05_T5_Supervised_Learning_BERT_Model_and_Implementation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mA3dZrvDewSm",
        "outputId": "7b553c6b-f8ac-4967-adef-bda06d27a95c"
      },
      "source": [
        "# Supervised Learning - BERT Model training and implementation\n",
        "Welcome to our BERT Model training and implementation workbook. We explore the training and testing of our BERT and Distilled Models on Air BnB dataset here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mCWT6FLy4vOl"
      },
      "source": [
        "## Modules download"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LVhb_7cDM9ax",
        "outputId": "be1fd8d1-3979-4c0a-dbb9-4b82839cb27f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Jun 19 05:31:39 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla V100-SXM2...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   32C    P0    24W / 300W |      0MiB / 16384MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Not connected to a GPU')\n",
        "else:\n",
        "  print(gpu_info)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ED4LBQSLNO73",
        "outputId": "d15357a3-5f5c-4ff3-aca1-8d60a9440e90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your runtime has 27.3 gigabytes of available RAM\n",
            "\n",
            "You are using a high-RAM runtime!\n"
          ]
        }
      ],
      "source": [
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('Not using a high-RAM runtime')\n",
        "else:\n",
        "  print('You are using a high-RAM runtime!')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnMBW2JT4vOl",
        "outputId": "3500386f-6cd7-49a1-9587-5aa87fbc2bbb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ],
      "source": [
        "#from google.colab import drive\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "import pandas as pd\n",
        "import folium\n",
        "import math\n",
        "import numpy as np\n",
        "import re\n",
        "import seaborn as sns\n",
        "#from google.colab import files\n",
        "from textblob import TextBlob\n",
        "import nltk\n",
        "import matplotlib.pyplot as plt\n",
        "nltk.download('punkt')\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U8iLXSfC4vOm",
        "outputId": "bebdf359-7190-4de1-aba8-5dcf9204ddc6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
      ],
      "source": [
        "\n",
        "!pip install yellowbrick\n",
        "!pip install textblob\n",
        "!pip install wordcloud\n",
        "!pip install vaderSentiment\n",
        "!pip install tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eWXaSlj2fHVG"
      },
      "outputs": [],
      "source": [
        "## All purpose\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from google.colab import drive\n",
        "\n",
        "\n",
        "## Visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "from wordcloud import WordCloud\n",
        "\n",
        "## NLP\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.tokenize import TweetTokenizer\n",
        "#from gensim.summarization import keywords\n",
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ggP3R7R1fR_o"
      },
      "outputs": [],
      "source": [
        "## Deeplearning/ML\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm, tree\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, ExtraTreesClassifier\n",
        "from sklearn.metrics import f1_score, accuracy_score, precision_score, recall_score, roc_curve, roc_auc_score, fbeta_score, jaccard_score, log_loss, classification_report\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "import tensorflow.keras.layers as L\n",
        "from tensorflow.keras.losses import MeanAbsoluteError\n",
        "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "srsy62zd4vOn"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
        "from sklearn.metrics import f1_score, mean_squared_error\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "from yellowbrick.classifier import ROCAUC, ConfusionMatrix, PrecisionRecallCurve\n",
        "#from sklearn.metrics import plot_confusion_matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEYlovAI4vOo"
      },
      "source": [
        "Run the following code if using Google Colab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E66vGFXS4vOp"
      },
      "source": [
        "### Data file description\n",
        "We used the file from the earlier supervised learning (traditional machine learning models) performed. This file has the sentiment and normalized sentiment score."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GiCNlfzYnmQz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "58c52f7c-d372-41ae-b237-932e5aedb9a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  listing_id reviewer_name  \\\n",
              "0           0         109         Edwin   \n",
              "1           1         109          Jenn   \n",
              "2           2        2708       Kuberan   \n",
              "3           3        2708       Camilla   \n",
              "4           4        2708        Fallon   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          comments2  \\\n",
              "0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The host canceled my reservation the day before arrival.   \n",
              "1                                                                                                                                                                                                                                                              Me and two friends stayed for four and a half months. It was a great place to stay! The apartment was very comfortable and I really enjoyed having the park with running path across the street. The only downside was it wasnt within walking distance to restaurants, bars, or coffee shops. But they are a short drive away. Overall, great stay!   \n",
              "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      i had a wonderful stay. Everything from start to the end was perfect.  Will come back again.   \n",
              "3  Charles is just amazing and he made my stay special. He is so nice, helpful and absolutely polite. Charles is always there when you need some advice or help and totally respects your privacy too. I could concentrate on my work while Charles was doing his work  also absolutely quiet. The apartment is perfectly tidy, very clean, with tasty furniture and beautiful details. My bedroom even had a desk in it. Also, the apartment is located in a perfect area, most sightseeing spots are in walking distance and there are some very nice restaurants around as well. I highly recommend this place!    \n",
              "4                                                                                                                                                                                                                                                                                                                                                                             Staying with Chas was an absolute pleasure. He was very accommodating and respectful of personal space. He is truly a nice person. He was very helpful and I am grateful that he opened up his home to me. I am glad to have met him.   \n",
              "\n",
              "     id  final_review_score score_label  Sentiment_Score Sentiment  \\\n",
              "0   109            4.285714        high           0.0000   Neutral   \n",
              "1   109            4.285714        high           0.9316  Positive   \n",
              "2  2708            4.912857        high           0.8126  Positive   \n",
              "3  2708            4.912857        high           0.9909  Positive   \n",
              "4  2708            4.912857        high           0.9672  Positive   \n",
              "\n",
              "   normalized_Sentiment_score  normalize_rating_score  \n",
              "0                        2.50                    4.25  \n",
              "1                        4.75                    4.25  \n",
              "2                        4.50                    5.00  \n",
              "3                        5.00                    5.00  \n",
              "4                        5.00                    5.00  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>listing_id</th>\n",
              "      <th>reviewer_name</th>\n",
              "      <th>comments2</th>\n",
              "      <th>id</th>\n",
              "      <th>final_review_score</th>\n",
              "      <th>score_label</th>\n",
              "      <th>Sentiment_Score</th>\n",
              "      <th>Sentiment</th>\n",
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              "      <th>normalize_rating_score</th>\n",
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              "      <td>0</td>\n",
              "      <td>109</td>\n",
              "      <td>Edwin</td>\n",
              "      <td>The host canceled my reservation the day before arrival.</td>\n",
              "      <td>109</td>\n",
              "      <td>4.285714</td>\n",
              "      <td>high</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>2.50</td>\n",
              "      <td>4.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>109</td>\n",
              "      <td>Jenn</td>\n",
              "      <td>Me and two friends stayed for four and a half months. It was a great place to stay! The apartment was very comfortable and I really enjoyed having the park with running path across the street. The only downside was it wasnt within walking distance to restaurants, bars, or coffee shops. But they are a short drive away. Overall, great stay!</td>\n",
              "      <td>109</td>\n",
              "      <td>4.285714</td>\n",
              "      <td>high</td>\n",
              "      <td>0.9316</td>\n",
              "      <td>Positive</td>\n",
              "      <td>4.75</td>\n",
              "      <td>4.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2708</td>\n",
              "      <td>Kuberan</td>\n",
              "      <td>i had a wonderful stay. Everything from start to the end was perfect.  Will come back again.</td>\n",
              "      <td>2708</td>\n",
              "      <td>4.912857</td>\n",
              "      <td>high</td>\n",
              "      <td>0.8126</td>\n",
              "      <td>Positive</td>\n",
              "      <td>4.50</td>\n",
              "      <td>5.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2708</td>\n",
              "      <td>Camilla</td>\n",
              "      <td>Charles is just amazing and he made my stay special. He is so nice, helpful and absolutely polite. Charles is always there when you need some advice or help and totally respects your privacy too. I could concentrate on my work while Charles was doing his work  also absolutely quiet. The apartment is perfectly tidy, very clean, with tasty furniture and beautiful details. My bedroom even had a desk in it. Also, the apartment is located in a perfect area, most sightseeing spots are in walking distance and there are some very nice restaurants around as well. I highly recommend this place!</td>\n",
              "      <td>2708</td>\n",
              "      <td>4.912857</td>\n",
              "      <td>high</td>\n",
              "      <td>0.9909</td>\n",
              "      <td>Positive</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2708</td>\n",
              "      <td>Fallon</td>\n",
              "      <td>Staying with Chas was an absolute pleasure. He was very accommodating and respectful of personal space. He is truly a nice person. He was very helpful and I am grateful that he opened up his home to me. I am glad to have met him.</td>\n",
              "      <td>2708</td>\n",
              "      <td>4.912857</td>\n",
              "      <td>high</td>\n",
              "      <td>0.9672</td>\n",
              "      <td>Positive</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "drive.mount(\"/content/drive\")\n",
        "path=\"/content/drive/MyDrive/M2/Reviews_w_sentiment_score_data.csv\"\n",
        "df = pd.read_csv(path)\n",
        "pd.set_option('display.max_columns', None)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tH8dX4C6i8pl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40465317-1bb0-4c70-b476-1a1e3867c219"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0                    0\n",
              "listing_id                    0\n",
              "reviewer_name                 2\n",
              "comments2                     0\n",
              "id                            0\n",
              "final_review_score            0\n",
              "score_label                   0\n",
              "Sentiment_Score               0\n",
              "Sentiment                     0\n",
              "normalized_Sentiment_score    0\n",
              "normalize_rating_score        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MONkmno6zzPX"
      },
      "source": [
        "***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kWB1Qae74vOv"
      },
      "outputs": [],
      "source": [
        "df2 = df.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OMo7QY-k4vOv"
      },
      "outputs": [],
      "source": [
        "### this is when the labelling starts\n",
        "\n",
        "def label_score(row):\n",
        "    if row['normalized_Sentiment_score'] == 5.0:\n",
        "        return 4\n",
        "    elif row['normalized_Sentiment_score'] == 4.75:\n",
        "        return 3\n",
        "    elif row['normalized_Sentiment_score'] == 4.5:\n",
        "        return 2\n",
        "    elif row['normalized_Sentiment_score'] == 4.25:\n",
        "        return 1\n",
        "    else:  # for 4.0 and lower\n",
        "        return 0\n",
        "\n",
        "df2['label'] = df2.apply(label_score, axis=1)"
      ]
    },
    {
      "cell_type": "code",
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          "height": 356
        },
        "outputId": "d01b9f2d-728b-4c8a-dd0c-fccf0782d50b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Unnamed: 0    listing_id            id  final_review_score  \\\n",
              "count  1.311152e+06  1.311152e+06  1.311152e+06        1.311152e+06   \n",
              "mean   6.555755e+05  4.434812e+16  4.434812e+16        4.841691e+00   \n",
              "std    3.784971e+05  1.645802e+17  1.645802e+17        1.485141e-01   \n",
              "min    0.000000e+00  1.090000e+02  1.090000e+02        2.857143e-01   \n",
              "25%    3.277878e+05  9.960144e+06  9.960144e+06        4.797143e+00   \n",
              "50%    6.555755e+05  2.182754e+07  2.182754e+07        4.880000e+00   \n",
              "75%    9.833632e+05  3.974507e+07  3.974507e+07        4.931429e+00   \n",
              "max    1.311151e+06  8.379426e+17  8.379426e+17        5.000000e+00   \n",
              "\n",
              "       Sentiment_Score  normalized_Sentiment_score  normalize_rating_score  \\\n",
              "count     1.311152e+06                1.311152e+06            1.311152e+06   \n",
              "mean      8.269747e-01                4.577832e+00            4.858903e+00   \n",
              "std       2.491615e-01                6.343306e-01            1.803327e-01   \n",
              "min      -9.987000e-01                0.000000e+00            2.500000e-01   \n",
              "25%       8.002000e-01                4.500000e+00            4.750000e+00   \n",
              "50%       9.151000e-01                4.750000e+00            5.000000e+00   \n",
              "75%       9.623000e-01                5.000000e+00            5.000000e+00   \n",
              "max       9.998000e-01                5.000000e+00            5.000000e+00   \n",
              "\n",
              "              label  \n",
              "count  1.311152e+06  \n",
              "mean   2.671066e+00  \n",
              "std    1.350041e+00  \n",
              "min    0.000000e+00  \n",
              "25%    2.000000e+00  \n",
              "50%    3.000000e+00  \n",
              "75%    4.000000e+00  \n",
              "max    4.000000e+00  "
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              "\n",
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>listing_id</th>\n",
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              "      <th>normalize_rating_score</th>\n",
              "      <th>label</th>\n",
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>1.311152e+06</td>\n",
              "      <td>1.311152e+06</td>\n",
              "      <td>1.311152e+06</td>\n",
              "      <td>1.311152e+06</td>\n",
              "      <td>1.311152e+06</td>\n",
              "      <td>1.311152e+06</td>\n",
              "      <td>1.311152e+06</td>\n",
              "      <td>1.311152e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.555755e+05</td>\n",
              "      <td>4.434812e+16</td>\n",
              "      <td>4.434812e+16</td>\n",
              "      <td>4.841691e+00</td>\n",
              "      <td>8.269747e-01</td>\n",
              "      <td>4.577832e+00</td>\n",
              "      <td>4.858903e+00</td>\n",
              "      <td>2.671066e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.784971e+05</td>\n",
              "      <td>1.645802e+17</td>\n",
              "      <td>1.645802e+17</td>\n",
              "      <td>1.485141e-01</td>\n",
              "      <td>2.491615e-01</td>\n",
              "      <td>6.343306e-01</td>\n",
              "      <td>1.803327e-01</td>\n",
              "      <td>1.350041e+00</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.090000e+02</td>\n",
              "      <td>1.090000e+02</td>\n",
              "      <td>2.857143e-01</td>\n",
              "      <td>-9.987000e-01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.500000e-01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.277878e+05</td>\n",
              "      <td>9.960144e+06</td>\n",
              "      <td>9.960144e+06</td>\n",
              "      <td>4.797143e+00</td>\n",
              "      <td>8.002000e-01</td>\n",
              "      <td>4.500000e+00</td>\n",
              "      <td>4.750000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.555755e+05</td>\n",
              "      <td>2.182754e+07</td>\n",
              "      <td>2.182754e+07</td>\n",
              "      <td>4.880000e+00</td>\n",
              "      <td>9.151000e-01</td>\n",
              "      <td>4.750000e+00</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.833632e+05</td>\n",
              "      <td>3.974507e+07</td>\n",
              "      <td>3.974507e+07</td>\n",
              "      <td>4.931429e+00</td>\n",
              "      <td>9.623000e-01</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.311151e+06</td>\n",
              "      <td>8.379426e+17</td>\n",
              "      <td>8.379426e+17</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>9.998000e-01</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3dfc93e-214d-433a-97dc-97977523dda2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c3dfc93e-214d-433a-97dc-97977523dda2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c3dfc93e-214d-433a-97dc-97977523dda2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "df2.describe()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_labels(df2, label = \"label\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "NtVTWVBwYZXh",
        "outputId": "3f59833a-f7bf-4494-cf69-ae72e37151da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fh0DOtKZ4vOw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "016a2780-5bca-42ff-b221-18d717db54a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bad: (178216, 12)\n",
            "Nay: (79067, 12)\n",
            "Average: (174935, 12)\n",
            "Good: (442500, 12)\n",
            "Awesome: (436434, 12)\n",
            "Nay-lowest-count: 79067\n"
          ]
        }
      ],
      "source": [
        "RANDOM_SEED = 42\n",
        "label_count_1, label_count_2, label_count_0, label_count_4, label_count3 = df2['label'].value_counts().sort_values()\n",
        "\n",
        "label_0 = df2[df2['label'] == 0]\n",
        "label_1 = df2[df2['label'] == 1]\n",
        "label_2 = df2[df2['label'] == 2]\n",
        "label_3 = df2[df2['label'] == 3]\n",
        "label_4 = df2[df2['label'] == 4]\n",
        "\n",
        "print('Bad:', label_0.shape)\n",
        "print('Nay:', label_1.shape)\n",
        "print('Average:', label_2.shape)\n",
        "print('Good:', label_3.shape)\n",
        "print('Awesome:', label_4.shape)\n",
        "print('Nay-lowest-count:', label_count_1)\n",
        "\n",
        "### because label 1 is the lowest:\n",
        "label_0_under = label_0.sample(label_count_1, random_state = RANDOM_SEED)\n",
        "label_2_under = label_2.sample(label_count_1, random_state = RANDOM_SEED)\n",
        "label_3_under = label_3.sample(label_count_1, random_state = RANDOM_SEED)\n",
        "label_4_under = label_4.sample(label_count_1, random_state = RANDOM_SEED)\n",
        "\n",
        "cleaned_df = pd.concat([label_0_under, label_1, label_2_under, label_3_under, label_4_under], axis = 0)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rx2VV4oE4vOw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "outputId": "996567a2-963e-42a0-8be2-3cf164293bbe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79067\n",
            "79067\n",
            "79067\n",
            "79067\n",
            "79067\n"
          ]
        }
      ],
      "source": [
        "plot_labels(cleaned_df, label = \"label\")\n",
        "print(len(cleaned_df[cleaned_df['label']==0]))\n",
        "print(len(cleaned_df[cleaned_df['label']==1]))\n",
        "print(len(cleaned_df[cleaned_df['label']==2]))\n",
        "print(len(cleaned_df[cleaned_df['label']==3]))\n",
        "print(len(cleaned_df[cleaned_df['label']==4]))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W19_axCF4vOw"
      },
      "outputs": [],
      "source": [
        "\n",
        "\"\"\"\n",
        "    Splits the dataframe into training, validation and testing sets.\n",
        "\n",
        "    Args:\n",
        "        df (pd.DataFrame): The input dataframe.\n",
        "        fractions (list-like): A list-like object of proportions for the train, validation and test set respectively,\n",
        "            e.g., for a 60%/20%/20% split, fractions could be [0.6, 0.2, 0.2].\n",
        "        RANDOM_SEED (int): Seed for the random number generator.\n",
        "\n",
        "    Returns:\n",
        "        train_df (pd.DataFrame): The training set.\n",
        "        val_df (pd.DataFrame): The validation set.\n",
        "        test_df (pd.DataFrame): The testing set.\n",
        "\n",
        "    Raises:\n",
        "        ValueError: If the fractions do not add up to 1.\n",
        "        ValueError: If the number of instances across all sets do not match the number of instances in the original dataframe.\"\"\"\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "fractions = np.array([0.8, 0.1, 0.1])\n",
        "\n",
        "\n",
        "def data_split(df, fractions, RANDOM_SEED):\n",
        "\n",
        "    # shuffling the data:\n",
        "\n",
        "    df = df.sample(frac = 1, random_state = RANDOM_SEED)\n",
        "    # split into training, validation and testing sets\n",
        "    train_df, val_df, test_df = np.array_split(df, (fractions[:-1].cumsum() * len(df)).astype(int))\n",
        "\n",
        "    if len(train_df) + len(val_df) + len(test_df) != len(df):\n",
        "        raise ValueError(\"Number of instances in splits do not match original dataframe\")\n",
        "        print(\"Check the splitting method please!\")\n",
        "    else:\n",
        "        return train_df, val_df, test_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8ySBFVWG4vOw"
      },
      "outputs": [],
      "source": [
        "df_train, df_dev, df_test = data_split(cleaned_df, fractions, RANDOM_SEED)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xfBMIgIH4vOx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "b3bbbd6b-5f42-40a0-85a8-c313a5bb780c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_labels(df_dev, label = 'label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "flELA2zp4vOx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "2c181f56-cf4c-447e-d40c-7082c0d7ab00"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_labels(df_test, label = 'label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z3gk-OSh4vOx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0c9646f-c7bd-4c3b-bbf4-660acfe6da33"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(316268, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "df_train.head()\n",
        "df_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1fWC1QO4vOx"
      },
      "source": [
        " ***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGQFMuZ94vOx"
      },
      "source": [
        "## Feature Engineering / Exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TykFj7bB4vOx"
      },
      "source": [
        "### Tokenization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AC7VYNEo4vOx"
      },
      "source": [
        "In this part, we will tokenize reviews and count numbers of tokens for each review. This information will be useful in BERT model (sensitivity analysis). But please note we will not use these tokens directly for training models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WoV8lwNX4vOy"
      },
      "outputs": [],
      "source": [
        "from nltk.tokenize import word_tokenize"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pXhHTbxJ4vOy"
      },
      "source": [
        "Parse through the first line, see what it does"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8uKFwQOe4vOy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94762a2e-009c-4899-a3f6-685135963654"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Great place. \n",
            " ['Great', 'place']\n"
          ]
        }
      ],
      "source": [
        "ds_temp = df_train[\"comments2\"].iloc[:10].map(lambda comments2: re.sub(r\"[^a-zA-Z0-9' ]\", ' ', comments2)).map(lambda comments2: comments2.split())\n",
        "print(df_train[\"comments2\"].iloc[0], '\\n', ds_temp.iloc[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gfmm9UrP4vOy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a846eab1-2f66-4e4f-c30a-cd1cec9bb887"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2.64 s, sys: 318 ms, total: 2.96 s\n",
            "Wall time: 2.95 s\n"
          ]
        }
      ],
      "source": [
        "%time df_train[\"tokens\"] = df_train[\"comments2\"].map(lambda comments2: re.sub(r\"[^a-zA-Z0-9' ]\", ' ', comments2)).map(lambda comments2: comments2.split())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0JmfYX2_4vOy"
      },
      "outputs": [],
      "source": [
        "num_train = df_train.shape[0]\n",
        "num_review = df2.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9_Du9ymm4vOy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "outputId": "9cedc59e-a52d-43c1-fc1a-50f9d619380f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df_train[\"num_tokens\"] = df_train[\"tokens\"].map(len)\n",
        "ax = df_train[\"num_tokens\"].hist(bins=20,\n",
        "                                 figsize=(4,3),\n",
        "                                 weights=np.ones(num_train)/num_train)\n",
        "ax.set_xlabel(\"Number of Tokens\")\n",
        "ax.set_ylabel(\"Ratio\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W7stl2aO4vOy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d10ec317-4181-4004-c93a-6ffc752bbf4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9662533041597633\n",
            "0.995254025067348\n"
          ]
        }
      ],
      "source": [
        "### to see number of reviews with tokens more than 128 vs 256\n",
        "\n",
        "print(len(df_train[df_train[\"num_tokens\"] <= 128]) / len(df_train[\"num_tokens\"]))\n",
        "print(len(df_train[df_train[\"num_tokens\"] <= 256]) / len(df_train[\"num_tokens\"]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywDIh6Cc4vOy"
      },
      "source": [
        "df_train.drop(columns=[\"tokens\", \"num_tokens\"], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tyBAdjph4vOy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e1489a0-dbae-4116-f11e-d13018632bcd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 8.81 s, sys: 149 ms, total: 8.96 s\n",
            "Wall time: 9.67 s\n"
          ]
        }
      ],
      "source": [
        "%time df2[\"num_tokens\"] = df2[\"comments2\"].\\\n",
        "map(lambda comments2: re.sub(r\"[^a-zA-Z0-9' ]\", ' ', comments2)).\\\n",
        "map(lambda comments2: len(comments2.split()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bWA5evKX4vOy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "763855c6-9f2f-472f-986e-e86f586ddb3a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x350 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ax = df2[\"num_tokens\"].hist(bins=100,\n",
        "                                  figsize=(5,3.5),\n",
        "                                  grid=False,\n",
        "                                  weights=np.ones(num_review)/num_review)\n",
        "ax.set_xlabel(\"Number of Tokens\")\n",
        "ax.set_ylabel(\"Ratio\")\n",
        "plt.savefig(\"num_tokens.eps\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j-BjO5Qq4vOz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b3e0e00-2661-4e15-91cd-ecba93c1125b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9550608930162178\n",
            "0.9943065334911589\n"
          ]
        }
      ],
      "source": [
        "print(len(df2[df2[\"num_tokens\"] <= 128]) / len(df2[\"num_tokens\"]))\n",
        "print(len(df2[df2[\"num_tokens\"] <= 256]) / len(df2[\"num_tokens\"]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fSbnmTMY4vOz"
      },
      "outputs": [],
      "source": [
        "df2.drop(columns=[\"num_tokens\"], inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFU-b3Eo4vOz"
      },
      "source": [
        "From the result, we can see most reviews are shorter than 256 tokens, which is important for our use later"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XxXyrwJy4vOz"
      },
      "source": [
        " ***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f38t88DH4vOz"
      },
      "source": [
        "# Vectorization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlCXFm6Z4vOz"
      },
      "source": [
        "We will try following settings for vectorization.\n",
        "\n",
        "* Word representation: `CountVectorizer` vs. `TfidfVectorizer`\n",
        "* N-grams: unigram & bigram\n",
        "* Minimum document frequency: 5\n",
        "* Convert to lowercase\n",
        "* Use stop words\n",
        "* Try binary counter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jZ73mMYb4vOz"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "\n",
        "def extract_features(df_train, df_dev, df_test):\n",
        "\n",
        "\n",
        "    ### vectorization\n",
        "    vectorizer = TfidfVectorizer(analyzer='word',\n",
        "                               stop_words='english',\n",
        "                               ngram_range=(1, 2),\n",
        "                               lowercase=True,\n",
        "                               min_df=5,\n",
        "                               binary=False)\n",
        "    X_train = vectorizer.fit_transform(df_train[\"comments2\"])\n",
        "    X_dev = vectorizer.transform(df_dev[\"comments2\"])\n",
        "    X_test = vectorizer.transform(df_test[\"comments2\"])\n",
        "    y_train = df_train[\"label\"].tolist()\n",
        "    y_dev = df_dev[\"label\"].tolist()\n",
        "    y_test = df_test[\"label\"].tolist()\n",
        "\n",
        "    return X_train, X_dev, X_test, y_train, y_dev, y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VGasvl7g4vOz"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VlzKGabP4vOz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e329c780-181e-4878-8975-fca416018e4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 16.2 s, sys: 312 ms, total: 16.5 s\n",
            "Wall time: 16.5 s\n"
          ]
        }
      ],
      "source": [
        "%time X_train, X_dev, X_test, y_train, y_dev, y_test = extract_features(df_train, df_dev, df_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gJVrVseW4vOz"
      },
      "outputs": [],
      "source": [
        "#print(X_train.shape), print(X_dev.shape), print(y_train.shape), print(y_dev.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0moCfC0P4vOz"
      },
      "source": [
        "***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8HT0-ZVKwTpK"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Liz6U9wrwVEk"
      },
      "source": [
        "# BERT IMPLEMENTATION"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nY2vyHT0wT-P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a139332-eed7-4faf-b302-5bdaa067c87b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Installing collected packages: tokenizers, sentencepiece, safetensors, pathtools, xxhash, watchdog, validators, smmap, setproctitle, sentry-sdk, pympler, multidict, importlib-metadata, frozenlist, docker-pycreds, dill, blinker, async-timeout, yarl, pydeck, multiprocess, huggingface-hub, gitdb, aiosignal, transformers, seqeval, GitPython, aiohttp, wandb, streamlit, datasets, simpletransformers\n",
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          ]
        }
      ],
      "source": [
        "%pip install simpletransformers\n",
        "from simpletransformers.classification import ClassificationModel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AXmXou6pw5qx"
      },
      "outputs": [],
      "source": [
        "from transformers import BertTokenizerFast, BertForSequenceClassification\n",
        "from torch.utils.data import DataLoader\n",
        "from transformers import AdamW\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import torch\n",
        "from torch.utils.data import DataLoader, TensorDataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jtmYuQtaxpLo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c38a620-ddcc-4dd7-b0db-9fa26792a2d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'listing_id', 'reviewer_name', 'comments2', 'id',\n",
              "       'final_review_score', 'score_label', 'Sentiment_Score', 'Sentiment',\n",
              "       'normalized_Sentiment_score', 'normalize_rating_score', 'label',\n",
              "       'tokens', 'num_tokens'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "df_train.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1_6WZdk7xxF_"
      },
      "outputs": [],
      "source": [
        "# Make a new DataFrame with just the text and label columns\n",
        "train_Bert_df = df_train[['comments2', 'label']]\n",
        "train_Bert_texts  = train_Bert_df['comments2'].values.tolist()\n",
        "train_Bert_labels = train_Bert_df['label'].values.tolist()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGupWpcZndhS"
      },
      "source": [
        "***\n",
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPbSaQzHmuof"
      },
      "source": [
        "## **LOAD** The Above codes till here.\n",
        "\n",
        "\n",
        "\n",
        "BERT Training and model implementation starts here.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NP9rJQyK7y6q"
      },
      "source": [
        "The next few steps is to train the BERT model using pretrain model from pre-exisiting tensor dataset trained on wiki pages, youtube etc. It is then applied on our dataset\n",
        "\n",
        "- It takes 6 hours to train\n",
        "- saves model in the drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "outputId": "e8a4218d-9211-4790-ce4a-0088f8174a41"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
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          "output_type": "display_data",
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              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
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          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "7dada3f252914182a6e5ba4b427dce80"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "# Only for model training, don't run otherwise -- > Load the BERT tokenizer and model\n",
        "tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=df_train['label'].nunique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1fPhEbn_x_U2"
      },
      "outputs": [],
      "source": [
        "# Only for model training, don't run otherwise -- ># Use a GPU if available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "# Encode the labels\n",
        "le = LabelEncoder()\n",
        "df_train['encoded_labels'] = le.fit_transform(df_train['label'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OSG6O0SmwUD0"
      },
      "outputs": [],
      "source": [
        "## Only for model training, don't run otherwise -- > Tokenize the text data\n",
        "encoded_data_train = tokenizer.batch_encode_plus(\n",
        "    df_train['comments2'].tolist(),\n",
        "    truncation=True,\n",
        "    padding=True,\n",
        "    max_length=256\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3IOAwTdfwUGm"
      },
      "outputs": [],
      "source": [
        "# Only for model training, don't run otherwise -- >\n",
        "input_ids_train = torch.tensor(encoded_data_train['input_ids'])\n",
        "attention_masks_train = torch.tensor(encoded_data_train['attention_mask'])\n",
        "labels_train = torch.tensor(df_train['encoded_labels'].values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wh2nH59twUTW",
        "outputId": "f0a57436-b533-485a-9dda-ea94a2c1b87e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning:\n",
            "\n",
            "This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Only for model training, don't run otherwise -- >\n",
        "from torch.utils.data import TensorDataset\n",
        "from torch.utils.data import RandomSampler\n",
        "\n",
        "# Create a DataLoader\n",
        "dataset_train = TensorDataset(input_ids_train, attention_masks_train, labels_train)\n",
        "dataloader_train = DataLoader(dataset_train, sampler=RandomSampler(dataset_train), batch_size=32)\n",
        "\n",
        "# Define the optimizer\n",
        "optimizer = AdamW(model.parameters(), lr=1e-5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UmL8l9Fyy4st"
      },
      "outputs": [],
      "source": [
        "# Only for model training, don't run otherwise -- > IMPORTANT FUNCTION, takes 5.0hours\n",
        "# Training loop\n",
        "num_epochs = 5\n",
        "model.train()\n",
        "for epoch in range(num_epochs):\n",
        "    for batch in dataloader_train:\n",
        "        batch = tuple(b.to(device) for b in batch)\n",
        "        inputs = {'input_ids': batch[0],\n",
        "                  'attention_mask': batch[1],\n",
        "                  'labels': batch[2]}\n",
        "\n",
        "        outputs = model(**inputs)\n",
        "        loss = outputs[0]\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R_ckHMvfy42Y"
      },
      "outputs": [],
      "source": [
        "# Only for model training, don't run otherwise -- > save to drive\n",
        "model.save_pretrained(\"/content/drive/MyDrive\")\n",
        "\n",
        "#/content/drive/MyDrive/M2/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Jvhm-WD8cf2"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDdU037U8doQ"
      },
      "source": [
        "Loading of BERT Model begins here"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 885,
          "referenced_widgets": [
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        },
        "id": "bWnFcFhbW_ZV",
        "outputId": "33738efd-9de4-4638-bf00-a78e4abe669e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79c422b553394642a488e7959ab05006"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "056c90cc910c467a8b246d209d800769"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "202fdbb44c774fbcbb4da9fe65f9ac07"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "994610d556c64b329b5ca2ba50426ecc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=5, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "### LOAD MODEL FROM HERE\n",
        "###Step 1\n",
        "from torch.utils.data import SequentialSampler\n",
        "# Load the trained model and tokenizer\n",
        "model = BertForSequenceClassification.from_pretrained(\"/content/drive/MyDrive/M2/Supervised_Learning/Full_Model\")\n",
        "tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Use a GPU if available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F6ycW32aemn3"
      },
      "outputs": [],
      "source": [
        "### Step 2\n",
        "# Encode the labels\n",
        "le = LabelEncoder()\n",
        "df_test['encoded_labels'] = le.fit_transform(df_test['label'])\n",
        "\n",
        "# Tokenize the text data\n",
        "encoded_data_test = tokenizer.batch_encode_plus(\n",
        "    df_test['comments2'].tolist(),\n",
        "    truncation=True,\n",
        "    padding=True,\n",
        "    max_length=256\n",
        ")\n",
        "\n",
        "input_ids_test = torch.tensor(encoded_data_test['input_ids'])\n",
        "attention_masks_test = torch.tensor(encoded_data_test['attention_mask'])\n",
        "labels_test = torch.tensor(df_test['encoded_labels'].values)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DyrLnlCzfO7B",
        "outputId": "4a62825c-9be9-45cb-efc8-d0b9a0c22bdf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=5, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "### Step 3\n",
        "# Create a DataLoader\n",
        "dataset_test = TensorDataset(input_ids_test, attention_masks_test, labels_test)\n",
        "dataloader_test = DataLoader(dataset_test, sampler=SequentialSampler(dataset_test), batch_size=32)\n",
        "\n",
        "# Evaluation mode\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CjTwuGIcfoJ3"
      },
      "outputs": [],
      "source": [
        "#### Step 4, to do the prediction\n",
        "\n",
        "# Variable to store predictions\n",
        "predictions = []\n",
        "\n",
        "# Predict on test data\n",
        "for batch in dataloader_test:\n",
        "    batch = tuple(b.to(device) for b in batch)\n",
        "\n",
        "    inputs = {'input_ids': batch[0],\n",
        "              'attention_mask': batch[1],\n",
        "              'labels': batch[2]}\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**inputs)\n",
        "\n",
        "    logits = outputs[1]\n",
        "    logits = logits.detach().cpu().numpy()\n",
        "    predictions.append(logits)\n",
        "\n",
        "predictions = np.concatenate(predictions, axis=0)\n",
        "\n",
        "# Convert logits to predicted class\n",
        "preds = np.argmax(predictions, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8FaQfwPp0WOy",
        "outputId": "b9808f0c-04a3-4a99-b445-bcd90170832c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-2.039121  , -1.3176852 , -0.4517939 ,  6.234849  , -2.5249166 ],\n",
              "       [-0.06209285,  7.0985374 , -0.7435171 , -3.6768692 , -2.768035  ],\n",
              "       [ 1.4395556 ,  5.4386663 ,  0.83590084, -3.2408392 , -4.3329663 ],\n",
              "       ...,\n",
              "       [-0.23559546,  6.804977  , -1.2970654 , -3.324782  , -2.207187  ],\n",
              "       [-1.0485826 ,  6.8679914 ,  0.43491995, -3.3395922 , -3.1136487 ],\n",
              "       [-1.6688787 ,  7.4077744 , -1.1784041 , -2.6579235 , -2.0752192 ]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5jX0vUke0hF2",
        "outputId": "6f1867f3-1afd-455f-e95d-5b78d285939a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9422016492133354"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "### Step 5: Analysis and evaluation\n",
        "\n",
        "y = df_test['label'].tolist()\n",
        "\n",
        "accuracy_score(y, preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yi39Sm4S08hG",
        "outputId": "9d758c4f-415f-40a1-eabd-5d6426b2018d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0      0.975     0.967     0.971      7943\n",
            "           1      0.941     0.941     0.941      7954\n",
            "           2      0.951     0.907     0.928      7878\n",
            "           3      0.926     0.902     0.914      7908\n",
            "           4      0.919     0.994     0.955      7851\n",
            "\n",
            "    accuracy                          0.942     39534\n",
            "   macro avg      0.943     0.942     0.942     39534\n",
            "weighted avg      0.943     0.942     0.942     39534\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y, preds, digits=3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "6_J2dkqy1NNT",
        "outputId": "30281b94-7713-409e-89c6-5f61fa1f1341"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "### Step 5: Analysis and evaluation\n",
        "# Confusion Matrix\n",
        "df_cm = pd.DataFrame(confusion_matrix(y, preds, normalize='true'), range(5), range(5))\n",
        "\n",
        "labels = [\"Bad\", \"Nay\", \"Average\", \"Good\", \"Awesome!!\"]\n",
        "\n",
        "model_name = \"BERT_base\"\n",
        "plt.figure(figsize=(6,4))\n",
        "ax = sns.heatmap(df_cm, annot=True, cmap=plt.cm.Blues, square=True)\n",
        "ax.set_xticklabels(labels)  # Set x-axis labels\n",
        "ax.set_yticklabels(labels)  # Set y-axis labels\n",
        "ax.set_xlabel('Predicted label')\n",
        "ax.set_ylabel('True label')\n",
        "\n",
        "# Save the plot\n",
        "#plt.savefig(model_name + \"_\" + label.lower() + \".eps\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GPVL-DZeZOxw"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uChmRibzOZ-h"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ucB-F8uOat3"
      },
      "source": [
        "DistilBERT model is a distilled form of the BERT model. The size of a BERT model was reduced by 40% via knowledge distillation during the pre-training phase while retaining 97% of its language understanding abilities and being 60% faster."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278,
          "referenced_widgets": [
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        },
        "id": "NvE0SPuBdUoM",
        "outputId": "999aa1c7-d96e-4383-8384-76dda9f0cceb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a93ffa83f0447b1a53882e83c4c0636"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d94e36aeb0ff4f49a1e97d2f7181b0f0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a5c700a78164e3c908e7037272215bd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "012f5394552449efa7de2f8e1506908c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "953a59f8f4564694986068802e2958e8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification 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",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.bias', 'classifier.weight', 'pre_classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "# Only for model training, don't run otherwise -- ># Use a GPU if available\n",
        "\n",
        "from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "\n",
        "\n",
        "# Load pre-trained model and tokenizer\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')\n",
        "\n",
        "\n",
        "# Adjust num_labels to the number of unique labels in your dataset\n",
        "distilled_model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=df_train['label'].nunique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "43qM4YV5BPRb",
        "outputId": "6ef3da8e-e9bb-4687-9fd7-11b946fe2e5c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "250000"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8CP2e-cqQPNr"
      },
      "outputs": [],
      "source": [
        "# Only for model training, don't run otherwise -- ># Use a GPU if available\n",
        "\n",
        "distilled_device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "distilled_model.to(distilled_device)\n",
        "\n",
        "# Encode the labels\n",
        "distilled_le = LabelEncoder()\n",
        "df_train['distilled_encoded_labels'] = distilled_le.fit_transform(df_train['label'])\n",
        "\n",
        "\n",
        "\n",
        "## Only for model training, don't run otherwise -- > Tokenize the text data\n",
        "distilled_encoded_data_train = tokenizer.batch_encode_plus(\n",
        "    df_train['comments2'].tolist(),\n",
        "    truncation=True,\n",
        "    padding=True,\n",
        "    max_length=256\n",
        ")\n",
        "\n",
        "\n",
        "# Only for model training, don't run otherwise -- >\n",
        "distilled_input_ids_train = torch.tensor(distilled_encoded_data_train['input_ids'])\n",
        "distilled_attention_masks_train = torch.tensor(distilled_encoded_data_train['attention_mask'])\n",
        "distilled_labels_train = torch.tensor(df_train['distilled_encoded_labels'].values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0NF1Juu2UhvR",
        "outputId": "68cf77fe-a3fe-47d5-ce3a-978228540bdc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning:\n",
            "\n",
            "This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Only for model training, don't run otherwise -- >\n",
        "from torch.utils.data import TensorDataset\n",
        "from torch.utils.data import RandomSampler\n",
        "\n",
        "# Create a DataLoader\n",
        "distilled_dataset_train = TensorDataset(distilled_input_ids_train, distilled_attention_masks_train, distilled_labels_train)\n",
        "distilled_dataloader_train = DataLoader(distilled_dataset_train, sampler=RandomSampler(distilled_dataset_train), batch_size=32)\n",
        "# Define the optimizer\n",
        "distilled_optimizer = AdamW(distilled_model.parameters(), lr=1e-5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZdK7-jY8Uhzf"
      },
      "outputs": [],
      "source": [
        "# Only for model training, don't run otherwise -- > IMPORTANT FUNCTION, takes 2.5hours\n",
        "# Training loop\n",
        "num_epochs = 5\n",
        "distilled_model.train()\n",
        "for epoch in range(num_epochs):\n",
        "    for batch in distilled_dataloader_train:\n",
        "        batch = tuple(b.to(distilled_device) for b in batch)\n",
        "        inputs = {'input_ids': batch[0],\n",
        "                  'attention_mask': batch[1],\n",
        "                  'labels': batch[2]}\n",
        "\n",
        "        outputs = distilled_model(**inputs)\n",
        "        loss = outputs[0]\n",
        "        loss.backward()\n",
        "        distilled_optimizer.step()\n",
        "        distilled_optimizer.zero_grad()\n",
        "\n",
        "\n",
        "distilled_model.save_pretrained(\"/content/drive/MyDrive/M2/Supervised_Learning/Distilled_Model\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704,
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        "id": "5ixv_ZPzUh3K",
        "outputId": "e4fd0119-f4ba-4c34-c7dd-9d93d9d7151b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9dfc7937bcc44bdd9922e4646ed8db0e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "531e65c7930d440a829f3401977ccdaf"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26eb92d9f65641c295b58ee4273ce0ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a79ceedb8ae9455d8ec0ac22a631501a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DistilBertForSequenceClassification(\n",
              "  (distilbert): DistilBertModel(\n",
              "    (embeddings): Embeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer): Transformer(\n",
              "      (layer): ModuleList(\n",
              "        (0-5): 6 x TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (activation): GELUActivation()\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
              "  (classifier): Linear(in_features=768, out_features=5, bias=True)\n",
              "  (dropout): Dropout(p=0.2, inplace=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "### LOAD MODEL FROM HERE\n",
        "###Step 1\n",
        "\n",
        "from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "\n",
        "distilled_model = DistilBertForSequenceClassification.from_pretrained(\"/content/drive/MyDrive/M2/Supervised_Learning/Distilled_Model\")\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')\n",
        "\n",
        "# Use a GPU if available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "distilled_model.to(device)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NfHSwXCitf-4"
      },
      "outputs": [],
      "source": [
        "### Step 2\n",
        "# Encode the labels\n",
        "distilled_le = LabelEncoder()\n",
        "df_test['distilled_encoded_labels'] = distilled_le.fit_transform(df_test['label'])\n",
        "# Tokenize the text data\n",
        "distilled_encoded_data_test = tokenizer.batch_encode_plus(\n",
        "    df_test['comments2'].tolist(),\n",
        "    truncation=True,\n",
        "    padding=True,\n",
        "    max_length=256\n",
        ")\n",
        "distilled_input_ids_test = torch.tensor(distilled_encoded_data_test['input_ids'])\n",
        "distilled_attention_masks_test = torch.tensor(distilled_encoded_data_test['attention_mask'])\n",
        "distilled_labels_test = torch.tensor(df_test['distilled_encoded_labels'].values)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IEShhabntgI-",
        "outputId": "b3d01730-dacb-4d80-a37b-3ef245de45ca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DistilBertForSequenceClassification(\n",
              "  (distilbert): DistilBertModel(\n",
              "    (embeddings): Embeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer): Transformer(\n",
              "      (layer): ModuleList(\n",
              "        (0-5): 6 x TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (activation): GELUActivation()\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
              "  (classifier): Linear(in_features=768, out_features=5, bias=True)\n",
              "  (dropout): Dropout(p=0.2, inplace=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "### Step 3\n",
        "# Create a DataLoader\n",
        "from torch.utils.data import SequentialSampler\n",
        "distilled_dataset_test = TensorDataset(distilled_input_ids_test, distilled_attention_masks_test, distilled_labels_test)\n",
        "distilled_dataloader_test = DataLoader(distilled_dataset_test, sampler=SequentialSampler(distilled_dataset_test), batch_size=32)\n",
        "# Evaluation mode\n",
        "distilled_model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OaZnxXDJtgex"
      },
      "outputs": [],
      "source": [
        "#### Step 4, to do the prediction\n",
        "# Variable to store predictions\n",
        "distilled_predictions = []\n",
        "# Predict on test data\n",
        "for batch in distilled_dataloader_test:\n",
        "    batch = tuple(b.to(device) for b in batch)\n",
        "\n",
        "    inputs = {'input_ids': batch[0],\n",
        "              'attention_mask': batch[1],\n",
        "              'labels': batch[2]}\n",
        "    with torch.no_grad():\n",
        "        outputs = distilled_model(**inputs)\n",
        "    logits = outputs[1]\n",
        "    logits = logits.detach().cpu().numpy()\n",
        "    distilled_predictions.append(logits)\n",
        "distilled_predictions = np.concatenate(distilled_predictions, axis=0)\n",
        "# Convert logits to predicted class\n",
        "distilled_preds = np.argmax(distilled_predictions, axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v5xG3By4tgzo",
        "outputId": "1e386f9d-23e5-4c64-9e4a-755fa65c9b7b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9370668285526382"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "### Step 5: Analysis and evaluation\n",
        "y = df_test['label'].tolist()\n",
        "accuracy_score(y, distilled_preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eIbGe1YT75y8",
        "outputId": "871e0ee5-fdd2-4bf0-f82d-250f4531a46f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0      0.978     0.960     0.969      7943\n",
            "           1      0.944     0.927     0.936      7954\n",
            "           2      0.930     0.942     0.936      7878\n",
            "           3      0.950     0.861     0.904      7908\n",
            "           4      0.890     0.995     0.940      7851\n",
            "\n",
            "    accuracy                          0.937     39534\n",
            "   macro avg      0.938     0.937     0.937     39534\n",
            "weighted avg      0.939     0.937     0.937     39534\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y, distilled_preds, digits=3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "b__kPWRF8SqH",
        "outputId": "450bf717-99d8-4011-dfd7-1800dbc636f5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "### Step 5: Analysis and evaluation\n",
        "# Confusion Matrix\n",
        "distilled_df_cm = pd.DataFrame(confusion_matrix(y, distilled_preds, normalize='true'), range(5), range(5))\n",
        "\n",
        "labels = [\"Bad\", \"Nay\", \"Average\", \"Good\", \"Awesome!!\"]\n",
        "\n",
        "model_name = \"distilled_BERT_base\"\n",
        "plt.figure(figsize=(6,4))\n",
        "ax = sns.heatmap(distilled_df_cm, annot=True, cmap=plt.cm.Blues, square=True)\n",
        "ax.set_xticklabels(labels)  # Set x-axis labels\n",
        "ax.set_yticklabels(labels)  # Set y-axis labels\n",
        "ax.set_xlabel('Predicted label')\n",
        "ax.set_ylabel('True label')\n",
        "\n",
        "# Save the plot\n",
        "#plt.savefig(model_name + \"_\" + label.lower() + \".eps\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### testing the model\n",
        "new_sentence = \"This is a great residence, value for money. Tops!\"\n",
        "tokenized_sentence = tokenizer.encode(new_sentence, add_special_tokens=True)\n",
        "input_ids = torch.tensor([tokenized_sentence]).to(device)\n",
        "with torch.no_grad():\n",
        "    outputs = distilled_model(input_ids)\n",
        "\n",
        "predictions = torch.nn.functional.softmax(outputs[0], dim=-1)\n",
        "predicted_class = torch.argmax(predictions).item()\n",
        "\n",
        "print(predicted_class)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9icp6yvgqJ0",
        "outputId": "9f9dbc8d-8db5-475f-cbff-edfff954e71b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*** Note *** Following function not in use. It was our initial code, but the GPU couldn't take the load. So we broke up the function to mangeable pieces in order to get it to run.  "
      ],
      "metadata": {
        "id": "zDG_zB0nm71a"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gyFjsUe2D3T_"
      },
      "outputs": [],
      "source": [
        "\n",
        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
        "from transformers import AdamW\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "\n",
        "def train_and_evaluate_model(data, max_len, model, tokenizer):\n",
        "    # Split data into input texts and labels\n",
        "    input_texts = data['comments2'].tolist()\n",
        "    labels = data['label'].tolist()  # Replace 'labels' with your actual column name for labels\n",
        "\n",
        "    # Tokenize the data\n",
        "    encoded_data = tokenizer.batch_encode_plus(\n",
        "        input_texts,\n",
        "        truncation=True,\n",
        "        padding=True,\n",
        "        max_length=max_len,\n",
        "        return_tensors='pt'\n",
        "    )\n",
        "\n",
        "    # Prepare inputs for the model\n",
        "    input_ids = encoded_data['input_ids']\n",
        "    attention_masks = encoded_data['attention_mask']\n",
        "\n",
        "    # Convert to torch.Tensor and move to the device\n",
        "    input_ids = input_ids.to(device)\n",
        "    attention_masks = attention_masks.to(device)\n",
        "    labels = torch.Tensor(labels).to(device)\n",
        "\n",
        "    # Create the DataLoader for our training set.\n",
        "    batch_size = 32  # adjust based on your resources\n",
        "    dataset = TensorDataset(input_ids, attention_masks, labels)\n",
        "    dataloader = DataLoader(dataset, sampler=RandomSampler(dataset), batch_size=batch_size)\n",
        "\n",
        "    # Optimizer\n",
        "    optimizer = AdamW(model.parameters(), lr=1e-5)  # you can adjust learning rate\n",
        "\n",
        "    # Training loop\n",
        "    epochs = 4  # adjust based on your needs\n",
        "    for epoch in range(epochs):\n",
        "        model.train()  # enter training mode\n",
        "        for batch in dataloader:\n",
        "            inputs = {\n",
        "                'input_ids': batch[0],\n",
        "                'attention_mask': batch[1],\n",
        "                'labels': batch[2].type(torch.long),  # ensure labels are integers\n",
        "            }\n",
        "\n",
        "            model.zero_grad()  # clear previous gradients\n",
        "            outputs = model(**inputs)  # forward pass\n",
        "            loss = outputs[0]\n",
        "            loss.backward()  # compute the loss gradients\n",
        "            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)  # prevent the \"exploding gradients\" problem\n",
        "            optimizer.step()  # update parameters\n",
        "\n",
        "    # After training, evaluate on the validation set\n",
        "    with torch.no_grad():\n",
        "        model.eval()  # enter evaluation mode\n",
        "        predictions = []\n",
        "        true_labels = []\n",
        "        for batch in validation_dataloader:\n",
        "            inputs = {\n",
        "                'input_ids': batch[0],\n",
        "                'attention_mask': batch[1],\n",
        "            }\n",
        "            outputs = model(**inputs)\n",
        "            predictions.extend(torch.argmax(outputs[0], dim=1).tolist())\n",
        "            true_labels.extend(batch[2].tolist())\n",
        "\n",
        "    performance = accuracy_score(true_labels, predictions)\n",
        "\n",
        "    return performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4wKkZ7HsZz9x"
      },
      "outputs": [],
      "source": [
        "### Following functions, Colab + not enough ram to run. Got to manually run as per above"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OqH-8RJP4vO3"
      },
      "source": [
        "***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zxk7PD4f4vO4",
        "outputId": "dd060f71-1dff-492c-e0a2-015fba6c413c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['vocab_layer_norm', 'vocab_projector', 'vocab_transform', 'activation_13']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification 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",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier', 'dropout_39', 'pre_classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"tf_distil_bert_for_sequence_classification_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " distilbert (TFDistilBertMai  multiple                 66362880  \n",
            " nLayer)                                                         \n",
            "                                                                 \n",
            " pre_classifier (Dense)      multiple                  590592    \n",
            "                                                                 \n",
            " classifier (Dense)          multiple                  3845      \n",
            "                                                                 \n",
            " dropout_39 (Dropout)        multiple                  0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 66,957,317\n",
            "Trainable params: 66,957,317\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "from transformers import TFAutoModelForSequenceClassification, DistilBertConfig\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras import models\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=5, id2label={0: \"Bad\", 1: \"Nay\", 2: \"Average\", 3: \"Good\", 4: \"Awesome!!\" })\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)\n",
        "model.compile(optimizer=optimizer, loss=model.hf_compute_loss)\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----- END of Code ----"
      ],
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        "id": "AG5WZX-ApF0h"
      }
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