8703 lines
518 KiB
Plaintext
8703 lines
518 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"# !unzip -q /content/en-ner-conll-2003.zip -d /content/"
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],
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"metadata": {
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"id": "am0MK_BNa4eB"
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},
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"id": "am0MK_BNa4eB",
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"execution_count": 1,
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"outputs": []
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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": "36c02fac",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "36c02fac",
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"outputId": "992e4fef-67b1-490e-adbb-22fba93a0dc4"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"/usr/local/lib/python3.10/dist-packages/torchtext/vocab/__init__.py:4: UserWarning: \n",
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"/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
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"Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
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" warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n",
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"/usr/local/lib/python3.10/dist-packages/torchtext/utils.py:4: UserWarning: \n",
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"/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
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"Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
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" warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import gensim\n",
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"from gensim.models import KeyedVectors\n",
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"import numpy as np\n",
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"from tensorflow.keras.models import Sequential\n",
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"from tensorflow.keras.layers import Dense\n",
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"import matplotlib.pyplot as plt\n",
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"from keras.regularizers import l2\n",
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"from collections import Counter\n",
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"from torchtext.vocab import vocab\n",
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"from tqdm.notebook import tqdm\n",
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"import torch"
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]
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},
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{
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"cell_type": "markdown",
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"id": "db84429c",
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"metadata": {
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"id": "db84429c"
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},
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"source": [
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"### Declare path"
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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": "fc2539ae",
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"metadata": {
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"id": "fc2539ae"
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},
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"outputs": [],
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"source": [
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"data_dir_path = 'en-ner-conll-2003'\n",
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"train_path = os.path.join(data_dir_path, 'train', 'train.tsv')\n",
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"dev_texts_path = os.path.join(data_dir_path, 'dev-0', 'in.tsv')\n",
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"dev_labels_path = os.path.join(data_dir_path, 'dev-0', 'expected.tsv')\n",
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"dev_predicted_path = os.path.join(data_dir_path, 'dev-0', 'out.tsv')\n",
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"test_texts_path = os.path.join(data_dir_path, 'test-A', 'in.tsv')\n",
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"test_predicted_path = os.path.join(data_dir_path, 'test-A', 'out.tsv')\n",
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"word2vec_file_path = 'word2vec_100_3_polish.bin'"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e4ea0458",
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"metadata": {
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"id": "e4ea0458"
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},
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"source": [
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"### Load files"
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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": "4e038df7",
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"metadata": {
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"id": "4e038df7"
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},
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"outputs": [],
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"source": [
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"train_data = pd.read_csv(train_path, sep='\\t', usecols=[0, 1], header=None, names=['label', 'text'])\n",
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"dev_texts_data = pd.read_csv(dev_texts_path, sep='\\t', usecols=[0], header=None, names=['text'])\n",
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"dev_labels_data = pd.read_csv(dev_labels_path, sep='\\t', usecols=[0], header=None, names=['label'])\n",
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"test_texts_data = pd.read_csv(test_texts_path, sep='\\t', usecols=[0], header=None, names=['text'])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f2bc73a6",
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"metadata": {
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|||
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"id": "f2bc73a6"
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},
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"source": [
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"### Build Vocab"
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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": "e0022f7b",
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"metadata": {
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|||
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"id": "e0022f7b"
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},
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"outputs": [],
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"source": [
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"def build_vocab(dataset):\n",
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" counter = Counter()\n",
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" for document in dataset:\n",
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" counter.update(document)\n",
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" return vocab(counter, specials=[\"<unk>\", \"<pad>\", \"<bos>\", \"<eos>\"])"
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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": "7d9fb428",
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"metadata": {
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|||
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"id": "7d9fb428"
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},
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"outputs": [],
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"source": [
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"train_X = train_data['text'].apply(lambda x: gensim.utils.simple_preprocess(x))\n",
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"v = build_vocab(train_X)"
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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": "7e793633",
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"metadata": {
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|||
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"id": "7e793633"
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|||
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},
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"outputs": [],
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"source": [
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"itos = v.get_itos()"
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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": 8,
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"id": "d3cca05a",
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"metadata": {
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|||
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"colab": {
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|||
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"base_uri": "https://localhost:8080/"
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},
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|||
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"id": "d3cca05a",
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"outputId": "053dc5b9-23f8-4f63-9352-68d1bd318fa6"
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},
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"outputs": [
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{
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|||
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"['<unk>', '<pad>', '<bos>', '<eos>', 'eu', 'rejects', 'german', 'call', 'to', 'boycott', 'british', 'lamb', 'peter', 'blackburn', 'brussels', 'the', 'european', 'commission', 'said', 'on', 'thursday', 'it', 'disagreed', 'with', 'advice', 'consumers', 'shun', 'until', 'scientists', 'determine', 'whether', 'mad', 'cow', 'disease', 'can', 'be', 'transmitted', 'sheep', 'germany', 'representative', 'union', 'veterinary', 'committee', 'werner', 'zwingmann', 'wednesday', 'should', 'buy', 'sheepmeat', 'from', 'countries', 'other', 'than', 'britain', 'scientific', 'was', 'clearer', 'we', 'do', 'support', 'any', 'such', 'recommendation', 'because', 'see', 'grounds', 'for', 'chief', 'spokesman', 'nikolaus', 'van', 'der', 'pas', 'told', 'news', 'briefing', 'he', 'further', 'study', 'required', 'and', 'if', 'found', 'that', 'action', 'needed', 'taken', 'by', 'proposal', 'last', 'month', 'farm', 'commissioner', 'franz', 'fischler', 'ban', 'brains', 'spleens', 'spinal', 'cords', 'human', 'animal', 'food', 'chains', 'highly', 'specific', 'precautionary', 'move', 'protect', 'health', 'proposed', 'wide', 'measures', 'after', 'reports', 'france', 'under', 'laboratory', 'conditions', 'could', 'contract', 'bovine', 'spongiform', 'encephalopathy', 'bse', 'but', 'agreed', 'review', 'his', 'standing', 'mational', 'officials', 'questioned', 'justified', 'as', 'there', 'only', 'slight', 'risk', 'spanish', 'minister', 'loyola', 'de', 'palacio', 'had', 'earlier', 'accused', 'at', 'an', 'ministers', 'meeting', 'of', 'causing', 'unjustified', 'alarm', 'through', 'dangerous', 'generalisation', 'backed', 'committees', 'are', 'due', 're', 'examine', 'issue', 'early', 'next', 'make', 'recommendations', 'senior', 'have', 'long', 'been', 'known', 'scrapie', 'brain', 'wasting', 'similar', 'which', 'is', 'believed', 'transferred', 'cattle', 'feed', 'containing', 'waste', 'farmers', 'denied', 'danger', 'their', 'expressed', 'concern', 'government', 'avoid', 'might', 'influence', 'across', 'europe', 'what', 'extremely', 'careful', 'how', 'going', 'take', 'lead', 'welsh', 'national', 'nfu', 'chairman', 'john', 'lloyd', 'jones', 'bbc', 'radio', 'bonn', 'has', 'led', 'efforts', 'public', 'consumer', 'confidence', 'collapsed', 'in', 'march', 'report', 'suggested', 'humans', 'illness', 'eating', 'contaminated', 'beef', 'imported', 'year', 'nearly', 'half', 'total', 'imports', 'brought', 'tonnes', 'mutton', 'some', 'percent', 'overall', 'rare', 'hendrix', 'song', 'draft', 'sells', 'almost', 'london', 'handwritten', 'guitar', 'legend', 'jimi', 'sold', 'auction', 'late', 'musician', 'favourite', 'possessions', 'florida', 'restaurant', 'paid', 'pounds', 'ai', 'no', 'telling', 'penned', 'piece', 'hotel', 'stationery', 'end', 'january', 'concert', 'english', 'city', 'nottingham', 'threw', 'sheet', 'paper', 'into', 'audience', 'where', 'retrieved', 'fan', 'buyers', 'also', 'snapped', 'up', 'items', 'were', 'put', 'former', 'girlfriend', 'kathy', 'etchingham', 'who', 'lived', 'him', 'they', 'included', 'black', 'lacquer', 'mother', 'pearl', 'inlaid', 'box', 'used', 'store', 'drugs', 'anonymous', 'australian', 'purchaser', 'bought', 'guitarist', 'died', 'overdose', 'aged', 'china', 'says', 'taiwan', 'spoils', 'atmosphere', 'talks', 'beijing', 'taipei', 'spoiling', 'resumption', 'strait', 'visit', 'ukraine', 'taiwanese', 'vice', 'president', 'lien', 'chan', 'this', 'week', 'infuriated', 'speaking', 'hours', 'chinese', 'state', 'media', 'time', 'right', 'engage', 'political', 'foreign', 'ministry', 'shen', 'guofang', 'reuters', 'necessary', 'opening', 'disrupted', 'authorities', 'quoted', 'top', 'negotiator', 'tang', 'shubei', 'visiting', 'group', 'rivals', 'hold', 'now', 'two', 'sides', 'hostility', 'overseas', 'edition', 'people', 'daily', 'saying', 'television', 'interview', 'read', 'comments', 'gave', 'details', 'why', 'considered', 'considers', 'renegade', 'province', 'opposed', 'all', 'gain', 'greater', 'international', 'recognition', 'rival', 'island', 'practical', 'steps', 'towards', 'goal', 'consultations', 'held', 'set', 'format', 'official', 'xinhua',
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]
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}
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],
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"source": [
|
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"print(itos)"
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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": 9,
|
|||
|
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"id": "fa981cf6",
|
|||
|
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"metadata": {
|
|||
|
|
"id": "fa981cf6"
|
|||
|
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},
|
|||
|
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"outputs": [],
|
|||
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"source": [
|
|||
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"v.set_default_index(v[\"<unk>\"])"
|
|||
|
|
]
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "4ed6fe85",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "4ed6fe85"
|
|||
|
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},
|
|||
|
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"source": [
|
|||
|
|
"### Preprocess data"
|
|||
|
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]
|
|||
|
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},
|
|||
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{
|
|||
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"cell_type": "code",
|
|||
|
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"source": [
|
|||
|
|
"reformat_ner_dict = {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "q3jPiHDvdkEJ"
|
|||
|
|
},
|
|||
|
|
"id": "q3jPiHDvdkEJ",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"id": "149c6b1f",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "149c6b1f"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def fit_data_Y(column):\n",
|
|||
|
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" dt = [\n",
|
|||
|
|
" [reformat_ner_dict[token] for token in row.split()] for row in column\n",
|
|||
|
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" ]\n",
|
|||
|
|
" return [torch.tensor([0] + document + [0], dtype=torch.long) for document in dt]\n",
|
|||
|
|
"\n",
|
|||
|
|
"def fit_data_X(dt):\n",
|
|||
|
|
" return [\n",
|
|||
|
|
" torch.tensor(\n",
|
|||
|
|
" [v[\"<bos>\"]] + [v[token] for token in document.split()] + [v[\"<eos>\"]],\n",
|
|||
|
|
" dtype=torch.long,\n",
|
|||
|
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" )\n",
|
|||
|
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" for document in dt\n",
|
|||
|
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" ]\n",
|
|||
|
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"\n",
|
|||
|
|
"\n",
|
|||
|
|
"train_X = fit_data_X(train_data['text'])\n",
|
|||
|
|
"train_Y = fit_data_Y(train_data['label'])\n",
|
|||
|
|
"dev_X = fit_data_X(dev_texts_data['text'])\n",
|
|||
|
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"dev_Y = fit_data_Y(dev_labels_data['label'])\n",
|
|||
|
|
"test_X = fit_data_X(test_texts_data['text'])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "1fa44315",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "1fa44315"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### Create model"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"num_tags = len(reformat_ner_dict)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "jAUWEqOse936"
|
|||
|
|
},
|
|||
|
|
"id": "jAUWEqOse936",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"id": "1eeecf36",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "1eeecf36"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"class LSTM(torch.nn.Module):\n",
|
|||
|
|
"\n",
|
|||
|
|
" def __init__(self):\n",
|
|||
|
|
" super(LSTM, self).__init__()\n",
|
|||
|
|
" self.emb = torch.nn.Embedding(len(v.get_itos()), 100)\n",
|
|||
|
|
" self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True)\n",
|
|||
|
|
" self.fc1 = torch.nn.Linear(256, num_tags)\n",
|
|||
|
|
"\n",
|
|||
|
|
" def forward(self, x):\n",
|
|||
|
|
" emb = torch.relu(self.emb(x))\n",
|
|||
|
|
" lstm_output, (h_n, c_n) = self.rec(emb)\n",
|
|||
|
|
" out_weights = self.fc1(lstm_output)\n",
|
|||
|
|
" return out_weights"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"lstm = LSTM()\n",
|
|||
|
|
"criterion = torch.nn.CrossEntropyLoss()\n",
|
|||
|
|
"optimizer = torch.optim.Adam(lstm.parameters())"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "CIRuZkFjfaBc"
|
|||
|
|
},
|
|||
|
|
"id": "CIRuZkFjfaBc",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"### Evaluation model"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "wJ4trgNPfcPF"
|
|||
|
|
},
|
|||
|
|
"id": "wJ4trgNPfcPF"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"def get_accuracy(y_true, y_pred):\n",
|
|||
|
|
" hit = 0\n",
|
|||
|
|
" missed = 0\n",
|
|||
|
|
"\n",
|
|||
|
|
" for p, t in zip(y_pred, y_true):\n",
|
|||
|
|
" if p == t:\n",
|
|||
|
|
" hit += 1\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" missed += 1\n",
|
|||
|
|
"\n",
|
|||
|
|
" accuracy = hit / (hit + missed)\n",
|
|||
|
|
" return accuracy"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "8zkCdkWTf472"
|
|||
|
|
},
|
|||
|
|
"id": "8zkCdkWTf472",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"def eval_model(dataset_tokens, dataset_labels, model):\n",
|
|||
|
|
" Y_true = []\n",
|
|||
|
|
" Y_pred = []\n",
|
|||
|
|
" for i in tqdm(range(len(dataset_labels))):\n",
|
|||
|
|
" batch_tokens = dataset_tokens[i].unsqueeze(0)\n",
|
|||
|
|
" tags = list(dataset_labels[i].numpy())\n",
|
|||
|
|
" Y_true += tags\n",
|
|||
|
|
"\n",
|
|||
|
|
" Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n",
|
|||
|
|
" Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
|
|||
|
|
" Y_pred += list(Y_batch_pred.numpy())\n",
|
|||
|
|
"\n",
|
|||
|
|
" return get_accuracy(Y_true, Y_pred)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "ljxy_LC-feYD"
|
|||
|
|
},
|
|||
|
|
"id": "ljxy_LC-feYD",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "ec76b0f6",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "ec76b0f6"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### Train model"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"id": "e72a055c",
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 839,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"148e531ca4d246bab191b479727a590f",
|
|||
|
|
"457151c3036747b2a00fda8f10de0286",
|
|||
|
|
"c6ff63b96e3242ab85ab11b705e9589d",
|
|||
|
|
"244d3485460448fca41d62275736ff13",
|
|||
|
|
"e763556731d44e848d1ebdaa135a2f4f",
|
|||
|
|
"4861711062e848e1a8fe201dd10b8d9b",
|
|||
|
|
"3892a6398cc04afba3286f09fc9121f7",
|
|||
|
|
"206dc08bbad24bbb9fd3a882d7364b9b",
|
|||
|
|
"9d9270f1f9f54d00904557d9dd69551c",
|
|||
|
|
"d927c26539ce4962939e455a4e64f9a0",
|
|||
|
|
"97eaa10132f0495d9c27a2351fb66e1a",
|
|||
|
|
"8c158138b4074904a47ca2c4ae43e0bc",
|
|||
|
|
"95ea84f571a74e9aa61667dfb445221e",
|
|||
|
|
"f017002d6ff6489e95b622b4338ec770",
|
|||
|
|
"03a0450df07f4ceeafc67a300e5268d6",
|
|||
|
|
"9ea9df37e643499282419a51f54dcc14",
|
|||
|
|
"0b35eabf7fb54d989ea02464a068afef",
|
|||
|
|
"1ae3188960b3468caffafd939ad1e34e",
|
|||
|
|
"b0371c1241af4fc5a79eb7b76fecbd26",
|
|||
|
|
"de23364b23324aa3a707d7a1c8a433a1",
|
|||
|
|
"bb9e8c1382314edabd9060436197f3d8",
|
|||
|
|
"dc677bc444ba4712b3b5699dd399b72b",
|
|||
|
|
"959aeae4749c4517812a883a25a557ec",
|
|||
|
|
"bcf0daafcc0b4f949b798536f53f1b1f",
|
|||
|
|
"296e7e033c2644cd9fa99ca16835ad6d",
|
|||
|
|
"8684d8e2d4594ba9832d1f121e6a26c2",
|
|||
|
|
"eb9f9fc6a7df4d95844ce267550ad053",
|
|||
|
|
"5c32d0aeac4f4295803f19f9beaad8c5",
|
|||
|
|
"96b00bbbfc3642f1a4349f9033f4d1db",
|
|||
|
|
"863e645d3dea462e9b3b534540c369f5",
|
|||
|
|
"d8e76e7b5d22455e91aa31044a46218a",
|
|||
|
|
"495403faaa6842bcbdd1370249bf3a9e",
|
|||
|
|
"3450230a9a874ac8b17db364ef0b663e",
|
|||
|
|
"08f2359db54e489dac411bb4af009c4f",
|
|||
|
|
"fcf7382c160d45a285398f633a1aae2e",
|
|||
|
|
"cf2be2eb1c8549429f0ab5d355072054",
|
|||
|
|
"dd4417e0dc2a484094f3978849c5e967",
|
|||
|
|
"c190f534152d420eb0fecf6c601c76a5",
|
|||
|
|
"2d2f5b7bd5394192950ebbcb5a546a13",
|
|||
|
|
"37a6cf2788da477093dba368b412f58a",
|
|||
|
|
"456509b0022e468980e9566e2c6f5f93",
|
|||
|
|
"e73075478fe149fd82413a6598f7be95",
|
|||
|
|
"4b575258b55b44fa91b2b5816b3aa5e4",
|
|||
|
|
"e87ebc94f0f04d698ea9e9048adf2092",
|
|||
|
|
"6a62c46dff6242eba89cc6daefd1c927",
|
|||
|
|
"451da3f9e9f440189994ce1fa231eabb",
|
|||
|
|
"8c99f8c0e3944db88b996f24772d1ac0",
|
|||
|
|
"9346f044bdd74dd496f8c7f7767c32c3",
|
|||
|
|
"43b70046458142359f48c87f625b78d6",
|
|||
|
|
"c218900434e14a98986b5c3d50f06579",
|
|||
|
|
"9e13c900f0ef4b72ad4130f3c7c19caa",
|
|||
|
|
"5f9f77b3b4eb42088f82c9155fb411fe",
|
|||
|
|
"ba926e6770914b298720c2a07cbcb254",
|
|||
|
|
"2ab28e82faa24acaad98ef7b3d0880be",
|
|||
|
|
"256489af0a6f4cd1b04d3c3bb0bb5e67",
|
|||
|
|
"213563fffa7542bebc7599b2521a959d",
|
|||
|
|
"674d4b5cd7f249609a35f2171e59a02c",
|
|||
|
|
"f4ae0fb1d67f4af7901544581a3ee85c",
|
|||
|
|
"c080ae836ada47c9b2873b05f55e776b",
|
|||
|
|
"57df7a4adf0247ccbcb67c99c37ce918",
|
|||
|
|
"2273e6bf4d1445f38833e730431f54fe",
|
|||
|
|
"50edc713f99b4e96952a3dddc85c458d",
|
|||
|
|
"8db9818f174e44709f5e08567fae3953",
|
|||
|
|
"8d310f1f89154ed1a402404d78ddfadb",
|
|||
|
|
"763e2dd4da164107b13d00f6ee0b3041",
|
|||
|
|
"c0fd3b99e12841a5b2e100df3467c443",
|
|||
|
|
"7bd3cb10e88840a1a490f0baf6defbb7",
|
|||
|
|
"4a89142efc0544168fb2079920ed59f3",
|
|||
|
|
"20a622b813b643bdb94df651e6eec425",
|
|||
|
|
"a856e372370244a1bea060e9fc5e2cad",
|
|||
|
|
"1e077d79557841988d1670936223f052",
|
|||
|
|
"962363bd73a94abeb6a9a7d50390dc59",
|
|||
|
|
"ebca11c693804c449430a73f6ef1224d",
|
|||
|
|
"4209fb153c1e4772a383121dd2897d5e",
|
|||
|
|
"25adbfba9fe44125b8e309805b2f0231",
|
|||
|
|
"7c41fdaedd244f038ee2bb873bc93f2a",
|
|||
|
|
"a6b7eebc3d0447b8b70873d964949b8b",
|
|||
|
|
"ac9ce3362c6b4b2296e27b6379585d8f",
|
|||
|
|
"1bb8f3fceda346a7895b491318cf587d",
|
|||
|
|
"9336e75985d94b12b0dd52eb45b1e13d",
|
|||
|
|
"aaf440e12be44e45b325e4d9b8540c55",
|
|||
|
|
"304dd57ede2843b1b10983ff9524df6b",
|
|||
|
|
"b86bf4876bc8424dac3c9d411ce051dd",
|
|||
|
|
"71c3b4c9ec48446483f4ec4a8a0ada24",
|
|||
|
|
"cb414c91bdab4dac843514faf86a4f5a",
|
|||
|
|
"f2f8f5377c4349a7aa8647c3c14d5cdc",
|
|||
|
|
"aa817c0c68744271aa57dff0854337f0",
|
|||
|
|
"8b3afd2aeacf47239a2c4ca5b0ddbd92",
|
|||
|
|
"7f6e13e163fa43b1bde4a8eea8d79a24",
|
|||
|
|
"a5c09bd711be4fa0b2213962d2db1ed9",
|
|||
|
|
"6b72d8add11a49d7a24c6629165972dd",
|
|||
|
|
"1503312de0424b57bf796a45e81b808b",
|
|||
|
|
"82c4dc09581c483fba09f77e7149e8df",
|
|||
|
|
"5b4aae1a44bd4ec79c88109d2590dd78",
|
|||
|
|
"102a1498441b4c89a62ce694a48568d3",
|
|||
|
|
"b132aa0f21dd48f5b967216b47765609",
|
|||
|
|
"361fe6ea841b4427bfb2958dbcf5338f",
|
|||
|
|
"d6c33da42ed44b3aaedbeb423a8165f5",
|
|||
|
|
"f6a8f84f98e042d1bee438edd601f94c",
|
|||
|
|
"6fa02cd2222449eb9f8ecc4432b6b29c",
|
|||
|
|
"086201a45bf3473781ab959ef3921772",
|
|||
|
|
"84803943513f4f4ebfd96cde770bc388",
|
|||
|
|
"c9a50c2295d649739f7887eb0ac2fff3",
|
|||
|
|
"e818a157602f4f8aa437916eb20020d9",
|
|||
|
|
"b125a4ba43924d4992a61678f6018ac2",
|
|||
|
|
"3d15a2eda255463f814359ea5490c75c",
|
|||
|
|
"e0ef7e80e55d47c8ace642f8425424f2",
|
|||
|
|
"ac2b6ad7d59d4e408d93f42b03b469d8",
|
|||
|
|
"1a46055450e34ede97899dee700a6cb4",
|
|||
|
|
"1bb102a75f62483ebc169dd0fa214c96",
|
|||
|
|
"adb59a8be3af4c2cba18da270aaf1c6b",
|
|||
|
|
"78a3fef9d42e4c638cfe7a8140060181",
|
|||
|
|
"1bfb8fc8ef7b48b59ccd32c499192274",
|
|||
|
|
"bdecceb1d56c450cbd05ff6e768e5141",
|
|||
|
|
"d4c8869293a945d6ac1a6b85ea03bec5",
|
|||
|
|
"7df5b964fefe49c29846d08617f93112",
|
|||
|
|
"eec1169eb3a24ce693175e7f2509be6e",
|
|||
|
|
"90ac5b30118d48cfb0af1c64f044034d",
|
|||
|
|
"fc3a5516568349629c437fabae0e1e98",
|
|||
|
|
"cfb299907f864c40b405e177569e8d91",
|
|||
|
|
"a503916db1b046f8bada5c2d9b097583",
|
|||
|
|
"4d34b7c5122d43aa9118fa936efccca0",
|
|||
|
|
"3713bf83d3cb403b995993371d142d11",
|
|||
|
|
"2e441f0cae254471a279d58ac02af735",
|
|||
|
|
"a8a85ee7e82641f8abf0eb81ecb10e78",
|
|||
|
|
"3be3da1cfd314abca30f0644a4fe345f",
|
|||
|
|
"35f0df0886ea43bb9c19350899575ef3",
|
|||
|
|
"ab73e9ae63934e179c66defe5d3f28fa",
|
|||
|
|
"1b62ebb513a24f2cb96a7c4da11d9f9f",
|
|||
|
|
"97b9f3a10eaf4cce8d850dea370b542e",
|
|||
|
|
"89475f22e0f542349094069968977635",
|
|||
|
|
"5d7bd40e004b4a7b918046df28627c21",
|
|||
|
|
"42cf1f97293d41bcb4b45c8c2ca5c830",
|
|||
|
|
"6d64b5f1caac4603b01db1057344338d",
|
|||
|
|
"3933951d52ac47a7ba9af4974f63f986",
|
|||
|
|
"1fe3351b4f844c96b98e999ad81b78b7",
|
|||
|
|
"50cd05f7b25f4ded8da36e6a55d20e1d",
|
|||
|
|
"4cc02516efbd49919248e3da16103480",
|
|||
|
|
"31e1e7a3c64b4c9f9ffc6de78f6300e6",
|
|||
|
|
"6e79dd14fc3a4bc4b4de2496aa2a7500",
|
|||
|
|
"564bb8efcf7e4a2cae681f62667c127e",
|
|||
|
|
"a84add25e5524910acffd8c4c8f6309f",
|
|||
|
|
"ec118e739daa49e786e4a96e9b7eed1b",
|
|||
|
|
"ae44eb677dbf4b4ba5a7ae1021afdc15",
|
|||
|
|
"be1b47cdff6f4ecd99c4a376e1d4aa79",
|
|||
|
|
"ac4ebccf036a4f6e8ca851fcafe4f452",
|
|||
|
|
"bf15cb330dff40b7b8c184b3287deb11",
|
|||
|
|
"4ad5b2acaf8841e29e76505bafd73e53",
|
|||
|
|
"578cb2c7783342af81a1f24b2fd6be94",
|
|||
|
|
"142428523a4749d9b3a4a1bd722cb569",
|
|||
|
|
"722ef77397c44236aee9b6ba2342b90c",
|
|||
|
|
"c48802e345a5433ead32f94a38229cbb",
|
|||
|
|
"f0684fccf68a45c28dda3d128d1a394f",
|
|||
|
|
"f761c4e9f3f84d17a7206579e5d01921",
|
|||
|
|
"1f7327bfb595432293d11a5f409c51ae",
|
|||
|
|
"00bdfc35a66b4435b5ff22c8e6ce93f5",
|
|||
|
|
"ef71d9a843584a7894b187738cfdcbf5",
|
|||
|
|
"bb9ae680067b4dedaf3da5be5dff7cb4",
|
|||
|
|
"49749095ea064e1bbd43e0c8db94d08d",
|
|||
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],
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"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"model_id": "1d95795fdb024322b26aeab371976977"
|
|||
|
|
}
|
|||
|
|
},
|
|||
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|
"metadata": {}
|
|||
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|
},
|
|||
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|
{
|
|||
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|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"dev_accuracy: 0.8430657599679131\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
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|
"output_type": "display_data",
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|
"version_major": 2,
|
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|
"version_minor": 0,
|
|||
|
|
"model_id": "5c92317b1c77454487c16811bb41dedc"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
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|
"output_type": "display_data",
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|
"version_major": 2,
|
|||
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|
"version_minor": 0,
|
|||
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|
"model_id": "5c42fdfa9dee47a5a9fc25ca34219f92"
|
|||
|
|
}
|
|||
|
|
},
|
|||
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|
"metadata": {}
|
|||
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|
},
|
|||
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|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"dev_accuracy: 0.837796940803267\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
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|
"version_major": 2,
|
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|
|
"version_minor": 0,
|
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|
|
"model_id": "236f1edf23624260bd2ef1c6b393d3dd"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
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"data": {
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],
|
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"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"model_id": "808e1edf72374db08e0452bf9b875444"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"dev_accuracy: 0.8345517857468414\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"NUM_EPOCHS = 10\n",
|
|||
|
|
"dev_accuracy_history = list()\n",
|
|||
|
|
"for i in range(NUM_EPOCHS):\n",
|
|||
|
|
" lstm.train()\n",
|
|||
|
|
" for i in tqdm(range(len(train_Y))):\n",
|
|||
|
|
" batch_tokens = train_X[i].unsqueeze(0)\n",
|
|||
|
|
" tags = train_Y[i].unsqueeze(1)\n",
|
|||
|
|
"\n",
|
|||
|
|
" predicted_tags = lstm(batch_tokens)\n",
|
|||
|
|
"\n",
|
|||
|
|
" optimizer.zero_grad()\n",
|
|||
|
|
" loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))\n",
|
|||
|
|
"\n",
|
|||
|
|
" loss.backward()\n",
|
|||
|
|
" optimizer.step()\n",
|
|||
|
|
"\n",
|
|||
|
|
" lstm.eval()\n",
|
|||
|
|
" dev_accuracy = eval_model(dev_X, dev_Y, lstm)\n",
|
|||
|
|
" dev_accuracy_history.append(dev_accuracy)\n",
|
|||
|
|
" print(\"dev_accuracy:\", dev_accuracy)\n",
|
|||
|
|
""
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"id": "561f4db3",
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 472
|
|||
|
|
},
|
|||
|
|
"id": "561f4db3",
|
|||
|
|
"outputId": "6a9cc103-0ce0-492b-9c11-6576f6df771d"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
],
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAHHCAYAAABEEKc/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB3r0lEQVR4nO3dd1QU198G8Gd36b03RVFRFAsoKIpdUGxYYtcIGkuKGhWTN3YsUUw0SvxZ0iwxsRtNNLYoRo0dISgWVGwgSLNQpe3O+weycQEVFJwFns85e5TZuzPfWZB9vHPvHYkgCAKIiIiISEkqdgFERERE6oYBiYiIiKgIBiQiIiKiIhiQiIiIiIpgQCIiIiIqggGJiIiIqAgGJCIiIqIiGJCIiIiIimBAIiIiIiqCAYmoEpJIJJg3b16ZX3fv3j1IJBJs3Lix3GsiehccHBzQu3dvscugaoABiegNbdy4ERKJBBKJBKdOnSr2vCAIsLe3h0QiqdS/0A8cOACJRAI7OzsoFAqxy6l0Hj16hM8//xxOTk7Q0dGBmZkZfHx88Oeff4pdWokcHByUP9dFH927dxe7PKJ3RkPsAogqOx0dHWzZsgXt2rVT2X7ixAk8ePAA2traIlVWPjZv3gwHBwfcu3cPx44dg7e3t9glVRo3btyAl5cXkpOTMXr0aLi7u+Pp06fYvHkzfH198dlnn2Hp0qVil1mMq6srpk2bVmy7nZ2dCNUQiYMBiegt9ezZEzt37sTKlSuhofHfP6ktW7bAzc0NKSkpIlb3djIzM/HHH38gKCgIGzZswObNm9U2IGVmZkJfX1/sMpTy8vIwcOBAPHnyBCdPnoSHh4fyualTp2LEiBFYtmwZ3N3dMWTIkHdWV35+PhQKBbS0tF7apkaNGnj//fffWU1E6oiX2Ije0rBhw/Do0SMcOXJEuS03Nxe7du3C8OHDS3xNZmYmpk2bBnt7e2hra8PJyQnLli2DIAgq7XJycjB16lRYWlrC0NAQffr0wYMHD0rcZ1xcHD744ANYW1tDW1sbjRs3xvr169/q3Pbs2YNnz55h0KBBGDp0KHbv3o3s7Oxi7bKzszFv3jw0aNAAOjo6sLW1xXvvvYfbt28r2ygUCnz77bdo2rQpdHR0YGlpie7du+PixYsAXj0+quiYq3nz5kEikeDatWsYPnw4TE1NlT14ly9fxqhRo1C3bl3o6OjAxsYGH3zwAR49elTiezZmzBjY2dlBW1sbderUwccff4zc3FzcuXMHEokEK1asKPa6M2fOQCKRYOvWrS9973777TdcuXIF06dPVwlHACCTyfD999/DxMREeV6JiYnQ0NDA/Pnzi+3rxo0bkEgkWLVqlXLb06dPMWXKFOXPkKOjI7766iuVy6CF7+myZcsQHByMevXqQVtbG9euXXtp3aU1atQoGBgY4M6dO/Dx8YG+vj7s7OywYMGCYj/Hpf15B4Bff/0VrVq1gp6eHkxNTdGhQwf89ddfxdqdOnUKrVq1go6ODurWrYtNmzapPJ+Xl4f58+ejfv360NHRgbm5Odq1a6fy75ToVdiDRPSWHBwc0KZNG2zduhU9evQAABw8eBCpqakYOnQoVq5cqdJeEAT06dMHf//9N8aMGQNXV1ccPnwYn3/+OeLi4lQ+kMeOHYtff/0Vw4cPh6enJ44dO4ZevXoVqyExMRGtW7eGRCLBxIkTYWlpiYMHD2LMmDFIS0vDlClT3ujcNm/ejM6dO8PGxgZDhw7F9OnTsW/fPgwaNEjZRi6Xo3fv3ggJCcHQoUMxefJkpKen48iRI7hy5Qrq1asHABgzZgw2btyIHj16YOzYscjPz8c///yDc+fOwd3d/Y3qGzRoEOrXr4/FixcrP2yPHDmCO3fuYPTo0bCxscHVq1fxww8/4OrVqzh37hwkEgkAID4+Hq1atcLTp08xfvx4NGzYEHFxcdi1axeysrJQt25dtG3bFps3b8bUqVOLvS+Ghobo27fvS2vbt28fAMDPz6/E542NjdG3b1/8/PPPiI6OhqOjIzp27IgdO3YgMDBQpe327dshk8mU73tWVhY6duyIuLg4fPjhh6hVqxbOnDmDGTNm4OHDhwgODlZ5/YYNG5CdnY3x48dDW1sbZmZmr3xf8/LySuz51NfXh66urvJruVyO7t27o3Xr1vj6669x6NAhBAYGIj8/HwsWLABQtp/3+fPnY968efD09MSCBQugpaWF8+fP49ixY+jWrZuyXXR0NAYOHIgxY8bA398f69evx6hRo+Dm5obGjRsDKAjRQUFBGDt2LFq1aoW0tDRcvHgR4eHh6Nq16yvPnwgAIBDRG9mwYYMAQAgNDRVWrVolGBoaCllZWYIgCMKgQYOEzp07C4IgCLVr1xZ69eqlfN3vv/8uABC+/PJLlf0NHDhQkEgkQnR0tCAIghARESEAED755BOVdsOHDxcACIGBgcptY8aMEWxtbYWUlBSVtkOHDhWMjY2Vdd29e1cAIGzYsOG155eYmChoaGgIP/74o3Kbp6en0LdvX5V269evFwAIy5cvL7YPhUIhCIIgHDt2TAAgfPrppy9t86raip5vYGCgAEAYNmxYsbaF5/qirVu3CgCEkydPKrf5+fkJUqlUCA0NfWlN33//vQBAuH79uvK53NxcwcLCQvD39y/2uhe5uroKxsbGr2yzfPlyAYCwd+9eleNFRkaqtHN2dha6dOmi/HrhwoWCvr6+cPPmTZV206dPF2QymRATEyMIwn/vqZGRkZCUlPTKWgrVrl1bAFDiIygoSNnO399fACBMmjRJuU2hUAi9evUStLS0hOTkZEEQSv/zfuvWLUEqlQr9+/cX5HK5StvC78eL9b34vUxKShK0tbWFadOmKbe5uLio/LsjKiteYiMqB4MHD8azZ8/w559/Ij09HX/++edLL68dOHAAMpkMn376qcr2adOmQRAEHDx4UNkOQLF2RXuDBEHAb7/9Bl9fXwiCgJSUFOXDx8cHqampCA8PL/M5bdu2DVKpFAMGDFBuGzZsGA4ePIgnT54ot/3222+wsLDApEmTiu2jsLfmt99+g0QiKdYz8mKbN/HRRx8V2/ZiD0d2djZSUlLQunVrAFC+DwqFAr///jt8fX1L7L0qrGnw4MHQ0dHB5s2blc8dPnwYKSkprx2jk56eDkNDw1e2KXw+LS0NAPDee+9BQ0MD27dvV7a5cuUKrl27pjJOaefOnWjfvj1MTU1Vvt/e3t6Qy+U4efKkynEGDBgAS0vLV9byIg8PDxw5cqTYY9iwYcXaTpw4Ufn3wh7M3NxcHD16FEDpf95///13KBQKzJ07F1Kp6kdT0Z8RZ2dntG/fXvm1paUlnJyccOfOHeU2ExMTXL16Fbdu3Sr1eRO9iJfYiMqBpaUlvL29sWXLFmRlZUEul2PgwIEltr1//z7s7OyKfXg2atRI+Xzhn1KpVHmJqpCTk5PK18nJyXj69Cl++OEH/PDDDyUeMykpqcznVDgW5NGjR8rxO82bN0dubi527tyJ8ePHAwBu374NJycnlQHqRd2+fRt2dnavvbRTVnXq1Cm27fHjx5g/fz62bdtW7LxTU1MBFLxnaWlpaNKkySv3b2JiAl9fX2zZsgULFy4EUHB5rUaNGujSpcsrX2toaPjaAfrp6enKtgBgYWEBLy8v7NixQ3m87du3Q0NDA++9957ydbdu3cLly5dfGnqKnndJ79OrWFhYlGowvlQqRd26dVW2NWjQAEDB+Ceg9D/vt2/fhlQqhbOz82uPW6tWrWLbTE1NVYL7ggUL0LdvXzRo0ABNmjRB9+7dMXLkSDRr1uy1+ycCGJCIys3w4cMxbtw4JCQkoEePHjAxMXknxy0clPv+++/D39+/xDZl/VC4desWQkNDAQD169cv9vzmzZuVAam8vKwnSS6Xv/Q1L/YWFRo8eDDOnDmDzz//HK6urjAwMIBCoUD37t3faB0nPz8/7Ny5E2fOnEHTpk2xd+9efPLJJ8V6OYpq1KgRIiIiEBMTU+IHOlAwoByASigYOnQoRo8ejYiICLi6umLHjh3w8vKChYWFso1CoUDXrl3xf//3f
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.plot(dev_accuracy_history, label='Validation Accuracy')\n",
|
|||
|
|
"plt.xlabel('Epoch')\n",
|
|||
|
|
"plt.ylabel('Accuracy')\n",
|
|||
|
|
"plt.title('Model Accuracy Over Epochs')\n",
|
|||
|
|
"plt.legend()\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "4d0b9315",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "4d0b9315"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"### Predict and save results"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"reversed_ner_dict = {v: k for k, v in reformat_ner_dict.items()}\n",
|
|||
|
|
"print(reversed_ner_dict)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "T9rzUXtEpEbd",
|
|||
|
|
"outputId": "dca59368-894e-47f2-c47f-a57dd99ff1c0"
|
|||
|
|
},
|
|||
|
|
"id": "T9rzUXtEpEbd",
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"{0: 'O', 1: 'B-PER', 2: 'I-PER', 3: 'B-ORG', 4: 'I-ORG', 5: 'B-LOC', 6: 'I-LOC', 7: 'B-MISC', 8: 'I-MISC'}\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"id": "54f93a9b",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "54f93a9b"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def predict_and_save(X, filename):\n",
|
|||
|
|
" Y_predicted = []\n",
|
|||
|
|
" for i in tqdm(range(len(X))):\n",
|
|||
|
|
" batch_tokens = X[i].unsqueeze(0)\n",
|
|||
|
|
" Y_batch_pred_weights = lstm(batch_tokens).squeeze(0)\n",
|
|||
|
|
" Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
|
|||
|
|
" Y_processed_pred = \" \".join(reversed_ner_dict[item] for item in Y_batch_pred.numpy()[1:-1])\n",
|
|||
|
|
" Y_predicted.append(Y_processed_pred)\n",
|
|||
|
|
" Y_predicted_df = pd.DataFrame(Y_predicted, columns=['predicted_label'])\n",
|
|||
|
|
" Y_predicted_df.to_csv(filename, sep='\\t', index=False, header=None)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"id": "9d3b3867",
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 81,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"b7a8863307a64d8f8b203cdb96760077",
|
|||
|
|
"acb7d3bfb2ae43f1954624d99eb10eec",
|
|||
|
|
"b6ff9cf63a18425ca23d15b29668a463",
|
|||
|
|
"ecb30112228140788f41d5b6063dc614",
|
|||
|
|
"1d94ae2bd9fd4f08a0a52f58e75653a8",
|
|||
|
|
"ae5ffdb4e84f40978b60679b533f5fd9",
|
|||
|
|
"e0fda431ac3043dfb47bb07732d674e4",
|
|||
|
|
"e48dba51dc3c4e149250c7c047a11508",
|
|||
|
|
"eba9b1f144b443a9a684906c2b90cfd2",
|
|||
|
|
"e9145b0563704a41be8268b4672ddebc",
|
|||
|
|
"182700175c48491794c1376c5715295f",
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