1002 lines
374 KiB
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1002 lines
374 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "263af9e9",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2025-05-12 02:34:48.724737: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
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"2025-05-12 02:34:48.725382: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
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"2025-05-12 02:34:48.727445: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
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"2025-05-12 02:34:48.733495: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
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"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
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"E0000 00:00:1746992088.743638 16048 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
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"E0000 00:00:1746992088.746546 16048 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
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"W0000 00:00:1746992088.754190 16048 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
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"W0000 00:00:1746992088.754206 16048 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
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"W0000 00:00:1746992088.754207 16048 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
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"W0000 00:00:1746992088.754208 16048 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
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"2025-05-12 02:34:48.756870: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
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"To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
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]
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}
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],
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"source": [
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"import pickle, tensorflow as tf, numpy as np\n",
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"from tensorflow.keras.models import Model\n",
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"from tensorflow.keras.layers import (\n",
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" Input,\n",
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" Embedding,\n",
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" SpatialDropout1D,\n",
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" Bidirectional,\n",
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" LSTM,\n",
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" TimeDistributed,\n",
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" Dense,\n",
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")\n",
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"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
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"from sklearn.model_selection import train_test_split\n",
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"from collections import Counter\n",
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"from itertools import zip_longest"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4fc87f1b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"total kalimat 638\n",
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"NER Label Count || SRL Label Count \n",
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"-------------------------------------------------------\n",
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"O 4251 || O 2178 \n",
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"TIME 235 || ARGM-TMP 1291 \n",
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"PER 539 || ARG0 962 \n",
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"LOC 586 || V 737 \n",
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"DATE 985 || ARG1 1305 \n",
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"ETH 430 || ARGM-LOC 503 \n",
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"EVENT 125 || ARG2 292 \n",
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"MISC 17 || ARGM-MOD 39 \n",
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"ORG 37 || ARGM-MNR 37 \n",
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"QUANT 47 || ARGM-NEG 6 \n",
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"MAT 115 || ARGM-DIR 41 \n",
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"UNIT 45 || ARGM-CAU 21 \n"
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]
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}
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],
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"source": [
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"data = []\n",
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"# with open(\"../dataset/new_ner_srl.tsv\", encoding=\"utf-8\") as f:\n",
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"with open(\"../dataset/ner_srl_without_bio.tsv\", encoding=\"utf-8\") as f:\n",
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" tok, ner, srl = [], [], []\n",
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" for line in f:\n",
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" line = line.strip()\n",
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" if not line:\n",
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" if tok:\n",
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" data.append({\"tokens\": tok, \"labels_ner\": ner, \"labels_srl\": srl})\n",
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" tok, ner, srl = [], [], []\n",
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" else:\n",
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" t, n, s = line.split(\"\\t\")\n",
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" tok.append(t.lower())\n",
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" ner.append(n.strip())\n",
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" srl.append(s.strip())\n",
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"\n",
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"print(\"total kalimat \", len(data))\n",
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"# ——————————————————\n",
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"sentences = [d[\"tokens\"] for d in data]\n",
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"labels_ner = [d[\"labels_ner\"] for d in data]\n",
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"labels_srl = [d[\"labels_srl\"] for d in data]\n",
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"\n",
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"ner_counter = Counter(label for seq in labels_ner for label in seq)\n",
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"\n",
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"srl_counter = Counter(label for seq in labels_srl for label in seq)\n",
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"\n",
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"\n",
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"print(f\"{'NER Label':<15} {'Count':<10} || {'SRL Label':<15} {'Count':<10}\")\n",
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"print(\"-\" * 55)\n",
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"\n",
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"for (ner_label, ner_count), (srl_label, srl_count) in zip_longest(ner_counter.items(), srl_counter.items(), fillvalue=('', '')):\n",
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" print(f\"{ner_label:<15} {ner_count:<10} || {srl_label:<15} {srl_count:<10}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "8dda2d6c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"NER -> Total Labels: 7412, O Count: 4251, O Percentage: 57.35%\n",
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"SRL -> Total Labels: 7412, O Count: 2178, O Percentage: 29.38%\n"
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]
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}
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],
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"source": [
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"\n",
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"\n",
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"def calculate_o_percentage(labels):\n",
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" counter = Counter(label for seq in labels for label in seq)\n",
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" total = sum(counter.values())\n",
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" count_o = counter.get(\"O\", 0)\n",
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" percent_o = (count_o / total) * 100 if total > 0 else 0\n",
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" return percent_o, total, count_o\n",
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"\n",
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"# Hitung persentase 'O' untuk NER\n",
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"o_ner_percent, total_ner, o_ner_count = calculate_o_percentage(labels_ner)\n",
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"\n",
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"# Hitung persentase 'O' untuk SRL\n",
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"o_srl_percent, total_srl, o_srl_count = calculate_o_percentage(labels_srl)\n",
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"\n",
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"print(f\"NER -> Total Labels: {total_ner}, O Count: {o_ner_count}, O Percentage: {o_ner_percent:.2f}%\")\n",
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"print(f\"SRL -> Total Labels: {total_srl}, O Count: {o_srl_count}, O Percentage: {o_srl_percent:.2f}%\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "48553e6b",
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"metadata": {},
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"outputs": [],
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"source": [
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"PAD_TOKEN = \"<PAD>\"\n",
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"words = sorted({w for s in sentences for w in s})\n",
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"\n",
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"ner_tags = sorted({t for seq in labels_ner for t in seq})\n",
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"srl_tags = sorted({t for seq in labels_srl for t in seq})\n",
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"\n",
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"ner_tags.insert(0, PAD_TOKEN)\n",
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"srl_tags.insert(0, PAD_TOKEN)\n",
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"\n",
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"word2idx = {w: i + 2 for i, w in enumerate(words)}\n",
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"word2idx[\"PAD\"] = 0\n",
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"word2idx[\"UNK\"] = 1\n",
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"\n",
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"tag2idx_ner = {t: i for i, t in enumerate(ner_tags)}\n",
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"tag2idx_srl = {t: i for i, t in enumerate(srl_tags)}\n",
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"idx2tag_ner = {i: t for t, i in tag2idx_ner.items()}\n",
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"idx2tag_srl = {i: t for t, i in tag2idx_srl.items()}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "096967e8",
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"metadata": {},
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"outputs": [],
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"source": [
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"X = [[word2idx.get(w, 1) for w in s] for s in sentences]\n",
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"y_ner = [[tag2idx_ner[t] for t in seq] for seq in labels_ner]\n",
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"y_srl = [[tag2idx_srl[t] for t in seq] for seq in labels_srl]\n",
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"\n",
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"maxlen = max(map(len, X))\n",
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"pad_id = tag2idx_ner[PAD_TOKEN]\n",
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"\n",
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"X = pad_sequences(X, maxlen=maxlen, padding=\"post\", value=0)\n",
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"y_ner = pad_sequences(y_ner, maxlen=maxlen, padding=\"post\", value=pad_id)\n",
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"y_srl = pad_sequences(y_srl, maxlen=maxlen, padding=\"post\", value=pad_id)\n",
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"\n",
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"mask = (y_ner != pad_id).astype(\"float32\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "a26893cc",
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"metadata": {},
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"outputs": [],
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"source": [
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"splits = train_test_split(\n",
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" X, y_ner, y_srl, mask, test_size=0.2, random_state=42, shuffle=True\n",
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")\n",
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"X_tr, X_te, ner_tr, ner_te, srl_tr, srl_te, m_tr, m_te = splits"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "1b4a1c61",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2025-05-12 02:34:51.102561: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1mModel: \"functional\"\u001b[0m\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
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"┃<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",
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"┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
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"│ tokens (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ - │\n",
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"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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"│ embed (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">86,208</span> │ tokens[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
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"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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"│ spatial_dropout1d │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ embed[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SpatialDropout1D</span>) │ │ │ │\n",
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"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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"│ not_equal │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ tokens[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">NotEqual</span>) │ │ │ │\n",
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"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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"│ bidirectional │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">66,048</span> │ spatial_dropout1… │\n",
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||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ │ │ not_equal[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
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||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ bidirectional_1 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</span> │ bidirectional[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
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||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ │ │ not_equal[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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"│ time_distributed │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
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||
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>) │ │ │ not_equal[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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"│ time_distributed_1 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ bidirectional_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>) │ │ │ not_equal[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
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"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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"│ ner_output │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">845</span> │ time_distributed… │\n",
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>) │ │ │ not_equal[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
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"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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"│ srl_output │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">845</span> │ time_distributed… │\n",
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>) │ │ │ not_equal[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
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"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
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"</pre>\n"
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],
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"text/plain": [
|
||
"┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
|
||
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n",
|
||
"┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
|
||
"│ tokens (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ embed (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m86,208\u001b[0m │ tokens[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ spatial_dropout1d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ embed[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"│ (\u001b[38;5;33mSpatialDropout1D\u001b[0m) │ │ │ │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ not_equal │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ tokens[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"│ (\u001b[38;5;33mNotEqual\u001b[0m) │ │ │ │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ bidirectional │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m66,048\u001b[0m │ spatial_dropout1… │\n",
|
||
"│ (\u001b[38;5;33mBidirectional\u001b[0m) │ │ │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ bidirectional_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m98,816\u001b[0m │ bidirectional[\u001b[38;5;34m0\u001b[0m]… │\n",
|
||
"│ (\u001b[38;5;33mBidirectional\u001b[0m) │ │ │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ time_distributed │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n",
|
||
"│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ time_distributed_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │ bidirectional_1[\u001b[38;5;34m…\u001b[0m │\n",
|
||
"│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ ner_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m13\u001b[0m) │ \u001b[38;5;34m845\u001b[0m │ time_distributed… │\n",
|
||
"│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
|
||
"│ srl_output │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m13\u001b[0m) │ \u001b[38;5;34m845\u001b[0m │ time_distributed… │\n",
|
||
"│ (\u001b[38;5;33mTimeDistributed\u001b[0m) │ │ │ not_equal[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
|
||
"└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">269,274</span> (1.03 MB)\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m269,274\u001b[0m (1.03 MB)\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">269,274</span> (1.03 MB)\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m269,274\u001b[0m (1.03 MB)\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"embed_dim = 64\n",
|
||
"lstm_units = 64\n",
|
||
"drop_embed = 0.45\n",
|
||
"drop_lstm = 0.35\n",
|
||
"\n",
|
||
"inp = Input(shape=(maxlen,), name=\"tokens\")\n",
|
||
"emb = Embedding(len(word2idx), embed_dim, mask_zero=True, name=\"embed\")(inp)\n",
|
||
"emb = SpatialDropout1D(drop_embed)(emb)\n",
|
||
"\n",
|
||
"x = Bidirectional(\n",
|
||
" LSTM(\n",
|
||
" lstm_units,\n",
|
||
" return_sequences=True,\n",
|
||
" dropout=drop_lstm,\n",
|
||
" recurrent_dropout=drop_lstm,\n",
|
||
" )\n",
|
||
")(emb)\n",
|
||
"x = Bidirectional(\n",
|
||
" LSTM(\n",
|
||
" lstm_units,\n",
|
||
" return_sequences=True,\n",
|
||
" dropout=drop_lstm,\n",
|
||
" recurrent_dropout=drop_lstm,\n",
|
||
" )\n",
|
||
")(x)\n",
|
||
"\n",
|
||
"ner_head = TimeDistributed(Dense(lstm_units, activation=\"relu\"))(x)\n",
|
||
"ner_out = TimeDistributed(\n",
|
||
" Dense(len(tag2idx_ner), activation=\"softmax\"), name=\"ner_output\"\n",
|
||
")(ner_head)\n",
|
||
"\n",
|
||
"srl_head = TimeDistributed(Dense(lstm_units, activation=\"relu\"))(x)\n",
|
||
"srl_out = TimeDistributed(\n",
|
||
" Dense(len(tag2idx_srl), activation=\"softmax\"), name=\"srl_output\"\n",
|
||
")(srl_head)\n",
|
||
"\n",
|
||
"model = Model(inp, [ner_out, srl_out])\n",
|
||
"\n",
|
||
"model.compile(\n",
|
||
" optimizer=tf.keras.optimizers.Adam(3e-4),\n",
|
||
" loss={\n",
|
||
" \"ner_output\": \"sparse_categorical_crossentropy\",\n",
|
||
" \"srl_output\": \"sparse_categorical_crossentropy\",\n",
|
||
" },\n",
|
||
" metrics={\n",
|
||
" \"ner_output\": [\"sparse_categorical_accuracy\"],\n",
|
||
" \"srl_output\": [\"sparse_categorical_accuracy\"],\n",
|
||
" },\n",
|
||
" # sample_weight_mode=\"temporal\"\n",
|
||
")\n",
|
||
"model.summary()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "f41d6012",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/30\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2025-05-12 02:34:58.809821: E tensorflow/core/util/util.cc:131] oneDNN supports DT_BOOL only on platforms with AVX-512. Falling back to the default Eigen-based implementation if present.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 239ms/step - loss: 5.1207 - ner_output_loss: 2.5598 - ner_output_sparse_categorical_accuracy: 0.2926 - srl_output_loss: 2.5609 - srl_output_sparse_categorical_accuracy: 0.2646 - val_loss: 5.0921 - val_ner_output_loss: 2.5409 - val_ner_output_sparse_categorical_accuracy: 0.2015 - val_srl_output_loss: 2.5512 - val_srl_output_sparse_categorical_accuracy: 0.1043 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 2/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 5.0802 - ner_output_loss: 2.5334 - ner_output_sparse_categorical_accuracy: 0.1957 - srl_output_loss: 2.5467 - srl_output_sparse_categorical_accuracy: 0.0940 - val_loss: 5.0274 - val_ner_output_loss: 2.4995 - val_ner_output_sparse_categorical_accuracy: 0.2015 - val_srl_output_loss: 2.5279 - val_srl_output_sparse_categorical_accuracy: 0.1165 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 3/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 5.0040 - ner_output_loss: 2.4842 - ner_output_sparse_categorical_accuracy: 0.1962 - srl_output_loss: 2.5197 - srl_output_sparse_categorical_accuracy: 0.1071 - val_loss: 4.8936 - val_ner_output_loss: 2.4134 - val_ner_output_sparse_categorical_accuracy: 0.2015 - val_srl_output_loss: 2.4802 - val_srl_output_sparse_categorical_accuracy: 0.1165 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 4/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 4.8397 - ner_output_loss: 2.3788 - ner_output_sparse_categorical_accuracy: 0.1934 - srl_output_loss: 2.4608 - srl_output_sparse_categorical_accuracy: 0.1129 - val_loss: 4.5720 - val_ner_output_loss: 2.2053 - val_ner_output_sparse_categorical_accuracy: 0.2015 - val_srl_output_loss: 2.3667 - val_srl_output_sparse_categorical_accuracy: 0.1160 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 5/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.4450 - ner_output_loss: 2.1235 - ner_output_sparse_categorical_accuracy: 0.1937 - srl_output_loss: 2.3212 - srl_output_sparse_categorical_accuracy: 0.1115 - val_loss: 3.8883 - val_ner_output_loss: 1.7636 - val_ner_output_sparse_categorical_accuracy: 0.2015 - val_srl_output_loss: 2.1246 - val_srl_output_sparse_categorical_accuracy: 0.1165 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 6/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.8528 - ner_output_loss: 1.7479 - ner_output_sparse_categorical_accuracy: 0.1952 - srl_output_loss: 2.1048 - srl_output_sparse_categorical_accuracy: 0.1138 - val_loss: 3.6268 - val_ner_output_loss: 1.6653 - val_ner_output_sparse_categorical_accuracy: 0.2015 - val_srl_output_loss: 1.9616 - val_srl_output_sparse_categorical_accuracy: 0.1135 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 7/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.6429 - ner_output_loss: 1.6612 - ner_output_sparse_categorical_accuracy: 0.1938 - srl_output_loss: 1.9816 - srl_output_sparse_categorical_accuracy: 0.1126 - val_loss: 3.3747 - val_ner_output_loss: 1.5140 - val_ner_output_sparse_categorical_accuracy: 0.2015 - val_srl_output_loss: 1.8608 - val_srl_output_sparse_categorical_accuracy: 0.1379 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 8/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.3915 - ner_output_loss: 1.5105 - ner_output_sparse_categorical_accuracy: 0.1908 - srl_output_loss: 1.8809 - srl_output_sparse_categorical_accuracy: 0.1282 - val_loss: 3.2546 - val_ner_output_loss: 1.4605 - val_ner_output_sparse_categorical_accuracy: 0.2015 - val_srl_output_loss: 1.7942 - val_srl_output_sparse_categorical_accuracy: 0.1312 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 9/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.2453 - ner_output_loss: 1.4581 - ner_output_sparse_categorical_accuracy: 0.1914 - srl_output_loss: 1.7871 - srl_output_sparse_categorical_accuracy: 0.1278 - val_loss: 3.1529 - val_ner_output_loss: 1.4262 - val_ner_output_sparse_categorical_accuracy: 0.1962 - val_srl_output_loss: 1.7268 - val_srl_output_sparse_categorical_accuracy: 0.1372 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 10/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.1824 - ner_output_loss: 1.4328 - ner_output_sparse_categorical_accuracy: 0.1914 - srl_output_loss: 1.7496 - srl_output_sparse_categorical_accuracy: 0.1333 - val_loss: 3.0830 - val_ner_output_loss: 1.4026 - val_ner_output_sparse_categorical_accuracy: 0.2004 - val_srl_output_loss: 1.6804 - val_srl_output_sparse_categorical_accuracy: 0.1441 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 11/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.0779 - ner_output_loss: 1.3960 - ner_output_sparse_categorical_accuracy: 0.1933 - srl_output_loss: 1.6819 - srl_output_sparse_categorical_accuracy: 0.1404 - val_loss: 3.0347 - val_ner_output_loss: 1.3791 - val_ner_output_sparse_categorical_accuracy: 0.2038 - val_srl_output_loss: 1.6556 - val_srl_output_sparse_categorical_accuracy: 0.1436 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 12/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.0401 - ner_output_loss: 1.3610 - ner_output_sparse_categorical_accuracy: 0.1988 - srl_output_loss: 1.6790 - srl_output_sparse_categorical_accuracy: 0.1425 - val_loss: 3.0043 - val_ner_output_loss: 1.3633 - val_ner_output_sparse_categorical_accuracy: 0.2031 - val_srl_output_loss: 1.6410 - val_srl_output_sparse_categorical_accuracy: 0.1441 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 13/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.9387 - ner_output_loss: 1.3281 - ner_output_sparse_categorical_accuracy: 0.1962 - srl_output_loss: 1.6106 - srl_output_sparse_categorical_accuracy: 0.1417 - val_loss: 2.9795 - val_ner_output_loss: 1.3491 - val_ner_output_sparse_categorical_accuracy: 0.2029 - val_srl_output_loss: 1.6304 - val_srl_output_sparse_categorical_accuracy: 0.1455 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 14/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.9423 - ner_output_loss: 1.3144 - ner_output_sparse_categorical_accuracy: 0.2022 - srl_output_loss: 1.6279 - srl_output_sparse_categorical_accuracy: 0.1425 - val_loss: 2.9585 - val_ner_output_loss: 1.3351 - val_ner_output_sparse_categorical_accuracy: 0.2031 - val_srl_output_loss: 1.6233 - val_srl_output_sparse_categorical_accuracy: 0.1452 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 15/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.9059 - ner_output_loss: 1.3101 - ner_output_sparse_categorical_accuracy: 0.1994 - srl_output_loss: 1.5958 - srl_output_sparse_categorical_accuracy: 0.1454 - val_loss: 2.9432 - val_ner_output_loss: 1.3262 - val_ner_output_sparse_categorical_accuracy: 0.2027 - val_srl_output_loss: 1.6170 - val_srl_output_sparse_categorical_accuracy: 0.1459 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 16/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.9055 - ner_output_loss: 1.2973 - ner_output_sparse_categorical_accuracy: 0.1947 - srl_output_loss: 1.6083 - srl_output_sparse_categorical_accuracy: 0.1426 - val_loss: 2.9266 - val_ner_output_loss: 1.3165 - val_ner_output_sparse_categorical_accuracy: 0.2015 - val_srl_output_loss: 1.6101 - val_srl_output_sparse_categorical_accuracy: 0.1420 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 17/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.8617 - ner_output_loss: 1.2864 - ner_output_sparse_categorical_accuracy: 0.1981 - srl_output_loss: 1.5752 - srl_output_sparse_categorical_accuracy: 0.1503 - val_loss: 2.9099 - val_ner_output_loss: 1.3069 - val_ner_output_sparse_categorical_accuracy: 0.2011 - val_srl_output_loss: 1.6030 - val_srl_output_sparse_categorical_accuracy: 0.1438 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 18/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.8199 - ner_output_loss: 1.2665 - ner_output_sparse_categorical_accuracy: 0.1957 - srl_output_loss: 1.5536 - srl_output_sparse_categorical_accuracy: 0.1460 - val_loss: 2.8892 - val_ner_output_loss: 1.2952 - val_ner_output_sparse_categorical_accuracy: 0.2008 - val_srl_output_loss: 1.5940 - val_srl_output_sparse_categorical_accuracy: 0.1512 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 19/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.8937 - ner_output_loss: 1.2911 - ner_output_sparse_categorical_accuracy: 0.1984 - srl_output_loss: 1.6025 - srl_output_sparse_categorical_accuracy: 0.1455 - val_loss: 2.8620 - val_ner_output_loss: 1.2787 - val_ner_output_sparse_categorical_accuracy: 0.2006 - val_srl_output_loss: 1.5834 - val_srl_output_sparse_categorical_accuracy: 0.1533 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 20/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.8192 - ner_output_loss: 1.2545 - ner_output_sparse_categorical_accuracy: 0.1988 - srl_output_loss: 1.5647 - srl_output_sparse_categorical_accuracy: 0.1529 - val_loss: 2.8301 - val_ner_output_loss: 1.2602 - val_ner_output_sparse_categorical_accuracy: 0.2013 - val_srl_output_loss: 1.5698 - val_srl_output_sparse_categorical_accuracy: 0.1540 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 21/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.7594 - ner_output_loss: 1.2302 - ner_output_sparse_categorical_accuracy: 0.1960 - srl_output_loss: 1.5291 - srl_output_sparse_categorical_accuracy: 0.1559 - val_loss: 2.7871 - val_ner_output_loss: 1.2358 - val_ner_output_sparse_categorical_accuracy: 0.2038 - val_srl_output_loss: 1.5512 - val_srl_output_sparse_categorical_accuracy: 0.1565 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 22/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.7990 - ner_output_loss: 1.2422 - ner_output_sparse_categorical_accuracy: 0.1976 - srl_output_loss: 1.5568 - srl_output_sparse_categorical_accuracy: 0.1513 - val_loss: 2.7361 - val_ner_output_loss: 1.2072 - val_ner_output_sparse_categorical_accuracy: 0.2171 - val_srl_output_loss: 1.5289 - val_srl_output_sparse_categorical_accuracy: 0.1613 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 23/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.6867 - ner_output_loss: 1.1842 - ner_output_sparse_categorical_accuracy: 0.2120 - srl_output_loss: 1.5023 - srl_output_sparse_categorical_accuracy: 0.1636 - val_loss: 2.6829 - val_ner_output_loss: 1.1777 - val_ner_output_sparse_categorical_accuracy: 0.2199 - val_srl_output_loss: 1.5052 - val_srl_output_sparse_categorical_accuracy: 0.1618 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 24/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.6152 - ner_output_loss: 1.1454 - ner_output_sparse_categorical_accuracy: 0.2129 - srl_output_loss: 1.4698 - srl_output_sparse_categorical_accuracy: 0.1625 - val_loss: 2.6377 - val_ner_output_loss: 1.1529 - val_ner_output_sparse_categorical_accuracy: 0.2268 - val_srl_output_loss: 1.4847 - val_srl_output_sparse_categorical_accuracy: 0.1677 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 25/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 2.5303 - ner_output_loss: 1.0935 - ner_output_sparse_categorical_accuracy: 0.2241 - srl_output_loss: 1.4368 - srl_output_sparse_categorical_accuracy: 0.1707 - val_loss: 2.5891 - val_ner_output_loss: 1.1272 - val_ner_output_sparse_categorical_accuracy: 0.2318 - val_srl_output_loss: 1.4619 - val_srl_output_sparse_categorical_accuracy: 0.1700 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 26/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.5297 - ner_output_loss: 1.0978 - ner_output_sparse_categorical_accuracy: 0.2190 - srl_output_loss: 1.4318 - srl_output_sparse_categorical_accuracy: 0.1648 - val_loss: 2.5424 - val_ner_output_loss: 1.1029 - val_ner_output_sparse_categorical_accuracy: 0.2383 - val_srl_output_loss: 1.4394 - val_srl_output_sparse_categorical_accuracy: 0.1721 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 27/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.4495 - ner_output_loss: 1.0693 - ner_output_sparse_categorical_accuracy: 0.2250 - srl_output_loss: 1.3802 - srl_output_sparse_categorical_accuracy: 0.1723 - val_loss: 2.4932 - val_ner_output_loss: 1.0773 - val_ner_output_sparse_categorical_accuracy: 0.2399 - val_srl_output_loss: 1.4159 - val_srl_output_sparse_categorical_accuracy: 0.1737 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 28/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.4468 - ner_output_loss: 1.0679 - ner_output_sparse_categorical_accuracy: 0.2334 - srl_output_loss: 1.3787 - srl_output_sparse_categorical_accuracy: 0.1790 - val_loss: 2.4424 - val_ner_output_loss: 1.0533 - val_ner_output_sparse_categorical_accuracy: 0.2403 - val_srl_output_loss: 1.3891 - val_srl_output_sparse_categorical_accuracy: 0.1772 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 29/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.4241 - ner_output_loss: 1.0521 - ner_output_sparse_categorical_accuracy: 0.2321 - srl_output_loss: 1.3720 - srl_output_sparse_categorical_accuracy: 0.1788 - val_loss: 2.4031 - val_ner_output_loss: 1.0369 - val_ner_output_sparse_categorical_accuracy: 0.2394 - val_srl_output_loss: 1.3663 - val_srl_output_sparse_categorical_accuracy: 0.1790 - learning_rate: 3.0000e-04\n",
|
||
"Epoch 30/30\n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.3343 - ner_output_loss: 1.0166 - ner_output_sparse_categorical_accuracy: 0.2329 - srl_output_loss: 1.3177 - srl_output_sparse_categorical_accuracy: 0.1790 - val_loss: 2.3498 - val_ner_output_loss: 1.0117 - val_ner_output_sparse_categorical_accuracy: 0.2410 - val_srl_output_loss: 1.3381 - val_srl_output_sparse_categorical_accuracy: 0.1836 - learning_rate: 3.0000e-04\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"callbacks = [\n",
|
||
" tf.keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True),\n",
|
||
" tf.keras.callbacks.ReduceLROnPlateau(patience=2, factor=0.5, min_lr=1e-5),\n",
|
||
"]\n",
|
||
"\n",
|
||
"history = model.fit(\n",
|
||
" X_tr,\n",
|
||
" [ner_tr, srl_tr], # y → LIST (pos 0 = ner_output, 1 = srl_output)\n",
|
||
" sample_weight=[m_tr, m_tr], # sama‑persis urutan\n",
|
||
" validation_data=(X_te, [ner_te, srl_te], [m_te, m_te]),\n",
|
||
" \n",
|
||
" batch_size=64,\n",
|
||
" epochs=30,\n",
|
||
" callbacks=callbacks,\n",
|
||
" verbose=1,\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# 7. Save artefacts\n",
|
||
"# =========================\n",
|
||
"model.save(\"lstm_ner_srl_model.keras\")\n",
|
||
"for fname, obj in [\n",
|
||
" (\"word2idx.pkl\", word2idx),\n",
|
||
" (\"tag2idx_ner.pkl\", tag2idx_ner),\n",
|
||
" (\"tag2idx_srl.pkl\", tag2idx_srl),\n",
|
||
"]:\n",
|
||
" with open(fname, \"wb\") as f:\n",
|
||
" pickle.dump(obj, f)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "430794b9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"def plot_training_history(history):\n",
|
||
" history_data = history.history\n",
|
||
" epochs = range(1, len(next(iter(history_data.values()))) + 1)\n",
|
||
"\n",
|
||
" # Coba deteksi metric secara dinamis agar fleksibel\n",
|
||
" ner_acc_key = next((k for k in history_data if \"ner_output\" in k and \"accuracy\" in k), None)\n",
|
||
" srl_acc_key = next((k for k in history_data if \"srl_output\" in k and \"accuracy\" in k), None)\n",
|
||
" val_ner_acc_key = f\"val_{ner_acc_key}\" if ner_acc_key else None\n",
|
||
" val_srl_acc_key = f\"val_{srl_acc_key}\" if srl_acc_key else None\n",
|
||
"\n",
|
||
" # --- Plot Accuracy ---\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
" if ner_acc_key:\n",
|
||
" plt.plot(epochs, history_data[ner_acc_key], marker=\"o\", label=\"NER Accuracy (Train)\")\n",
|
||
" if srl_acc_key:\n",
|
||
" plt.plot(epochs, history_data[srl_acc_key], marker=\"s\", label=\"SRL Accuracy (Train)\")\n",
|
||
" if val_ner_acc_key in history_data:\n",
|
||
" plt.plot(epochs, history_data[val_ner_acc_key], marker=\"o\", linestyle=\"--\", label=\"NER Accuracy (Val)\")\n",
|
||
" if val_srl_acc_key in history_data:\n",
|
||
" plt.plot(epochs, history_data[val_srl_acc_key], marker=\"s\", linestyle=\"--\", label=\"SRL Accuracy (Val)\")\n",
|
||
"\n",
|
||
" plt.title(\"Accuracy per Epoch\")\n",
|
||
" plt.xlabel(\"Epochs\")\n",
|
||
" plt.ylabel(\"Accuracy\")\n",
|
||
" plt.legend()\n",
|
||
" plt.grid(True)\n",
|
||
" plt.savefig(\"accuracy_plot.png\")\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" # --- Plot Loss ---\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
" if \"ner_output_loss\" in history_data:\n",
|
||
" plt.plot(epochs, history_data[\"ner_output_loss\"], marker=\"o\", label=\"NER Loss (Train)\")\n",
|
||
" if \"srl_output_loss\" in history_data:\n",
|
||
" plt.plot(epochs, history_data[\"srl_output_loss\"], marker=\"s\", label=\"SRL Loss (Train)\")\n",
|
||
" if \"val_ner_output_loss\" in history_data:\n",
|
||
" plt.plot(epochs, history_data[\"val_ner_output_loss\"], marker=\"o\", linestyle=\"--\", label=\"NER Loss (Val)\")\n",
|
||
" if \"val_srl_output_loss\" in history_data:\n",
|
||
" plt.plot(epochs, history_data[\"val_srl_output_loss\"], marker=\"s\", linestyle=\"--\", label=\"SRL Loss (Val)\")\n",
|
||
"\n",
|
||
" plt.title(\"Loss per Epoch\")\n",
|
||
" plt.xlabel(\"Epochs\")\n",
|
||
" plt.ylabel(\"Loss\")\n",
|
||
" plt.legend()\n",
|
||
" plt.grid(True)\n",
|
||
" plt.savefig(\"loss_plot.png\")\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
"plot_training_history(history)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "e690a0e0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x1000 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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Hx0cnT56UzuPy7UOHDjnfuGjevLnbbb766ivDNzd+fzn2xZ6dNmvx4sV65ZVXJEnTp09XWlqaKlSooC+//FIvvvjiFZkBAC41CjQA4LLbsWOHtm3bds7tfn+p54XcwKhDhw5q166dJGncuHHnvf+FOnsncf1aptPT08vc/vTp05o4caI+++wz57I777xTkrRq1SotXry41D7FxcWaOHGiJCkuLk5xcXGX+FmUrUGDBvL395ckLViwoNT6kpISPf/88273LS4uLvOM49nj6g9FryyenpeR6tWr69VXX9Xjjz+uZ5991rAM/1FQUJBsNpskaePGjaXWFxcX6x//+EeZ+5+Vl5d3QbOfj4MHD2rQoEFyOBzq37+/Bg0apJYtWzo/Dz1+/PjzfsMFADwBBRoAcNl9//33atiwobp27aqUlBTt3LnTue7UqVPKzMzUkCFDnGerWrdureuvv/6CftfZO/1+8803ZX6OOj8/X4cPHy7zYfZMnZ+fnxYuXKgaNWro6NGj6tSpkx588EGtWbNGp0+fdm63c+dOzZgxQ7GxsXrmmWdc1vXu3dt5R+i+fftq7ty5zptX7dixQ71799aqVaukX2/IdaVVqFBBvXv3liRNnjxZaWlpzrOoWVlZ6tWrl+HXPe3evVvR0dF67rnnlJmZ6TwrrF+/IioxMVGSFBAQoPbt25uax9PzKstDDz2kqVOnavz48ab3CQwM1HXXXSdJeuyxx7Rs2TLnmxKbN2/WrbfeqrVr1yogIMDt/iEhIc4brs2aNcvlv8Gl5nA4NHjwYB04cEBRUVF66623nOtGjRqlm2++WcXFxerfv7/sdvtlmwMALgurv4gaAPDn97///c8hyeXh6+vrCA0NddhsNpfl11xzjWPPnj2ljjF48GCHJEedOnXO+fvi4+MdkhytWrVyWf7ll1+WmqOsx5EjR87ree7Zs8fRsWNHl2N4eXk5QkNDHb6+vi7L27Rp4/jpp59c9t+9e7ejcePGLhmFhIS4HOtf//qX299dp04dhyTHrFmzDOc7m+HgwYNLrXvmmWcckhzt27c33H/Xrl2OiIgI5zwVKlRwBAUFOSQ5Kleu7Fi+fLlz3Zdffuncb8eOHS7P3dvbu1Qmvr6+jg8//LDU73R3PE/I61zat29/Qfue/e/g7nW+du1aR0BAgPP5+fn5OSpXruyQ5PDx8XGkpKSU+bz++c9/uuwbGRnpqFOnjqNfv37ObWbNmmX675nR73r55ZedM61atarUfvv27XNUrVrVIckxYMCA80gHAKzHGWgAwGXXuXNnbd++Xf/61790xx13qGHDhvLz81NeXp4qVaqk6Oho9e3bVx988IHWrFnjvJPwhTp7FnrNmjX65JNPLtGzOLeIiAgtWbJE6enpevDBB9W0aVOFhITIbrfL399fzZo10wMPPKDly5fr22+/Vb169Vz2r1mzptauXatXXnlFbdu2lb+/v44fP67IyEjdddddWrdunR5++OEr9nz+qFatWlq9erXuvfde59nMwMBADRo0SOvXrzc8e1yzZk198sknevTRR9W2bVvVqFFDBQUF8vHxUaNGjfTggw9q8+bN6tOnz3nN4+l5XWotWrRQRkaG+vbtq6uuukolJSWqXLmy+vbtq2+++UZ33XVXmfuPHTtW//rXv9SyZUtVqFBBu3fv1i+//HJJ74qdmZnp/JqqZ555xu33PYeHhys5OVk2m01z5851+egGAHg6m+NK3UkCAAAAAIByjDPQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACT5WD4DLp6SkRHv37lXlypVls9msHgcAAAAAPI7D4dDRo0cVEREhL6+yzzFToP/E9u7dq8jISKvHAAAAAACPt2vXLtWqVavMbSjQf2KVK1eWJA2fvVx+lQKtHsejPNwuyuoRPFalivzPAgAAKP/yj520egSPFMC/9Uo5etSu2KvrOPtTWUjvT+zsZdt+lQIp0H9QOSjI6hE8Fv+jCgAA/gxKvCnQ7gTybz1DZj72yk3EAAAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYIKP1QOg/Nq9Y4/WrVyvg3sP6djRY+o28FbVb3S1222XLvpS363ZrBtvvUHXXBcvSdr1824tSFrodvs7h/VVeK3ql3X+K+nbDT/prXnL9F3WLh3Isevfk4aqy41NneuPHS/S5Lf/o89Xfqcj+cdVu0aohva5UXf1vM7Sua30btoKvT5nqQ7m2BUXXVMvPnGHWjSua/VYliMXY2TjHrkYIxv3yMUY2bhHLtKM95fo8/Tv9FP2QVX0q6BrGtfV6Pu76era1Zzb3PnIdK3e+JPLfgO6X6tJj99hwcTWmfLuf/VS0v9cltWvU02rUp+2bKbzwRlok1atWiVvb2917drVZfnOnTtls9mcj9DQULVv314rV64sdQy73a5x48apcePG8vf3V1hYmFq1aqUpU6boyJEjzu0cDofGjx+vGjVqyN/fX506ddL27duvyPM8H6dOnlLVGlfpb93bl7ndj9//pH279iugcoDL8ojaNXTfU0NdHnEtGymoSpCq16xmeLzy6PiJIjWqH6HnHuvjdv2zbyzS8tVb9dq4RC2f85Tu6dteT09boMVfbb7is3qCjxav09PTFmr0vbdo+XujFRddU71HTNeh3KNWj2YpcjFGNu6RizGycY9cjJGNe+RyxuoNP+muntfpoxmPKGXq/So+fVqDnnhbxwuLXLa7s1tbZSyY4Hw89UB3y2a2Umy9Gtr82XPOx6dvj7R6JNMo0CYlJSVpxIgRSk9P1969e0utX7Jkifbt26f09HRFRESoW7duOnDggHN9bm6u2rZtq1mzZmnUqFFavXq11q9fr0mTJikzM1Nz5851bjtlyhS99tpreuutt7R69WoFBASoc+fOOnHixBV7vmZExdRVu5uvVf3G7s86S1JBfoGWf7pCt/RNkJe368vN28dbAZUDnI+KlSrqpy071PiahrLZbFfgGVw5N7VtpCfv66pbfnfW+ffWbd6hO7q0Urvm0YqsEabEHu3U6OoIbdjyyxWf1RPMmLtMg3q208Ae1yq2Xg29MuZOVaroqzmfrLJ6NEuRizGycY9cjJGNe+RijGzcI5czZr90v/rc0loNosLVqH5NvfRUf+09cETfbdvtsp2/XwVVDQtyPioHVLRsZit5e3upeliQ8xEWEmj1SKZRoE0oKChQamqqhg0bpq5duyo5ObnUNmFhYQoPD1dcXJzGjh0ru92u1atXO9ePHTtW2dnZysjI0JAhQ9S0aVPVqVNHCQkJmjdvnoYPHy79evZ52rRpevrpp3XbbbepadOmSklJ0d69e7Vo0aIr+rwvlqPEof/N/0ItbrhGYdXDzrn9z1t26MTxE2rUotEVmc+TtIiL0hdfb9a+Q3lyOBz6ev12/bzrkG5sFWv1aFfcyVPF2rB1lzq0jnEu8/LyUvvWMVrz3Q5LZ7MSuRgjG/fIxRjZuEcuxsjGPXIxdrSgUJIUUrmSy/KPl6zXNT3GqfPdUzTlnU9VeOKkRRNaa8euQ4rr9rRa3v6sHhg/W7v351o9kmkUaBPS0tIUGxurmJgYJSYmaubMmXI4HG63LSwsVEpKiiTJ19dXklRSUqLU1FQlJiYqIiLC7X5nz7ju2LFD+/fvV6dOnZzrgoOD1aZNG61aVfY7eUVFRbLb7S4PK61ZuU5eXjbFX9vM1Pbfr/tBdaJrq3Jw+XkH6lL558jeiq4brla3T1DU3x7XXaPe0qTHeqttvPHZ/T+rnLwCnT5doqqhlV2WVw0N0sEca1/TViIXY2TjHrkYIxv3yMUY2bhHLu6VlJTon298rJZxUYqpV8O5vEena/TKPwZq7rRhGjawoxYuXqdHJ71v6axWuKZxXb02bqBSXx2mKU/2Vfa+HHV/4F8qOOZZV9sa4SZiJiQlJSkxMVGS1KVLF+Xn52vFihXq0KGDc5t27drJy8tLx48fl8PhUIsWLdSxY0dJ0qFDh5SXl6eYmBiX47Zo0UJZWVmSpO7du2vevHnav3+/JKl6ddcbaFWvXt25zsjzzz+vZ5999hI964tzYM9BbfhmowY82M/U5dhH8wv0y/Zs3Xpnlysyn6eZtSBd67/fqVkv3Kua1UO1euNP+scrC1T9qmDd0DLGxBEAAADgCcZP+0hZO/bpw9dHuCwf0P1a559j60WoWliQBj72pn7Zc1h1al5lwaTW6NTut6tNG0fXVIvGddS85wQtWpqpxB7XlrmvJ6BAn0NWVpYyMjK0cOGZu0X7+PioX79+SkpKcinQqampio2N1ebNm/Xkk08qOTlZFSpUKPPYCxcu1MmTJzV69GgVFhZe9KxjxozRY4895vzZbrcrMjLyoo97Ifbs3Kvjx44r6aXfLnd3lDi08v++UuY3G3TPE3e7bP/Duh9UsVJF1WsYZcG01iosOqkX3/lM/540VB3bNZYkNaofoe+379Fb8778yxXosJBAeXt7lbr5yKFcu6qFBVk2l9XIxRjZuEcuxsjGPXIxRjbukUtp46ct0LJVPyj1tQdVo1pImdvGN6wtSdr5FyvQfxRcuZKurl1NO3YfsnoUU7iE+xySkpJUXFysiIgI+fj4yMfHR2+++aYWLFig/Px853aRkZGKjo5Wr169NHnyZPXq1UtFRWfuule1alWFhIQ4zzafVbt2bdWvX1+VK/922Ut4eLgkudyA7OzPZ9cZ8fPzU1BQkMvDKg2bxyhxxAANfKi/8xFQOUAtbmiuXnff5rKtw+HQ9+u3qGHzWHl7e1s2s1WKi0t0qvi0bF6uZ+q9vW2GHxX4M/Ot4KP42EitWPPb35eSkhKlr9mmVk3+em+wnEUuxsjGPXIxRjbukYsxsnGPXH7jcDg0ftoCLf7qO73/6jBF1jj3/X9++PHMjYn/qm82nFVwvEg79xxW9bBgq0cxhQJdhuLiYqWkpOjll1/Whg0bnI+NGzcqIiJC8+bNc7tfnz595OPjoxkzZki/3kyhb9++mjNnjts7eP9eVFSUwsPDtXTpUueyszcku/Zaz7qk4WTRSR3ce0gH9555t8h+xK6Dew/JnndU/pX8dVX1MJeHl7eXKgUGKLRqFZfj7Pp5t+xH7Ipr+ee9edix40X6fvtufb/9zJ0Yd+3L1ffbd2vPgSOqHFBRbeOv1qQZn+ibzO3K3pujtP+u1vz/rVWXG5pYPbolhg+4SSmLvtG8T79V1o79euyFVB0rLNLA7m2tHs1S5GKMbNwjF2Nk4x65GCMb98jljPHTFmjRF+s07elEBfr76VCOXYdy7DpRdOYmYb/sOazXUhbru6xd2r0vV198vVmPPz9XrZvVU8Or3d8j6c/qmdcW6ev1Z/7Nm7HpZ909+t/y9rLp9oRrrB7NFC7hLsOnn36qI0eO6J577lFwsOs7Ir1791ZSUpK6dCn9mV2bzaaHH35YEyZM0P33369KlSpp8uTJWr58uVq3bq2JEyeqZcuWCggI0KZNm7Rq1SrFxcU59x05cqSee+45RUdHKyoqSuPGjVNERIR69ux5xZ67GQf2HNSCpIXOn9P/+5UkqWHzWHXuc7Pp43y/9gfVqF1DoVVDL8ucnmBjVrb6Pjzd+fOzb5y5o/odXVrp1X8M1IwJg/XC259qxMQ5yrMfV63wKhp93626q+d1Fk5tndsTWuhwXoEmv/2ZDuYcVZMGNTX/tQf/8u/QkosxsnGPXIyRjXvkYoxs3COXM+Z8/I0kqf/IGS7LXxp9p/rc0loVKnjr63XbNGt+uo4XnlREtRB1ubGpHrrL/L+Z/yz2HszT/eNn60j+MYWFBKpNs6v1f/9+TFdVqWxib+vZHH/Fa0RN6t69u0pKSvTZZ5+VWpeRkaE2bdpo48aNatasmTIzMxUfH+9cf/z4cdWqVUtPPfWUnnzySUlSfn6+XnzxRS1cuFA7duyQl5eXoqOjddttt2nkyJEKDT1TIB0Oh5555hm98847ysvL0/XXX68ZM2aoQYMG5zW/3W5XcHCwHv1wrfwq/fXubF2WUTf+9e5ubVZARd5XAwAA5V/esb/mV0SdSyD/1ivFbrerZrUqys/PP+fHYCnQf2IUaGMUaGMUaAAA8GdAgXaPAl3a+RRoPgMNAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYIKP1QPg8hvWpo4qBwVZPYZHmb0u2+oRPNbw6+pZPQIAADCppMRh9Qgea+v+o1aP4JGa1w6xegSPc/o8/h5xBhoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAk+Vg+AP48Z7y/R5+nf6afsg6roV0HXNK6r0fd309W1qzm3ufOR6Vq98SeX/QZ0v1aTHr/Dgokvnx0/7dbKZWu1d9cBHbUf08ChPdSoaX3n+qX/9402ZWYpP++ovL29VTOyum6+9TpF1q0hSTqSk68vF3+rn7fv0tGjxxQUFKhmLRuqw81t5OPjbeEzu3LeTVuh1+cs1cEcu+Kia+rFJ+5Qi8Z1rR7LcuRijGzcIxdjZOMeuRgjm9JmLlipWR99pey9uZKk2HrheuKeLurUrrHVo11Rp0tK9P6HX2rZV5t0JK9AoVUq6+b28ep/e3vZbDbndtl7Dmnm3C/03Q87dbqkRLVrVtXTj/VTtatCLJ3/clqV+aNmzF2mTVm7dOCwXbOev0e3tG/qXP/Z8o1KWfi1NmXt0hH7cS1JfkJxDWpZOnNZOAONS2b1hp90V8/r9NGMR5Qy9X4Vnz6tQU+8reOFRS7b3dmtrTIWTHA+nnqgu2UzXy4ni06pRkRVde9zk9v1V1Wrou69b9LDTw7S3x/up5DQIM16a4GOFRyXJB06mCuHQ7qtbyc9Mnqwbu3VQRlfb9IXn311hZ+JNT5avE5PT1uo0ffeouXvjVZcdE31HjFdh3KPWj2apcjFGNm4Ry7GyMY9cjFGNu5FVAvR+OE9tGz2E1o6+wnd0LKBEp94V1t/3mf1aFfUhx9/pc+WrNXwIV31zssPaeiAmzX/P1/rk/+tdm6zd3+uRj2TpMiIq/Ti+CGa8eJwDbi9vXwr/LnPaR4/cVKN69fU84/3cb++8KRaN6unp4f3uOKzXQgKtEmrVq2St7e3unbt6rJ8586dstlszkdoaKjat2+vlStXljqG3W7XuHHj1LhxY/n7+yssLEytWrXSlClTdOTIEed2H330kRISEhQWFiabzaYNGzZcked4sWa/dL/63NJaDaLC1ah+Tb30VH/tPXBE323b7bKdv18FVQ0Lcj4qB1S0bObLJaZRlG7uep0aN412u75Zi4aqH1NHoVeFqHqNq3Rrz/YqOnFS+/celiQ1aBil3gM6Kzq2rkKvClHDuKt1w00t9P2mH6/wM7HGjLnLNKhnOw3sca1i69XQK2PuVKWKvprzySqrR7MUuRgjG/fIxRjZuEcuxsjGvS43NNHN1zXW1bWrqX7tanp6WHcFVPLT2s07rR7titqybZfatohR62saqHq1KrqhbWNd0/RqZf20x7nN7NSlahUfrXsGJqh+VA1FhIeqbctYhQQHWjr75dbx2kZ66v6uurV9M7fr77illR4f2kU3tGpwxWe7EBRok5KSkjRixAilp6dr7969pdYvWbJE+/btU3p6uiIiItStWzcdOHDAuT43N1dt27bVrFmzNGrUKK1evVrr16/XpEmTlJmZqblz5zq3PXbsmK6//nq9+OKLV+z5XQ5HCwolSSGVK7ks/3jJel3TY5w63z1FU975VIUnTlo0oWcoLj6tNd98p4oV/RQeUdVwuxOFJ+Vf6c/3ZsMfnTxVrA1bd6lD6xjnMi8vL7VvHaM13+2wdDYrkYsxsnGPXIyRjXvkYoxszDl9ukQfLV6n44Un1TLur3Vpe8MGkdqweYd2/3oy5Odf9uv7rGy1jD9zMqWkpERrMrepZo0w/WNyiu78+xSN/Mc7+mbNFosnx/n6c18vcIkUFBQoNTVVa9eu1f79+5WcnKyxY8e6bBMWFqbw8HCFh4dr7Nix+uCDD7R69Wr16HHmUoSxY8cqOztb27ZtU0REhHO/OnXqKCEhQQ6Hw7nsrrvukn49u30+ioqKVFT02+XSdrv9gp/zxSopKdE/3/hYLeOiFFOvhnN5j07XqGb1Kqp+VZC2/rRPL779qX7edUhv/XOIZbNaZev3Pyt19mc6deqUAoMCNGR4bwUE+rvdNufQEa1amalbbrvxis95peXkFej06RJVDa3ssrxqaJC27zxguN+fHbkYIxv3yMUY2bhHLsbIpmw//LhXXe59WSdOFivA308pL96r2N/9+++voO9t1+t4YZH+/vgb8vKyqaTEocH9btJN15/5rG+e/ZgKT5xU2idfaXDfmzR0wM1at/FHPfdKql4Yd7eaNvprveFQnlGgTUhLS1NsbKxiYmKUmJiokSNHasyYMS43BDirsLBQKSkpkiRfX1/p1zKZmpqqxMREl/L8e+6Odb6ef/55Pfvssxd9nEth/LSPlLVjnz58fYTL8gHdr3X+ObZehKqFBWngY2/qlz2HVafmVRZMap169SP10BOJOnasUGtXfacPkj/VA48OUOAfztjn5x1V8tsfKS6+gVpd29TweAAAAFaoX6ealr/3lOwFhfpk2QY9OHGOPnnz4b9UiU7/9nt9+dUmPTmit+rUqqafd+7X2yn/p9AqQbq5fbwcJWdOll3bIla9uraTJF1dt4Z+2LZL/12yhgJdjnAJtwlJSUlKTEyUJHXp0kX5+flasWKFyzbt2rVTYGCgAgICNHXqVLVo0UIdO3aUJB06dEh5eXmKiYlx2adFixYKDAxUYGCg+vfvf9FzjhkzRvn5+c7Hrl27LvqYF2L8tAVatuoHzZs2XDWqlX1HwfiGtSVJO/ccvkLTeQ5fvwoKq1pFtetG6Pb+neXl5aV132522caeX6Ck6R+qdt0I9ex7s2WzXklhIYHy9vYqdVOWQ7l2VQsLsmwuq5GLMbJxj1yMkY175GKMbMrmW8FH9SKrKr5hbY1/sIcaR0fondQVJvb880ias1h9b7teHdo1UVTt6up4YzP1uvVapX185r5IQUGV5O3tpdq1XD+uFxlxlQ4dzrdoalwICvQ5ZGVlKSMjw1lwfXx81K9fPyUlJblsl5qaqszMTC1YsED169dXcnKyKlSoUOaxFy5cqA0bNqhz584qLCy86Fn9/PwUFBTk8riSHA6Hxk9boMVffaf3Xx2myBph59znhx/PfJ6c//M5k19xcbHz5/y8o/r3G2mqWau6eg/oLC+vi79KoTzwreCj+NhIrViT5VxWUlKi9DXb1KpJlKWzWYlcjJGNe+RijGzcIxdjZHN+SkocKjp1yuoxrqiik6dKXVHq5WVznnmu4OOjBvVqOj8jfdae/Tl/6q+w+jPiEu5zSEpKUnFxscul1w6HQ35+fnrjjTecyyIjIxUdHa3o6GgVFxerV69e2rx5s/z8/FS1alWFhIQoKyvL5di1a585+1q5cmXl5eVdwWd1eYyftkAfL1mvdyYNVaC/nw7lnPkMduXAiqro56tf9hzWx0vX629tGqpKUIC2/LxXz03/WK2b1VPDq91f2l5eFRWdVM6h3/6bHsnN197dB1UpoKIqVfLX8i9WKzaunioHBer4sUJ9u3KD7PkFios/c/fB/LyjSnrjQ4WEBqnLbTfqWMFvb7BUDgqw5DldScMH3KThz76n5g1r65rGdfXmvC91rLBIA7u3tXo0S5GLMbJxj1yMkY175GKMbNybOP0TdWrXSLWqV1HB8SLN/3ytvl7/oz7813CrR7ui2lwTow8WrVS1q0JUp1ZV/bhzvz76bJUSOjR3btO7+3V64V8fKq5hHTVrHKW1G37U6nXb9OL4uy2d/XI7drxIO3Yfcv6cvS9Hm7ftVkhQJdUKD9UR+zHt2X9E+389E/9j9kHp1xNsnniSjQJdhuLiYqWkpOjll19WQkKCy7qePXtq3rx56tKlS6n9+vTpo/Hjx2vGjBl69NFH5eXlpb59+2rOnDkaP3684eegy7s5H38jSeo/cobL8pdG36k+t7RWhQre+nrdNs2an67jhScVUS1EXW5sqofu+vNdmrwn+4CSpn/o/Pm/i85cxtS8VSPd1reTDh3M1fpZ3+t4wQlVCqiomrXDdd/D/VS9xpnPgf+Ula2cw3nKOZynKRPedTn2pGmPXeFnc+XdntBCh/MKNPntz3Qw56iaNKip+a896JH/I3olkYsxsnGPXIyRjXvkYoxs3Dt85KiGP/ueDhy2KyiwohrVj9CH/xquv7WJtXq0K2rYkFuVkrZM02d+qrz8YwqtUlm3dmqpAb3bO7e5rnVDPXRvN6V9vFJvJf+fakVcpacf66e42DqWzn65bdiard4P/Xbi8ZnXFkmS+t7aWq89PVCfr9yskZN++0aiB8bPliQ9PrSLnrj3FgsmLpvN8fvbP8PFokWL1K9fPx08eFDBwcEu60aPHq1ly5bpww8/VFRUlDIzMxUfH+9c/+abb2rChAnasWOHKlWqpJycHLVr107Hjh3TxIkT1bJlSwUEBGjTpk166qmnFBcXpwULFki/fuVVdna29u7dq65du+qDDz5QTEyM8y7fZtntdgUHB2tb9iFVvsKXc3u6uRt2m9jqr2n4dfWsHgEAAJhUUsI/5Y1k7Mi1egSP1Lw2l4z/kd1uV+3wUOXn55/zY7B8BroMSUlJ6tSpU6nyLEm9e/fW2rVrDb8qavDgwTp16pTzMu+wsDBlZGRo0KBBeumll9S6dWs1adJEEyZMUL9+/fTuu7+dZfzkk0/UvHlzde3aVZJ05513qnnz5nrrrbcu23MFAAAAAJSNM9B/YpyBNsYZaGOcgQYAoPzgDLQxzkC7xxno0jgDDQAAAADAJUaBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAm+Fg9AC6/4ABfBQX4Wj2GR0m8JtLqEQAAAC6al5fN6hE8Vr2qgVaP4JH8KnhbPYLHOZ9MOAMNAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAEH6sHwJ/fu2kr9PqcpTqYY1dcdE29+MQdatG4rtVjXVHT5yzR/9I36adfDqqiXwW1iKurpx7orqtrV3NuM/eTb/TxkvXavG23Co4XadNnkxVc2d/Sua3E68Y9cjFGNu6RizGycY9cjJGNe+Ry5t9x8z75RnsO5EqSouuEa/hdN6t9m4Yu2zkcDt035t9auWarpj97tzpd38Siia1XXl83nIE2adWqVfL29lbXrl1dlu/cuVM2m835CA0NVfv27bVy5cpSx7Db7Ro3bpwaN24sf39/hYWFqVWrVpoyZYqOHDkiSTp16pRGjx6tJk2aKCAgQBERERo0aJD27t17xZ7rpfTR4nV6etpCjb73Fi1/b7Tiomuq94jpOpR71OrRrqjVG37SoF7Xa9Fbj2jOKw/oVPFp3fX4WzpeWOTcpvDEKbVvHasHEztZOqsn4HXjHrkYIxv3yMUY2bhHLsbIxj1yOSP8qmCNuq+rPnrzUS2Y8ajaNq+vB8fP0vad+122m70gXTabZWN6jPL8uqFAm5SUlKQRI0YoPT3dbZldsmSJ9u3bp/T0dEVERKhbt246cOCAc31ubq7atm2rWbNmadSoUVq9erXWr1+vSZMmKTMzU3PnzpUkHT9+XOvXr9e4ceO0fv16ffTRR8rKylKPHj2u6PO9VGbMXaZBPdtpYI9rFVuvhl4Zc6cqVfTVnE9WWT3aFZUy9X7dcUtrNYiqoUb1a+rlsQO058ARfZe127nNPX3ba3hiJzUvB++8XW68btwjF2Nk4x65GCMb98jFGNm4Ry5n3NSusdq3aai6taoqKrKqHr3nVlXy99WGH35xbrPlxz2a+eEKTX6in6WzeoLy/LqhQJtQUFCg1NRUDRs2TF27dlVycnKpbcLCwhQeHq64uDiNHTtWdrtdq1evdq4fO3assrOzlZGRoSFDhqhp06aqU6eOEhISNG/ePA0fPlySFBwcrC+++EJ9+/ZVTEyM2rZtqzfeeEPr1q1Tdnb2FX3eF+vkqWJt2LpLHVrHOJd5eXmpfesYrfluh6WzWe1oQaEkKSSoktWjeBxeN+6RizGycY9cjJGNe+RijGzcIxf3Tp8u0WfLMnX8xEk1b1RHklR44qQen/S+xj98u6qGBlk9oqXK++uGAm1CWlqaYmNjFRMTo8TERM2cOVMOh8PttoWFhUpJSZEk+fr6SpJKSkqUmpqqxMRERUREuN3PVsa1HPn5+bLZbAoJCSlzzqKiItntdpeHlXLyCnT6dImqhlZ2WV41NEgHc6ydzUolJSV69vVFatkkSjH1alg9jsfhdeMeuRgjG/fIxRjZuEcuxsjGPXJxlfXzPjXvOkZNuozWM9Pma/qzQ1S/brgk6fkZH6t54zrqdF2c1WNarry/bijQJiQlJSkxMVGS1KVLF+Xn52vFihUu27Rr106BgYEKCAjQ1KlT1aJFC3Xs2FGSdOjQIeXl5SkmJsZlnxYtWigwMFCBgYHq37+/29994sQJjR49Wv3791dQUNnvVj3//PMKDg52PiIjIy/ymeNyGPfqAm3bsU9vPDPI6lEAAABwiURFVtWidx5X2vSH1b9HO41+cZ5+3LlfS7/ZrG83/KixD/a0ekRcAtyF+xyysrKUkZGhhQsXSpJ8fHzUr18/JSUlqUOHDs7tUlNTFRsbq82bN+vJJ59UcnKyKlSoUOaxFy5cqJMnT2r06NEqLCwstf7UqVPq27evHA6H3nzzzXPOOmbMGD322GPOn+12u6UlOiwkUN7eXqVuBnAo165qYX/NS1fGvbpAS7/5QWmvP6Qa1cq+ouCviteNe+RijGzcIxdjZOMeuRgjG/fIxZVvBR/VqXmVJCmuQaS+y9qllI9Wys+vgrL35qhVj6ddth/x7Gy1bFJP770y3KKJrVHeXzecgT6HpKQkFRcXKyIiQj4+PvLx8dGbb76pBQsWKD8/37ldZGSkoqOj1atXL02ePFm9evVSUdGZOyxXrVpVISEhysrKcjl27dq1Vb9+fVWuXLnU7z1bnn/55Rd98cUX5zz7LEl+fn4KCgpyeVjJt4KP4mMjtWLNb8+7pKRE6Wu2qVWTKEtnu9IcDofGvbpAn6/8TvOmDVftiDCrR/JYvG7cIxdjZOMeuRgjG/fIxRjZuEcuZSspcejkqWL9vf9N+uTdx7XoncecD0kaM+y2v+QNxcr764YCXYbi4mKlpKTo5Zdf1oYNG5yPjRs3KiIiQvPmzXO7X58+feTj46MZM2ZIv34ovm/fvpozZ46pr6M6W563b9+uJUuWKCys/Jat4QNuUsqibzTv02+VtWO/HnshVccKizSwe1urR7uinn51gRZ9sVavjU9UQCU/Hcyx62COXSeKTjq3OZhj1/fb92jnnsOSpKyf9+r77XuUZz9m4eTW4HXjHrkYIxv3yMUY2bhHLsbIxj1yOePlf3+mNZt+0u79ucr6eZ9e/vdnytj4k7p3vEZVQ4PUIKqGy0OSIqqFKLJG+f13/sUoz68bLuEuw6effqojR47onnvuUXBwsMu63r17KykpSV26dCm1n81m08MPP6wJEybo/vvvV6VKlTR58mQtX75crVu31sSJE9WyZUsFBARo06ZNWrVqleLiztxQ4NSpU+rTp4/Wr1+vTz/9VKdPn9b+/We+Py40NNR5Y7Ly4vaEFjqcV6DJb3+mgzlH1aRBTc1/7cFycXnGpTRn0deSpH4PT3dZPnVMf91xS2tJ0vsff6NpyZ87190x4o1S2/xV8Lpxj1yMkY175GKMbNwjF2Nk4x65nJFzpECjX5ing7l2VQ7wV0y9Gkp64T5d1zLGxN5/PeX5dWNzGN1OGurevbtKSkr02WeflVqXkZGhNm3aaOPGjWrWrJkyMzMVHx/vXH/8+HHVqlVLTz31lJ588knp17tpv/jii1q4cKF27NghLy8vRUdH67bbbtPIkSMVGhqqnTt3KirK/aULX375pcvnrs/FbrcrODhYB3LyLb+c29PYC09ZPYLHCvIv+7P7AAAA5cFBe5HVI3ikakF+Vo/gcex2u6qHBSs//9y9iQL9J0aBNkaBNkaBBgAAfwYUaPco0KWdT4HmM9AAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwwcfqAQArBPlXsHoEAAAAXEbVgvysHgF/QpyBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBA47J7N22FmvYYr/DrRqrT3S9p3fc7rR7JY5CNMbJxj1yMkY175GKMbNwjF2Nk4x65GCMbY+U1Gwo0LquPFq/T09MWavS9t2j5e6MVF11TvUdM16Hco1aPZjmyMUY27pGLMbJxj1yMkY175GKMbNwjF2NkY6w8Z+ORBXrVqlXy9vZW165dXZbv3LlTNpvN+QgNDVX79u21cuXKUsew2+0aN26cGjduLH9/f4WFhalVq1aaMmWKjhw54tyuQ4cOstlseuGFF0odo2vXrrLZbJowYcI5Z87MzNQdd9yh6tWrq2LFioqOjtZ9992nbdu2ldq2c+fO8vb21po1a0qt69Chg0aOHFlqeXJyskJCQs45h6eZMXeZBvVsp4E9rlVsvRp6ZcydqlTRV3M+WWX1aJYjG2Nk4x65GCMb98jFGNm4Ry7GyMY9cjFGNsbKczYeWaCTkpI0YsQIpaena+/evaXWL1myRPv27VN6eroiIiLUrVs3HThwwLk+NzdXbdu21axZszRq1CitXr1a69ev16RJk5SZmam5c+e6HC8yMlLJyckuy/bs2aOlS5eqRo0a55z3008/Vdu2bVVUVKT3339fW7Zs0Zw5cxQcHKxx48a5bJudna1vvvlGDz30kGbOnHkB6ZQfJ08Va8PWXerQOsa5zMvLS+1bx2jNdzssnc1qZGOMbNwjF2Nk4x65GCMb98jFGNm4Ry7GyMZYec/Gx+oB/qigoECpqalau3at9u/fr+TkZI0dO9Zlm7CwMIWHhys8PFxjx47VBx98oNWrV6tHjx6SpLFjxyo7O1vbtm1TRESEc786deooISFBDofD5XjdunVTWlqavv76a1133XWSpNmzZyshIUHZ2dllznv8+HENGTJEt956qxYuXOhcHhUVpTZt2igvL89l+1mzZqlbt24aNmyY2rZtq1deeUX+/v4XkdhvioqKVFRU5PzZbrdfkuNeqJy8Ap0+XaKqoZVdllcNDdL2nQcM9/srIBtjZOMeuRgjG/fIxRjZuEcuxsjGPXIxRjbGyns2HncGOi0tTbGxsYqJiVFiYqJmzpxZqvCeVVhYqJSUFEmSr6+vJKmkpESpqalKTEx0Kc+/Z7PZXH729fXVwIEDNWvWLOey5ORkDR069Jzzfv755zp8+LCefPJJt+t/f9m1w+HQrFmzlJiYqNjYWNWvX1/z588/5+8w6/nnn1dwcLDzERkZecmODQAAAAB/dR5XoJOSkpSYmChJ6tKli/Lz87VixQqXbdq1a6fAwEAFBARo6tSpatGihTp27ChJOnTokPLy8hQTE+OyT4sWLRQYGKjAwED179+/1O8dOnSo0tLSdOzYMaWnpys/P1/dunU757zbt2+XJMXGxp5z2yVLluj48ePq3LmzJCkxMVFJSUnn3M+sMWPGKD8/3/nYtWvXJTv2hQgLCZS3t1epmwEcyrWrWliQZXN5ArIxRjbukYsxsnGPXIyRjXvkYoxs3CMXY2RjrLxn41EFOisrSxkZGc6C6+Pjo379+pUqmampqcrMzNSCBQtUv359JScnq0KFCmUee+HChdqwYYM6d+6swsLCUuubNWum6OhozZ8/XzNnztRdd90lHx/XK9wnT57sLOGBgYHKzs42PDvuzsyZM9WvXz/ncfv376+vv/5aP/30k+ljlMXPz09BQUEuDyv5VvBRfGykVqzJci4rKSlR+pptatUkytLZrEY2xsjGPXIxRjbukYsxsnGPXIyRjXvkYoxsjJX3bDzqM9BJSUkqLi52ufTa4XDIz89Pb7zxhnNZZGSkoqOjFR0dreLiYvXq1UubN2+Wn5+fqlatqpCQEGVlZbkcu3bt2pKkypUrl/pc8llDhw7V9OnT9cMPPygjI6PU+gceeEB9+/Z1/hwREaEGDRpIkrZu3aprr73W8Lnl5uZq4cKFOnXqlN58803n8tOnT2vmzJmaNGmSJCkoKEj5+fml9s/Ly1NwcLDh8T3V8AE3afiz76l5w9q6pnFdvTnvSx0rLNLA7m2tHs1yZGOMbNwjF2Nk4x65GCMb98jFGNm4Ry7GyMZYec7GYwp0cXGxUlJS9PLLLyshIcFlXc+ePTVv3jx16dKl1H59+vTR+PHjNWPGDD366KPy8vJS3759NWfOHI0fP97wc9DuDBgwQKNGjVKzZs3UqFGjUutDQ0MVGhrqsiwhIUFXXXWVpkyZ4nITsbPy8vIUEhKi999/X7Vq1dKiRYtc1i9evFgvv/yyJk6cKG9vb8XExGjx4sWljrN+/XpnWS9Pbk9oocN5BZr89mc6mHNUTRrU1PzXHiwXl2dcbmRjjGzcIxdjZOMeuRgjG/fIxRjZuEcuxsjGWHnOxuY4n2uQL6NFixapX79+OnjwYKkzraNHj9ayZcv04YcfKioqSpmZmYqPj3euf/PNNzVhwgTt2LFDlSpVUk5Ojtq1a6djx45p4sSJatmypQICArRp0yY99dRTiouL04IFC6Rfv3c5Pj5e06ZNk34tvBUqVFBAQIAkKT4+Xj179izzu6A//vhj3XHHHerSpYsefvhh1a9fX4cPH1ZaWpqys7P1wQcfKD4+Xl26dCn1fdP5+fmqVq2aPvroI3Xt2lU///yzGjdurPvuu0/33nuv/Pz89Nlnn2n06NH6z3/+4/ZNBCN2u13BwcE6kJNv+eXcAAAAAOCJ7Ha7qocFKz//3L3JYz4DnZSUpE6dOrm9TLl3795au3at4dcyDR48WKdOnXJe5h0WFqaMjAwNGjRIL730klq3bq0mTZpowoQJ6tevn959913DOUJCQpzl2azbbrtN33zzjSpUqKABAwYoNjZW/fv3V35+vp577jmtW7dOGzduVO/evUvtGxwcrI4dOzo/512vXj2lp6dr69at6tSpk9q0aaO0tDR9+OGH51WeAQAAAACXlsecgcalxxloAAAAAChbuTwDDQAAAACAJ6NAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATfKweAAAAAAAutaJTp60ewSP5VfC2eoRyjTPQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEDjsns3bYWa9hiv8OtGqtPdL2nd9zutHsljkI0xsnGPXIyRjXvkYoxs3CMXY2TjHrlIqzJ/1F1PvKNmPcYpvN0j+r8Vm1zWf7Z8o/o9MkMNu4xReLtHtHnbbstm9RTl9XVTbgr0qlWr5O3tra5du7os37lzp2w2m/MRGhqq9u3ba+XKlaWOYbfbNW7cODVu3Fj+/v4KCwtTq1atNGXKFB05csS5XYcOHWSz2fTCCy+UOkbXrl1ls9k0YcKEMuetW7eucyZ/f3/VrVtXffv21bJly9zOv2HDhvN+PuXBR4vX6elpCzX63lu0/L3Riouuqd4jputQ7lGrR7Mc2RgjG/fIxRjZuEcuxsjGPXIxRjbukcsZx0+cVOP6NfX8433cry88qdbN6unp4T2u+GyeqDy/bspNgU5KStKIESOUnp6uvXv3llq/ZMkS7du3T+np6YqIiFC3bt104MAB5/rc3Fy1bdtWs2bN0qhRo7R69WqtX79ekyZNUmZmpubOnetyvMjISCUnJ7ss27Nnj5YuXaoaNWqYmnnixInat2+fsrKylJKSopCQEHXq1EmTJk06577nej7lxYy5yzSoZzsN7HGtYuvV0Ctj7lSlir6a88kqq0ezHNkYIxv3yMUY2bhHLsbIxj1yMUY27pHLGR2vbaSn7u+qW9s3c7v+jlta6fGhXXRDqwZXfDZPVJ5fN+WiQBcUFCg1NVXDhg1T165dSxVbSQoLC1N4eLji4uI0duxY2e12rV692rl+7Nixys7OVkZGhoYMGaKmTZuqTp06SkhI0Lx58zR8+HCX43Xr1k2HDx/W119/7Vw2e/ZsJSQkqFq1aqbmrly5ssLDw1W7dm3deOONeueddzRu3DiNHz9eWVlZZe57rudTHpw8VawNW3epQ+sY5zIvLy+1bx2jNd/tsHQ2q5GNMbJxj1yMkY175GKMbNwjF2Nk4x654EKU99dNuSjQaWlpio2NVUxMjBITEzVz5kw5HA632xYWFiolJUWS5OvrK0kqKSlRamqqEhMTFRER4XY/m83m8rOvr68GDhyoWbNmOZclJydr6NChF/VcHnnkETkcDn388cemtnf3fIwUFRXJbre7PKyUk1eg06dLVDW0ssvyqqFBOphj7WxWIxtjZOMeuRgjG/fIxRjZuEcuxsjGPXLBhSjvr5tyUaCTkpKUmJgoSerSpYvy8/O1YsUKl23atWunwMBABQQEaOrUqWrRooU6duwoSTp06JDy8vIUExPjsk+LFi0UGBiowMBA9e/fv9TvHTp0qNLS0nTs2DGlp6crPz9f3bp1u6jnEhoaqmrVqmnnzrI/JF/W8zHy/PPPKzg42PmIjIy8qFkBAAAAAL/x+AKdlZWljIwMZ8H18fFRv379lJSU5LJdamqqMjMztWDBAtWvX1/JycmqUKFCmcdeuHChNmzYoM6dO6uwsLDU+mbNmik6Olrz58/XzJkzddddd8nHx8dlm8mTJztLeGBgoLKzs8/5nBwOR6kz3n90Ic9nzJgxys/Pdz527dp1zlkup7CQQHl7e5W6GcChXLuqhQVZNpcnIBtjZOMeuRgjG/fIxRjZuEcuxsjGPXLBhSjvrxuPL9BJSUkqLi5WRESEfHx85OPjozfffFMLFixQfn6+c7vIyEhFR0erV69emjx5snr16qWioiJJUtWqVRUSElLqc8e1a9dW/fr1Vbly5VK/96yhQ4dq+vTpmj9/vtvLtx944AFt2LDB+TC6RPysnJwcHTp0SFFRUWVuV9bzMeLn56egoCCXh5V8K/goPjZSK9b8lntJSYnS12xTqyZlP/8/O7IxRjbukYsxsnGPXIyRjXvkYoxs3CMXXIjy/rrx6AJdXFyslJQUvfzyyy4ldePGjYqIiNC8efPc7tenTx/5+PhoxowZ0q8fSu/bt6/mzJnj9g7eZRkwYIC+++47xcXFqVGjRqXWh4aGqn79+s7HH89Q/9G//vUveXl5qWfPnqZn+OPzKU+GD7hJKYu+0bxPv1XWjv167IVUHSss0sDuba0ezXJkY4xs3CMXY2TjHrkYIxv3yMUY2bhHLmccO16kzdt2O7/fOXtfjjZv263d+3MlSUfsx7R5225t27FfkvRj9kFt3ra7XHzm93Ioz6+bstuexT799FMdOXJE99xzj4KDg13W9e7dW0lJSerSpUup/Ww2mx5++GFNmDBB999/vypVqqTJkydr+fLlat26tSZOnKiWLVsqICBAmzZt0qpVqxQXF+d2hipVqmjfvn3nvHzanaNHj2r//v06deqUduzYoTlz5ujf//63nn/+edWvX9/0cdw9n/Li9oQWOpxXoMlvf6aDOUfVpEFNzX/twXJxecblRjbGyMY9cjFGNu6RizGycY9cjJGNe+Ryxoat2er90BvOn595bZEkqe+trfXa0wP1+crNGjnpt6/NfWD8bEnS40O76Il7b7FgYmuV59eNzWF0O2sP0L17d5WUlOizzz4rtS4jI0Nt2rTRxo0b1axZM2VmZio+Pt65/vjx46pVq5aeeuopPfnkk5Kk/Px8vfjii1q4cKF27NghLy8vRUdH67bbbtPIkSMVGhoqSerQoYPi4+M1bdo0t3PFx8erZ8+emjBhguHsdevW1S+//CL9evfs8PBwtW3bVg888ID+9re/ObfbuXOnoqKinPP/8eeyns+52O12BQcH60BOvuWXcwMAAABXUtGp01aP4JH8KnhbPYLHsdvtqh4WrPz8c/cmjy7QuDgUaAAAAPxVUaDdo0CXdj4F2qM/Aw0AAAAAgKegQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACY4GP1AIAVik+XWD2Cx/Lx5n01AADKi/zjp6wewWPtyS20egSP1KhWkNUjlGv8SxkAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADDBx+oB8Of3btoKvT5nqQ7m2BUXXVMvPnGHWjSua/VYlrqm5wTt2p9bavmQ3tdryhN9LZnJ0/C6cY9cjJGNe+RijGzcIxdjf/Vsps9Zos/TN+mn7IOq6FdB18TV1VP3d9fVtauV2tbhcOjuJ9/Rioytevu5oep8QxNLZr6SDuXka0bK5/p2fZZOnDylWuFhGjuitxrWryVJuq7XWLf7DR/URQN73XiFp7Veef37xBloXFYfLV6np6ct1Oh7b9Hy90YrLrqmeo+YrkO5R60ezVKLZz2uzZ8953zMf+1BSdJtNzW3ejSPwOvGPXIxRjbukYsxsnGPXIyRjbR640+6q9f1WvjmI3rv5QdUXHxag0a9peOFRaW2TfpwhWw2myVzWsFeUKgHxrwtHx8vvTzubr3/2kg9NORWVQ7wd27zycwxLo+xD/WWzWZTh2vjLJ3dCuX575PHFuhVq1bJ29tbXbt2dVm+c+dO2Ww25yM0NFTt27fXypUrSx3Dbrdr3Lhxaty4sfz9/RUWFqZWrVppypQpOnLkiHO7Dh06yGaz6YUXXih1jK5du8pms2nChAllzlu3bl1NmzbNcP2uXbs0dOhQRUREyNfXV3Xq1NEjjzyinJycUtv++OOPGjJkiGrVqiU/Pz9FRUWpf//+Wrt2bZkzeKIZc5dpUM92GtjjWsXWq6FXxtypShV9NeeTVVaPZqmrqlRW9bAg52Px15tVt9ZVandNfatH8wi8btwjF2Nk4x65GCMb98jFGNlIKS/drztuaa0GUTXUqH5NTR0zQHsOHNF323a7bPf99j36d9pyTRl9p2WzXmnvf7RC1a4K1j9G9FGjBpGKqB6qNvHRqlUjzLlNWJXKLo+VGT/omrgo1QwPtXR2K5Tnv08eW6CTkpI0YsQIpaena+/evaXWL1myRPv27VN6eroiIiLUrVs3HThwwLk+NzdXbdu21axZszRq1CitXr1a69ev16RJk5SZmam5c+e6HC8yMlLJyckuy/bs2aOlS5eqRo0aF/Vcfv75Z7Vs2VLbt2/XvHnz9OOPP+qtt97S0qVLde211yo397dLedeuXasWLVpo27Ztevvtt/XDDz9o4cKFio2N1eOPP35Rc1xpJ08Va8PWXerQOsa5zMvLS+1bx2jNdzssnc2TnDxVrPn/W6sB3dr+pd6pNcLrxj1yMUY27pGLMbJxj1yMkY17RwsKJUkhlSs5lxWeOKlH/vmeJo7srWphQRZOd2V9tWaLYuvX0tNT5qrr4Em6+7HX9cniNYbb5+Yd1TfrstStU8srOqcnKO9/nzzyM9AFBQVKTU3V2rVrtX//fiUnJ2vsWNfPDISFhSk8PFzh4eEaO3asPvjgA61evVo9evSQJI0dO1bZ2dnatm2bIiIinPvVqVNHCQkJcjgcLsfr1q2b0tLS9PXXX+u6666TJM2ePVsJCQnKzs6+qOfz4IMPytfXV4sXL5a//5nLOGrXrq3mzZvr6quv1j/+8Q+9+eabZz4rcvfdio6O1sqVK+Xl9dv7G/Hx8XrkkUfK/D1FRUUqKvrtEhq73X5Rc1+snLwCnT5doqqhlV2WVw0N0vadBwz3+6v574pNyi8oVP+ubawexSPwunGPXIyRjXvkYoxs3CMXY2RTWklJiSa+sUgtm0Qppt5vJ5smvrFILeLqKuH6P/9nnn9v74EjWvS/1erX4zoN6tNBW37crVeT/iMfH2/detM1pbb/vy8zVcnfT+3bNrZkXiuV979PHnkGOi0tTbGxsYqJiVFiYqJmzpxZqvCeVVhYqJSUFEmSr6+v9Otf6NTUVCUmJrqU59/745k+X19fDRw4ULNmzXIuS05O1tChQy/queTm5urzzz/X8OHDneX5rPDwcA0cOFCpqalyOBzasGGDvv/+ez3++OMu5fmskJCQMn/X888/r+DgYOcjMjLyombHlfH+f75Vx7YNFV412OpRAAAATBn36gJl7din18cPci774uvNWrV+u8Y/1MvS2axQ4nCoQb0IPZDYWQ3qRei2hNbqcXMrLfp8tdvtP126Vgk3NpOfb4UrPisujkcW6KSkJCUmJkqSunTpovz8fK1YscJlm3bt2ikwMFABAQGaOnWqWrRooY4dO0qSDh06pLy8PMXExLjs06JFCwUGBiowMFD9+/cv9XuHDh2qtLQ0HTt2TOnp6crPz1e3bt0u6rls375dDodDDRs2dLu+YcOGOnLkiA4dOqTt27dLkmJjYy/od40ZM0b5+fnOx65duy5q9osVFhIob2+vUjcDOJRr/0td0lOWXftylb4mS4m3XWv1KB6D14175GKMbNwjF2Nk4x65GCMbV+OnLdCyVT/og2kPqka1307wfLN+u37Zm6Om3cbq6pse19U3nfn44bDxs9TvkTcsnPjyC6tSWXUjXe9GXrdWVR04nF9q2w0/7FD2nsPq3qnVFZzQc5T3v08eV6CzsrKUkZHhLLg+Pj7q16+fkpKSXLZLTU1VZmamFixYoPr16ys5OVkVKpT9Ds7ChQu1YcMGde7cWYWFhaXWN2vWTNHR0Zo/f75mzpypu+66Sz4+rle5T5482VnCAwMDTV/ebXQG/Xy3KYufn5+CgoJcHlbyreCj+NhIrViT5VxWUlKi9DXb1KpJlKWzeYp5n36rq6pU1s3t/nqX7xjhdeMeuRgjG/fIxRjZuEcuxsjmDIfDofHTFujzld9p7rThivzdDbIkadiAjvrfzCf033+Pcj4kadyDPTX1qdInr/5MmsbWVvaeQy7LsvfmKLxq6StIP12yTjFX11R01MXdZ6m8Ku9/nzzuM9BJSUkqLi52ufTa4XDIz89Pb7zx2ztXkZGRio6OVnR0tIqLi9WrVy9t3rxZfn5+qlq1qkJCQpSVleVy7Nq1a0uSKleurLy8PLe/f+jQoZo+fbp++OEHZWRklFr/wAMPqG/f376n1+gS8bPq168vm82mLVu2qFev0pezbNmyRVWqVFHVqlXVoEEDSdLWrVvVvPmf4+uMhg+4ScOffU/NG9bWNY3r6s15X+pYYZEGdm9r9WiWKykp0bzPVqvfra3l4+Nt9TgehdeNe+RijGzcIxdjZOMeuRgjmzOXbX+8dJ3enXSPAvz9dDDnzP12ggIrqqKfr6qFBbk9gxhRvUqpsv1n06/79bp/zFuaPX+5Ol7XRD9s36VPFmfoyWGu//4/dvyEvvzmOz10962WzeoJyvPfJ1MF+pNPPjF9wLM38boQxcXFSklJ0csvv6yEhASXdT179tS8efPUpUuXUvv16dNH48eP14wZM/Too4/Ky8tLffv21Zw5czR+/PhzltzfGzBggEaNGqVmzZqpUaNGpdaHhoYqNNT8rebDwsJ08803O2f7/eeg9+/fr/fff1+DBg2SzWZTfHy8GjVqpJdffln9+vUr9TnovLy8c34O2tPcntBCh/MKNPntz3Qw56iaNKip+a89WC4uz7jcVqzJ0u79R8rF/1Bcabxu3CMXY2TjHrkYIxv3yMUY2UhzPv5aknTnI9Ndlr/0VH/dcUtri6byDA2ja+n50Yl6a87nSk5bphrVquiRod3UuX28y3ZLvtokh0O6+YZmls3qCcrz3yebw8R1w+5uaOX2YDabTp8+fcHDLFq0SP369dPBgwcVHOx6Q6XRo0dr2bJl+vDDDxUVFaXMzEzFx//2gnzzzTc1YcIE7dixQ5UqVVJOTo7atWunY8eOaeLEiWrZsqUCAgK0adMmPfXUU4qLi9OCBQukX78HOj4+3vk9znl5eapQoYICAgKkX++A3bNnzzK/C7pu3bq64447NHDgQJflderU0eHDh9WuXTs1bNhQzz33nKKiovT999/riSeeUFFRkb799ltnKc/IyFCnTp3UpEkT/eMf/1BsbKwKCgr0n//8R4sXLy71WfCy2O12BQcH60BOvuWXc3ua4tMlVo/gsXy8Pe6THQAAwED+8VNWj+Cx9uSW/sgmpEa16AV/ZLfbVT0sWPn55+5Npv6lXFJSYupxMeVZv16+3alTp1LlWZJ69+6ttWvXGn410+DBg3Xq1CnnZd5hYWHKyMjQoEGD9NJLL6l169Zq0qSJJkyYoH79+undd981nCMkJMRZns/H1KlT1bx5c5fHZ599pujoaK1du1b16tVT3759dfXVV+vvf/+7/va3v2nVqlUuZ7Rbt26ttWvXqn79+rrvvvvUsGFD9ejRQ99//72z4AMAAAAArjxTZ6CNnDhxQhUrVry0E+GS4Qy0Mc5AG+MMNAAA5QdnoI1xBto9zkCXdsnPQP/e6dOn9c9//lM1a9ZUYGCgfv75Z0nSuHHjSt0pGwAAAACAP4vzLtCTJk1ScnKypkyZIl9fX+fyuLg4/fvf/77U8wEAAAAA4BHOu0CnpKTonXfe0cCBA+Xt/dtX7zRr1kxbt2691PMBAAAAAOARzrtA79mzR/Xr1y+1vKSkRKdO8RkMAAAAAMCf03kX6EaNGmnlypWlls+fP1/Nmze/VHMBAAAAAOBRfM53h/Hjx2vw4MHas2ePSkpK9NFHHykrK0spKSn69NNPL8+UAAAAAABY7LzPQN922236z3/+oyVLliggIEDjx4/Xli1b9J///Ec333zz5ZkSAAAAAACLnfcZaEm64YYb9MUXX1z6aQAAAAAA8FAXVKAlae3atdqyZYv06+eiW7RocSnnAgAAAADAo5x3gd69e7f69++vr7/+WiEhIZKkvLw8tWvXTh988IFq1ap1OeYEAAAAAMBS5/0Z6HvvvVenTp3Sli1blJubq9zcXG3ZskUlJSW69957L8+UAAAAAABY7LzPQK9YsULffPONYmJinMtiYmL0+uuv64YbbrjU8wEAAAAA4BHO+wx0ZGSkTp06VWr56dOnFRERcanmAgAAAADAo5x3gX7ppZc0YsQIrV271rls7dq1euSRRzR16tRLPR8AAAAAAB7B5nA4HOfaqEqVKrLZbM6fjx07puLiYvn4nLkC/OyfAwIClJube3knhml2u13BwcE6kJOvoKAgq8fxKMWnS6wewWP5eJ/3+2oAAMAi+cdLXxmKM/bkFlo9gkdqVIte8Ed2u13Vw4KVn3/u3mTqM9DTpk27VLMBAAAAAFAumSrQgwcPvvyTAAAAAADgwc77Lty/d+LECZ08edJlGZcKAwAAAAD+jM77w47Hjh3TQw89pGrVqikgIEBVqlRxeQAAAAAA8Gd03gX6ySef1LJly/Tmm2/Kz89P//73v/Xss88qIiJCKSkpl2dKAAAAAAAsdt6XcP/nP/9RSkqKOnTooCFDhuiGG25Q/fr1VadOHb3//vsaOHDg5ZkUAAAAAAALnfcZ6NzcXNWrV0/69fPOZ7+26vrrr1d6evqlnxAAAAAAAA9w3gW6Xr162rFjhyQpNjZWaWlp0q9npkNCQi79hAAAAAAAeIDzLtBDhgzRxo0bJUlPPfWUpk+frooVK+rRRx/VE088cTlmBAAAAADAcjaHw+G4mAP88ssvWrdunerXr6+mTZteuslw0ex2u4KDg3UgJ5+vF/uD4tMlVo/gsXy8z/t9NQAAYJH846esHsFj7ckttHoEj9SoFr3gj+x2u6qHBSs//9y96aK+B1qS6tSpozp16lzsYQAAAAAA8GimCvRrr71m+oAPP/zwxcwDAAAAAIBHMnUJd1RUlLmD2Wz6+eefL8VcuAS4hNvYRX5y4U/NZrNZPQIAAMBF4yN77vFxvdIu+SXcZ++6DQAAAADAXxVvPwAAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZcUIFeuXKlEhMTde2112rPnj2SpPfee09fffXVpZ4PAAAAAACPcN4FesGCBercubP8/f2VmZmpoqIiSVJ+fr4mT558OWYEAAAAAMBy512gn3vuOb311lt69913VaFCBefy6667TuvXr7/U8wEAAAAA4BHOu0BnZWXpxhtvLLU8ODhYeXl5l2ouAAAAAAA8ynkX6PDwcP3444+lln/11VeqV6/epZoLAAAAAACPct4F+r777tMjjzyi1atXy2azae/evXr//fc1atQoDRs27PJMCQAAAACAxXzOd4ennnpKJSUl6tixo44fP64bb7xRfn5+GjVqlEaMGHF5pgQAAAAAwGI2h8PhuJAdT548qR9//FEFBQVq1KiRAgMDL/10uCh2u13BwcE6kJOvoKAgq8fxKBf4sv9LsNlsVo8AAABw0YpPl1g9gkfy8b6gbzL+U7Pb7aoeFqz8/HP3pvM+A32Wr6+vGjVqdKG7AwAAAABQrpx3gf7b3/5W5hmqZcuWXexMAAAAAAB4nPMu0PHx8S4/nzp1Shs2bNDmzZs1ePDgSzkbAAAAAAAe47wL9Kuvvup2+YQJE1RQUHApZgIAAAAAwONcsk+QJyYmaubMmZfqcAAAAAAAeJRLVqBXrVqlihUrXqrDAQAAAADgUc77Eu7bb7/d5WeHw6F9+/Zp7dq1Gjdu3KWcDQAAAAAAj3HeBTo4ONjlZy8vL8XExGjixIlKSEi4lLMBAAAAAOAxzqtAnz59WkOGDFGTJk1UpUqVyzcVAAAAAAAe5rw+A+3t7a2EhATl5eVdvokAAAAAAPBA530Tsbi4OP3888+XZxoAAAAAADzUeRfo5557TqNGjdKnn36qffv2yW63uzwAAAAAAPgzsjkcDoeZDSdOnKjHH39clStX/m1nm835Z4fDIZvNptOnT1+eSXHe7Ha7goODdSAnX0FBQVaP41FMvuz/kn7/9xoAAKC8Kj5dYvUIHsnH+5J9k/Gfht1uV/WwYOXnn7s3mS7Q3t7e2rdvn7Zs2VLmdu3btz+/aXHZUKCNUaCNUaABAMCfAQXaPQp0aedToE3fhfts4aAgAwAAAAD+is7r7QfOTAEAAAAA/qrO63ugGzRocM4SnZube7Ez4U/m3bQVen3OUh3MsSsuuqZefOIOtWhc1+qxLPVq8mJ9+uVGbf/lgCr6VVDrJlF6ZsRtiq5T3erRPAavG/fIxRjZuEcuxsjGPXIxRjbukUtpU979r15K+p/Lsvp1qmlV6tOWzeRpyuvr5rzOQD/77LN69dVXy3xcqFWrVsnb21tdu3Z1Wb5z507ZbDbnIzQ0VO3bt9fKlStLHcNut2vcuHFq3Lix/P39FRYWplatWmnKlCk6cuSIc7sOHTrIZrPphRdeKHWMrl27ymazacKECWXOW7duXdlsNn3wwQel1jVu3Fg2m03Jycmltv/2229dth05cqQ6dOjg/HnChAnO5+rt7a3IyEj9/e9/L7dvTHy0eJ2enrZQo++9RcvfG6246JrqPWK6DuUetXo0S329/kfdc8cN+jzpcX30+oM6dfq0eo+YrmOFRVaP5hF43bhHLsbIxj1yMUY27pGLMbJxj1yMxdaroc2fPed8fPr2SKtH8hjl+XVzXgX6zjvv1ODBg8t8XKikpCSNGDFC6enp2rt3b6n1S5Ys0b59+5Senq6IiAh169ZNBw4ccK7Pzc1V27ZtNWvWLI0aNUqrV6/W+vXrNWnSJGVmZmru3Lkux4uMjHQpuJK0Z88eLV26VDVq1DA1c2RkpGbNmuWy7Ntvv9X+/fsVEBBQavuKFStq9OjR5zxu48aNtW/fPmVnZ2vWrFn63//+p2HDhpmaydPMmLtMg3q208Ae1yq2Xg29MuZOVaroqzmfrLJ6NEvNf224BnRrq4ZX11Bcg1qaPj5Ru/cf0cYtu6wezSPwunGPXIyRjXvkYoxs3CMXY2TjHrkY8/b2UvWwIOcjLCTQ6pE8Rnl+3Zgu0Jfz888FBQVKTU3VsGHD1LVr11LFVpLCwsIUHh6uuLg4jR07Vna7XatXr3auHzt2rLKzs5WRkaEhQ4aoadOmqlOnjhISEjRv3jwNHz7c5XjdunXT4cOH9fXXXzuXzZ49WwkJCapWrZqpuQcOHKgVK1Zo167fSs/MmTM1cOBA+fiUvjr+73//u7799lv997//LfO4Pj4+Cg8PV82aNdWpUyfdcccd+uKLL0zN5ElOnirWhq271KF1jHOZl5eX2reO0Zrvdlg6m6exF5yQJIUEV7J6FMvxunGPXIyRjXvkYoxs3CMXY2TjHrmUbceuQ4rr9rRa3v6sHhg/W7v3l88rSi+18v66MV2gL+fX/qSlpSk2NlYxMTFKTEzUzJkzDX9fYWGhUlJSJEm+vr6SpJKSEqWmpioxMVERERFu9/vjGwC+vr4aOHCgyxnk5ORkDR061PTc1atXV+fOnTV79mxJ0vHjx5Wammp4jKioKD3wwAMaM2aMSkrM3VZ/586d+vzzz53PtSxFRUWy2+0uDyvl5BXo9OkSVQ2t7LK8amiQDuZYO5snKSkp0dhXFqhNs3pqdLX71+9fCa8b98jFGNm4Ry7GyMY9cjFGNu6Ri7FrGtfVa+MGKvXVYZryZF9l78tR9wf+pYJjJ6wezXLl/XVjukCXlJSYPjN7vpKSkpSYmChJ6tKli/Lz87VixQqXbdq1a6fAwEAFBARo6tSpatGihTp27ChJOnTokPLy8hQTE+OyT4sWLRQYGKjAwED179+/1O8dOnSo0tLSdOzYMaWnpys/P1/dunU7r9mHDh2q5ORkORwOzZ8/X1dffbXi4+MNt3/66ae1Y8cOvf/++4bbfPfddwoMDJS/v7+ioqL0/fffm7r0+/nnn1dwcLDzERkZeV7PBdZ4YsqH2vLzPv37ubutHgUAAACXQKd2jXRbx+ZqHF1TN7VtqHmvPKD8o4VatDTT6tFwkSz/Fu2srCxlZGQ4C66Pj4/69eunpKQkl+1SU1OVmZmpBQsWqH79+kpOTlaFChXKPPbChQu1YcMGde7cWYWFhaXWN2vWTNHR0Zo/f75mzpypu+66q9Sl15MnT3aW8MDAQGVnZ7us79q1qwoKCpSenq6ZM2ee8wx21apVNWrUKI0fP14nT550u01MTIw2bNigNWvWaPTo0ercubNGjBhR5nElacyYMcrPz3c+fn9puRXCQgLl7e1V6mYAh3LtqhZW9heU/1U8+VKaPv9qsz6ZMUI1q1exehyPwOvGPXIxRjbukYsxsnGPXIyRjXvkYl5w5Uq6unY17dh9yOpRLFfeXzeWF+ikpCQVFxcrIiJCPj4+8vHx0ZtvvqkFCxYoPz/fuV1kZKSio6PVq1cvTZ48Wb169VJR0Zk7FletWlUhISHKyspyOXbt2rVVv359Va5cudTvPWvo0KGaPn265s+f77b8PvDAA9qwYYPz8cdLxH18fHTXXXfpmWee0erVqzVw4MBzPufHHntMhYWFmjFjhtv1vr6+ql+/vuLi4vTCCy/I29tbzz777DmP6+fnp6CgIJeHlXwr+Cg+NlIr1vz236WkpETpa7apVZMoS2ezmsPh0JMvpemz5Zv08YwRqlPzKqtH8hi8btwjF2Nk4x65GCMb98jFGNm4Ry7mFRwv0s49h1U9LNjqUSxX3l83lhbo4uJipaSk6OWXX3YpqRs3blRERITmzZvndr8+ffrIx8fHWUC9vLzUt29fzZkzx+0dvMsyYMAAfffdd4qLi1OjRo1KrQ8NDVX9+vWdD3c3Bxs6dKhWrFih2267TVWqnPssYmBgoMaNG6dJkybp6NFz36r96aef1tSpU8/7uXmC4QNuUsqibzTv02+VtWO/HnshVccKizSwe1urR7PUE1PSlPZ/a/XOPwcrsFJFHThs14HDdhWecH9Vwl8Nrxv3yMUY2bhHLsbIxj1yMUY27pGLe8+8tkhfr9+u7L05ytj0s+4e/W95e9l0e8I1Vo/mEcrz66Z0G7yCPv30Ux05ckT33HOPgoNd343p3bu3kpKS1KVLl1L72Ww2Pfzww5owYYLuv/9+VapUSZMnT9by5cvVunVrTZw4US1btlRAQIA2bdqkVatWKS4uzu0MVapU0b59+855OXhZGjZsqMOHD6tSJfN3UP773/+uV199VXPnzlWbNm3K3Pbaa69V06ZNNXnyZL3xxhsXPKcVbk9oocN5BZr89mc6mHNUTRrU1PzXHiwXl2dcTjMXfCVJ6v7Aay7L3xg/UAO6ef7/cFxuvG7cIxdjZOMeuRgjG/fIxRjZuEcu7u09mKf7x8/WkfxjCgsJVJtmV+v//v2YrqpifGXsX0l5ft3YHJfz9trn0L17d5WUlOizzz4rtS4jI0Nt2rTRxo0b1axZM2VmZrrcnOv48eOqVauWnnrqKT355JOSpPz8fL344otauHChduzYIS8vL0VHR+u2227TyJEjFRoaKknq0KGD4uPjNW3aNLdzxcfHq2fPnpowYYLh7HXr1tXIkSM1cqT7L0QPCQnRtGnTdPfddxtuP2/ePA0YMEDt27fX8uXLJUkTJkzQokWLtGHDBpfjffDBB7r77ru1fft20zcHs9vtCg4O1oGcfMsv5/Y0Fr7sPd7l/Mo6AACAK6X4tLlvvfmr8fG2/FO8Hsdut6t6WLDy88/dmywt0Li8KNDGeNkbo0ADAIA/Awq0exTo0s6nQJMeAAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATPCxegAAAAAAuNR8vDlXiEuPVxUAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRoAAAAAABMo0AAAAAAAmECBBgAAAADABAo0AAAAAAAmUKABAAAAADCBAg0AAAAAgAkUaAAAAAAATKBAAwAAAABgAgUaAAAAAAATKNAAAAAAAJhAgQYAAAAAwAQKNAAAAAAAJlCgAQAAAAAwgQINAAAAAIAJFGgAAAAAAEygQAMAAAAAYAIFGgAAAAAAEyjQAAAAAACYQIEGAAAAAMAECjQAAAAAACZQoAEAAAAAMIECDQAAAACACRRoAAAAAABMoEADAAAAAGACBRqX3btpK9S0x3iFXzdSne5+Seu+32n1SJZ7NXmxOg5+SbU7jFKDzmOUOOodbf/lgNVjeRReN+6RizGycY9cjJGNe+RijGzcIxdjZGOsvGZDgcZl9dHidXp62kKNvvcWLX9vtOKia6r3iOk6lHvU6tEs9fX6H3XPHTfo86TH9dHrD+rU6dPqPWK6jhUWWT2aR+B14x65GCMb98jFGNm4Ry7GyMY9cjFGNsbKczYeUaBXrVolb29vde3a1WX5zp07ZbPZnI/Q0FC1b99eK1euLHUMu92ucePGqXHjxvL391dYWJhatWqlKVOm6MiRI87tOnToIJvNphdeeKHUMbp27SqbzaYJEyaUOW/dunVls9n0wQcflFrXuHFj2Ww2JScnuyz/5ptvdOutt6pKlSqqWLGimjRpoldeeUWnT5922e73zzcgIEDR0dG6++67tW7dujJn8lQz5i7ToJ7tNLDHtYqtV0OvjLlTlSr6as4nq6wezVLzXxuuAd3aquHVNRTXoJamj0/U7v1HtHHLLqtH8wi8btwjF2Nk4x65GCMb98jFGNm4Ry7GyMZYec7GIwp0UlKSRowYofT0dO3du7fU+iVLlmjfvn1KT09XRESEunXrpgMHfrvcNTc3V23bttWsWbM0atQorV69WuvXr9ekSZOUmZmpuXPnuhwvMjKyVMHds2ePli5dqho1apiaOTIyUrNmzXJZ9u2332r//v0KCAhwWb5w4UK1b99etWrV0pdffqmtW7fqkUce0XPPPac777xTDofDZftZs2Zp3759+v777zV9+nQVFBSoTZs2SklJMTWbpzh5qlgbtu5Sh9YxzmVeXl5q3zpGa77bYelsnsZecEKSFBJcyepRLMfrxj1yMUY27pGLMbJxj1yMkY175GKMbIyV92wsL9AFBQVKTU3VsGHD1LVr11LFVpLCwsIUHh6uuLg4jR07Vna7XatXr3auHzt2rLKzs5WRkaEhQ4aoadOmqlOnjhISEjRv3jwNHz7c5XjdunXT4cOH9fXXXzuXzZ49WwkJCapWrZqpuQcOHKgVK1Zo167fzhjOnDlTAwcOlI+Pj3PZsWPHdN9996lHjx565513FB8fr7p16+ree+/V7NmzNX/+fKWlpbkcOyQkROHh4apbt64SEhI0f/58DRw4UA899JDL2fQ/Kioqkt1ud3lYKSevQKdPl6hqaGWX5VVDg3Qwx9rZPElJSYnGvrJAbZrVU6OrI6wex3K8btwjF2Nk4x65GCMb98jFGNm4Ry7GyMZYec/G8gKdlpam2NhYxcTEKDExUTNnzix1RvaswsJC51lYX19f6dfykZqaqsTEREVEuC8fNpvN5WdfX18NHDjQ5QxycnKyhg4danru6tWrq3Pnzpo9e7Yk6fjx40pNTS11jMWLFysnJ0ejRo0qdYzu3burQYMGmjdv3jl/36OPPqqjR4/qiy++MNzm+eefV3BwsPMRGRlp+vnAOk9M+VBbft6nfz93t9WjAAAAACiD5QU6KSlJiYmJkqQuXbooPz9fK1ascNmmXbt2CgwMVEBAgKZOnaoWLVqoY8eOkqRDhw4pLy9PMTExLvu0aNFCgYGBCgwMVP/+/Uv93qFDhyotLU3Hjh1Tenq68vPz1a1bt/OafejQoUpOTpbD4dD8+fN19dVXKz4+3mWbbdu2SZIaNmzo9hixsbHObcoSGxsr/fq5cCNjxoxRfn6+8/H7s+NWCAsJlLe3V6mbARzKtataWJBlc3mSJ19K0+dfbdYnM0aoZvUqVo/jEXjduEcuxsjGPXIxRjbukYsxsnGPXIyRjbHyno2lBTorK0sZGRnOguvj46N+/fopKSnJZbvU1FRlZmZqwYIFql+/vpKTk1WhQoUyj71w4UJt2LBBnTt3VmFhYan1zZo1U3R0tObPn6+ZM2fqrrvucrn0WpImT57sLOGBgYHKzs52Wd+1a1cVFBQoPT1dM2fOLPMMttFZdbPO7v/Hs+m/5+fnp6CgIJeHlXwr+Cg+NlIr1mQ5l5WUlCh9zTa1ahJl6WxWczgcevKlNH22fJM+njFCdWpeZfVIHoPXjXvkYoxs3CMXY2TjHrkYIxv3yMUY2Rgr79n4mNjmsklKSlJxcbHLpdcOh0N+fn564403nMsiIyMVHR2t6OhoFRcXq1evXtq8ebP8/PxUtWpVhYSEKCsry+XYtWvXliRVrlxZeXl5bn//0KFDNX36dP3www/KyMgotf6BBx5Q3759nT//8RJxHx8f3XXXXXrmmWe0evVqLVy4sNQxGjRoIEnasmWL2rVrV2r9li1b1KhRozJzOrudJEVFef6L6veGD7hJw599T80b1tY1jevqzXlf6lhhkQZ2b2v1aJZ6Ykqa5n++Tu9PvU+BlSrqwOEzn/cICqwo/4q+Vo9nOV437pGLMbJxj1yMkY175GKMbNwjF2NkY6w8Z2NZgS4uLlZKSopefvllJSQkuKzr2bOn5s2bpy5dupTar0+fPho/frxmzJihRx99VF5eXurbt6/mzJmj8ePHG34O2p0BAwZo1KhRatasmdsSGxoaqtDQ0DKPMXToUE2dOlX9+vVTlSqlL8FNSEhQaGioXn755VIF+pNPPtH27dv1z3/+85yzTps2TUFBQerUqZOp5+Ypbk9oocN5BZr89mc6mHNUTRrU1PzXHiwXl2dcTjMXfCVJ6v7Aay7L3xg/UAO6ef7/cFxuvG7cIxdjZOMeuRgjG/fIxRjZuEcuxsjGWHnOxua42GuLL9CiRYvUr18/HTx4UMHBwS7rRo8erWXLlunDDz9UVFSUMjMzXT5b/Oabb2rChAnasWOHKlWqpJycHLVr107Hjh3TxIkT1bJlSwUEBGjTpk166qmnFBcXpwULFki/fg90fHy8pk2bJknKy8tThQoVnF89FR8fr549e5b5XdB169bVyJEjNXLkSElSTk6OKlWqJH9/f+nXu2hPmzZNd9995qZQ8+fP15133qmhQ4fqoYceUlBQkJYuXaonnnhCHTt2VFpamvPSbJvNplmzZqlLly4qKirStm3b9Pbbb2vRokVKSUnRgAEDTGdst9sVHBysAzn5ll/O7WksetmXC2V9TAAAAAD4s7Hb7aoeFqz8/HP3Jss+A52UlKROnTqVKs+S1Lt3b61du9bwa5gGDx6sU6dOOS/zDgsLU0ZGhgYNGqSXXnpJrVu3VpMmTTRhwgT169dP7777ruEcISEhpb63+XyFhYU5y7M7ffr00Zdffqns7GzdcMMNiomJ0auvvqp//OMf+uCDD0oVliFDhqhGjRqKjY3VsGHDFBgYqIyMjPMqzwAAAACAS8uyM9C4/DgDbYyXvTHOQAMA8P/t3Xd0FOXbxvFrU4EkhN5LqKEX6SjSERCVohRBVIo/BBQQkF5VqgqCIBaKgiBIU0EpgjQLKtIJoQYQQicJLSHlef+QrKzZxdVXMmHz/ZyzBzMzu7lzuzs71zxTAKQn98UINAAAAAAA9xMCNAAAAAAAbiBAAwAAAADgBgI0AAAAAABuIEADAAAAAOAGAjQAAAAAAG4gQAMAAAAA4AYCNAAAAAAAbiBAAwAAAADgBgI0AAAAAABuIEADAAAAAOAGAjQAAAAAAG4gQAMAAAAA4AYCNAAAAAAAbiBAAwAAAADgBgI0AAAAAABuIEADAAAAAOAGAjQAAAAAAG4gQAMAAAAA4AYCNAAAAAAAbiBAAwAAAADgBgI0AAAAAABuIEADAAAAAOAGAjQAAAAAAG4gQAMAAAAA4AYCNAAAAAAAbiBAAwAAAADgBgI0AAAAAABu8LG6AMAKNpvN6hIAAAAA3GcYgQYAAAAAwA0EaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIAGAAAAAMANBGgAAAAAANxAgAYAAAAAwA0EaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIAGAAAAAMANBGgAAAAAANxAgAYAAAAAwA0EaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIAGAAAAAMANBGgAAAAAANxAgAYAAAAAwA0EaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIAGAAAAAMANBGgAAAAAANxAgAYAAAAAwA0EaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIAGAAAAAMANBGjccx8u2awKj49Ungf7qtFzk7Vjf4TVJaUZ9MY1euMcfXGN3jhHX1yjN87RF9fojXP0xTV649r92hsCNO6p5et2aPjUFRrUrZk2zR+kciXyq81LM3Th8lWrS7McvXGN3jhHX1yjN87RF9fojXP0xTV64xx9cY3euHY/9ybNBegff/xR3t7eevTRRx2mR0REyGaz2R/ZsmVT3bp1tXXr1hSvERMToxEjRqhs2bLKmDGjsmfPrmrVqmnSpEm6cuWKfbl69erJZrNpwoQJKV7j0Ucflc1m0+jRo+9ab0hIiGw2m3766SeH6X379lW9evXsP48ePdqh/uRHqVKlHJ535MgRdenSRYUKFZK/v7/y58+vhg0b6tNPP1VCQoIbHUxbZi7cqM4ta6vj47VUqmhevT2kvTJl8NOCL3+0ujTL0RvX6I1z9MU1euMcfXGN3jhHX1yjN87RF9fojWv3c2/SXICePXu2XnrpJW3ZskVnzpxJMf/bb79VZGSktmzZonz58qlFixY6d+6cff7ly5dVs2ZNzZ07VwMGDND27dv122+/6Y033tDOnTu1cOFCh9crWLCg5s2b5zDt9OnT2rBhg/LmzetWzRkyZNCgQYP+drmyZcsqMjLS4bFt2zb7/J9//lkPPPCAwsLCNGPGDO3bt0+bNm1St27d9N5772n//v1u1ZNW3IpP0K6Dp1Sveqh9mpeXl+pWD9Uve49bWpvV6I1r9MY5+uIavXGOvrhGb5yjL67RG+foi2v0xrX7vTc+Vhdwp2vXrmnx4sX69ddfdfbsWc2bN09Dhw51WCZ79uzKkyeP8uTJo6FDh+qzzz7T9u3b9fjjj0uShg4dqpMnT+rQoUPKly+f/XmFCxdWkyZNZIxxeL0WLVpoyZIl+v777/Xggw9Kkj7++GM1adJEJ0+edKvuF154QbNmzdLXX3+t5s2bu1zOx8dHefLkcTrPGKPnnntOJUuW1Pfffy8vrz/3bZQoUUIdOnRIUftfxcXFKS4uzv5zTEyMW/XfK5eirikxMUk5swU5TM+ZLbMOR5xz+bz0gN64Rm+coy+u0Rvn6Itr9MY5+uIavXGOvrhGb1y733uTpkaglyxZolKlSik0NFSdOnXSnDlzXIbGmzdv6pNPPpEk+fn5SZKSkpK0ePFiderUySE838lmszn87Ofnp44dO2ru3Ln2afPmzVOXLl3crrtIkSLq0aOHhgwZoqSkJLefd6ddu3YpLCxMAwYMcAjPd6v9r8aPH6/g4GD7o2DBgv+qFgAAAABASmkqQM+ePVudOnWSJDVt2lTR0dHavHmzwzK1a9dWYGCgAgIC9Oabb6pKlSpq2LChJOnChQuKiopSaGiow3OqVKmiwMBABQYGqkOHDil+b5cuXbRkyRJdv35dW7ZsUXR0tFq0aPGPah8+fLiOHz+uTz/91OUye/futdeR/OjRo4ck6dChQ5LkUPv58+cdlp05c+ZdaxgyZIiio6Ptj1OnTv2jv+G/lj1LoLy9vVJcDODC5Rjlyp7ZsrrSAnrjGr1xjr64Rm+coy+u0Rvn6Itr9MY5+uIavXHtfu9NmgnQ4eHh+vnnn+0B18fHR+3atdPs2bMdllu8eLF27typZcuWqXjx4po3b558fX3v+torVqzQrl279Mgjj+jmzZsp5lesWFElSpTQ0qVLNWfOHD3zzDPy8XE8un3cuHEOYfavh3fnzJlTAwYM0MiRI3Xr1i2ndYSGhmrXrl0Oj7Fjx7qsO3v27PblsmTJ4vJ1k/n7+ytz5swODyv5+fqoUqmC2vxLuH1aUlKStvxySNXKF7G0NqvRG9fojXP0xTV64xx9cY3eOEdfXKM3ztEX1+iNa/d7b9LMOdCzZ89WQkKCw6HXxhj5+/vr3XfftU8rWLCgSpQooRIlSighIUGtWrXSvn375O/vr5w5cypLliwKDw93eO1ChQpJkoKCghQVFeX093fp0kUzZszQgQMH9PPPP6eY36NHD7Vt29b+s7NDxF955RXNnDnT5Uixn5+fihcv7nReiRIlpNs7EipXrixJ8vb2ti//10B/v+j5dAP1HDNflUsX0gNlQ/Teou90/WacOj5W0+rSLEdvXKM3ztEX1+iNc/TFNXrjHH1xjd44R19cozeu3c+9SROpLCEhQZ988oneeustNWnSxGFey5YttWjRIjVt2jTF85588kmNHDlSM2fOVL9+/eTl5aW2bdtqwYIFGjlypMvzoJ15+umnNWDAAFWsWFFlypRJMT9btmzKli3bXV8jMDBQI0aM0OjRo+0XNXNX5cqVVapUKb355ptq27aty/Og7zetm1TRxahrGvf+ap2/dFXlS+bX0mm97ovDM+41euMavXGOvrhGb5yjL67RG+foi2v0xjn64hq9ce1+7o3N/N2lnVPBypUr1a5dO50/f17BwcEO8wYNGqSNGzfq888/V5EiRbRz505VqlTJPv+9997T6NGjdfz4cWXKlEmXLl1S7dq1df36dY0dO1ZVq1ZVQECA9uzZo8GDB6tcuXJatmyZdPs+0JUqVdLUqVMlSVFRUfL19VVAQIAkqVKlSmrZsuVd7wUdEhKivn37qm/fvpKk+Ph4lS5dWqdPn1aNGjW0adMm6fZ9oJcuXapvv/3W4fk2m025c+eWJP30009q3LixypUrpyFDhqh06dKKj4/Xli1b1L9/f02YMEEvvfSS232NiYlRcHCwzl2KtvxwbgAAAABIi2JiYpQ7e7Cio/8+N6WJYc7Zs2erUaNGKcKzJLVp00a//vqry1syPfvss4qPj7cf5p09e3b9/PPP6ty5syZPnqzq1aurfPnyGj16tNq1a6cPP/zQZR1ZsmSxh+d/y9fXV6+99ppiY2NTzNu/f7/y5s3r8ChcuLB9fs2aNbVjxw6FhoaqV69eKlOmjGrXrq1FixZpypQpevHFF/9ftQEAAAAA/r00MQKNe4MRaAAAAAC4u/tuBBoAAAAAgLSOAA0AAAAAgBsI0AAAAAAAuIEADQAAAACAGwjQAAAAAAC4gQANAAAAAIAbCNAAAAAAALiBAA0AAAAAgBsI0AAAAAAAuIEADQAAAACAGwjQAAAAAAC4gQANAAAAAIAbCNAAAAAAALiBAA0AAAAAgBsI0AAAAAAAuIEADQAAAACAGwjQAAAAAAC4gQANAAAAAIAbCNAAAAAAALiBAA0AAAAAgBsI0AAAAAAAuIEADQAAAACAGwjQAAAAAAC4gQANAAAAAIAbCNAAAAAAALiBAA0AAAAAgBsI0AAAAAAAuMHH6gJw791KSNKthCSry0hTvGxWV5B2+XizXw0AgPtFnxX7rS4hzepetYDVJaRJpfIFWV1CmpOQ6H5WYksZAAAAAAA3EKABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwAwEaAAAAAAA3EKABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwAwEaAAAAAAA3EKABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwAwEaAAAAAAA3EKABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwAwEaAAAAAAA3EKABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwg4/VBcCzXbseqwkfrNY3W/bo4uVrKlcyv17v10aVyxS2ujRLPdBytE6dvZxi+vNtHtKkgW0tqSmt+XDJZk1fsEHnL8WoXIn8mjjwKVUpG2J1WZajL67RG+foi2v0xjn64lp6602x7JnUoEQOFcySQcEZffXRTye1N/KqwzK5g/z0WNncKp4jQF42m85djdOc7ad05Wa8Mvl6q1npnArNFaismXx1PS5BeyKv6usD5xWbkGTZ33UvPPW/yTp7ISrF9FZNa+iVFx5X3K14zZj3jTZs26P4hERVr1RCr7zwuLJlCbSkXqtFno/S2BlfasOPB3QzLl5FCuTQtOEdVal0IatL+1tpegT6xx9/lLe3tx599FGH6REREbLZbPZHtmzZVLduXW3dujXFa8TExGjEiBEqW7asMmbMqOzZs6tatWqaNGmSrly5Yl+uXr16stlsmjBhQorXePTRR2Wz2TR69OgU80aPHu1Qi7OHJD333HOy2Wzq0aNHitfo1auXbDabnnvuOfu05OVtNpv8/PxUvHhxjR07VgkJCf+ik9bpN36RtvwSrndHPqNNCwarXo1SeurlGYo8n3IFk56sm9tf+1a/bn8sndZLkvREg8pWl5YmLF+3Q8OnrtCgbs20af4glSuRX21emqELl6+68WzPRV9cozfO0RfX6I1z9MW19NgbPx8vnY6O1dLdkU7nZw/wVZ+Hi+j81VuavjVCEzce0dqDFxSf+Ec4Ds7go+AMvvpi31lN2HBEn+44o9K5A9XhgXyp/Jfcex9M6qmVswfbH1NGPS9Jql+7nCRp+tyv9f2vBzV2YAdNf62bLl6O0bCJn1pctTWiYm7o0RemysfHW59NeVHbFg3VmJdbKjgoo9WluSVNB+jZs2frpZde0pYtW3TmzJkU87/99ltFRkZqy5Ytypcvn1q0aKFz587Z51++fFk1a9bU3LlzNWDAAG3fvl2//fab3njjDe3cuVMLFy50eL2CBQtq3rx5DtNOnz6tDRs2KG/evE5rHDBggCIjI+2PAgUKaOzYsQ7T7nz9zz77TDdv3rRPi42N1cKFC1WoUMq9LU2bNlVkZKQOHz6s/v37a/To0Zo8efI/7KJ1bsbe0upNuzWi1xOqVbm4ihTMqYHdmqtIgRyat2Kb1eVZKkfWIOXOntn+WPf9PoUUyKHaDxS3urQ0YebCjercsrY6Pl5LpYrm1dtD2itTBj8t+PJHq0uzFH1xjd44R19cozfO0RfX0mNvws5d09dh57Un0vlOghZlcuvA2Wv6cv85nY6O1aXr8dp39qqu3UqUJEVejdOcn09p/9lrunQ9XocvXtfq/edVLk+QvGyp/MfcY1mDA5Q9a5D98cOv4cqfJ5sqlS2ia9djtXrDDvV+rrmqlC+m0GL5NaR3G+0LP6n94SetLj3VTZv/rfLlzqLpIzrqgbKFVThfdtWvUVpFCuS0ujS3pNkAfe3aNS1evFgvvviiHn300RTBVpKyZ8+uPHnyqFy5cho6dKhiYmK0fft2+/yhQ4fq5MmT+vnnn/X888+rQoUKKly4sJo0aaJFixapZ8+eDq/XokULXbx4Ud9//7192scff6wmTZooV65cTusMDAxUnjx57A9vb28FBQU5TEv2wAMPqGDBglq+fLl92vLly1WoUCFVrpxy5NHf31958uRR4cKF9eKLL6pRo0b68ssv/0U3rZGYmKTExCT5+zmeKZDB308/7z5mWV1pza34BC1d86ueblHTfsRCenYrPkG7Dp5Sveqh9mleXl6qWz1Uv+w9bmltVqIvrtEb5+iLa/TGOfriGr1JySapTO5Anb92Sz1qF9brzUPVr24Rlc8bdNfnZfD1UmxCkpJMqpWa6uLjE7Ruyy41b1BFNptN4cdOKyEhUVUrFrMvU7hATuXOkUX7Dp2ytFYrrN26V5VKF1KXoXNUutlQ1e88UfNX/mB1WW5LswF6yZIlKlWqlEJDQ9WpUyfNmTNHxjj/pN28eVOffPKJJMnPz0+SlJSUpMWLF6tTp07Kl8/5YSJ/DSt+fn7q2LGj5s6da582b948denS5T/7u7p06eLw+nPmzNHzzz/v1nMzZsyoW7duuZwfFxenmJgYh4eVAgMyqGq5EE2Zu1ZnL0QrMTFJS9f8ol/3Hde5S9bWlpZ8vXmPoq/dVIdHa1hdSppwKeqaEhOTlDOb4xdwzmyZdT4dv2/oi2v0xjn64hq9cY6+uEZvUgr091EGX281KplDB89d03vfn9DeyKvqUqOgimXP5PQ5AX7eeqRUTv0QccXpfE+x9ecwXbseq+YNHpAkXb5yTb4+3goKcDxEOVuWAF2+4rmnALhy4swlzVu+TUUL5tTiqS/q+dYPaeiUZfps9XY3nm29NBugZ8+erU6dOkm3D2WOjo7W5s2bHZapXbu2AgMDFRAQoDfffFNVqlRRw4YNJUkXLlxQVFSUQkNDHZ5TpUoVBQYGKjAwUB06dEjxe7t06aIlS5bo+vXr2rJli6Kjo9WiRYv/7O/q1KmTtm3bphMnTujEiRP6/vvv7X+nK8YYffvtt1q7dq0aNGjgcrnx48crODjY/ihYsOB/Vve/NWPUMzLGqOLjI1Sw7iv6cMlmtWpcRV6MtNp9+tVPaliztPLkDLa6FAAAALckb8rti4zRpqOXdDo6Vt8euqj9Z6/qwSLZUizv7+OlF2oV0tmYOH0Tdj71C05Fqzb8qhoPlFCObJmtLiVNSkoyqhBaQMNffEwVQguqc8sH1enxWvp4xfduPNt6aTJAh4eH6+eff7YHXB8fH7Vr106zZ892WG7x4sXauXOnli1bpuLFi2vevHny9fW962uvWLFCu3bt0iOPPOJwLnKyihUrqkSJElq6dKnmzJmjZ555Rj4+jocgjxs3zh7CAwMDdfKk++cu5MyZ035I+ty5c/Xoo48qR44cTpddtWqVAgMDlSFDBjVr1kzt2rVzeiGzZEOGDFF0dLT9ceqU9YeEhBTIqZXv9dGxjZO1c+UYrZ0zQPEJiSqcP7vVpaUJpyIva8sv4er0RC2rS0kzsmcJlLe3V4qLsly4HKNc2dPvFxF9cY3eOEdfXKM3ztEX1+hNStfjEpWYZHT2apzD9HNXbylrJsftcX8fL71Yu7DiEpI0e/spjz58++z5K9qx56haNKpqn5Yta6DiExJ19bpj9rgcdV3Zst79kHdPlDtHZpUMyeMwrWRIbv1+7v44MiFNBujZs2crISFB+fLlk4+Pj3x8fPTee+9p2bJlio6Oti9XsGBBlShRQq1atdK4cePUqlUrxcX98SHOmTOnsmTJovDwcIfXLlSokIoXL66gINdv1i5dumjGjBlaunSp08O3e/TooV27dtkfrg4Rv9vrz5s3Tx9//PFdDw+vX7++du3apcOHD+vmzZv6+OOPFRAQ4HJ5f39/Zc6c2eGRVgRk9FfuHMGKirmhTdsP6pE65a0uKU1YtOon5cgapMa1y1pdSprh5+ujSqUKavMvf352k5KStOWXQ6pWvoiltVmJvrhGb5yjL67RG+foi2v0JqVEY3Tyyk3lCvR3mJ4r0E9Xbvx5yqG/j5defLCwEpKMPvzppBI8OT1L+nrjb8qSOUC1qvx5FGxo0fzy8fHWjj1H7dNOnr6gcxejVK6k9UeMprbqFYrqyEnHoxCOnrqggnmyWlbTP5HmAnRCQoI++eQTvfXWWw4hdffu3cqXL58WLVrk9HlPPvmkfHx8NHPmTOn2hR3atm2rBQsWOL2C9908/fTT2rt3r8qVK6cyZcqkmJ8tWzYVL17c/vjrCPXfadq0qW7duqX4+Hg98sgjLpcLCAhQ8eLFVahQoX/8O9KK734K08YfD+jEmUva/PNBte49XcUL51KHFjWtLs1ySUlJWrR6u9o1ry4fH2+ry0lTej7dQJ+s/EGLVv2k8ONn9cqExbp+M04dH0vf7xv64hq9cY6+uEZvnKMvrqXH3vh5eyl/cAblD84gScqeyU/5gzMoa8Y/Rpg3Hr6oygUyq1ZIVuUI8FOdotlUNk+Qth3/YyTR38dLPR8sLH9vLy3aeVoZfLwV5O+jIH8feeLJfElJSfp6429qVv8B+Xj/uW0XGJBBjzasonfnfqPf9h5T+NHTGv/ucpULLaSyoWn/vsf/tR7t62nHvghNmbdOx05d0LK1v2r+yh/UpU0dq0tzS5pLZatWrdKVK1fUtWtXBQc7nhPapk0bzZ49W02bNk3xPJvNppdfflmjR4/W//73P2XKlEnjxo3Tpk2bVL16dY0dO1ZVq1ZVQECA9uzZox9//FHlypVzWkPWrFkVGRn5t4eD/1ve3t4KCwuz/7cni7l2U2/M+kqR56OUJXOAWtSrqCE9WsiXwKjNv4Tr97NXPPqL999q3aSKLkZd07j3V+v8pasqXzK/lk7rlW4Pk0tGX1yjN87RF9fojXP0xbX02JtCWTPopTp/jrC3qvDHYbfbT1zRwt/OaE/kVS3ZFanGJXOodYU8On/7tlXHLt2QJBXMkkEh2f64oNjIJiUdXnvM2kO6fCM+Vf+ee+3XPUd17mKUmjeskmLeS883l5fNpuGTFyo+PkHVK5XQKy88bkmdVqtcprA+nthNr7/3ld6as0aF8mbX631b68mm1awuzS024+rS1hZ57LHHlJSUpNWrV6eY9/PPP6tGjRravXu3KlasqJ07d6pSpUr2+Tdu3FCBAgU0ePBgvfrqq5Kk6OhoTZw4UStWrNDx48fl5eWlEiVK6IknnlDfvn2VLdsfFzmoV6+eKlWqpKlTpzqtq1KlSmrZsuVdz0GWpJCQEPXt21d9+/Z1mP7cc88pKipKK1eudPq8li1bKkuWLPbbdf3d8u6IiYlRcHCwTp27kqYO504LPO3eg/8lH+80d2AKAABwoc+K/VaXkGZ1r1rA6hLSpFL50t95138nJiZG+XNlVXR09N/mpjQXoPHfIUC7RoB2jQANAMD9gwDtGgHaOQJ0Sv8kQLOlDAAAAACAGwjQAAAAAAC4gQANAAAAAIAbCNAAAAAAALiBAA0AAAAAgBsI0AAAAAAAuIEADQAAAACAGwjQAAAAAAC4gQANAAAAAIAbCNAAAAAAALiBAA0AAAAAgBsI0AAAAAAAuIEADQAAAACAGwjQAAAAAAC4gQANAAAAAIAbCNAAAAAAALiBAA0AAAAAgBsI0AAAAAAAuIEADQAAAACAGwjQAAAAAAC4gQANAAAAAIAbCNAAAAAAALiBAA0AAAAAgBsI0AAAAAAAuIEADQAAAACAGwjQAAAAAAC4gQANAAAAAIAbCNAAAAAAALjBx+oCcO/FxScqNj7R6jLSlJMXb1hdQppVrmCw1SUAAAA3vVSrsNUlpFnVHhtsdQlp0qXt060uIc3xstncX/aeVgIAAAAAgIcgQAMAAAAA4AYCNAAAAAAAbiBAAwAAAADgBgI0AAAAAABuIEADAAAAAOAGAjQAAAAAAG4gQAMAAAAA4AYCNAAAAAAAbiBAAwAAAADgBgI0AAAAAABuIEADAAAAAOAGAjQAAAAAAG4gQAMAAAAA4AYCNAAAAAAAbiBAAwAAAADgBgI0AAAAAABuIEADAAAAAOAGAjQAAAAAAG4gQAMAAAAA4AYCNAAAAAAAbiBAAwAAAADgBgI0AAAAAABuIEADAAAAAOAGAjQAAAAAAG4gQAMAAAAA4AYCNAAAAAAAbiBAAwAAAADgBgI0AAAAAABu8LG6AHiOGQu+1Zote3T0xHll8PdVlXIhGtzjMRUrlEuSFBVzXW/PWaOtv4Tr9LkoZc8SoCZ1yqt/12bKHJjR6vLvqaf+N1lnL0SlmN6qaQ298sLjirsVrxnzvtGGbXsUn5Co6pVK6JUXHle2LIGW1JsWfLhks6Yv2KDzl2JUrkR+TRz4lKqUDbG6LMvRF9fojXP0xTV64xx9cS299+aDhev14aINDtMK58+ppbP6S5IuXrmqaXO+1vZdh3XjZpwK58+pLm3rq8GD5S2q+N7o0uYhdWlTRwXzZpMkHTx2VpNnf6NvfzggSXq21YN68pGqqhBaQJkDM6pw/YGKuXYzxes0ebCsBnZrprLF8ynuVoK+/+2wOg38MNX/ntQ2Z9lWzV2+TSfPXJYklSqaRwO7NlWj2mWtLs0tjEDjP7N911F1bvWQVs7qowVv91B8QqKe6T9LN27GSZLOXYzRuYsxGtbzca3/+FW9OeRpbd5+UK9O/Mzq0u+5Dyb11MrZg+2PKaOelyTVr11OkjR97tf6/teDGjuwg6a/1k0XL8do2MRPLa7aOsvX7dDwqSs0qFszbZo/SOVK5Febl2bowuWrVpdmKfriGr1xjr64Rm+coy+u0Zs/FC2UW998Msz++GhiD/u80W8v0YnTF/T2iGe16N2+ql+7rIZMWqjwo6ctrfm/duZ8lMa8+4Xqd56kBs9O1tZfD+nTN19QqaJ5JEkZM/hqw48HNGXeOpev8Vj9Spo1prMWfvWT6nScoKbd3tbStb+m4l9hnXy5smhkz8e18eOB2vDxQNWpWlKdBn6og8cirS7NLQToNOrUqVPq0qWL8uXLJz8/PxUuXFh9+vTRpUuXrC7NpU/e/J+ealZdJYvkVZni+fXW0Kd1+twV7Q3/XZIUWjSv3n/9eTV6sJwK58+hB6uU0MDuzbXhh/1KSEi0uvx7KmtwgLJnDbI/fvg1XPnzZFOlskV07XqsVm/Yod7PNVeV8sUUWiy/hvRuo33hJ7U//KTVpVti5sKN6tyytjo+XkuliubV20PaK1MGPy348kerS7MUfXGN3jhHX1yjN87RF9fozR+8vb2UI2uQ/ZElOMA+b8/BE2rXorbKliyoAnmyq2u7hgoKyKiwI54VoNds3af1PxzQsVMXdPTkeb3+3le6fiNOVcsVkSTNWrRJUz9er1/2Rjh9vre3l8b3b6OR01Zq7vJtOnryvMKPn9XKb3em8l9ijaZ1yqvxg2VVrFAuFS+US8NffEwBmfz16z7n/UprCNBp0LFjx1S1alUdPnxYixYt0pEjRzRr1ixt2LBBtWrV0uXLl60u0S1Xbx+qkiVzJpfLxFyPVWCmDPLx8U7FyqwVH5+gdVt2qXmDKrLZbAo/dloJCYmqWrGYfZnCBXIqd44s2nfolKW1WuFWfIJ2HTyletVD7dO8vLxUt3qoftl73NLarERfXKM3ztEX1+iNc/TFNXrzp1NnLqrZs2/oiW6TNPzNz3T2/J+nqFUoVVjrt+5R9NUbSkpK0rotuxV3K15Vyhe1tOZ7ycvLptaNqyhTRj+33wsVQwsqf+6sSjJGmxcMUtg3b+jzd15U6WJ573m9aU1iYpKWr9uhGzdvqWq5++N0CM6BToN69eolPz8/rVu3Thkz/nFucKFChVS5cmUVK1ZMw4YN03vvvZfieXFxcYqLi7P/HBMTk6p13ykpKUljpq9U1fJFFFrU+crgctQ1Tf94nTo8XivV67PS1p/DdO16rJo3eECSdPnKNfn6eCsowPE88GxZAnT5Svo6LEySLkVdU2JiknJmC3KYnjNbZh2OOGdZXVajL67RG+foi2v0xjn64hq9+UPZkoU0qu9TKpw/py5euaoPF32r7oNn6bN3+ykgk7/GD3paQyctVKOnx8rb20sZ/H01eegzKpgvh9Wl/+fKFMuntXP6K4Ofj67fjNMzAz9U+PGzbj03JP8f/RjcvbmGTVmuk5GX1LtjQ301q4+qthmrqJgb97h66x04ckZNu72l2FsJCsjor08mdlMpF5khrWEEOo25fPmy1q5dq549e9rDc7I8efKoY8eOWrx4sYwxKZ47fvx4BQcH2x8FCxZMxcodjZiyTIeOR+rdUZ2dzr96PVbPD/pQxUNyq9/zTVO9Piut2vCrajxQQjmyZba6FAAAALc9WDVUjR6qoBJF8qrWAyX1zqjndfX6TX27bY8kadan63T1eqxmvN5Nn0zprY4t62jIpIU6EuFesLyfHD5xTg93HK9Gz7+pOcu2aeboZxRaJI9bz/XyskmS3pq7Vl99t0u7D55Sr7ELZIxRy4aV73HlaUPxwrm0af5grZvdX8+3fki9xi7gHGj8O4cPH5YxRqVLl3Y6v3Tp0rpy5YouXLiQYt6QIUMUHR1tf5w6Zc3hvyOmLNOGHw5o0dReypsrS4r5127EqvOA9xWQyV8fvN5Fvuno8O2z569ox56jatGoqn1atqyBik9I1NXrjldnvBx1XdmyBjl5Fc+WPUugvL29UlyU5cLlGOXKnn53OtAX1+iNc/TFNXrjHH1xjd44FxSYUYXy5dSpyEv6PfKSlqz6USNeflLVKxZXySL51L1DI5UuXkCfr/a888TjExJ1/PeL2n3wlMbO+FL7Dp9Wj/b13Hru2YvRkqTwOwLjrfgERZy+pAJ5st2zmtMSP18fFS2YU5VKF9LIXo+rbIl8+mDxZqvLcgsBOo1yNsL8d/z9/ZU5c2aHR2oyxmjElGVau3WvFk3tqUL5sqdY5ur1WHXqP0t+vt6aPb6bMvj7pmqNVvt642/KkjlAtar8eQ5VaNH88vHx1o49R+3TTp6+oHMXo1SupHVHEVjFz9dHlUoV1OZfwu3TkpKStOWXQ6pWvoiltVmJvrhGb5yjL67RG+foi2v0xrkbN+N0+uwl5cgapNi4eOmO0dVk3l42Jf2L7dr7jZfNJj8/986O3X3wlGLj4lW8cG77NB9vLxXKm02nzt4f1zr6ryUlGcXFx1tdhls4BzqNKV68uGw2m8LCwtSqVasU88PCwpQ1a1blzJnTkvruZviUZfry2x36cFxXBWTy1/lLf5yDnTkwgzL4++nq9Vg903+Wbsbe0jvDO+nq9VhdvR4r3bFn15MlJSXp642/qVn9B+Tj/eeoe2BABj3asIrenfuNMgdmUkAmf039aJXKhRZS2dBCltZslZ5PN1DPMfNVuXQhPVA2RO8t+k7Xb8ap42M1rS7NUvTFNXrjHH1xjd44R19cozfS1NmrVad6aeXNlUUXLl/VBwvXy8vLS4/UraiggIwqmDe7xs9Yrj5dHlVwUCZt+mm/tu86oikjn7W69P/UyF6P69sf9uvU2SsKypRBTzatqoeqlFCbl2ZKknJlD1Ku7JlVtOAf5zqXLZ5PV2/E6vezVxQVc0NXr8dq7vJtGvxCc50+d0Wnzl7WS50aSZJWfvubpX9bahg740s1ql1GBXJn1bUbcVq69ld9/9sRff5OT6tLcwsBOo3Jnj27GjdurJkzZ6pfv34O50GfPXtWn376qTp37iybzXbX17HCgpXfS5LavTzDYfqbQzroqWbVte/Q79p54IQk6eEObzgss23xCPvN6D3Vr3uO6tzFKDVvWCXFvJeeby4vm03DJy9UfHyCqlcqoVdeeNySOtOC1k2q6GLUNY17f7XOX7qq8iXza+m0Xun6MDnRl7uiN87RF9fojXP0xTV6I52/FK3hby5SdMwNZQ0OUMUyIZr7Zk9lDQ6UJE0d/bzenfeNXnntY924GaeCebNrdN+n9GDVUlaX/p/KkTVQ743urNw5MivmWqz2HzmtNi/N1KafD0qSnm9dR4NfaG5f/usP+0mSeo6Zr0WrtkuSRr6zQgmJSZo1prMy+Ptqx/4TeqLnNEVfvenit3qOi1euqueY+Tp3MUaZAzOoTPF8+vydnqpf4/54n9jMvzlWGPfU4cOHVbt2bZUuXVqvv/66ihQpov3792vgwIGKi4vTTz/9pGzZ/j5sxsTEKDg4WEd+v6igVD6cO607edHzr274b5UrGGx1CQAAwE1Hzl6zuoQ0q9pjg60uIU26tH261SWkOTExMcqbM4uio6P/9jRYzz5m9j5VokQJ/frrrypatKjatm2rYsWK6YUXXlD9+vX1448/uhWeAQAAAAD/LQ7hTqMKFy6sefPmWV0GAAAAAOA2RqABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwAwEaAAAAAAA3EKABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwAwEaAAAAAAA3EKABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwAwEaAAAAAAA3EKABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwAwEaAAAAAAA3EKABAAAAAHADARoAAAAAADcQoAEAAAAAcIOP1QXg3vPx9pKvN/tK7pQ5o6/VJQAAAPy/Fc8TaHUJadb+dZOtLiFNioyKtbqENOfqVfd7QqoCAAAAAMANBGgAAAAAANxAgAYAAAAAwA0EaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIAGAAAAAMANBGgAAAAAANxAgAYAAAAAwA0EaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIAGAAAAAMANBGgAAAAAANxAgAYAAAAAwA0EaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIAGAAAAAMANBGgAAAAAANxAgAYAAAAAwA0EaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIAGAAAAAMANBGgAAAAAANxAgAYAAAAAwA0EaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIDGf+qnXUfU+dUPVPnxEcr3YB99s2WPy2UHTVqsfA/20YeLN6VqjVabveQ7VWz2qibN+tI+7eLlqxo6+TM1eHqsarQcpna9p+rbbXstrdNqHy7ZrAqPj1SeB/uq0XOTtWN/hNUlpQn0xTV64xx9cY3eOEdfXKM3KX3/2xG17zdLpZsNVdZqvbV6026rS7Lch59tVNkmAzX+vS/s0+Juxeu16ctVu80oVX18mPqM/VgXr1y1tE4rfLR4o8o9MlATbvcmOuaGxs1YqRZdJ6nKY0PUqNMbGjdzpa5ev2l1qS4RoNOgxx57TE2bNnU6b+vWrbLZbNqzx3UwtdKNm7dUtnh+jev/5F2X+2bzbu3Yf0J5cgSnWm1pwb7wU1r69U8qWSSvw/Rhb36miN8v6J1Rz2nZe6+o4YPlNXD8AoUdOW1ZrVZavm6Hhk9doUHdmmnT/EEqVyK/2rw0Qxcup78vmjvRF9fojXP0xTV64xx9cY3eOHfjZpzKlcyvya+2s7qUNGFv+Cl9vvonlSzquK03cdaX2vRTmN4e/ow+fvNFXbgUoz5jPrasTivYe3PHdvD5yzE6fylaA7q30Ir3++uNAe30/a/hGvn255bWejcE6DSoa9euWr9+vX7//fcU8+bOnauqVauqQoUKltT2dxrUKqNBLzyqZnUrulwm8kKUhk9ZphmjnpGPj3eq1melGzfjNGTyIo3q86QyB2Z0mLc77IQ6PF5b5UMLqUDe7HqhQ0MFBWRU2JGU74H0YObCjercsrY6Pl5LpYrm1dtD2itTBj8t+PJHq0uzFH1xjd44R19cozfO0RfX6I1zjR8sq+EvPqYW9V1v+6UX12/GadCEhRrT70kF37Gtd/X6TS1b84te/d9jqlm5uMqWLKDX+7fTrgMntDvshKU1p5YbN+M0eOJCje77pDIH/dmbEiF5NHXks6pXs4wK5cuhGpWK6+XnmmrT9gNKSEy0tGZXCNBpUIsWLZQzZ07NmzfPYfq1a9f0+eefq2vXrpbV9v+VlJSkl8cu0ItPN1DoX/bMebpxM1bq4WqlVLNyiRTzKpYurLVbdiv66g0lJSXpm027FHcrXlUrFLOkVivdik/QroOnVK96qH2al5eX6lYP1S97j1tam5Xoi2v0xjn64hq9cY6+uEZv4I7Xp6/Qw9VLq9YDJR2m7z90WgkJiar1wJ/bgEUL5VLeXFm060D6CNCvv+u8N85cvR6rwEwZ5OOdNgfaCNBpkI+Pjzp37qx58+bJGGOf/vnnnysxMVEdOnRw+ry4uDjFxMQ4PNKaGQs2yNvbS12fqmt1Kanqm027FHb0tF5+vpnT+ZOHdlJCQpIebjta1R4fqtenL9eUEc+qUL4cqV6r1S5FXVNiYpJyZgtymJ4zW2adv5T23tOphb64Rm+coy+u0Rvn6Itr9AZ/5+vvdinsyGn165pyW+/ilavy9fVOcQRi9qxB6eI86K83/dGbvl2cbwff6Ur0db2/8Fs92axGqtT2bxCg06guXbro6NGj2rx5s33a3Llz1aZNGwUHOz9vePz48QoODrY/ChYsmIoV/709B0/po883a+qwjrLZbFaXk2rOXojSpPe/1PhXO8jfz9fpMjM+Waur12/qg3HdtXDay3qmdR29On6BDh+PTPV6AQAA4L7I81Ga8N4XmjjY9bZeepXcmwmD/r43167HqueI2SpWKLd6PtMk1Wr8p3ysLgDOlSpVSrVr19acOXNUr149HTlyRFu3btXYsWNdPmfIkCF65ZVX7D/HxMSkqRC9ffdRXbxyTdXajLZPS0xM0ph3V+rDJZv187JRltZ3rxw4/LsuR11T+97v2KclJiVpx77j+uyrH/TFhwP12Vc/aNmsV1S8cB5JUmjRfPpt33F9tuoHjXipjYXVp77sWQLl7e2V4qIsFy7HKFf2zJbVZTX64hq9cY6+uEZvnKMvrtEb3M2Bw7/rUtQ1PdXTcVvv173HteiLH/TB+G6Kj09UzLWbDqPQl65cVY6sQS5e1TMcOPLHdnDbXn/ZDt57XIu+/EG/rRovb28vXb8Rq/8N+0gBGf31zqhn5ZuGr5NEgE7DunbtqpdeekkzZszQ3LlzVaxYMdWt6/rQZ39/f/n7+6dqjf9Em6bVVKea43kPT/ebpTZNq6pd87R7mMb/V41KxbX0vVccpo16e4lCCubS80/VU2zcLUmS119G5b28vGSSjNIbP18fVSpVUJt/Cdej9f64IElSUpK2/HJI3Z562OryLENfXKM3ztEX1+iNc/TFNXqDu6lZubhWvt/fYdqwtxaraMFc6tq2vvLkCpaPj7d+2nlYTer8cSHg46fOK/J8lCqVKWxR1amjZqXiWvGX3gx/a7GK3O6Nt7eXrl2P1f+GfShfXx9NH/N8mh/FJ0CnYW3btlWfPn20cOFCffLJJ3rxxRfT/KHP12/E6fjvF+w/nzpzSfsO/a4smTOpQJ5syhYc4LC8j4+3cmXLrOKFc1tQbeoIyJRBJULyOEzLmMFPWYIyqURIHsUnJKpQvux6bfpyvdLtUWUJCtDGH/fpp52HNX30c5bVbaWeTzdQzzHzVbl0IT1QNkTvLfpO12/GqeNjNa0uzVL0xTV64xx9cY3eOEdfXKM3zl27Eafjp/7c9jtx5pL2hv+uLMGZVDBPNktrSy0BmTKoRBHHbb1MGfwUnDmTfXqbptU06f2vFByUSYGZMmjczJWqVKawKpb27AD9d9vB167H6oWhH+pm3C2982oHXb8Rq+s3YiVJWYP/OPIjrSFAp2GBgYFq166dhgwZopiYGD33XNoPU7sPntSTL71r/3n09JWSpLbNqmvq8I4WVpZ2+fp4692xXfTO3G/08uh5unEzToXy5dBr/duqTvXSVpdnidZNquhi1DWNe3+1zl+6qvIl82vptF7p/jA5+uIavXGOvrhGb5yjL67RG+d2hZ3QYz2m2X8eNmW5JKnDozU0c/QzFlaWtgzq8bhsNpv6vvaJ4m8l6MGqoRr+Uiury7LcgSOntefgSUlS8+cnOsxb+/EQ5U+DO2Fs5s7LPCPN+fHHH1W7dm01b95cq1ev/kfPjYmJUXBwsCIiLytz5vS9cv+rCzFxVpeQZhXKkcnqEgAAAP7fzly5aXUJaRLpL6WrV2NUuXgeRUdH/21uYgQ6jatVq5bYxwEAAAAA1kt7B5UDAAAAAJAGEaABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwAwEaAAAAAAA3EKABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwAwEaAAAAAAA3EKABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwAwEaAAAAAAA3EKABAAAAAHADARoAAAAAADcQoAEAAAAAcAMBGgAAAAAANxCgAQAAAABwAwEaAAAAAAA3EKABAAAAAHADARoAAAAAADf4WF0A7h1jjCTp6tUYq0tJc65djbO6hDQrxi/B6hIAAAD+365evWl1CWnS7YiAO1y7elW6Iz/dDQHag129/UYoXzLE6lIAAAAAIE27evWqgoOD77qMzbgTs3FfSkpK0pkzZxQUFCSbzWZpLTExMSpYsKBOnTqlzJkzW1pLWkJfXKM3rtEb5+iLa/TGOfriGr1xjr64Rm+coy+upaXeGGN09epV5cuXT15edz/LmRFoD+bl5aUCBQpYXYaDzJkzW/4BSYvoi2v0xjV64xx9cY3eOEdfXKM3ztEX1+iNc/TFtbTSm78beU7GRcQAAAAAAHADARoAAAAAADcQoJEq/P39NWrUKPn7+1tdSppCX1yjN67RG+foi2v0xjn64hq9cY6+uEZvnKMvrt2vveEiYgAAAAAAuIERaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIAGAAAAAMANBGgAAAAAwH/i0qVLSkpKsrqMe4YADQBpQGxsrCR59BcO7h1uqAHgXmM9kxI9SSkqKkqhoaFauHCh1aXcMwRoAJbgS+dPkZGRqlixorZs2SIvLy9CtBO8XxydPn1aX375paZPny5JstlsVpeUJiUmJlpdQprFZwp/Jzo6Wr///rtOnTol3V7P8P30h5s3byouLk6nTp2y7wDHHzJlyqQ6deroyy+/VExMjNXl3BMEaPwr58+fV3h4uH7++WeH6en9C5mNNdeuXLmi06dPa9++fRIb/A6uXbumEiVKqH379vrxxx8J0bfdvHlT0dHREu8XB/v27dPjjz+uxYsXa//+/bp586bVJaUZkZGRWrNmjVasWKHLly/L29ub9bKky5cva8+ePZo1a5bmz5+vy5cvp/vv62Rnz57V2rVrtWbNGsXFxVldTpqxf/9+tWzZUrVr11aTJk00dOhQSZKXl1e6f++EhYWpU6dOqlq1qooVK6ZatWpp8ODBVpeVZvj5+alhw4bauHGjLl68KHng0XU2k94/BfjH9uzZo1atWsnf318HDx5U48aN9fzzz6t9+/bS7RCdHjd2Dxw4oClTpmj06NHKnz+/1eWkKfv27VOPHj105coVRUREqHv37po6darVZaUpBw8e1NixY7V+/Xp9+eWXqlWrlpKSkuTllT73c4aFhWngwIE6c+aMAgICNG7cONWqVUs+Pj5Wl2apsLAw1a5dWz179lS/fv2UI0cOq0tKM/bs2aP27dsrKSlJFy5cUL58+fTtt98qd+7cVpdmqbCwMPXp00eXL1/WgQMHZLPZFBwcrOHDh6tdu3bKnj271SVa5sCBA+rSpYvy58+v3Llza+bMmVaXlCbs3r1bDz30kDp16qQqVapo3bp1+v7779W7d28NGTLE6vIstXfvXtWpU0edOnVS5cqVlS1bNn388cdas2aNGjdurOXLl8vX19fqMi1zZwZ44IEHFBoaqkWLFlld1n/PAP/A2bNnTdGiRc2rr75q9u3bZ/bs2WMaN25satWqZcaOHWuSkpKMMcb+b3px9OhRU7BgQWOz2cwjjzxizp49a3VJaUZYWJjJnj27GTx4sFm7dq35/PPPjZeXl5k5c6bVpaUJ8fHx9v8+cOCA6dChg8mRI4f54YcfjDHGJCYmWlidNXbt2mWyZMliunbtat58801TtmxZU7ZsWfP7779bXZqlrl27Zlq0aGFeeOEFh+npbX3rzK5du0ymTJnMoEGDzLFjx8y8efOMj4+PefbZZ018fHy67dGuXbtMjhw5TL9+/cy2bdtMTEyM2blzp2nTpo3x8fExEyZMMNHR0VaXaYm9e/earFmzmhEjRjh8Z2/ZssXs3r3b0tqsdOjQIZMhQwYzevRo+7TLly+bmjVrmkaNGllam9XOnz9vKleubAYPHpxi+rvvvmsCAgJMu3btLKvPKrGxsQ4/J2/XTJo0yVSpUsUcOXLEGA/7riJA4x/5/vvvTbFixcyJEyfs086fP2969+5tqlevbt566y1L67PCjRs3zKuvvmratGljNm7caEJCQkz9+vUJ0caYqKgo88QTT5iXX37ZYXr37t3N008/bYyHrVDdderUKbN69Wr7zwkJCfb/Tg7R+fPnNzt27LCoQuvs2bPHBAUFmWHDhtmnLViwwNhsNjNr1iz7tPT4vrl06ZIpWbKkWbRokdP5f+1JeulRRESEyZAhg3nllVfs0xITE01ISIhp2rSpw7LppSfmjs/SiBEjjHGyM+7pp582gYGBZsOGDcaks96cO3fOPPDAA6ZPnz4O0ydOnGhsNpt57rnnzL59+yyrzyrx8fGmX79+JkeOHOajjz4y5o73xeDBg02dOnXMtWvX0tV75U6//fabKVeunNm7d6/9ezv5cxUVFWVef/11kylTJrNixQqLK009x44dMy1btjRz5swxN27ccJh36tQpkzVrVjNq1CjL6rtX0uexgfjXMmTIoNjYWJ04cUKSlJCQoJw5c2rMmDEqX768li1bpt27d0vp6HxoLy8vlS1bVu3atVP9+vX17bff6tixY+rQoYPOnTvn9DnppTfx8fG6du2aqlev7jC9ZMmSOnz4sOSB58X8nVu3bqlPnz4aO3asvvjiC0lyOE+zdOnSGjRokKpUqaLhw4d77AU4nElISNArr7yiGzduaODAgfbpO3fulCRdv35da9as0Y0bN9Lleb8nT57U4cOHVaxYMafzbTab4uLiNGXKFPvP6cEPP/ygAgUK6MyZM7px44YkadKkSTpx4oTOnj2rPn36qHfv3vrtt9906dIlq8tNFZcvX1bFihVVvXp1jR07Vrrj3NXkdc0nn3yiYsWK6bXXXpPS0ftFksLDwxUbG6tnn33W/h309ttva8SIERo1apQ2b96sKVOm2K/ZkR4YY+Tj46MePXqoTZs2+uijjzR16lTZbDadO3dO06dP1xNPPKGAgIB09V650+7du3XkyBGVK1dO3t7eMsbYT7MKDg7W008/LV9fXx05csTqUlNNbGysEhIS9MILL6hp06YaOnSorl69qri4OBUoUECvvvqqli1bpvDwcKtL/U8RoPGPFChQQBkyZNCCBQskST4+PkpMTFS2bNk0ceJERUREaPHixVI6+jL29/fXk08+qaeeekqSVKxYMa1fv94eos+fPy/dvsDYrl27pHTUmxw5cui9995Tx44dpTsushYYGKgMGTJIt8OjpHQTFP38/DRixAhlzZpVM2fO1MqVK6W/hOiKFSuqTZs22rdvX7rpi26vT2bMmKEiRYroiSeekCRNnDhR77//vrp06aJTp05pwIABql27tlq1aqX33ntPBw8etLrsVGGMUWBgoAIDA7VhwwYlJCQ4XW7Lli1av369rl69muo1prbkK9+2atVKw4cP1/Hjx9W9e3eNGTNGb731lmbOnKk5c+aocOHCunDhgpo3b64HHnhAY8aMsbr0ey5btmzq2bOnfv75Z82bN8++Y8Fms9nXNd7e3mrRooUuXrxov1hfevHjjz8qMjJSlStXtgegatWq6euvv9aoUaM0a9YsrV+/Xq+//rrLHeGe5PDhwxo3bpwuX76skiVLasCAASpXrpyWLVum0aNHq1q1anr++efVv39/KR0NAvxV8eLFJUnLli2TnGzLFSlSREWLFtXp06ctqc8KpUuX1ldffaUdO3aoVKlSWrJkicqVK6fhw4dr3759aty4saKjo+07FTxm0MTqIXCkbefPnzebN282q1atMlFRUcYYY9auXWt8fHzM66+/bl8u+XCeF154wbRp08ayelPLnX2JiYmxT7/zsKbw8HBTuHBhU79+fXPy5Enz4osvmoYNG9r76Klc9ebOw5Rnz55tqlWrZv950KBB5qWXXjK3bt1K9XpTW/LhXnv37jWNGjUyTZo0cTjcK7kH27dvN2XLljURERGW1ZpaDh8+bDZt2mT/+ciRI6ZQoUImd+7cJnv27Obbb791WP7TTz813bp1M4UKFTInT560oGLrPPHEEyZv3rzm119/dTp/0KBB5plnnjE3b95M9dpS0++//25atGhhf9/ExsaaOXPmmGrVqhmbzWZWrlyZ4jnbtm0z06ZN8/hDc+Pi4uz//dJLLxl/f38zd+7cFIdXGmNMnz59TM2aNR3Wz57qzr9x7ty5Jjg42OzatSvFoe3J3+PDhw83NWrUMFevXk31WlPTxYsXTeHChU2WLFnMoEGDzMWLF425vV7u1q2byZUrl6lZs6Z9+Tuv25HenDp1yuTKlcs8/vjjDt/Nye+hy5cvm9q1a5v58+dbWKV1YmNjzZUrV8yAAQPMgw8+aHx9fc2oUaNMjhw5TOXKlT3qs0SAhkv79+83Dz30kGnVqpXDxSSMMWbatGnGy8vLDBs2zCEktWrVyvTo0cOCalPP3fqSvBJN/gI+dOiQKVasmMmcObPx9/d3udHrKdzpjbm98VKuXDljjDHDhg0zXl5e5ueff071elPLnRtuiYmJKUL0I488YhYvXuzwnAEDBpiHHnrI43e4GGNMz549jc1mcwjKR44cMVWqVDGhoaHm8uXLxjg5h/POdY+n+f33383nn39uBg8ebGbMmGG++uorY26vU8qWLWuKFCliNm7caK5du2bM7Qs8Dh482OTKlcscOHDA4urvvQ0bNpiHH37YNGjQwGzbts2Y2xtvc+fONdWrVzdPPvmkvTd3BkpPlhxs/vq56NWrl/H39zfz5s1zCNFRUVGmbdu2Hnl+4l/t3LnTNGvWzP6e+O2334yvr68ZMmSIQyBMSkoySUlJJjEx0bz00kume/fuHr8z6uTJk6Zo0aKmcOHC5oknnjD9+/c3ly5dMub2BVK7d+9uatasaaZPn25/Tnq8uGWyZcuWGT8/P/PMM8+k2Bk3fPhwExISki52fP+dCxcumLlz55q6deuaTJkymaxZs5rz589bXdZ/hgANp/bu3WuyZ89uRo4c6bAi+O6778yZM2eMMcbMmTPH+Pv7m0ceecR06NDBdOnSxQQEBHj03v279SX5CsF//WLp0KGDyZ49u0f3xbjZm+QNlY8++sg0adLEjBs3zvj5+Xn0xbIOHDhgGjZsaN577z2za9euFPP37NljmjdvbmrVqmWGDRtmvvjiC/Pyyy+bXLlypZsrwSYmJprnn3/eZM6c2axfv94+/ciRIyYkJMTUrVvXREZG2qd7+tX+d+/ebYoXL26qV69uypQpYzJlymQCAgJMt27dTFxcnPn+++9N1apVjb+/v6latap5+OGHTe3atU1ISIj57bffrC4/1axdu9a0aNHCPPzwww4hes6cOaZGjRqmVatW9sDk6aNm4eHhpnfv3qZ27domNDTUdOnSxeFic8kh+s6R6OHDh5tixYrZr5DrqXbt2mUyZsxohgwZYswd640RI0YYLy8vM27cOIcrkcfGxppBgwaZnDlzmrCwMMvqTk3z5883lSpVMs8++6ypVauWGThwoD1EJ49EP/TQQ2bSpElWl2q5hIQEM2vWLOPj42P/rA0bNsw8/fTTJmvWrOlqHezMX7+Xz507Z7Zv326OHj1qWU33AgEaKURGRpoKFSqY3r17O0yfNGmSyZw5s+nQoYM5deqUMbc39Hr37m1atmxpunbtavbu3WtR1ffe3foSHBzs0JfkUcYJEyYYm81mdu7caVHVqeOf9MbcDtA2m81kz57d/PLLLxZUnDqSkpJMr169jJeXlxk/frzJkiWLGTdunFm7dq3DcgcOHDCDBg0yRYsWNZUqVTLNmjXz6M+SM0lJSaZTp04uQ3TDhg3N6dOnLa0xNRw6dMh+27dz584Zc/tWcMOHDze+vr72q9ffunXLvP7666Z79+6mffv2ZubMmeb48eMWV39vORv1WrVqlWnRooWpU6dOihD94IMPmoYNG5rr169bUG3q2b17t8mWLZt59tlnTb9+/cyIESNM/vz5TZ48eUz//v3ty/Xu3dv4+/ubJUuWmKFDh5qMGTN6/Mb+zp07TcaMGc3QoUMdpsfHx5srV66Y3r17G5vNZpo3b24mTpxoxowZY9q2bWty5Mjh0Tt2k0NO8o6lPXv2mLZt25pNmzaZCRMmmCpVqjiE6CNHjph27dqZxo0b248ISu9++ukn07p1a1O2bFnz4IMPmp49e6abHS4gQMOJL7/80lSqVMlhRfDmm2+abNmymZ49e5q6deuaZ555xhw7dsyYO87Z9PQ9/O72JfmczLi4OPPFF1+kixXqP+3Njz/+aAoWLJguQuKuXbtMSEiI2bZtm1m3bp1p3bq1qVGjhmnZsqXZunWruXLlijG3N2ji4uLMlStXnJ6r6EmuXbtmTp06ZdavX29++eUXhz3WySF63bp19mlHjx41wcHB5tFHH/XoczUTExNNz549TceOHVPMu3TpknnrrbeMl5dXujjk9q/27dtnmjdvboYNG+ZwJJQxxmzatMk0bdrUPPzww2bLli3G3A7RM2fONI0aNXLYeedpTp06ZYoXL+5w2zdze0dMu3btTPbs2c3YsWPt0/v162dsNpvJkCGDRwdEc/s9kyFDBvPGG284TJ82bZr9Gi5Xrlwx8+bNM2XKlDF58uQxFSpUMN27dzcHDx60qOp778iRI+aNN95IceRBhw4dTOPGjY0xxowdO9ZUr17dDBw40B6Yjx075vC5wx+j0cnfX+n5sPb0iACNFAYPHmxKlCjhMG3GjBlm69atxhhjPvzwQ1OnTh3TsmVLc+XKlRTn/Xqqf9qX9OSf9CZ5j7YnXUzClYSEBBMXF2d69OhhJk+ebMzt8xOjoqKMzWYzZcuWNZUqVTJff/11utiZYG4favrUU0+Z8uXLmwwZMhibzWZatmxpVq1aZV/GWYg+fvy4OXz4sEVVp56HHnrI5X17IyMjTfPmzU21atXMjRs3PH6dmyw+Pt40bNjQ2Gw2U7BgQZMxY0ZTs2ZN07p1a7N06VITHR1tvv76a9OpUydTt25d89NPPxlzeyemp19D4IsvvjAPP/ywOXv2rH0ndvK/R44cMQ0aNDAVKlQw4eHh9udMmDDB408PiYmJMY0bNzbZsmVzOAd+/PjxJjAw0OGiheb2Tr0rV66YW7duefRgwLlz50y+fPmMzWYzuXPnNq+//rr57LPP7POaNm1qNm/ebJKSksyQIUPsI6uMOjt35zo4vayP8QduY4UUcuXKpfPnzysiIsI+rWfPnnrooYckSd26dVNoaKhu3LihoKAg+y0gPP3WTP+kL4GBgRZWmvr+SW8yZ84s3b6Vlafz9vaWn5+fKlWqpMmTJ9s/MwMHDlSePHk0cuRI1a5dW23atNGrr77q8bce2rNnj+rVq6ccOXJo9OjR+vHHHzVv3jxt27ZNw4YN05IlSyRJ8+fPV8uWLdWhQwetXr1akhQSEmK/hYinMsbo0qVLunjxonT7vr13zsuTJ486d+6s3bt36/Llyx6/ztUd96adNWuWqlSpoipVqui1115T//79df36dY0ePVpFixbVokWLFBkZqUuXLqlbt27avXu3/Pz8FBwcbPWfcE/98ssvOn78uHLnzi0fHx/p9u3gjDEqVqyYRo0apX379jncg3XQoEGqUKGChVXfexkyZNCTTz6psmXL2m8xOX36dL355ptavny56tat63ArpoCAAAUFBcnX19feR09z6dIl5cqVSw0bNlS9evVUunRpXbx4UW+88YZatWqlNWvWKCEhQd99951sNpveeOMN1ahRQ4cOHVJ8fLzV5adJd66D08P6GH8iQMMu+d5sRYoUUXx8vD799FNFRUU5zEv+18/PT8WKFbPft9aT/Zu+eMx97v4G7xlHERERmjJlivr166e9e/fap//vf/9TzZo1NXPmTD399NP66quvtGbNGrVt21YzZszQqlWr9P777ysoKMjS+u+lvXv3qmbNmurWrZveffddtW7dWpUqVVLnzp21bt06RUdH680331RYWJgkae7cuapbt6569uxpv4etJzPGKCkpSaGhodq6datD4DHG2DfOrl69qpIlSypnzpwWVps6Tpw4oVWrVikmJkbFixfX/PnzdejQIW3dulXFihXTmjVrtH37dr311lsqUKCATpw4oQMHDujIkSMeH5yTZcuWTfHx8YqIiHD6vVO+fHnlypVLZ86ckdLB/XtPnTqlH374Qb6+vnr++efVrVs3XbhwQSVLltSIESO0atUqNW7cWLoj8Lzzzjs6c+aMvL29La7+3tm5c6dy5sypffv26fXXX1eJEiWUIUMG5cqVSxs3blSePHn07bffasOGDZowYYJ+//132Ww2vfnmm1q4cKFy5cpl9Z8ApC1WD4HDWhcvXjRhYWEpzvdp27atyZQpk5k+fbrDZedv3rxpBg0aZHLnzu3R5wjRF9fojXN79uwxJUuWNM8//7yZNGlSikPUJ0yYYDJmzGhKly5t9uzZY0w6OuTrxIkTJiAgwLRt29Y+LTEx0X67GHP7vtc2m81MnDjR4bmefuGwv74HNm/ebGw2m+nevbvDubvJ536/9NJLpnXr1h5/CPfJkyeNr6+vCQ0NNcuWLbPfmiksLMyUK1fONG7c2H6+c7Lr16+bn376yaPPeY6IiDBfffWV/cJov/76q/H29nY4z/nOz9WRI0dMxYoVzcaNGy2rObXcuHHDPPPMM6Zs2bJm8+bNxty+Rssnn3xiatSoYapXr26/JVXy52n06NHGZrN59Ck0u3btMkFBQWbgwIH2aREREeZ///ufqVq1qpkzZ44xxpjo6Gjz9ttv2w/p5pxewDUCdDq2d+9eU7lyZVOqVCljs9nMiBEj7BursbGxpkWLFsbPz8+0bt3aLF++3EyaNMk8++yzJnv27B595U764hq9ce7QoUMmR44cZvDgwS7vGXr16lVTunTpFFcqTw9Onz5tihYtalq0aOGwIZ8cAJMvRNi6dWvz+OOPm9jYWI8Oh+b2xn6yv96Wa+rUqcbHx8c89dRT5ssvvzTm9rnjw4cPN5kzZ/bojf1kR48eNYGBgcbX19dUqlTJLF261L5T6uDBg6ZcuXKmadOm5rvvvrO61FTz150Kyf14+eWXjc1mM2+//XaK5wwePNiUK1fO4TZwnuzrr782bdq0MQ8++KD9vZEcomvVqmVatGhhv0bJsGHDPP5iamFhYSZz5symT58+xtxexyTvPDh58qTp0aOHqVatmpkyZYrFlQL3FwJ0OrVr1y4TEBBgXn31VbNx40YzatQo4+3t7XDfSGOMefXVV03FihXt97vr3LmzOXDggGV132v0xTV641x8fLzp3r27eeqpp+xB0PxlZDF5g2Xy5Mmmfv36DvfJ9nTJoxjHjx83lSpVMo0aNTIbNmxIMd8YYxo0aGBatmxpSZ2pKSIiwrRp08asWbPGPu3O90tsbKyZP3++yZo1q/Hz8zMBAQGmfPnypmzZsh69IypZ8nvirbfeMq+88opp2rSpKVSokNMQ3aJFC4fbnnkyZzsVkpKSzLFjx8wzzzxjbDabadOmjZkxY4aZM2eO+d///meCgoLSxXvmzs/PunXrzBNPPOEyRD/11FOmf//+JmPGjObXX3+1sOp7a+fOnSZLlizGZrOZDz74wH4BzzuPUEgO0TVq1CBEA/8AATodOnDggPH19bVf6dXcHt3ImjWrad++fYrlo6OjTUREhImPjzexsbGpXG3qoS+u0RvX4uLiTOXKle23RfmrOzfsDh8+bGw2m/n4449TsULrJe9AOHbsmKlUqZJp3Lixw0h0QkKCOX36tGnRooWZPXu2MR5+ePuJEydMoUKFTLNmzRz68NdDJo8ePWrWrl1rZs2aZbZt25ZuRhGT/99/9tlnpkqVKubChQvmueeeSxGiw8PDTf78+c2TTz7p8bd+c7ZToUCBAmb58uXGGGPOnj1r3nvvPVO4cGGTM2dOU7ZsWdO6dWuPP1rh5MmTZu/evQ6nDRljzNq1a81jjz2WIkQvWLDAlCpVyvj7+3t0eP7tt99MpkyZzGuvvWYGDx5sQkJCzNSpU12G6F69epnQ0FAzc+ZMiysH7g+eealB3NX69euVkJCgSpUq2actWbJEUVFRunDhgiZOnKjSpUuraNGiKleunDJnzmy/crInX2SDvrhGb1y7du2aLl26pIwZM0q3L5p259WTbTabjDEaOHCg+vbtq6FDh6pq1aoWVpz6vL29lZCQoCJFimj58uVq1aqVxo0bJ2OMGjRoIG9vb7377ruKiIhQo0aNJA++omlSUpIKFSqkzZs3q1WrVho/frwkqX79+vLy8rK/f4wxKlq0qPz9/dWkSROry77nTpw4oX379qlChQoqWLCgJKldu3b65JNP9Prrr2vu3Llq1aqVBg4cKElq2rSpSpYsqU2bNsnLy8v++fNUyZ+H/Pnza+HChVqzZo0GDhyol19+WZL06KOPqkePHurQoYOuX7+ugIAA+fv7K0OGDBZXfu+cPHlSISEh8vHxUZ48edSxY0cVKVJEzz77rJo0aaLcuXNr9OjRGjlypEaPHq0GDRqobdu28vHxUfXq1VWkSBGr/4R7IjIyUo8//rh69Oih4cOHS5JiY2M1depU2Ww2derUSdmyZZNur48KFiyoAQMGyN/fX82aNbO4euA+YXWChzWGDBlifH19zZo1a8zEiRNNlixZzIwZM8ysWbPMoEGDTLFixUyZMmVM3bp1zZIlS6wuN9XQF9fojXM3btwwJUuWNI8//rh92l9HT3fs2GFatWrl0Rc3SvbX83nNHfelTb4Q1J0j0T/99JMZOXKkCQgIMLt27bKo6tT1dyPySUlJJi4uzn5qQPIFozzViRMnjM1mM5kzZzZlypQxc+bMsV9ob/Xq1aZ58+b20yMee+wxU6JECfPpp5+aa9euWVz5vRUREWFWrVplTp486TC9efPm9nNaW7ZsaUJCQszSpUs9/n3yV2fOnDGlSpUy+fPnNx06dDDNmjUzRYsWNUWLFjWNGzc2K1euNGPGjDGdO3c2devWTXHROU918uRJs3btWmPuWNcYY0zfvn1NSEiIeeedd5yORHvy/a+B/xoBOp25c2U6cOBAY7PZjI+Pj8M5ieb2oaZr1qwxTZo0MYcOHbKg0tRFX1yjN64lh8Q5c+YYm81mxo0bZ8wdh1sm/ztixAjTrFkz+8VrPNXRo0fNBx98YKKiooy53Z/kjbLjx4+bokWL2g+bPH78uKlatarJkiWLCQwM9OjDKZ1J7suxY8dMxYoVTaNGjRxCdO/evY2fn5/55ZdfLKwydZw5c8aEhoaaUqVKmWeeecZUrVrVNG/e3PTq1cvs3bvX5M2b10yaNMm+fIMGDUzFihXtO2Q80b/ZqbBw4UKPP2Um2Z2HH1esWNG0a9fOfP311+b69etm/vz5plu3bqZ06dKmUKFCJlOmTMZms5lGjRqZmzdvevTpIX915zrYuAjRAP45AnQ6EBYWZoYOHWoiIiJSnGP3xhtvGJvNZj7//HP7tPTy5UJfXKM3/8ypU6dMt27djM1mM8OGDTNHjx41xhizb98+M2DAAJMlSxaPPxfx7NmzJkeOHPaNs+QQbW6Hgbx585rnnnvOJCYm2t9Tx44dM40aNbIHA0/0T0fk161bZ3r37m0yZsyYLi7+lPxeOHHihKlQoYLp1KmTmTdvnvnuu+9MnTp1TKtWrUxQUJCpUqWKw3munn40BzsV/l7yzt2jR4+aihUrmvr165sffvjBPv/YsWNm165dpn///qZNmzZm3759FlZ7byWvX+78vk5ez/z1+gB9+/Y1JUqUMOPHjzeXL19O5UoBz0CA9nC3bt0y1apVMzabzZQoUcIMGDDALF682GGZV155xfj6+ppPP/00xfM9NRjRF9fojXN3C0Lm9ohq//79ja+vr8mZM6fJmTOnqVSpkilbtqzZuXOnJTWnpuPHj5usWbOaLFmymAYNGpgpU6bYQ3Tv3r1Nz549nV6Z/M4jHDzNvxmRz5QpkwkKCvLoW+v81Z1BqEKFCqZp06b2kffNmzebAQMG2C+8lx4OM2Wngvv+eiRHw4YNU1yVPTEx0aPfN+6sZ3bt2uUQrrt3724qVqxIgAb+JQJ0OjBp0iTz9ttvm3Xr1plRo0aZrFmzmo4dO5oZM2bYN2hHjRplMmbMaL8CbnpAX1yjN47utoFy9OhRU6xYMRMWFmZu3bpl9u7da6ZNm2bGjh1r1q1bZ79PtidLfk989NFHplWrVqZVq1amQoUK5p133jFxcXHmwoULHh2UnWFE/p/5axCqV6+e2bp1q9VlWYadCo7+6ZEc6eX+4O6uZ5L7dud6+OzZs5bUDHgCAnQ68N1335nMmTPbv3zPnDljRo8ebTJmzGhq1KhhPvjgAxMeHm7eeOMNkyNHDhMdHW11yamCvrhGb/7kzgZK586dTVJSkseOvrvy142yb7/91jRq1MgcPHjQDBo0yJQuXdpMmzbN/v5IT/1hRN65fxKE/npeeHrDToU//JMjOY4dO2aqVq1qqlevni4uGvZP1zPGye3yAPxzXm5cqBv3uXr16umFF17Q1KlTFRsbq7x58yosLEyFCxdWaGioFixYoHLlyqlkyZIKDw+3337I09EX1+jNn27evKnExERFRUXpiy++0Ny5cxUdHS1Jmjx5slq1aqV58+bJZrN57K2XnDl+/Lhmz56tiIgI+63KGjZsqKCgII0YMUITJkxQvXr19OGHH2ru3Lm6evWq/ZZens4Yo5CQEE2ePFn169dXcHCw5s6dq48//li3bt3SqFGjNG3aNIf3S3IPPfm2b8eOHdNHH32k6Oho+3shISFBPj4+ioiIUKVKlbRjxw777c6ioqI0ePBgbd261erS76nkz8Sdn43kvly9elVFihTRihUrFBUVpTFjxui7776zsNrUde7cOdWoUUPjxo3Txx9/bH/v+Pj46OTJk6pdu7YefvhhVa5cWUlJSSpSpIg+++wz+fv7q3DhwlaXf0/9m/WMJIfbLAL4l6xO8Egdn3/+ualVq5ZJTEw0Xbt2Nblz57ZfUOPgwYNmypQpHn2BDVfoi2v0hkOTXTl9+rQJDg42NpvN5M+f37zzzjtm3bp1xhhjdu/ebVq0aGGOHz9ujDGma9eu5oEHHjDjx4/3+AscMSLv2r85pP3IkSOmTp065sSJExZWfm8xunp3//ZIDk8+rJ31DGA9AnQ68vDDDxsvLy+TL1++dHO/VXfQF9fSa2/YQLm7M2fOmPr165uaNWuaJ554wrRs2dI89NBDpkOHDmbt2rWmVKlS5rXXXrMv//TTT5uHHnrIoy9Yc+zYMfPhhx/adxwka9WqlXnqqaeMMca8+OKLpnz58mbq1Kn2nQnp5b1DEEqJnQp39//ZgempnyvWM0DaQIBOB5JXnKtXrzYlS5Y0K1ascJieXtEX19Jzb9hAcc/x48dN8+bNTdu2bc3UqVPNwYMHTatWrUynTp2Mj4+PyZ8/vzlz5ox9+Tv/29MwIn93BCHn2KngHDswnWM9A6QdnAiRDiSf/1KlShUlJSVpx44dDtPTK/riWnrtzZkzZ1S5cmW98MILeuihhzRt2jStX79ekjR69GjdvHlTERERmjlzpqpXr65PPvlEM2bMsJ/f68kuXLigLVu2aNWqVYqOjlZISIjeffddXb9+XStXrtSRI0e0fPlyjRw5UqNGjdKECROUN29eJSYmSpLy5s1r9Z9wz9hsNj3wwAOqUaOGqlatqu+++05jx47V008/rbNnz+rIkSNasGCBJOmjjz5SqVKltHr1aiUkJFhd+j2VfE5vUlKSJCkkJERXr17V+PHj1axZM82aNUvvv/++/Pz85O3t7fT8eE/9XHGevHNcW8E11jNAGmJ1gkfqmj9/vgkICDDbt2+3upQ0hb64lp56w6HJzu3fv9889NBDplWrVmb06NEO844cOWJatGhh6tSpYxYtWmRZjVZjRN4RR3I4x+iqa4yw/j3WM0DaYDPpYbcd7E6fPq1OnTpp/vz5KlCggNXlpBn0xbX01puIiAj16tVLgYGBql27tpo2baohQ4YoICBAn332mXLnzq1ffvnFPqIaGRnp0aOr+/btU7169dSrVy916dLFfmXb7777TsWKFVOhQoV09OhR9e3bV9euXVP37t319NNPW132PXfhwgWFhYUpJiZGderUUXBwsI4fP66XXnpJ169f14ABA/Too4/q8OHDWrx4sUJCQtSpUyclJiZ69Aiibh/JUaZMGcXExChfvnx69dVXVbp0aTVu3Fh79uzRsGHDNH36dIWEhKhbt27auXOnnnrqKfXq1UtBQUFWl3/PHD9+XBs2bFCjRo0UEhJin966dWv5+PhoyZIl6tmzp7Zt26auXbuqS5cuCgoKkjHGY0fi7xQZGamOHTvq5s2byp07t2w2my5evKiCBQvqueeeU58+fdSxY0cNHz5cktSxY0edPHlSX375pbJmzWp1+fcE6xkgbSJAp0OxsbHKkCGD1WWkOfTFNU/uDRsorp09e1aPPPKIHn74YU2fPt0+ffLkyXrjjTfUrFkzjR8/XiEhITp69Kj69++vkydPasiQIXrqqacsrf1eOnDggP73v/8pZ86cqlixokaNGmWfl7wzITo6Wj179lT79u0trdUKBKGU2KngHnZg/on1DJB2EaABpFtsoNzdV199pZEjR2rRokUqVaqUJOmtt97SuHHj1L59e+3fv1+FChXS2LFjFRISoiNHjtjPU/TUe7AyIu8egpAjdio4xw5M51jPAGmc1ceQA4AV9u7da7Jnz25GjhxpIiIi7NM3btxov0VM8vm99erVM59++qmF1Vpj8ODBpkSJEg7TZsyYYbZu3WqMMebDDz80derUMS1btjQXL140xsOvEBwZGWkqVKhgevfu7TB90qRJJjg42LRv395+juaRI0fME088YSpXrmyWLFliUcWp5/z582bz5s3mq6++sl9J+tixY+bRRx819erVM6tWrTLGGHPo0CHz2muvmfnz5xtzx7nA6QHnrzri2grOsZ4B0j4CNIB0hw0U97z99tsmODg4xYWg7tStWzfTpEkTjw7Oyb788ktTqVIlExYWZp/25ptvmmzZspmePXuaunXrmmeeecber8OHD5v27ds77KDxRAQh59ip4Bo7MF1jPQOkfdzGCkC688svv8jLy0u9evWyT3vrrbc0YcIEdezYUZGRkRo5cqQiIiJUrFgxvfnmmwoNDVX16tUtrTu1JN92qEiRIoqPj9enn36qqKgoh3nJ//r5+alYsWL2nz3ZDz/8oOvXr9sPZ5ekjBkz6osvvtCMGTPUqVMnRUREqF+/frp06ZKKFy+u+fPne+zh7Lp9qOnDDz+sBg0aaMqUKfbTIL777judPHlSxYoV09SpUxUcHKz3339fCxcutLrkVHHgwAG1bt1aU6dO1Y4dOxQcHCzd/ky98847CgwM1MSJE/XZZ5+pRIkSGj58uDp16iR5+G2qdPvaCh07dlSHDh00ZswY++dj8uTJatWqlQYNGmRf9ya/d9588019/vnnVpeeKljPAPcBqxM8AKQ2Dk1O6eLFiyYsLMwcPHjQYXrbtm1NpkyZzPTp08358+ft02/evGkGDRpkcufOneI5nooReUccyeEco6t3xwjr3bGeAdI+RqABpDu5cuXS+fPnFRERYZ/Ws2dPPfTQQ5Kkbt26KTQ0VDdu3LCPHPn4+FhW7722b98+NW7cWK1atVLp0qU1cuRInTlzRpL0ySefqEGDBurfv7969OihFStWaPLkyerRo4c++ugjffPNNwoNDbX6T7inGJF3jiM5UmJ09e8xwuoc6xng/kGABpBusIGS0u7du1WzZk01btxYM2fO1MiRIzVu3Dht2bJFkuTv76+vvvpKffv21dGjR9W2bVvNnj1bxhht3bpVlStXtvpPuCcuXbqkgwcPKjw8XF5ef3xVtmzZUi1atNC4ceO0YMECXbhwwT7v1q1bGjx4sJYtW6Y+ffrIz8/P4r/g3iMIpcROhb/HDsw/sZ4B7lNWD4EDwL3EocmuHThwwPj6+poRI0bYp4WHh5usWbOa9u3bp1g+OjraREREmPj4eBMbG5vK1aaevXv3msqVK5tSpUoZm81mRowYYU6fPm2MMSY2Nta0aNHC+Pn5mdatW5vly5ebSZMmmWeffdZkz57d/Pbbb1aXn2o41DQlTg9xLTEx0RhjzIoVK0ymTJnM66+/bq5cueIwL/nfnj17mhdffNHExcVZWPG9xXoGuH955i49ALh9aHLnzp118+ZNhYeHa/jw4erRo4fy5cunTz75RE8++aT69++v7777Tp06ddKRI0e0f/9+rVq1SuvXr/f4Q5PXr1+vhIQEVapUyT5tyZIlioqK0oULFzRx4kSVLl1aRYsWVbly5ZQ5c2ZlzpxZ8uALHe3evVsPPvigevXqpaZNm2rz5s16/fXXVaZMGbVv394+Ij9o0CCtXbtWbdu2VbFixVSjRg1t3bpVpUuXtvpPuOeSkpLk5eXlcCRHr169lCVLFvu85H/Ty5Ecye4cXQ0JCZFuj64m69atm7Zv366TJ096/Oiqbo+wXrhwQTabzb4+vXOENTg4WO3atVPOnDml2yOso0eP1rJly7R582aPHWFlPQPc56xO8ABwL+zatcsEBASYV1991WzcuNGMGjXKeHt7p7iVzquvvmoqVqxofHx8TGhoqOncubM5cOCAZXWntiFDhhhfX1+zZs0aM3HiRJMlSxYzY8YMM2vWLDNo0CBTrFgxU6ZMGVO3bl2Pv/gTI/KucSTH3TG6mhIjrM6xngHufwRoAB6HDZS/d+e9ZgcOHGhsNpvx8fExGzZscFju8OHDZs2aNaZJkybm0KFDFlSaet555x1js9nMsmXL7NNee+01Y7PZTMOGDc2ECRPMF198Yfbu3ZviuUlJSalcbeohCDnHTgXX2IHpGusZ4P5HgAbgcdhAcS4sLMwMHTrURERE2EfDkr3xxhvGZrOZzz//3D7Nk3vhCiPyjghCzrFTwTV2YP491jPA/Y0ADcAjsYHi6NatW6ZatWrGZrOZEiVKmAEDBpjFixc7LPPKK68YX19fp/el9fQwzYh8SgQh59ipcHfswHSN9QzgGQjQADwKGyiuTZo0ybz99ttm3bp1ZtSoUSZr1qymY8eOZsaMGfYN11GjRpmMGTOa2bNnW13uPceI/N0RhFJip4J72IH5J9YzgOchQAO477GB4p7vvvvOZM6c2fzyyy/GGGPOnDljRo8ebTJmzGhq1KhhPvjgAxMeHm7eeOMNkyNHDhMdHW11yfcMI/LuIQg5YqfC3bED0xHrGcAzEaAB3NfYQPlnBgwYYDp27Ghu3rxpjDGmXbt2plSpUqZz587m4YcfNr6+vubzzz83ly5dsrrUe44RedcIQq6xU8EROzDvjvUM4HkI0ADue2yguO/zzz83tWrVMomJiaZr164md+7cZt++fcYYYw4ePGimTJli/9nTMSLviCB0d+xUSIkdmH+P9QzgeQjQAO57bKD8Mw8//LDx8vIy+fLlM7t27bK6HEsxIv8HgpBz7FT4e+zA/HusZwDPQoAG4BHYQPl7yRuzq1evNiVLljQrVqxwmJ4eMSL/J4KQI3YquIcdmH+P9QzgWWzGGCMAuM8tXbpUb7/9trZt26YXXnhBq1at0oYNG1S2bFmFh4frm2++UePGjVW2bFmrS7XcuXPn9NBDD6l9+/Z67bXXrC7HcnXr1tW2bduUJ08eff3116pYsaLVJVli06ZNeuKJJ7RhwwZVrVpVkZGR+uCDDzRx4kRVqFBBXbt2Vd26dbV06VJNmTJFR48eVebMma0u+56aPHmyfHx8VK5cOX3//feaNm2amjdvrtq1a+vFF1+UzWbT6NGjNWnSJL377rvq0qWL1SVbYuDAgYqMjNRHH32kDBkyqH379tq9e7eqV6+uiIgI/fjjj1q4cKEaNGigbNmyWV2uJVjPAB7E6gQPAP8VDk123/z5801AQIDZvn271aVYhhH5lDiSwxGjq+5hhNU11jOA5/GyOsADwP9X8oE0gwYNUvHixTVjxgxVrFhRHGDjWv369VWtWjXly5fP6lIsY7PZJElVqlRRUlKSduzY4TA9PapRo4aOHTsmPz8/devWTZs2bdLSpUv18ccf64MPPtCkSZNUunTpdDOKWK9ePb3wwguaOnWqYmNjlTdvXoWFhalw4cIKDQ3VggULVK5cOZUsWVLh4eEePyLvypNPPilfX1/5+vrqm2++0dq1a+1H+4SGhqpv377p9ugf1jOA5yFAA7jvsYHyz+XPn1/ffPONChQoYHUplsudO7dGjRqlKVOm6Oeff7a6HEsRhFJip8LdsQPTPaxnAM9BgAbgMdhA+WcyZMhgdQlpBiPyBCFX2Klwd+zAdB/rGcAzEKABeBQ2UPBvMCJPEHKGnQruYwfm32M9A3gGAjQAj8IGCv4tRuT/QBD6EzsV/hl2YP491jPA/Y/bWAEAAAenT59Wp06dNH/+fHZG3bZgwQL16NFDGzduVPXq1a0uJ82KjY0lJALwaARoAACQAkHIETsVAAAiQAMAALiHnQoAAAI0AAAAAABu4CJiAAAAAAC4gQANAAAAAIAbCNAAAAAAALiBAA0AAAAAgBsI0AAAAAAAuIEADQAAAACAGwjQAADAwXPPPaeWLVvaf65Xr5769u2b6nVs2rRJNptNUVFRLpex2WxauXKl2685evRoVapU6f9VV0REhGw2m3bt2vX/eh0AwP2HAA0AwH3gueeek81mk81mk5+fn4oXL66xY8cqISHhnv/u5cuX67XXXnNrWXdCLwAA9ysfqwsAAADuadq0qebOnau4uDh9/fXX6tWrl3x9fTVkyJAUy966dUt+fn7/ye/Nli3bf/I6AADc7xiBBgDgPuHv7688efKocOHCevHFF9WoUSN9+eWX0h2HXb/xxhvKly+fQkNDJUmnTp1S27ZtlSVLFmXLlk1PPPGEIiIi7K+ZmJioV155RVmyZFH27Nn16quvyhjj8Hv/egh3XFycBg0apIIFC8rf31/FixfX7NmzFRERofr160uSsmbNKpvNpueee06SlJSUpPHjx6tIkSLKmDGjKlasqKVLlzr8nq+//lolS5ZUxowZVb9+fYc63TVo0CCVLFlSmTJlUtGiRTVixAjFx8enWO79999XwYIFlSlTJrVt21bR0dEO8z/66COVLl1aGTJkUKlSpTRz5sx/XAsAwPMQoAEAuE9lzJhRt27dsv+8YcMGhYeHa/369Vq1apXi4+P1yCOPKCgoSFu3btX333+vwMBANW3a1P68t956S/PmzdOcOXO0bds2Xb58WStWrLjr7+3cubMWLVqkadOmKSwsTO+//74CAwNVsGBBLVu2TJIUHh6uyMhIvfPOO5Kk8ePH65NPPtGsWbO0f/9+9evXT506ddLmzZul20G/devWeuyxx7Rr1y5169ZNgwcP/sc9CQoK0rx583TgwAG98847+vDDDzVlyhSHZY4cOaIlS5boq6++0po1a7Rz50717NnTPv/TTz/VyJEj9cYbbygsLEzjxo3TiBEj9PHHH//jegAAHsYAAIA079lnnzVPPPGEMcaYpKQks379euPv728GDBhgn587d24TFxdnf878+fNNaGioSUpKsk+Li4szGTNmNGvXrjXGGJM3b14zadIk+/z4+HhToEAB++8yxpi6deuaPn36GGOMCQ8PN5LM+vXrndb53XffGUnmypUr9mmxsbEmU6ZM5ocffnBYtmvXrqZDhw7GGGOGDBliypQp4zB/0KBBKV7rrySZFStWuJw/efJkU6VKFfvPo0aNMt7e3ub333+3T/vmm2+Ml5eXiYyMNMYYU6xYMbNw4UKH13nttddMrVq1jDHGHD9+3EgyO3fudPl7AQCeiXOgAQC4T6xatUqBgYGKj49XUlKSnn76aY0ePdo+v3z58g7nPe/evVtHjhxRUFCQw+vExsbq6NGjio6OVmRkpGrUqGGf5+Pjo6pVq6Y4jDvZrl275O3trbp167pd95EjR3Tjxg01btzYYfqtW7dUuXJlSVJYWJhDHZJUq1Ytt39HssWLF2vatGk6evSorl27poSEBGXOnNlhmUKFCil//vwOvycpKUnh4eEKCgrS0aNH1bVrV3Xv3t2+TEJCgoKDg/9xPQAAz0KABgDgPlG/fn2999578vPzU758+eTj4/g1HhAQ4PDztWvXVKVKFX366acpXitnzpz/qoaMGTP+4+dcu3ZNkrR69WqH4Krb53X/V3788Ud17NhRY8aM0SOPPKLg4GB99tlneuutt/5xrR9++GGKQO/t7f2f1QoAuD8RoAEAuE8EBASoePHibi//wAMPaPHixcqVK1eKUdhkefPm1fbt2/Xwww9Lt0dad+zYoQceeMDp8uXLl1dSUpI2b96sRo0apZifPAKemJhon1amTBn5+/vr5MmTLkeuS5cubb8gWrKffvrJ7b9Vkn744QcVLlxYw4YNs087ceJEiuVOnjypM2fOKF++fPbf4+XlpdDQUOXOnVv58uXTsWPH1LFjx3/0+wEAno+LiAEA4KE6duyoHDly6IknntDWrVt1/Phxbdq0SS+//LJ+//13SVKfPn00YcIErVy5UgcPHlTPnj3veg/nkJAQPfvss+rSpYtWrlxpf80lS5ZIkgoXLiybzaZVq1bpwoULunbtmoKCgjRgwAD169dPH3/8sY4eParffvtN06dPt1+Yq0ePHjp8+LAGDhyo8PBwLVy4UPPmzftHf2+JEiV08uRJffbZZzp69KimTZvm9IJoGTJk0LPPPqvdu3dr69atevnll9W2bVvlyZNHkjRmzBiNHz9e06ZN06FDh7R3717NnTtXb7/99j+qBwDgeQjQAAB4qEyZMmnLli0qVKiQWrdurdKlS6tr166KjY21j0j3799fzzzzjJ599lnVqlVLQUFBatWq1V1f97333tOTTz6pnj17qlSpUurevbuuX78uScqfP7/GjBmjwYMHK3fu3Ordu7ck6bXXXtOIESM0fvx4lS5dWk2bNtXq1atVpEgR6fZ5ycuWLdPKlStVsWJFzZo1S+PGjftHf+/jjz+ufv36qXfv3qpUqZJ++OEHjRgxIsVyxYsXV+vWrdW8eXM1adJEFSpUcLhNVbdu3fTRRx9p7ty5Kl++vOrWrat58+bZawUApF824+oqIQAAAAAAwI4RaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIAGAAAAAMANBGgAAAAAANxAgAYAAAAAwA0EaAAAAAAA3ECABgAAAADADQRoAAAAAADcQIAGAAAAAMANBGgAAAAAANzwf8mdBGu7VuRSAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 1000x1000 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 1. Prediksi\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"pred_ner_prob, pred_srl_prob = model.predict(X_te, verbose=0)\n",
|
||
"\n",
|
||
"pred_ner = pred_ner_prob.argmax(-1)\n",
|
||
"pred_srl = pred_srl_prob.argmax(-1)\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 2. Siapkan masker PAD\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"pad_id = tag2idx_ner[\"<PAD>\"]\n",
|
||
"\n",
|
||
"mask_ner = ner_te != pad_id\n",
|
||
"mask_srl = srl_te != pad_id\n",
|
||
"\n",
|
||
"true_ner_flat = ner_te[mask_ner]\n",
|
||
"pred_ner_flat = pred_ner[mask_ner]\n",
|
||
"\n",
|
||
"true_srl_flat = srl_te[mask_srl]\n",
|
||
"pred_srl_flat = pred_srl[mask_srl]\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 3. Hitung confusion matrix TANPA PAD\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# Buang ID PAD dari label list\n",
|
||
"labels_ner_no_pad = [i for i in range(len(tag2idx_ner)) if i != pad_id]\n",
|
||
"labels_srl_no_pad = [i for i in range(len(tag2idx_srl)) if i != pad_id]\n",
|
||
"\n",
|
||
"cm_ner = confusion_matrix(true_ner_flat, pred_ner_flat, labels=labels_ner_no_pad)\n",
|
||
"\n",
|
||
"cm_srl = confusion_matrix(true_srl_flat, pred_srl_flat, labels=labels_srl_no_pad)\n",
|
||
"\n",
|
||
"# Siapkan label display TANPA PAD\n",
|
||
"display_labels_ner = [idx2tag_ner[i] for i in labels_ner_no_pad]\n",
|
||
"display_labels_srl = [idx2tag_srl[i] for i in labels_srl_no_pad]\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 4. Plot NER CM (tanpa PAD)\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"fig, ax = plt.subplots(figsize=(10, 10))\n",
|
||
"disp_ner = ConfusionMatrixDisplay(\n",
|
||
" confusion_matrix=cm_ner, display_labels=display_labels_ner\n",
|
||
")\n",
|
||
"disp_ner.plot(\n",
|
||
" include_values=True, # Tampilkan angka\n",
|
||
" values_format=\"d\", # Format integer\n",
|
||
" cmap=plt.cm.Blues, # Biru-putih\n",
|
||
" ax=ax,\n",
|
||
" colorbar=False,\n",
|
||
")\n",
|
||
"ax.set_title(\"NER Confusion Matrix\", fontsize=18)\n",
|
||
"plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 5. Plot SRL CM (tanpa PAD)\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"fig, ax = plt.subplots(figsize=(10, 10))\n",
|
||
"disp_srl = ConfusionMatrixDisplay(\n",
|
||
" confusion_matrix=cm_srl, display_labels=display_labels_srl\n",
|
||
")\n",
|
||
"disp_srl.plot(\n",
|
||
" include_values=True, values_format=\"d\", cmap=plt.cm.Blues, ax=ax, colorbar=False\n",
|
||
")\n",
|
||
"ax.set_title(\"SRL Confusion Matrix\", fontsize=18)\n",
|
||
"plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "a49f1dfe",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"NER TAG accuracy : 70.26%\n",
|
||
"SRL TAG accuracy : 53.52%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.metrics import accuracy_score, classification_report\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"# 3b. Akurasi token‑level (tanpa PAD)\n",
|
||
"# ------------------------------------------------------------------\n",
|
||
"acc_ner = accuracy_score(true_ner_flat, pred_ner_flat)\n",
|
||
"acc_srl = accuracy_score(true_srl_flat, pred_srl_flat)\n",
|
||
"\n",
|
||
"print(f\"NER TAG accuracy : {acc_ner:.2%}\")\n",
|
||
"print(f\"SRL TAG accuracy : {acc_srl:.2%}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "9adad755",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"[NER] Classification report:\n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" DATE 0.63 0.84 0.72 184\n",
|
||
" ETH 0.59 0.51 0.54 87\n",
|
||
" EVENT 0.00 0.00 0.00 14\n",
|
||
" LOC 0.00 0.00 0.00 112\n",
|
||
" MAT 0.00 0.00 0.00 32\n",
|
||
" MISC 0.00 0.00 0.00 3\n",
|
||
" O 0.72 0.92 0.81 877\n",
|
||
" ORG 0.00 0.00 0.00 0\n",
|
||
" PER 0.80 0.38 0.52 102\n",
|
||
" QUANT 0.00 0.00 0.00 13\n",
|
||
" TIME 0.00 0.00 0.00 55\n",
|
||
" UNIT 0.00 0.00 0.00 14\n",
|
||
"\n",
|
||
" accuracy 0.70 1493\n",
|
||
" macro avg 0.23 0.22 0.22 1493\n",
|
||
"weighted avg 0.59 0.70 0.63 1493\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# (Opsional) tampilkan ringkasan metrik per‑label\n",
|
||
"print(\"\\n[NER] Classification report:\")\n",
|
||
"print(\n",
|
||
" classification_report(\n",
|
||
" true_ner_flat,\n",
|
||
" pred_ner_flat,\n",
|
||
" labels=labels_ner_no_pad,\n",
|
||
" target_names=display_labels_ner,\n",
|
||
" digits=2,\n",
|
||
" )\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "7cd28380",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"[SRL] Classification report:\n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" ARG0 0.68 0.75 0.71 195\n",
|
||
" ARG1 0.43 0.51 0.47 257\n",
|
||
" ARG2 0.00 0.00 0.00 55\n",
|
||
" ARGM-CAU 0.00 0.00 0.00 0\n",
|
||
" ARGM-DIR 0.00 0.00 0.00 11\n",
|
||
" ARGM-LOC 0.00 0.00 0.00 98\n",
|
||
" ARGM-MNR 0.00 0.00 0.00 7\n",
|
||
" ARGM-MOD 0.00 0.00 0.00 2\n",
|
||
" ARGM-NEG 0.00 0.00 0.00 0\n",
|
||
" ARGM-TMP 0.65 0.64 0.65 254\n",
|
||
" O 0.48 0.67 0.56 469\n",
|
||
" V 0.63 0.29 0.40 145\n",
|
||
"\n",
|
||
" accuracy 0.54 1493\n",
|
||
" macro avg 0.24 0.24 0.23 1493\n",
|
||
"weighted avg 0.49 0.54 0.50 1493\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
|
||
"/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
|
||
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"\\n[SRL] Classification report:\")\n",
|
||
"print(\n",
|
||
" classification_report(\n",
|
||
" true_srl_flat,\n",
|
||
" pred_srl_flat,\n",
|
||
" labels=labels_srl_no_pad,\n",
|
||
" target_names=display_labels_srl,\n",
|
||
" digits=2,\n",
|
||
" )\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "333745fd",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# def plot_training_history(history):\n",
|
||
"# epochs = range(1, len(history['loss']) + 1)\n",
|
||
"\n",
|
||
"# plt.figure(figsize=(14, 6))\n",
|
||
"\n",
|
||
"# # Plot Loss\n",
|
||
"# plt.subplot(1, 2, 1)\n",
|
||
"# plt.plot(epochs, history['loss'], label='Training Loss')\n",
|
||
"# plt.plot(epochs, history['val_loss'], label='Validation Loss')\n",
|
||
"# plt.title('Loss During Training')\n",
|
||
"# plt.xlabel('Epochs')\n",
|
||
"# plt.ylabel('Loss')\n",
|
||
"# plt.legend()\n",
|
||
"\n",
|
||
"# # Plot Accuracy\n",
|
||
"# plt.subplot(1, 2, 2)\n",
|
||
"# plt.plot(epochs, history['ner_output_accuracy'], label='NER Train Acc')\n",
|
||
"# plt.plot(epochs, history['val_ner_output_accuracy'], label='NER Val Acc')\n",
|
||
"# plt.plot(epochs, history['srl_output_accuracy'], label='SRL Train Acc')\n",
|
||
"# plt.plot(epochs, history['val_srl_output_accuracy'], label='SRL Val Acc')\n",
|
||
"# plt.title('Accuracy During Training')\n",
|
||
"# plt.xlabel('Epochs')\n",
|
||
"# plt.ylabel('Accuracy')\n",
|
||
"# plt.legend()\n",
|
||
"\n",
|
||
"# plt.tight_layout()\n",
|
||
"# plt.show()\n",
|
||
"\n",
|
||
"# plot_training_history(history.history)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "df36e200",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# def token_level_accuracy(y_true, y_pred):\n",
|
||
"# total, correct = 0, 0\n",
|
||
"# for true_seq, pred_seq in zip(y_true, y_pred):\n",
|
||
"# for t, p in zip(true_seq, pred_seq):\n",
|
||
"# if t.sum() == 0:\n",
|
||
"# continue\n",
|
||
"# total += 1\n",
|
||
"# if t.argmax() == p.argmax():\n",
|
||
"# correct += 1\n",
|
||
"# return correct / total\n",
|
||
"\n",
|
||
"# def decode_predictions(pred, true, idx2tag):\n",
|
||
"# true_out, pred_out = [], []\n",
|
||
"# for pred_seq, true_seq in zip(pred, true):\n",
|
||
"# t_labels, p_labels = [], []\n",
|
||
"# for p_tok, t_tok in zip(pred_seq, true_seq):\n",
|
||
"# if t_tok.sum() == 0:\n",
|
||
"# continue\n",
|
||
"# t_labels.append(idx2tag[t_tok.argmax()])\n",
|
||
"# p_labels.append(idx2tag[p_tok.argmax()])\n",
|
||
"# true_out.append(t_labels)\n",
|
||
"# pred_out.append(p_labels)\n",
|
||
"# return true_out, pred_out\n",
|
||
"\n",
|
||
"# results = model.evaluate(X_test, {\"ner_output\": y_ner_test, \"srl_output\": y_srl_test}, verbose=0)\n",
|
||
"# for name, value in zip(model.metrics_names, results):\n",
|
||
"# print(f\"{name}: {value}\")\n",
|
||
"\n",
|
||
"# y_pred_ner, y_pred_srl = model.predict(X_test, verbose=0)\n",
|
||
"\n",
|
||
"# true_ner, pred_ner = decode_predictions(y_pred_ner, y_ner_test, idx2tag_ner)\n",
|
||
"# true_srl, pred_srl = decode_predictions(y_pred_srl, y_srl_test, idx2tag_srl)\n",
|
||
"\n",
|
||
"# acc_ner = token_level_accuracy(y_ner_test, y_pred_ner)\n",
|
||
"# acc_srl = token_level_accuracy(y_srl_test, y_pred_srl)\n",
|
||
"\n",
|
||
"# print(f\"NER Token Accuracy {acc_ner:.2%}\")\n",
|
||
"# print(f\"SRL Token Accuracy {acc_srl:.2%}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "9127cce0",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# print(\"[NER] Classification Report:\")\n",
|
||
"# print(classification_report(true_ner, pred_ner, digits=2))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "300897b8",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# print(\"SRL Classification Resport:\")\n",
|
||
"# print(classification_report(true_srl, pred_srl, digits=2))"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "myenv",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.16"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|