{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import gensim.downloader as gn\n",
    "import csv\n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STEP 3 - PREPROCESSING\n"
     ]
    }
   ],
   "source": [
    "names = ['content', 'id', 'label']\n",
    "train_data_content = pd.read_table('train/in.tsv', error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = ['content', 'id'])\n",
    "train_data_labels = pd.read_table('train/expected.tsv', error_bad_lines = False, header = None, quoting=csv.QUOTE_NONE, names = ['label'])\n",
    "dev_data = pd.read_table('dev-0/in.tsv', error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = ['content', 'id'])\n",
    "test_data = pd.read_table('test-A/in.tsv', error_bad_lines = False, header = None, quoting = csv.QUOTE_NONE, names = ['content', 'id'])\n",
    "\n",
    "print('STEP 3 - PREPROCESSING')\n",
    "# lowercase all content\n",
    "X_train = train_data_content['content'].str.lower()\n",
    "y_train = train_data_labels['label']\n",
    "X_dev = dev_data['content'].str.lower()\n",
    "X_test = test_data['content'].str.lower()\n",
    "\n",
    "# tokenize datasets\n",
    "X_train = [word_tokenize(content) for content in X_train]\n",
    "X_dev = [word_tokenize(content) for content in X_dev]\n",
    "X_test = [word_tokenize(content) for content in X_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"
     ]
    }
   ],
   "source": [
    "w2v = gn.load('word2vec-google-news-300')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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