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Transformer.ipynb
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223
Transformer.ipynb
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{
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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": "5d0842c8-c292-41ce-a27b-73986bf43e1c",
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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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"C:\\Users\\obses\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-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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"C:\\Users\\obses\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-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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"from collections import Counter\n",
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"import torch\n",
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"from datasets import load_dataset\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 pandas as pd\n",
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"from nltk.tokenize import word_tokenize\n",
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"import string\n",
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"\n",
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"from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer"
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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": "4ccf08e2-fec1-4d68-a1fd-d33af6bd54bc",
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset = pd.read_csv('train.tsv', sep='\\t', header=None)"
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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": "c027b2f3-48eb-442a-a212-c5fa9d1cfba5",
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"metadata": {},
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"outputs": [],
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"source": [
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"X_test = pd.read_csv('../dev-0/in.tsv', sep='\\t', header=None)\n",
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"Y_test = pd.read_csv('../dev-0/expected.tsv', sep='\\t', header=None)"
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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": "9fc7a069-52ce-4343-a8a2-dd77619f5d25",
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train = dataset[dataset.columns[1]]\n",
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"Y_train = dataset[dataset.columns[0]]\n",
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"\n",
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"X_test = X_test[X_test.columns[0]]\n",
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"Y_test = Y_test[Y_test.columns[0]]"
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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": "7cd49893-c730-4089-9b21-8683c11fd6cc",
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train = [text.split() for text in X_train]\n",
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"X_test = [text.split() for text in X_test]"
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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": "bd029e57-f5c3-4b2b-bcf0-ea25d71ee952",
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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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"Some weights of the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
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"- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
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"- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "9f4a002be4b64bfdba0a227b9a6815f2",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"0it [00:00, ?it/s]"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"215\n"
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]
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}
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],
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"source": [
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"model = AutoModelForTokenClassification.from_pretrained(\"dbmdz/bert-large-cased-finetuned-conll03-english\")\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-cased\")\n",
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"\n",
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"recognizer = pipeline(\"ner\", model=model, tokenizer=tokenizer)\n",
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"full_tags = []\n",
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"for idx, el in tqdm(enumerate(X_test)):\n",
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" out_tags = []\n",
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" tags_corrected = []\n",
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"\n",
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" temp = []\n",
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" tags_joined = []\n",
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" tag = 'NULL'\n",
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" t = \"\"\n",
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" for r in recognizer(\" \".join(el)):\n",
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" \n",
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" if len(t) == 0:\n",
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" t = r['word']\n",
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" tag = r['entity']\n",
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" continue\n",
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" if \"#\" in r['word']:\n",
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" t = t + str(r['word']).replace(\"#\",\"\")\n",
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" continue\n",
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" if \"#\" not in r['word'] and len(t) != 0:\n",
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" temp.append(t)\n",
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" tags_joined.append(tag)\n",
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" t = r['word']\n",
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" tag = r['entity']\n",
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"\n",
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" for tag in Y_test[idx].split():\n",
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" if tag == \"O\":\n",
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" out_tags.append(\"O\")\n",
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" if tag != \"O\" and len(tags_joined) > 0:\n",
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" out_tags.append(tags_joined[0])\n",
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" tags_joined = tags_joined[1:]\n",
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" continue\n",
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" if tag != \"O\" and len(tags_joined) == 0:\n",
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" out_tags.append(\"O\")\n",
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"\n",
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" #print(len(Y_test[idx].split()), len(out_tags))\n",
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"\n",
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" out_tags = \" \".join(out_tags).replace(\"I-\",\"B-\").split()\n",
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" \n",
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" last_tag = out_tags[0]\n",
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" tags_corrected.append(last_tag)\n",
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" \n",
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" for tag in out_tags[1:]:\n",
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"\n",
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" if tag == last_tag:\n",
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" tags_corrected.append(tag.replace(\"B-\",\"I-\"))\n",
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" last_tag = tag\n",
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" else:\n",
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" last_tag = tag\n",
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" tags_corrected.append(tag)\n",
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" \n",
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" #print(len(Y_test[idx].split()), len(tags_corrected))\n",
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"\n",
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" full_tags.append(tags_corrected)\n",
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"\n",
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"print(len(full_tags))\n",
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"\n",
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"# for idx, el in tqdm(enumerate(full_tags)):\n",
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"# if len(el) != len(len(Y_test[idx].split())):\n",
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"# print(\"Somethings wrong sir\")\n",
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" "
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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": "0feb1763-b28b-4cee-a996-f493c3f3d037",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"out.tsv\", 'w') as file:\n",
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" for el in full_tags:\n",
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" \n",
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" file.write(\" \".join(el)) \n",
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" file.write(f\"\\n\")\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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