYAooRrO2zNKEPyfIbbjVoUYHTbx6ImsRAQAAAABwGsEUUAwy3Zma8OcEfbH1C0nSrY1u1aiOo2S32c0uDQAAAACAUoNgCihipzJO6Zmlz2j5oeWSpCfbPalBLQZx5z0AAAAAAM5CMAUUoSOnjujRxY9qx4kd8rH5aNyV49QloovZZQEAAAAAUCoRTAFFZGP8Rg1dNFTH0o4pyDdI7177riKDIs0uCwAAAACAUotgCigCC/ct1PPLnleaK02NqjbS1GunqmalmmaXBQAAAABAqUYwBVwEwzA0feN0TV43WZJ0RdgVeuuqt+Rv9ze7NAAAAAAASj2CKeACOV1OvbrqVX2781tJUr+m/fTMJc/Iy0q3AgAAAACgIPgNGrgACekJGr5kuNYcXSOrxarnLnlO/Zr1M7ssAAAAAADKFIIpoJD2J+7Xo4se1d7EvfLz8tObV72pzrU7m10WAAAAAABlDsEUUAhrY9bq8d8eV0J6gmr419B7176nJtWamF0WAAAAAABlktXsAoCy4n+7/qf7F9yvhPQERVaP1OyeswmlAAAAAAC4CIyYAs7DMAxNXT9V/9nwH0lS14iuev2K1+Xr5Wt2aQAAAAAAlGkEU8A5pLvS9eLyF/Xz3p8lSYMjB2tY22GyWhhsCAAAAADAxSKYAvJxLPWYHv/tcf0T94+8LF566dKX1LtRb7PLAgAAAACg3CCYAvKw6+QuPbroUR06dUiVvStr0tWT1KFmB7PLAgAAAACgXCGYAs6y4vAKPbXkKZ1ynlJ45XBNvW6q6gXWM7ssAAAAAADKHYIp4Az/3fZfjV09Vi7DpbYhbTXpmkmq6lPV7LIAAAAAACiXCKYASS63SxPXTtT/bf4/SdKN9W/UmMvGyNvmbXZpAAAAAEopt9utjIwMs8uQJDmdTnl5eSktLU0ul8vsclDO2e122Wy2InktgilUeCnOFI1YNkK/HfhNkvRo60f1YMsHZbFYzC4NAAAAQCmVkZGhPXv2yO12m12KJMkwDNWoUUMHDhzgdxmUiCpVqqhGjRoX/f1GMIUKLSY5Ro8tfkxbjm+Rt9Vbr13xmnrU62F2WQAAAABKMcMwdOTIEdlsNoWHh8tqtZpdktxut06dOqVKlSqVinpQfhmGoZSUFMXGxkqSataseVGvRzCFCmvLsS0aumioYlNjVc2nmiZfM1mtQ1qbXRYAAACAUi4zM1MpKSmqVauW/Pz8zC5HOmNaoY+PD8EUip2vr68kKTY2ViEhIRc1rY9gChXSb/t/03PLnlNqZqrqB9bX1Oumqnbl2maXBQAAAKAMyF7DydubNWlRcWWHsk6nk2AKKCjDMPTZ5s/01l9vyZChS2teqreufksB3gFmlwYAAACgjGEtJ1RkRfX9z/g+VBhOt1OvrXpNb/71pgwZur3x7ZraZSqhFAAAAADANMeOHVNISIj27t1bLK8/c+ZMValSpVDn9OnTR2+//Xax1HM2gilUCEkZSRq6aKj+u/2/ssiip9s/rZc6vSS71W52aQAAAABQIu69915ZLBaNHz8+x/7vvvsux+iXJUuWyGKx5Pk4evSoJOnll1/27MteBP6BBx7Q8ePHC1TLwYMH5e3trcjIyCK+yrLn9ddf180336y6detKZ3z+J0+eLJLXv+uuu7R9+/ZCnTNq1Ci9/vrrSkhIKJIazoVgCuXeoVOH1H9ef604vEK+Xr6adM0kDWwxkGG3AAAAACocHx8fvfHGGzpx4sR5227btk1HjhzJ8QgJCfEcb9GihY4cOaL9+/drxowZ+uWXX/Twww8XqI6ZM2fqzjvvVGJiolavXn1R13SxXC6X3G63Ke+dkpKi6dOna/DgwYU+NyMjo0DtfH19c/y5FURkZKQaNGigzz//vNB1FRbBFMq1f+L+Ub+f+mlXwi6F+IZo5vUzdW2da80uCwAAAABM0aVLF9WoUUPjxo07b9uQkBDVqFEjx+PMO/55eXmpRo0aCgsLU5cuXXTHHXdo4cKF531dwzA0Y8YM9e/fX/369dP06dNztfnjjz909dVXy8/PT1WrVlX37t09YZrb7daECRPUsGFDORwO1alTR6+//rqUz2ij9evXy2KxeKbKZU9t++GHH9S8eXM5HA7t379ff/75p7p27aqgoCAFBgbqqquu0rp163LUdfLkST344IMKDQ2Vj4+PIiMj9eOPPyo5OVkBAQGaO3dujvbfffed/P39lZSUlOdnMW/ePDkcDnXq1EmStHfvXl1zzTWSpKpVq8pisejee++VJF199dUaOnSonnjiCQUFBal79+6SpIkTJyoqKkr+/v4KDw/XI488olOnTnne4+ypfC+//LJat26tzz77THXr1lVgYKD69OmTq8ZevXrpyy+/PO+f58UimEK59cueX3TfL/fpeNpxNa3WVLNumKXm1ZubXRYAAAAAmMZms2ns2LF69913dfDgwSJ73b1792r+/PkFulPhb7/9ppSUFHXp0kX33HOPvvzySyUnJ3uOr1+/Xtddd52aN2+ulStXavny5erVq5fnbogjR47U+PHj9eKLL2rz5s2aPXu2QkNDC1VvSkqK3njjDX388cfatGmTQkJClJSUpIEDB2r58uVatWqVGjVqpJ49e3oCG7fbrR49euiPP/7Q559/rs2bN2v8+PGy2Wzy9/dXnz59NGPGjBzvM2PGDN1+++2qXLlynnUsW7ZM7dq182yHh4fr66+/ls4YsTZ58mTP8U8//VTe3t76448/NG3aNEmS1WrVlClTtGnTJn366adavHixnn322XNe/65du/Tdd9/pxx9/1I8//qjff/891xTPDh06aM2aNUpPTy/UZ1tY3JUP5Y5hGPpww4d6b/17kqSra1+tNzq/IT+7n9mlAQAAACiHDMNQqtNlynv72m2FXqakd+/eat26tUaPHp3naKVstWvXzrEdERGhTZs2ebajo6NVqVIluVwupaWlSadH75zP9OnT1adPH9lsNkVGRqp+/fr66quvPCODJkyYoPbt2+v999/3nNOiRQtJUlJSkiZPnqz33ntPAwcOlCQ1aNBAV1xxRaE+A6fTqffff1+tWrXy7Lv22pyzaz788ENVqVJFv//+u2688Ub9+uuvWrNmjbZs2aLGjRtLkurXr+9pf//99+uyyy7TkSNHVLNmTcXGxmrevHn69ddf861j3759qlWrlmfbZrOpWrVq0ukRa2cvWt6oUSNNmDAhx74nnnjC87xu3bp67bXX9NBDD+X4/M7mdrs1c+ZMT2DWv39/LVq0yDPyTJJq1aqljIwMHT16VBEREfm+1sUimEK5kuHK0JiVY/TDrh8kSf2b99dT7Z6SzWozuzQAAAAA5VSq06XmL8035b03v9Jdft6F/9X+jTfe0LXXXqunn3463zbLli3LMdLHbs9586gmTZrohx9+UFpamj7//HOtX79ejz322Dnf9+TJk/rmm2+0fPlyz7577rlH06dP9wRT69ev1x133JHn+Vu2bFF6erquu+66Al9rXry9vdWyZcsc+2JiYjRq1CgtWbJEsbGxcrlcSklJ0f79+z111a5d2xNKna1Dhw5q0aKFPv30U40YMUKff/65IiIi1Llz53zrSE1NlY+PT4HrPnN0VbZff/1V48aN09atW5WYmKjMzEylpaUpJSVFfn55D9CoW7dujj/b7CDtTL6+vtLp0WXFial8KDdOpp3UkAVD9MOuH2Sz2DSq4yg9e8mzhFIAAAAAcJbOnTure/fuGjlyZL5t6tWrp4YNG3oeZ4+a8fb2VsOGDRUZGemZ0jZmzJhzvu/s2bOVlpamjh07ysvLS15eXnruuee0fPlyz53jsgORvJzrmE5Pa9PpUWzZnE5nnq9z9kizgQMHav369Zo8ebJWrFih9evXq3r16p5Fxs/33jo9amrmzJnS6Wl8gwYNOueItqCgoAItRJ/N398/x/bevXt14403qmXLlvr666+1du1aTZ06VTrP4uhnh4wWiyXXAvDZd1gMDg4ucH0XghFTKBf2JOzR0EVDtT9pvyrZK+mtq97S5WGXm10WAAAAgArA127T5le6m/beF2r8+PFq3bq1mjRpUiS1jBo1Stdee60efvjhHNPTzjR9+nQ99dRTntFR2R555BF98sknGj9+vFq2bKlFixblGXI1atRIvr6+WrRoke6///5cx7NDlCNHjqhq1arS6ZFOBfHHH3/o/fffV8+ePSVJBw4cUHx8vOd4y5YtdfDgQW3fvj3fUVP33HOPnn32WU2ZMkWbN2/2TDfMT5s2bXLd+S57na7sNbXOZe3atXK73Xr77bc9odx///vfAlzt+W3cuFG1a9dWUFBQkbxefhgxhTLvz6N/6p5592h/0n7V8q+lz3p8RigFAAAAoMRYLBb5eXuZ8ijs+lJnioqK0t13360pU6bkeTw2NlZHjx7N8chr9FG2Sy+9VC1bttTYsWPzPL5+/XqtW7dO999/vyIjI3M8+vbtq08//VSZmZkaOXKk/vzzTz3yyCPasGGDtm7dqg8++EDx8fHy8fHRc889p2effVb/93//p127dmnVqlWetbIaNmyo8PBwvfzyy9qxY4d++uknvf322wX6PBo1aqTPPvtMW7Zs0erVq3X33XfnGCV11VVXqXPnzrrtttu0cOFC7dmzRz///LN++eUXT5uqVavq1ltv1TPPPKNu3brlWqfrbN27d9emTZtyjJqKiIiQxWLRjz/+qLi4uBx32Dtbw4YN5XQ69e6772r37t367LPPPIuiX6xly5apW7duRfJa50IwhTLt2x3f6oGFDygxI1Etg1tq1g2z1LBqQ7PLAgAAAIAy4ZVXXsk1hStbkyZNVLNmzRyPtWvXnvP1nnzySX388cc6cOBArmPTp09X8+bN1bRp01zHevfu7VksvHHjxlqwYIH++ecfdejQQZdeeqm+//57eXllTfp68cUX9dRTT+mll15Ss2bNdNddd3nWR7Lb7friiy+0detWtWzZUm+88YZee+21An0W06dP14kTJ9S2bVv1799fw4YNU0hISI42X3/9tS655BL17dtXzZs317PPPptrZNPgwYOVkZGh++6777zvGRUVpbZt2+YY5RQWFqYxY8ZoxIgRCg0N1dChQ/M9v1WrVpo4caLeeOMNRUZGatasWRo3blyBrvdc0tLS9N1332nIkCEX/VrnYzHOnHhZASQmJiowMFAJCQkKCAgwu5yL4nQ6NW/ePPXs2TPX/NDyzm249e7f7+rj6I8lSd3rdtdrl78mH6+CLxoHFIWK3A+B0oJ+CJiPfoiKJi0tTXv27FG9evUKtXB1cXK73UpMTFRAQIBnShfM8dlnn+nJJ5/U4cOHPdPyzuWnn37SM888o40bN5aaP7sPPvhA3377rRYsWJBvm3P1g8JkL6wxhTInNTNVLyx/QQv3LZQkPdDyAT3a+lFZLaWjAwMAAAAAKp6UlBQdOXJE48eP14MPPligUEqSbrjhBu3YsUOHDh1SeHh4sddZEHa7Xe+++26JvBe/yaNMiU+N1+D5g7Vw30J5Wb30+hWv67E2jxFKAQAAAABMNWHCBDVt2lQ1atQ4590O8/LEE0+UmlBKp+8uWFSL4p8Pv82jzNh+Yrv6/dRP0fHRCnQE6qOuH+mmBjeZXRYAAAAAAHr55ZfldDq1aNEiVapUyexyygym8qFMWH5ouZ7+/WklO5MVERChqddNVURAhNllAQAAAACAi0AwhVLvi61faPya8XIbbrUPba9J10xSoCPQ7LIAAAAAAMBFIphCqeVyu/TmX29q1pZZkqSbG9ys0ZeOlt3GnV4AAAAAACgPCKZQKiU7k/Xs0me19OBSSdLjbR/X4MjBslgsZpcGAAAAAACKCMEUSp2jyUf16KJHtf3EdjlsDr1+xevqXre72WUBAAAAAIAiRjCFUmVT/CYNXTxU8anxqu5TXVOunaKWwS3NLgsAAAAAABQDq9kFANkW7Vuke3+5V/Gp8WpYpaFm3zCbUAoAAAAAUK4dO3ZMISEh2rt3b5G95syZM1WlShXP9rRp09SrV68ie/2iRDAF0xmGoRkbZ+jJJU8qzZWmy8Mu12c9PlOtSrXMLg0AAAAAyo17771XFotF48ePz7H/u+++y7Ge75IlS2SxWPJ8HD16VJL08ssve/bZbDaFh4frgQce0PHjxwtUy8GDB+Xt7a3IyMgivsqy5/XXX9fNN9+sunXrFtt73HfffVq3bp2WLVtWbO9xoQimYCqn26kxK8do4tqJMmToriZ36b1r31Ml70pmlwYAAAAA5Y6Pj4/eeOMNnThx4rxtt23bpiNHjuR4hISEeI63aNFCR44c0f79+zVjxgz98ssvevjhhwtUx8yZM3XnnXcqMTFRq1evvqhrulgul0tut9uU905JSdH06dM1ePDgYn0fb29v9evXT1OmTCnW97kQBFMwTUJ6gh5e+LC+3vG1rBarRnQYoRc6viAvK0ufAQAAAEBx6NKli2rUqKFx48adt21ISIhq1KiR42G1/hsjeHl5qUaNGgoLC1OXLl10xx13aOHChed9XcMwNGPGDPXv31/9+vXT9OnTc7X5448/dPXVV8vPz09Vq1ZV9+7dPWGa2+3WhAkT1LBhQzkcDtWpU0evv/66dMZor5MnT3pea/369bJYLJ6pctnT3H744Qc1b95cDodD+/fv159//qmuXbsqKChIgYGBuuqqq7Ru3bocdZ08eVIPPvigQkND5ePjo8jISP34449KTk5WQECA5s6dm6P9d999J39/fyUlJeX5WcybN08Oh0OdOnXyXFvt2rX1wQcf5Gj3999/y2q1at++fZKkiRMnKioqSv7+/goPD9cjjzyiU6dOnfNz79Wrl3744Qelpqaes11JI5iCKQ4kHlD/n/tr9dHV8vXy1ZRrpujuZnfnGD4KAAAAAGWCYUgZyeY8DKNQpdpsNo0dO1bvvvuuDh48WGQfwd69ezV//nx5e3uft+1vv/2mlJQUdenSRffcc4++/PJLJScne46vX79e1113nZo3b66VK1dq+fLl6tWrl1wulyRp5MiRGj9+vF588UVt3rxZs2fPVmhoaKHqTUlJ0RtvvKGPP/5YmzZtUkhIiJKSkjRw4EAtX75cq1atUqNGjdSzZ09PqOR2u9WjRw/98ccf+vzzz7V582aNHz9eNptN/v7+6tOnj2bMmJHjfWbMmKHbb79dlStXzrOOZcuWqV27dp5tq9Wqvn37avbs2TnazZo1S5dffrkiIiI87aZMmaJNmzbp008/1eLFi/Xss8+e85rbt2+vzMxM00eonY2hKShxf8f+rccXP64T6ScU6heq9657T02rNTW7LAAAAAC4MM4UaaxJa+Q+f1jy9i/UKb1791br1q01evToPEcrZatdu3aO7YiICG3atMmzHR0drUqVKsnlciktLU06PZLnfKZPn64+ffrIZrMpMjJS9evX11dffaV7771XkjRhwgS1b99e77//vuecFi1aSJKSkpI0efJkvffeexo4cKAkqUGDBrriiisK9Rk4nU69//77atWqlWfftddem6PNhx9+qCpVquj333/XjTfeqF9//VVr1qzRli1b1LhxY0lS/fr1Pe3vv/9+XXbZZTpy5Ihq1qyp2NhYzZs3T7/++mu+dezbt0+1auX83rn77rv19ttva//+/apTp47cbre+/PJLjRo1ytPmiSee8DyvW7euXnvtNT300EM5PrOz+fn5KTAw0DPqqrRgxBRK1I+7f9Tg+YN1Iv2Emldvrtk3zCaUAgAAAIAS9sYbb+jTTz/Vli1b8m2zbNkyrV+/3vOYN29ejuNNmjTR+vXr9eeff+q5555T9+7d9dhjj53zfU+ePKlvvvlG99xzj2ffPffckyMgyx4xlZctW7YoPT093+MF5e3trZYtc94FPiYmRkOGDFGjRo0UGBiogIAAnTp1Svv37/fUVbt2bU8odbYOHTqoRYsW+vTTTyVJn3/+uSIiItS5c+d860hNTZWPj0+Ofa1bt1azZs08o6Z+//13xcbG6o477vC0+fXXX3XdddcpLCxMlStXVv/+/XXs2DGlpKSc87p9fX3P26akMWIKJcIwDH3wzwf64J+sebLXhl+rcVeOk5/dz+zSAAAAAODi2P2yRi6Z9d4XoHPnzurevbtGjhzpGal0tnr16qlKlSr5voa3t7caNmwoSRo/frxuuOEGjRkzRq+++mq+58yePVtpaWnq2LGjZ59hGHK73dq+fbsaN24sX1/ffM8/1zGdnuKW/ZrZnE5nnq9z9lIyAwcO1LFjxzR58mRFRETI4XDo0ksvVUZGRoHeW6dHTU2dOlUjRozQjBkzNGjQoHMuWRMUFJTnQvR33323Zs+erREjRmj27Nm6/vrrVb16den0tMkbb7xRDz/8sF5//XVVq1ZNy5cv1+DBg5WRkSE/v/y/J44fP67g4ODzXkdJYsQUil26K10jlo3whFKDWgzSO9e8QygFAAAAoHywWLKm05nxuIh1esePH6///e9/WrlyZZF8DKNGjdJbb72lw4fzD+mmT5+up556KsdIrH/++UdXXnmlPvnkE0lSy5YttWjRojzPb9SokXx9ffM9nh26HDlyxLNv/fr1Bar/jz/+0LBhw9SzZ0+1aNFCDodD8fHxnuMtW7bUwYMHtX379nxf45577tG+ffs0ZcoUbd682TPdMD9t2rTR5s2bc+3v16+fNm7cqLVr12ru3Lm6++67PcfWrl0rt9utt99+W506dVLjxo3P+Zln27Vrl9LS0tSmTZvzti1JBFMoVsfTjuv++fdr3p558rJ4afSlozW8/XBZLXzrAQAAAMD/t3fn4TVd+xvA3535ZEZk0ISEEM0kIqTR25jFkLgh1YiIkCbmodQUt4lwDaWN0iJ+KpJKgxou8ihKo4rTIGgiZldNrSESKuFEcpxzfn9czu25IYMMW5z38zznqey99l7vPrV4fJ+11haTh4cHwsPD8eWXX77wfH5+Pu7cuaPxedHso+f8/Pzg6emJhQsXvvB8Tk4OTp06hejoaLi7u2t8wsLC8M033+Dp06eIjY1FdnY2xo0bh9OnT+PChQtISkpCQUEBjIyMMHPmTMyYMQPr16/HlStXcPToUfVSQGdnZzg4OCAhIQGXL1/G999/j8TExCp9H61bt0ZaWhrOnz+PY8eOITw8XGOWVJcuXeDv74+QkBDs378fV69exZ49e7B37151m0aNGmHQoEGYPn06evfuXW6frv8VEBCAs2fPlps15ejoiM6dO+PDDz+EQqHAgAED1OecnZ0hl8vx1Vdf4bfffkNaWhpWr15d6fMdPnwYLVu2RKtWrar0fdQXVgeozvz2528Y+v1Q5NzLgZm+GZJ6JeH9Nu+LHYuIiIiIiIiemTdvHpRK5QvPubi4wM7OTuNz8uTJCu83ZcoUrF27Fjdv3ix3Ljk5Ga6urmjbtvw+wwMHDlRvFt6mTRvs27cPubm56NSpE/z8/LBz507o6f1nN6K4uDh8/PHHiI+Px9tvv43Q0FDk5+cDAPT19bFx40ZcuHABnp6eWLx4MebPn1+l7yI5ORkPHjyAt7c3IiIiMGnSJFhbW2u02bZtGzp27IiwsDC4urpixowZ6rcFPvd8SV1UVFSlfXp4eMDb2xubN28udy48PBy5ubkYOHCgRoGsXbt2WLp0KRYvXgx3d3ekp6dj0aJFlfa1ceNGxMTEVNquvgkqVTXfLdnAFRUVwcLCAg8fPoS5ubnYcWpELpdj9+7d6NevH/T19cWOo+Ho7aOY+tNUFMuL8ZbpW1jVYxVaWraswpVEDcvrPA6JtAXHIZH4OA5J2zx58gRXr16Fk5NTuY2rxaJUKlFUVARzc3P1PkskjrS0NEyZMgW3bt2CgYFBpe2///57TJ8+HWfOnKmz/3dnz55F9+7dcenSJVhYWNTKPSsaB9WpvXDzc6p1Wy9txYKjC/BU9RReTb2wvPtyNDZqLHYsIiIiIiIiojojk8lw+/ZtfPrppxg9enSVilIA0L9/f1y+fBl//PEHHBwc6iTb7du3sX79+lorStWm16KMunLlSjg6OsLIyAi+vr44fvx4la7btGkTBEFAcHBwnWekyilVSiw9sRRzs+biqeop+jn1w9qAtSxKERERERER0RtvyZIlaNu2LWxtbREbG1utaz/66KM6K0oBQM+ePREQEFBn968J0QtT3333HaZOnYo5c+bg1KlTaNeuHQICAtTrQ1/m2rVrmDZtGt577716y0ovJ5PLMOWnKUg5mwIAGNduHD5971MY6hqKHY2IiIiIiIioziUkJEAulyMzMxOmpqZix2kwRC9MLV26FDExMRg5ciRcXV2xevVqGBsbq18T+SIKhQLh4eGYO3cuWrbkvkViy5flY+QPI3Hg5gHo6+hj0XuLMNZrLIQavLaUiIiIiIiIiN58ohamysrKcPLkSfTs2fO/gXR00LNnT2RlZb30unnz5sHa2hoffvhhPSWll7l4/yKGfj8U5wrPoZFhI6ztvRaBLQPFjkVEREREREREDYCom58XFBRAoVDAxsZG47iNjQ0uXLjwwmuOHDmC5ORk5OTkVKmP0tJSlJaWqn8uKioCnr05RC6X1yi/2J7nF+s5Dv1xCLHSWJQ8LYGjuSOWd1kOBzOHBv+9ElWH2OOQiDgOiV4HHIekbeRyOVQqFZRKJZRKpdhxAAAqlUr939clE73ZlEolVCoV5HI5dHV1Nc5V5++DBvVWvuLiYkRERODrr7+GlZVVla5ZtGgR5s6dW+74vn37YGxsXAcp69/+/fvrtT+VSoWssizsKdkDFVRoqdcSYQhD3uE85CGvXrMQvS7qexwSUXkch0Ti4zgkbaGnpwdbW1s8evQIZWVlYsfRUFxcLHYE0hJlZWUoKSnBoUOH8PTpU41zMpmsyvcRtTBlZWUFXV1d3L17V+P43bt3YWtrW679lStXcO3aNQQFBamPPa8E6+np4eLFi2jVqpXGNbGxsZg6dar656KiIjg4OKB3794wNzevg6eqP3K5HPv370evXr2gr69fL30+VT7FZyc/w+7LuwEAwa2CEdsxFvo69dM/0etGjHFIRJo4DonEx3FI2ubJkye4efMmTE1NYWRkJHYc4NkEguLiYpiZmXG/X6oXT548gUQigb+/f7lx8Hy1WlWIWpgyMDBAhw4dkJmZieDgYOBZoSkzMxMTJkwo175t27bIy9OckfPJJ5+guLgYy5cvf+GrFQ0NDWFoWP7NcPr6+m/MX5r19SyPyh5h2uFpkP4hhQABUzpMwQi3EfxDj+gN+zOFqKHiOCQSH8chaQuFQgFBEKCjowMdHdHfKQb8ZdLG81xEdU1HRweCILzwz/7q/F0g+u/WqVOn4uuvv8Y333yD8+fPY+zYsXj8+DFGjhwJABg+fDhiY2MBAEZGRnB3d9f4WFpawszMDO7u7jAwMBD5ad5ctx7dQsSeCEj/kMJI1whfdP0CI91HsihFRERERERE9AoiIiKwcOHCOru/IAjYsWNHldvv3bsXXl5e9b5HmeiFqdDQUHz++eeIj4+Hl5cXcnJysHfvXvWG6Ddu3MDt27fFjqnVTt87jaHfD8W///w3rCRWSO2Tih4teogdi4iIiIiIiKrh3r17GDt2LJo3bw5DQ0PY2toiICAAUqlU3cbR0RGCIEAQBBgbG8PDwwNr167VuM/BgwchCAL+/PPPamcICAiArq4usrOza+WZGqrc3Fzs3r0bkyZNUh9zdHTEsmXLaq2P27dvo2/fvlVu36dPH+jr6yM9Pb3WMlTFa7H5+YQJE164dA/PfsNXJDU1tY5SEQD8cO0H/OPIP1CqKEWbRm2wssdK2JqU3/+LiIiIiIiIXm8hISEoKyvDN998g5YtW+Lu3bvIzMxEYWGhRrt58+YhJiYGMpkMW7ZsQUxMDN56661qFTle5MaNG/jll18wYcIErFu3Dh07dqzhE9WMXC4XbfnxV199hcGDB8PU1LRa1/11GWllXrR3d2VGjBiBL7/8EhEREdW+9lWJPmOKXk8qlQpr89Zi2s/TUKoohb+9P9b3Xc+iFBERERERUQP0559/4vDhw1i8eDG6deuGFi1aoFOnToiNjcWAAQM02pqZmcHW1hYtW7bEzJkz0bhx41p562ZKSgoCAwMxduxYbNy4ESUlJeUyjh49GjY2NuqtfHbt2qU+L5VK0bVrVxgbG6NRo0YICAjAgwcPgJfMNvLy8kJCQoL6Z0EQkJSUhAEDBsDExAQLFiyAQqHAhx9+CCcnJ0gkEri4uGD58uXlsq9btw5ubm4wNDSEnZ2denJNVFQUAgMDNdrK5XJYW1sjOTn5hd+DQqHA1q1bNV7s1rVrV1y/fh1TpkxRz1jDs8k4lpaWyMjIgKurKwwNDXHjxg1kZ2ejV69esLKygoWFBbp06YJTp05p9PPXpXzXrl2DIAj417/+hW7dusHY2Bjt2rVDVlaWxjVBQUE4ceIErly58pL/i7WPhSkqR66QI04ah+Wn/jMYh709DF92+xIm+iZiRyMiIiIiInrtqFQqyOQyUT4qlapKGU1NTWFqaoodO3agtLS0StcolUps27YNDx48qPGeziqVCikpKRg2bBjatm0LZ2dnbN26VaOvvn37QiqV4ttvv8W5c+fw6aefQldXFwCQk5ODHj16wNXVFVlZWThy5AiCgoKgUCiqlSMhIQEDBw5EXl4eoqKioFQqYW9vjy1btuDcuXOIj4/H7NmzsXnzZvU1SUlJGD9+PEaNGoW8vDxkZGTA2dkZABAdHY29e/dqbEG0a9cuyGQyhIaGvjDD6dOn8fDhQ/j4+KiP/etf/4K9vT3mzZuH27dva9xPJpNh8eLFWLt2Lc6ePQtra2sUFxcjMjISR44cwdGjR9G6dWv069cPxcXFFT7/P/7xD0ybNg05OTlo06YNwsLC8PTpU/X55s2bw8bGBocPH67W91oTr8VSPnp9PCx9iI9++ggn7p6AjqCDWZ1mIaxtmNixiIiIiIiIXlslT0vgu8FXlL6PDT0GY33jStvp6ekhNTUVMTExWL16Nby9vdGlSxcMGTIEnp6eGm1nzpyJTz75BKWlpXj69CkaN26M6OjoGuX88ccfIZPJEBAQAAAYNmwYkpOT1UvGfvzxRxw/fhznz59HmzZtAAAtW7ZUX79kyRL4+Phg1apV6mNubm7VzjF06FD1y9aemzt3rvrXTk5OyMrKwubNm/HBBx8AAObPn4+PP/4YkydPVrd7vgyxc+fOcHFxQVpaGmbMmAE8mxlW0TK969evQ1dXF9bW1upjjRs3hq6urnq22l/J5XKsWrUK7dq1Ux/r3r27Rps1a9bA0tISP//8c7kZXH81bdo09O/fX/3cbm5u+Pe//422bduq2zRr1gzXr19/6T1qG2dMkdr1ousI3x2OE3dPwETfBCu6r2BRioiIiIiI6A0REhKCW7duISMjA3369MHBgwfh7e1dbu/m6dOnIycnBwcOHICvry+++OIL9QyhV7Vu3TqEhoZCT+8/82PCwsIglUrVS8ZycnJgb2+vLkr9r+czpmrqr7OUnlu5ciU6dOiApk2bwtTUFGvWrMGNGzcAAPn5+bh161aFfUdHRyMlJQUAcPfuXezZswdRUVEvbV9SUgJDQ8Mqv+XewMCgXPHw7t27iImJQevWrWFhYQFzc3M8evRInftl/nofOzs79TP+lUQigUwmq1K22sAZUwQAyL6TjSkHp+Bh6UPYmdhhRY8VaNPoxX8gEBERERER0X9J9CQ4NvSYaH1Xh5GREXr16oVevXohLi4O0dHRmDNnDkaMGKFuY2VlBWdnZzg7O2PLli3w8PCAj48PXF1dXynj/fv3sX37dsjlciQlJamPKxQKrFu3DgsWLIBEUvFzVHZeR0en3LJGuVxerp2JieYWNZs2bcK0adOQmJgIPz8/mJmZ4bPPPsOxY8eq1C8ADB8+HLNmzUJWVhZ++eUXODk54b333ntpeysrK8hkMpSVlVVpiaREIilXxIqMjERhYSGWL1+OFi1awNDQEH5+figrK6vwXn/d7P35PZVKpUab+/fvo2nTppXmqi2cMUXIuJKBUftH4WHpQ7g3cceG/htYlCIiIiIiIqoiQRBgrG8syqeqs25extXVFY8fP37peQcHB4SGhiI2NvaV+0hPT4e9vT1yc3ORk5Oj/iQmJiI1NRUKhQKenp74/fffcenSpRfew9PTE5mZmS/to2nTphr7MhUVFeHq1auVZpNKpejcuTPGjRuH9u3bw9nZWWPjbzMzMzg6OlbYd5MmTRAcHIyUlBSkpqaWWyr4v7y8vAAA586d0zhuYGBQ5T2zpFIpJk2ahH79+qk3ZS8oKKjStRV58uQJrly5gvbt29f4XlXFGVNaTKlSYsWvK/B13tcAgF4temHB3xZUu+JOREREREREr7fCwkIMHjwYUVFR8PT0hJmZGU6cOIElS5bg73//e4XXTp48Ge7u7jhx4oTGUri8vDyYmZmpfxYEQWMfpOeSk5Px/vvvw93dXeO4g4MDYmNjsXfvXvTv3x/+/v4ICQnB0qVL4ezsjAsXLkAQBPTp0wexsbHw8PDAuHHjMGbMGBgYGOCnn37C4MGDYWVlhe7duyM1NRVBQUGwtLREfHy8euP0irRu3Rrr16/HDz/8ACcnJ6SlpSE7OxtOTk7qNgkJCRgzZgysra3Rt29fFBcXQyqVYuLEieo20dHRCAwMhEKhQGRkZIV9Nm3aFN7e3jhy5Ii6SIVnbxY8dOgQhgwZAkNDQ1hZWVWYOy0tDT4+PigqKsL06dOrNLurMkePHlXPvqovnDGlpZ48fYIZh2aoi1LRHtH4vMvnLEoRERERERG9gUxNTdX7Rfn7+8Pd3R1xcXGIiYnBihUrKrzW1dUVvXv3Rnx8vMZxf39/tG/fXv3p0KFDuWtPnjyJ3NxchISElDtnYWGBHj16IDk5GQCwbds2dOzYEWFhYXB1dcWMGTPUM4jatGmDffv2ITc3F506dYKfnx927typ3rMqNjYWXbp0QWBgIPr374/g4GC0atWq0u9l9OjRGDRoEEJDQ+Hr64vCwkKMGzdOo01kZCSWLVuGVatWwc3NDYGBgbh8+bJGm549e8LOzg4BAQFo1qxZpf1GR0cjPT1d49i8efNw7do1tGrVqtKldMnJyXjw4AG8vb0RERGBSZMmaWym/qo2btyI8PBwGBtXvqF+bRFUVX235BuiqKgIFhYWePjwIczNzcWOUyNyuRy7d+9Gv379NNaJVqagpACTD0zG6YLT0NPRQ/w78RjYemCdZiV6U73qOCSi2sNxSCQ+jkPSNk+ePMHVq1fh5OQEIyMjseMAz/YJKioqgrm5OXR0OAelvj169AhvvfUWUlJSMGjQoErbl5SUwMXFBd999129zk6qSEFBAVxcXHDixAmNGWMvU9E4qE7thUv5tMy/H/wb4zPH49bjWzA3MMeybsvQ0baj2LGIiIiIiIiIGhylUomCggIkJibC0tISAwYMqNJ1EokE69evr5V9oWrLtWvXsGrVqioVpWoTC1NaRPqHFNN+noZH8kdwMHPAyh4r4WRRv7/hiIiIiIiIiN4UN27cgJOTE+zt7ZGamqpeWlgVXbt2rdNs1eXj46Oxh1h9YWFKS3x34TssOr4ICpUC3tbeWNZtGRoZNRI7FhEREREREVGD5ejoCC3bIanWsTD1hlMoFUg8mYi0c2kAgKCWQUjonAADXQOxoxERERERERGRlmNh6g0mk8sw89BMHPz9IABggtcEjPIcBUEQxI5GRERERERERMTC1JvqzuM7mHhgIi7cvwADHQMs+NsC9HHqI3YsIiIiIiIiIiI1FqbeQGcLz2JS5iTkl+SjsVFjLO+2HF7WXmLHIiIiIiIiIiLSwMLUG+bAjQOYdXgWSp6WoJVFK6zosQL2ZvZixyIiIiIiIiIiKoeFqTeESqXC+nPrkXgiESqo4Gfnh8SuiTAzMBM7GhERERERERHRC+mIHYBqTq6UY97Refj8xOdQQYXBbQZjZc+VLEoRERERERERvaYiIiKwcOHCWr2nIAjYsWMHAKCgoADW1tb4/fffa7WP2sbCVANXoizBpIOTsPXSVggQMN1nOuLeiYO+jr7Y0YiIiIiIiOg1cu/ePYwdOxbNmzeHoaEhbG1tERAQAKlUqm7j6OgIQRAgCAKMjY3h4eGBtWvXatzn4MGDEAQBf/75Z7UzBAQEQFdXF9nZ2bXyTA1Vbm4udu/ejUmTJtVZH1ZWVhg+fDjmzJlTZ33UBhamGrA/Hv2BNY/W4NidY5DoSbC823IMdxsOQRDEjkZERERERESvmZCQEPz666/45ptvcOnSJWRkZKBr164oLCzUaDdv3jzcvn0bZ86cwbBhwxATE4M9e/bUuP8bN27gl19+wYQJE7Bu3boa36+m5HK5aH1/9dVXGDx4MExNTeu0n5EjRyI9PR3379+v035qgoWpBur2o9sY/sNw3FPeQ1NJU6T2SUW35t3EjkVERERERESvoT///BOHDx/G4sWL0a1bN7Ro0QKdOnVCbGwsBgwYoNHWzMwMtra2aNmyJWbOnInGjRtj//79Nc6QkpKCwMBAjB07Fhs3bkRJSUm5jKNHj4aNjQ2MjIzg7u6OXbt2qc9LpVJ07doVxsbGaNSoEQICAvDgwQPg2UyvZcuWadzPy8sLCQkJ6p8FQUBSUhIGDBgAExMTLFiwAAqFAh9++CGcnJwgkUjg4uKC5cuXl8u+bt06uLm5wdDQEHZ2dpgwYQIAICoqCoGBgRpt5XI5rK2tkZyc/MLvQaFQYOvWrQgKClIfmz17Nnx9fcu1bdeuHebNmwcAyM7ORq9evWBlZQULCwt06dIFp06dqvA7d3NzQ7NmzbB9+/YK24mJhakGytbEFn52frDTtUNaQBpcm7iKHYmIiIiIiEgrqVQqKGUyUT4qlapKGU1NTWFqaoodO3agtLS0StcolUps27YNDx48gIGBQY2/o5SUFAwbNgxt27aFs7Mztm7dqtFX3759IZVK8e233+LcuXP49NNPoaurCwDIyclBjx494OrqiqysLBw5cgRBQUFQKBTVypGQkICBAwciLy8PUVFRUCqVsLe3x5YtW3Du3DnEx8dj9uzZ2Lx5s/qapKQkjB8/HqNGjUJeXh4yMjLg7OwMAIiOjsbevXtx+/Ztdftdu3ZBJpMhNDT0hRlOnz6Nhw8fwsfHR30sPDwcx48fx5UrV9THzp49i9OnT2Po0KEAgOLiYkRGRuLIkSM4evQoWrdujX79+qG4uLjCZ+7UqRMOHz5cre+pPvGtfA2UIAiI943H9w++h7WxtdhxiIiIiIiItJaqpAQXvTuI0rfLqZMQjI0rbaenp4fU1FTExMRg9erV8Pb2RpcuXTBkyBB4enpqtJ05cyY++eQTlJaW4unTp2jcuDGio6NrlPPHH3+ETCZDQEAAAGDYsGFITk5GRESE+vzx48dx/vx5tGnTBgDQsmVL9fVLliyBj48PVq1apT7m5uZW7RxDhw7FyJEjNY7NnTtX/WsnJydkZWVh8+bN+OCDDwAA8+fPx8cff4zJkyer23Xs2BEA0LlzZ7i4uCAtLQ0zZswAns0Mq2iZ3vXr16Grqwtr6//+W97NzQ3t2rXDhg0bEBcXBwBIT0+Hr6+vugjWvXt3jfusWbMGlpaW+Pnnn8vN2vqrZs2a4ddff63Gt1S/OGOqATPQNYChYCh2DCIiIiIiImoAQkJCcOvWLWRkZKBPnz44ePAgvL29kZqaqtFu+vTpyMnJwYEDB+Dr64svvvhCXRx5VevWrUNoaCj09P4zPyYsLAxSqVQ9QygnJwf29vbqotT/ej5jqqb+OkvpuZUrV6JDhw5o2rQpTE1NsWbNGty4cQMAkJ+fj1u3blXYd3R0NFJSUgAAd+/exZ49exAVFfXS9iUlJTA0NCy3P3R4eDg2bNgAPJthtnHjRoSHh6vP3717FzExMWjdujUsLCxgbm6OR48eqbO+jEQigUwmq7CNmDhjioiIiIiIiKgGBIkELqdOitZ3dRgZGaFXr17o1asX4uLiEB0djTlz5mDEiBHqNlZWVnB2doazszO2bNkCDw8P+Pj4wNX11baQuX//PrZv3w65XI6kpCT1cYVCgXXr1mHBggWQVPIclZ3X0dEpt6zxRZubm5iYaPy8adMmTJs2DYmJifDz84OZmRk+++wzHDt2rEr9AsDw4cMxa9YsZGVl4ZdffoGTkxPee++9l7a3srKCTCZDWVmZxhLJsLAwzJw5E6dOnUJJSQlu3rypsRwwMjIShYWFWL58OVq0aAFDQ0P4+fmhrKyswnz3799H06ZNK30OsbAwRURERERERFQDgiBUaTnd68jV1RU7dux46XkHBweEhoYiNjYWO3fufKU+0tPTYW9vX66fffv2ITExEfPmzYOnpyd+//13XLp06YWzpjw9PZGZmamx7O6vmjZtqrHPU1FREa5evVppNqlUis6dO2PcuHHqY3/d58nMzAyOjo7IzMxEt24vfuFYkyZNEBwcjJSUFGRlZZVbKvi/vLy8AADnzp1T/xoA7O3t0aVLF6Snp6OkpAS9evXSWO4nlUqxatUq9OvXDwBw8+ZNFBQUVPqMZ86cQdeuXSttJxYWpoiIiIiIiIjecIWFhRg8eDCioqLg6ekJMzMznDhxAkuWLMHf//73Cq+dPHky3N3dceLECY2lcHl5eTAzM1P/LAgC2rVrV+765ORkvP/++3B3d9c47uDggNjYWOzduxf9+/eHv78/QkJCsHTpUjg7O+PChQsQBAF9+vRBbGwsPDw8MG7cOIwZMwYGBgb46aefMHjwYFhZWaF79+5ITU1FUFAQLC0tER8fr944vSKtW7fG+vXr8cMPP8DJyQlpaWnIzs6Gk5OTuk1CQgLGjBkDa2tr9O3bF8XFxZBKpZg4caK6TXR0NAIDA6FQKBAZGVlhn02bNoW3tzeOHDmiUZjCs+V8c+bMQVlZGb744otyWdPS0uDj44OioiJMnz690hldMpkMJ0+exMKFCyv9LsTCPaaIiIiIiIiI3nCmpqbq/aL8/f3h7u6OuLg4xMTEYMWKFRVe6+rqit69eyM+Pl7juL+/P9q3b6/+dOhQfgP4kydPIjc3FyEhIeXOWVhYoEePHkhOTgYAbNu2DR07dkRYWBhcXV0xY8YM9Vv32rRpg3379iE3NxedOnWCn58fdu7cqd6zKjY2Fl26dEFgYCD69++P4OBgtGrVqtLvZfTo0Rg0aBBCQ0Ph6+uLwsJCjdlTeLaEbtmyZVi1ahXc3NwQGBiIy5cva7Tp2bMn7OzsEBAQgGbNmlXab3R0NNLT08sdf//991FYWAiZTIbg4GCNc8nJyXjw4AG8vb0RERGBSZMmacyoepGdO3eiefPmFS4tFJugquq7Jd8QRUVFsLCwwMOHD2Fubi52nBqRy+XYvXs3+vXrB319fbHjEGkljkMi8XEcEomP45C0zZMnT3D16lU4OTnByMhI7DgAAKVSiaKiIpibm0NHh3NQ6tujR4/w1ltvISUlBYMGDaq0fUlJCVxcXPDdd9/Bz8+vznK98847mDRpEoYOHVrr965oHFSn9sKlfEREREREREREr0CpVKKgoACJiYmwtLTEgAEDqnSdRCLB+vXrq7RH1KsqKCjAoEGDEBYWVmd91AYWpoiIiIiIiIiIXsGNGzfg5OQEe3t7pKamqpcWVkVdb0huZWWFGTNm1GkftYGFKSIiIiIiIiKiV+Do6Agt2yGp1nHhKRERERERERERiYKFKSIiIiIiIiIiEgULU0RERERERESvgEu4SJvV1u9/FqaIiIiIiIiIqkFXVxcAUFZWJnYUItHIZDIAgL6+fo3uw83PiYiIiIiIiKpBT08PxsbGuHfvHvT19aGjI/6cD6VSibKyMjx58uS1yENvLpVKBZlMhvz8fFhaWqoLta+KhSkiIiIiIiKiahAEAXZ2drh69SquX78udhzgWbGgpKQEEokEgiCIHYe0gKWlJWxtbWt8HxamiIiIiIiIiKrJwMAArVu3fm2W88nlchw6dAj+/v41XlpFVBl9ff0az5R6joUpIiIiIiIiolego6MDIyMjsWMAz/a9evr0KYyMjFiYogaFC0+JiIiIiIiIiEgULEwREREREREREZEoWJgiIiIiIiIiIiJRaN0eUyqVCgBQVFQkdpQak8vlkMlkKCoq4hpiIpFwHBKJj+OQSHwch0Ti4zik18nzmsvzGkxFtK4wVVxcDABwcHAQOwoRERERERER0RuruLgYFhYWFbYRVFUpX71BlEolbt26BTMzMwiCIHacGikqKoKDgwNu3rwJc3NzseMQaSWOQyLxcRwSiY/jkEh8HIf0OlGpVCguLkazZs2go1PxLlJaN2NKR0cH9vb2YseoVebm5vyDh0hkHIdE4uM4JBIfxyGR+DgO6XVR2Uyp57j5ORERERERERERiYKFKSIiIiIiIiIiEgULUw2YoaEh5syZA0NDQ7GjEGktjkMi8XEcEomP45BIfByH1FBp3ebnRERERERERET0euCMKSIiIiIiIiIiEgULU0REREREREREJAoWpoiIiIiIiIiISBQsTDVgK1euhKOjI4yMjODr64vjx4+LHYlIayxatAgdO3aEmZkZrK2tERwcjIsXL4odi0hrffrppxAEAR999JHYUYi0zh9//IFhw4ahSZMmkEgk8PDwwIkTJ8SORaQ1FAoF4uLi4OTkBIlEglatWuGf//wnuJ00NRQsTDVQ3333HaZOnYo5c+bg1KlTaNeuHQICApCfny92NCKt8PPPP2P8+PE4evQo9u/fD7lcjt69e+Px48diRyPSOtnZ2fi///s/eHp6ih2FSOs8ePAA7777LvT19bFnzx6cO3cOiYmJaNSokdjRiLTG4sWLkZSUhBUrVuD8+fNYvHgxlixZgq+++krsaERVwrfyNVC+vr7o2LEjVqxYAQBQKpVwcHDAxIkTMWvWLLHjEWmde/fuwdraGj///DP8/f3FjkOkNR49egRvb2+sWrUK8+fPh5eXF5YtWyZ2LCKtMWvWLEilUhw+fFjsKERaKzAwEDY2NkhOTlYfCwkJgUQiwbfffitqNqKq4IypBqisrAwnT55Ez5491cd0dHTQs2dPZGVliZqNSFs9fPgQANC4cWOxoxBplfHjx6N///4afycSUf3JyMiAj48PBg8eDGtra7Rv3x5ff/212LGItErnzp2RmZmJS5cuAQByc3Nx5MgR9O3bV+xoRFWiJ3YAqr6CggIoFArY2NhoHLexscGFCxdEy0WkrZRKJT766CO8++67cHd3FzsOkdbYtGkTTp06hezsbLGjEGmt3377DUlJSZg6dSpmz56N7OxsTJo0CQYGBoiMjBQ7HpFWmDVrFoqKitC2bVvo6upCoVBgwYIFCA8PFzsaUZWwMEVEVEPjx4/HmTNncOTIEbGjEGmNmzdvYvLkydi/fz+MjIzEjkOktZRKJXx8fLBw4UIAQPv27XHmzBmsXr2ahSmierJ582akp6djw4YNcHNzQ05ODj766CM0a9aM45AaBBamGiArKyvo6uri7t27Gsfv3r0LW1tb0XIRaaMJEyZg165dOHToEOzt7cWOQ6Q1Tp48ifz8fHh7e6uPKRQKHDp0CCtWrEBpaSl0dXVFzUikDezs7ODq6qpx7O2338a2bdtEy0SkbaZPn45Zs2ZhyJAhAAAPDw9cv34dixYtYmGKGgTuMdUAGRgYoEOHDsjMzFQfUyqVyMzMhJ+fn6jZiLSFSqXChAkTsH37dhw4cABOTk5iRyLSKj169EBeXh5ycnLUHx8fH4SHhyMnJ4dFKaJ68u677+LixYsaxy5duoQWLVqIlolI28hkMujoaP7TXldXF0qlUrRMRNXBGVMN1NSpUxEZGQkfHx906tQJy5Ytw+PHjzFy5EixoxFphfHjx2PDhg3YuXMnzMzMcOfOHQCAhYUFJBKJ2PGI3nhmZmbl9nQzMTFBkyZNuNcbUT2aMmUKOnfujIULF+KDDz7A8ePHsWbNGqxZs0bsaERaIygoCAsWLEDz5s3h5uaGX3/9FUuXLkVUVJTY0YiqRFCpVCqxQ9CrWbFiBT777DPcuXMHXl5e+PLLL+Hr6yt2LCKtIAjCC4+npKRgxIgR9Z6HiICuXbvCy8sLy5YtEzsKkVbZtWsXYmNjcfn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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -2477,7 +8807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 64,
    "id": "aeef32c1",
    "metadata": {},
    "outputs": [
@@ -2485,33 +8815,41 @@
      "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 0x7fc3e3f1b010> 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 264ms/step\n",
+      "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         3\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         3\n",
-      "   macro avg       0.00      0.00      0.00         3\n",
-      "weighted avg       0.00      0.00      0.00         3\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",
-      "         CAU       0.00      0.00      0.00         1\n",
-      "         LOC       0.00      0.00      0.00         1\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",
-      "         RG0       0.00      0.00      0.00         1\n",
-      "         RG1       0.00      0.00      0.00        10\n",
-      "         RG2       0.00      0.00      0.00         1\n",
-      "           _       0.00      0.00      0.00         2\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        17\n",
-      "   macro avg       0.00      0.00      0.00        17\n",
-      "weighted avg       0.00      0.00      0.00        17\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"
      ]
     },
@@ -2519,22 +8857,40 @@
      "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",
+      "/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: ARG2 seems not to be NE tag.\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: ARG0 seems not to be NE tag.\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: AM-MOD seems not to be NE tag.\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: AM-CAU seems not to be NE tag.\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/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/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"
      ]
     }
    ],
@@ -2590,7 +8946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 65,
    "id": "5a18da05",
    "metadata": {},
    "outputs": [],
@@ -2612,13 +8968,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 66,
    "id": "cee30988",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ]
@@ -2644,13 +9000,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 67,
    "id": "4ba2b85c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ]
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diff --git a/NER_SRL/tag2idx_ner.pkl b/NER_SRL/tag2idx_ner.pkl
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diff --git a/NER_SRL/tag2idx_srl.pkl b/NER_SRL/tag2idx_srl.pkl
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diff --git a/NER_SRL/test_model.py b/NER_SRL/test_model.py
new file mode 100644
index 0000000..55c879c
--- /dev/null
+++ b/NER_SRL/test_model.py
@@ -0,0 +1,52 @@
+import json
+import numpy as np
+import pickle
+
+from keras.models import load_model
+from keras.preprocessing.sequence import pad_sequences
+
+
+model = load_model("multi_task_bilstm_model.keras")
+
+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
+
+
+def predict_sentence(sentence):
+    tokens = sentence.strip().lower().split()
+    print(tokens)
+    x = [word2idx.get(w.lower(), word2idx["UNK"]) for w in tokens]
+    x = pad_sequences([x], maxlen=50, padding="post", value=word2idx["PAD"])
+
+    preds = model.predict(x)
+    pred_labels_ner = np.argmax(preds[0], axis=-1)[0]
+    pred_labels_srl = np.argmax(preds[1], axis=-1)[0]
+
+    print("Hasil prediksi NER:")
+    for token, label_idx in zip(tokens, pred_labels_ner[: len(tokens)]):
+        print(f"{token}\t{idx2tag_ner[int(label_idx)]}")
+
+    print("\nHasil prediksi SRL:")
+    for token, label_idx in zip(tokens, pred_labels_srl[: len(tokens)]):
+        print(f"{token}\t{idx2tag_srl[int(label_idx)]}")
+
+
+if __name__ == "__main__":
+    try:
+        sentence = "aku lahir di indonesia"
+        predict_sentence(sentence)
+    except KeyboardInterrupt:
+        print("\n\nSelesai.")
diff --git a/NER_SRL/train.py b/NER_SRL/train.py
new file mode 100644
index 0000000..f139e8d
--- /dev/null
+++ b/NER_SRL/train.py
@@ -0,0 +1,107 @@
+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
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zi40=d7dZE=V`a?wzGDya-S<6Q>8$hv1(gmB@B7gWymiSE`^kyt=*97~1O6!7zwnz8
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diff --git a/dataset/dataset_ner_srl.json b/dataset/dataset_ner_srl.json
index f537bb2..a7177f5 100644
--- a/dataset/dataset_ner_srl.json
+++ b/dataset/dataset_ner_srl.json
@@ -226,9 +226,9 @@
       "O",
       "O",
       "O",
-      "V",
       "O",
-      "V",
+      "O",
+      "O",
       "O",
       "O",
       "O",
@@ -1188,10 +1188,3067 @@
       "unsur-unsur",
       "iklim",
       "terhadap",
-      "tanaman",
-      ":"
+      "tanaman"
     ],
-    "labels_ner": ["O", "O", "O", "O", "O", "O", "O"],
-    "labels_srl": ["AM-TMP", "ARG1", "ARG1", "ARG1", "AM-DIR", "ARG2", "O"]
+    "labels_ner": ["O", "O", "O", "O", "O", "O"],
+    "labels_srl": ["AM-TMP", "ARG1", "ARG1", "ARG1", "AM-DIR", "ARG2"]
+  },
+  {
+    "tokens": [
+      "Penyinaran",
+      "matahari",
+      "Memengaruhi",
+      "fotosintesis",
+      "tanaman",
+      ",",
+      "dapat",
+      "meningkatkan",
+      "suhu",
+      "udara",
+      "."
+    ],
+    "labels_ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
+    "labels_srl": [
+      "ARG0",
+      "ARG0",
+      "V",
+      "ARG1",
+      "ARG1",
+      "O",
+      "O",
+      "V",
+      "ARG1",
+      "ARG1",
+      "O"
+    ]
+  },
+  {
+    "tokens": [
+      "Suhu",
+      "Mengurangi",
+      "kadar",
+      "air",
+      "sehingga",
+      "cenderung",
+      "menjadi",
+      "kering",
+      "."
+    ],
+    "labels_ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O"],
+    "labels_srl": ["ARG0", "V", "ARG1", "ARG1", "O", "AM-MNR", "V", "ARG1", "O"]
+  },
+  {
+    "tokens": ["Kelembaban", "Membatasi", "hilangnya", "air", "."],
+    "labels_ner": ["O", "O", "O", "O", "O"],
+    "labels_srl": ["ARG0", "V", "ARG1", "ARG1", "O"]
+  },
+  {
+    "tokens": [
+      "Angin",
+      "Membantu",
+      "proses",
+      "penyerbukan",
+      "secara",
+      "alami",
+      ",",
+      "mengurangi",
+      "kadar",
+      "air",
+      "."
+    ],
+    "labels_ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
+    "labels_srl": [
+      "ARG0",
+      "V",
+      "ARG1",
+      "ARG1",
+      "AM-MNR",
+      "AM-MNR",
+      "O",
+      "V",
+      "ARG1",
+      "ARG1",
+      "O"
+    ]
+  },
+  {
+    "tokens": [
+      "Hujan",
+      "Meningkatkan",
+      "kadar",
+      "air",
+      ",",
+      "mengikis",
+      "tanah",
+      "."
+    ],
+    "labels_ner": ["O", "O", "O", "O", "O", "O", "O", "O"],
+    "labels_srl": ["ARG0", "V", "ARG1", "ARG1", "O", "V", "ARG1", "O"]
+  },
+
+  {
+    "tokens": [
+      "Kalian",
+      "menemukan",
+      "berbagai",
+      "perbedaan",
+      "sosial",
+      "budaya",
+      "masyarakat",
+      "di",
+      "sekitar",
+      "tempat",
+      "tinggalmu",
+      "."
+    ],
+    "labels_ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
+    "labels_srl": [
+      "ARG0",
+      "V",
+      "ARG1",
+      "ARG1",
+      "ARG1",
+      "ARG1",
+      "ARG1",
+      "O",
+      "ARGM-LOC",
+      "ARGM-LOC",
+      "ARGM-LOC",
+      "O"
+    ]
+  },
+  {
+    "tokens": [
+      "Apabila",
+      "kalian",
+      "tinggal",
+      "di",
+      "perkotaan",
+      ",",
+      "perbedaan",
+      "sosial",
+      "budaya",
+      "akan",
+      "semakin",
+      "banyak",
+      "."
+    ],
+    "labels_ner": [
+      "O",
+      "O",
+      "O",
+      "O",
+      "B-LOC",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O"
+    ],
+    "labels_srl": [
+      "O",
+      "ARG0",
+      "V",
+      "O",
+      "ARGM-LOC",
+      "O",
+      "ARG1",
+      "ARG1",
+      "ARG1",
+      "O",
+      "ARGM-MOD",
+      "ARGM-EXT",
+      "O"
+    ]
+  },
+  {
+    "tokens": [
+      "Perbedaan",
+      "sosial",
+      "budaya",
+      "meliputi",
+      "perbedaan",
+      "nilai-nilai",
+      ",",
+      "norma",
+      ",",
+      "dan",
+      "karakteristik",
+      "dari",
+      "suatu",
+      "kelompok",
+      "."
+    ],
+    "labels_ner": [
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O"
+    ],
+    "labels_srl": [
+      "ARG1",
+      "ARG1",
+      "ARG1",
+      "V",
+      "ARG2",
+      "ARG2",
+      "O",
+      "ARG2",
+      "O",
+      "ARG2",
+      "ARG2",
+      "O",
+      "ARG2",
+      "ARG2",
+      "O"
+    ]
+  },
+  {
+    "tokens": [
+      "Keragaman",
+      "sosial",
+      "budaya",
+      "di",
+      "masyarakat",
+      "dapat",
+      "terjadi",
+      "saat",
+      "berbagai",
+      "jenis",
+      "suku",
+      "dan",
+      "agama",
+      "yang",
+      "ada",
+      "di",
+      "suatu",
+      "ruang",
+      "bertemu",
+      "dan",
+      "berinteraksi",
+      "setiap",
+      "harinya",
+      "."
+    ],
+    "labels_ner": [
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "B-MISC",
+      "O",
+      "B-REL",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O",
+      "O"
+    ],
+    "labels_srl": [
+      "ARG1",
+      "ARG1",
+      "ARG1",
+      "ARGM-LOC",
+      "ARGM-LOC",
+      "ARGM-MOD",
+      "V",
+      "ARGM-TMP",
+      "ARGM-TMP",
+      "ARGM-TMP",
+      "ARG2",
+      "ARG2",
+      "ARG2",
+      "ARG2",
+      "ARG2",
+      "ARGM-LOC",
+      "ARGM-LOC",
+      "ARGM-LOC",
+      "V",
+      "V",
+      "V",
+      "ARGM-TMP",
+      "ARGM-TMP",
+      "O"
+    ]
+  },
+  {
+    "tokens": [
+      "Ruang",
+      "tersebut",
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