tfidf-char-wb.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the Train Dataframe: (5749, 7)\n",
      "Shape of the Validation Dataframe: (5749, 7)\n",
      "Shape of the Test Dataframe: (5749, 7)\n"
     ]
    }
   ],
   "source": [
    "def on_bad_line(values):\n",
    "    return values[:7]\n",
    "\n",
    "columns_mapping = {\n",
    "    0: 'genre',\n",
    "    1: 'filename',\n",
    "    2: 'year',\n",
    "    3: 'index',\n",
    "    4: 'score',\n",
    "    5: 'sentence1',\n",
    "    6: 'sentence2'\n",
    "}\n",
    "\n",
    "# quotings 3 = csv.QUOTE_NONE\n",
    "train_df = pd.read_csv('Dataset/sts-train.csv', sep=\"\\t\", on_bad_lines=on_bad_line, engine='python', header=None, encoding='utf-8', quoting=3).rename(columns=columns_mapping)\n",
    "print(f'Shape of the Train Dataframe: {train_df.shape}')\n",
    "\n",
    "dev_df = pd.read_csv('Dataset/sts-train.csv', sep=\"\\t\", on_bad_lines=on_bad_line, engine='python', header=None, encoding='utf-8', quoting=3).rename(columns=columns_mapping)\n",
    "print(f'Shape of the Validation Dataframe: {dev_df.shape}')\n",
    "\n",
    "test_df = pd.read_csv('Dataset/sts-train.csv', sep=\"\\t\", on_bad_lines=on_bad_line, engine='python', header=None, encoding='utf-8', quoting=3).rename(columns=columns_mapping)\n",
    "print(f'Shape of the Test Dataframe: {test_df.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "TfidfVectorizer(analyzer='char_wb', ngram_range=(1, 3), strip_accents='unicode')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(analyzer='char_wb', ngram_range=(1, 3), lowercase=True, strip_accents='unicode')\n",
    "vectorizer.fit(train_df['sentence1'].tolist() + train_df['sentence2'].tolist())\n",
    "print(f'Vocabulary size: {len(vectorizer.vocabulary_)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation: 0.7296001092273372\n"
     ]
    }
   ],
   "source": [
    "y_pred = []\n",
    "for s1, s2 in zip(dev_df['sentence1'], dev_df['sentence2']):\n",
    "    vect = vectorizer.transform([s1, s2])\n",
    "    y_pred.append(cosine_similarity(vect)[0][1])\n",
    "\n",
    "print(f'Pearson Correlation: {pearsonr(y_pred, dev_df[\"score\"] / 5)[0]}')"
   ]
  }
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