{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJ6MhJYYBCwu",
        "outputId": "85eb2778-813b-4462-9ed5-c81a8bf27474"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Thu Jun 29 12:31:21 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 T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   51C    P8     9W /  70W |      0MiB / 15360MiB |      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": [
        "!nvidia-smi"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "wmj22-TcZMef"
      },
      "source": [
        "## Setup\n",
        "\n",
        "We'll need [the Transformers library](https://huggingface.co/transformers/) by Hugging Face:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kj_7Tz0-pK69",
        "outputId": "f8215e7a-c231-4ad9-c7fb-cf992933d875"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.5/55.5 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m30.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q -U watermark"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jjsbi1u3QFEM",
        "outputId": "55ef32b8-6924-434e-f353-15fac991287b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m51.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m25.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m108.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m88.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -qq transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AJqoaFpVpoM8",
        "outputId": "5ac2bfed-402a-42a2-b060-8b2c8c94d5ca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Python implementation: CPython\n",
            "Python version       : 3.10.12\n",
            "IPython version      : 7.34.0\n",
            "\n",
            "numpy       : 1.22.4\n",
            "pandas      : 1.5.3\n",
            "torch       : 2.0.1+cu118\n",
            "transformers: 4.30.2\n",
            "\n"
          ]
        }
      ],
      "source": [
        "%reload_ext watermark\n",
        "%watermark -v -p numpy,pandas,torch,transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w68CZpOwFoly",
        "outputId": "de8523be-0fd7-4114-fb67-08dd386bd2d8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@title Setup & Config\n",
        "import transformers\n",
        "from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\n",
        "import torch\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from collections import defaultdict\n",
        "from textwrap import wrap\n",
        "\n",
        "from torch import nn, optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.nn.functional as F\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
        "\n",
        "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
        "\n",
        "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
        "\n",
        "rcParams['figure.figsize'] = 12, 8\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)\n",
        "torch.manual_seed(RANDOM_SEED)\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SgPRhuMzi9ot",
        "outputId": "a12fde95-c69b-47b0-c59f-e2d0aa537cea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1S6qMioqPJjyBLpLVz4gmRTnJHnjitnuV\n",
            "To: /content/apps.csv\n",
            "100% 134k/134k [00:00<00:00, 87.9MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1zdmewp7ayS4js4VtrJEHzAheSW-5NBZv\n",
            "To: /content/reviews.csv\n",
            "100% 7.17M/7.17M [00:00<00:00, 101MB/s]\n"
          ]
        }
      ],
      "source": [
        "!gdown --id 1S6qMioqPJjyBLpLVz4gmRTnJHnjitnuV\n",
        "!gdown --id 1zdmewp7ayS4js4VtrJEHzAheSW-5NBZv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "id": "mUKLyKc7I6Qp",
        "outputId": "83ce462b-7ecd-4788-99e6-ef12f3d053cd"
      },
      "outputs": [
        {
          "data": {
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userName</th>\n",
              "      <th>userImage</th>\n",
              "      <th>content</th>\n",
              "      <th>score</th>\n",
              "      <th>thumbsUpCount</th>\n",
              "      <th>reviewCreatedVersion</th>\n",
              "      <th>at</th>\n",
              "      <th>replyContent</th>\n",
              "      <th>repliedAt</th>\n",
              "      <th>sortOrder</th>\n",
              "      <th>appId</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andrew Thomas</td>\n",
              "      <td>https://lh3.googleusercontent.com/a-/AOh14GiHd...</td>\n",
              "      <td>Update: After getting a response from the deve...</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>4.17.0.3</td>\n",
              "      <td>2020-04-05 22:25:57</td>\n",
              "      <td>According to our TOS, and the term you have ag...</td>\n",
              "      <td>2020-04-05 15:10:24</td>\n",
              "      <td>most_relevant</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Craig Haines</td>\n",
              "      <td>https://lh3.googleusercontent.com/-hoe0kwSJgPQ...</td>\n",
              "      <td>Used it for a fair amount of time without any ...</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>4.17.0.3</td>\n",
              "      <td>2020-04-04 13:40:01</td>\n",
              "      <td>It sounds like you logged in with a different ...</td>\n",
              "      <td>2020-04-05 15:11:35</td>\n",
              "      <td>most_relevant</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>steven adkins</td>\n",
              "      <td>https://lh3.googleusercontent.com/a-/AOh14GiXw...</td>\n",
              "      <td>Your app sucks now!!!!! Used to be good but no...</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>4.17.0.3</td>\n",
              "      <td>2020-04-01 16:18:13</td>\n",
              "      <td>This sounds odd! We are not aware of any issue...</td>\n",
              "      <td>2020-04-02 16:05:56</td>\n",
              "      <td>most_relevant</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lars Panzerbjørn</td>\n",
              "      <td>https://lh3.googleusercontent.com/a-/AOh14Gg-h...</td>\n",
              "      <td>It seems OK, but very basic. Recurring tasks n...</td>\n",
              "      <td>1</td>\n",
              "      <td>192</td>\n",
              "      <td>4.17.0.2</td>\n",
              "      <td>2020-03-12 08:17:34</td>\n",
              "      <td>We do offer this option as part of the Advance...</td>\n",
              "      <td>2020-03-15 06:20:13</td>\n",
              "      <td>most_relevant</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Scott Prewitt</td>\n",
              "      <td>https://lh3.googleusercontent.com/-K-X1-YsVd6U...</td>\n",
              "      <td>Absolutely worthless. This app runs a prohibit...</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>4.17.0.2</td>\n",
              "      <td>2020-03-14 17:41:01</td>\n",
              "      <td>We're sorry you feel this way! 90% of the app ...</td>\n",
              "      <td>2020-03-15 23:45:51</td>\n",
              "      <td>most_relevant</td>\n",
              "      <td>com.anydo</td>\n",
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            "text/plain": [
              "           userName                                          userImage  \\\n",
              "0     Andrew Thomas  https://lh3.googleusercontent.com/a-/AOh14GiHd...   \n",
              "1      Craig Haines  https://lh3.googleusercontent.com/-hoe0kwSJgPQ...   \n",
              "2     steven adkins  https://lh3.googleusercontent.com/a-/AOh14GiXw...   \n",
              "3  Lars Panzerbjørn  https://lh3.googleusercontent.com/a-/AOh14Gg-h...   \n",
              "4     Scott Prewitt  https://lh3.googleusercontent.com/-K-X1-YsVd6U...   \n",
              "\n",
              "                                             content  score  thumbsUpCount  \\\n",
              "0  Update: After getting a response from the deve...      1             21   \n",
              "1  Used it for a fair amount of time without any ...      1             11   \n",
              "2  Your app sucks now!!!!! Used to be good but no...      1             17   \n",
              "3  It seems OK, but very basic. Recurring tasks n...      1            192   \n",
              "4  Absolutely worthless. This app runs a prohibit...      1             42   \n",
              "\n",
              "  reviewCreatedVersion                   at  \\\n",
              "0             4.17.0.3  2020-04-05 22:25:57   \n",
              "1             4.17.0.3  2020-04-04 13:40:01   \n",
              "2             4.17.0.3  2020-04-01 16:18:13   \n",
              "3             4.17.0.2  2020-03-12 08:17:34   \n",
              "4             4.17.0.2  2020-03-14 17:41:01   \n",
              "\n",
              "                                        replyContent            repliedAt  \\\n",
              "0  According to our TOS, and the term you have ag...  2020-04-05 15:10:24   \n",
              "1  It sounds like you logged in with a different ...  2020-04-05 15:11:35   \n",
              "2  This sounds odd! We are not aware of any issue...  2020-04-02 16:05:56   \n",
              "3  We do offer this option as part of the Advance...  2020-03-15 06:20:13   \n",
              "4  We're sorry you feel this way! 90% of the app ...  2020-03-15 23:45:51   \n",
              "\n",
              "       sortOrder      appId  \n",
              "0  most_relevant  com.anydo  \n",
              "1  most_relevant  com.anydo  \n",
              "2  most_relevant  com.anydo  \n",
              "3  most_relevant  com.anydo  \n",
              "4  most_relevant  com.anydo  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv(\"reviews.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dB2jE6am7Dpo",
        "outputId": "4f8a8991-9197-495a-80f6-2cfa56cb8022"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(15746, 11)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VA_wGSLQLKCh",
        "outputId": "6a66788f-32df-4e4f-b787-9144c8f80c79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15746 entries, 0 to 15745\n",
            "Data columns (total 11 columns):\n",
            " #   Column                Non-Null Count  Dtype \n",
            "---  ------                --------------  ----- \n",
            " 0   userName              15746 non-null  object\n",
            " 1   userImage             15746 non-null  object\n",
            " 2   content               15746 non-null  object\n",
            " 3   score                 15746 non-null  int64 \n",
            " 4   thumbsUpCount         15746 non-null  int64 \n",
            " 5   reviewCreatedVersion  13533 non-null  object\n",
            " 6   at                    15746 non-null  object\n",
            " 7   replyContent          7367 non-null   object\n",
            " 8   repliedAt             7367 non-null   object\n",
            " 9   sortOrder             15746 non-null  object\n",
            " 10  appId                 15746 non-null  object\n",
            "dtypes: int64(2), object(9)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzqfEbgjo5p8",
        "outputId": "ca9cff56-5009-4835-c18a-4fe3a57aaf6c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0        1\n",
            "1        1\n",
            "2        1\n",
            "3        1\n",
            "4        1\n",
            "        ..\n",
            "15741    5\n",
            "15742    5\n",
            "15743    5\n",
            "15744    5\n",
            "15745    5\n",
            "Name: score, Length: 15746, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(df.score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "Wwh_rW4Efhs3",
        "outputId": "e1683828-bb18-4ad9-81bc-ebcdd2dbb2b8"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 693,
              "width": 1035
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.countplot(x='score', data = df)\n",
        "plt.xlabel('review score');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "ei0xmdi1Chp0"
      },
      "outputs": [],
      "source": [
        "def to_sentiment(rating):\n",
        "  rating = int(rating)\n",
        "  if rating <= 2:\n",
        "    return 0\n",
        "  elif rating == 3:\n",
        "    return 1\n",
        "  else:\n",
        "    return 2\n",
        "\n",
        "df['sentiment'] = df.score.apply(to_sentiment)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "V-155O-SFSqE"
      },
      "outputs": [],
      "source": [
        "class_names = ['negative', 'neutral', 'positive']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KM69l8w6oM-s",
        "outputId": "74047a77-7a49-404c-9622-c43c5cb5c742"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0        0\n",
            "1        0\n",
            "2        0\n",
            "3        0\n",
            "4        0\n",
            "        ..\n",
            "15741    2\n",
            "15742    2\n",
            "15743    2\n",
            "15744    2\n",
            "15745    2\n",
            "Name: sentiment, Length: 15746, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(df.sentiment)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "y3tY3ECJDPaz",
        "outputId": "364ebf85-98ef-4d13-ebfb-c9b73ea1d476"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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LL82WVrvjjjvi5JNPjh133DGn3+DBg7PAQd33c/vtt8fhhx+e95zWZ+2Xu7Wf3+jRo/MKFnzwwQc5S9kNHDiwwX4jRozICRWccMIJceONN65zJ92QIUPi8ccfjx/96EdRWVkZZWVlcdddd+UsNQcANI9tJUC6Oay9UL1kyZJYsmRJRES0atUqSktLG+zfUNCyrttuuy0LFRx00EFxzDHHRJcuXWLFihXxzjvvJMOjBQUF8fnPfz569uwZ3bp1i3bt2kV5eXl88MEH8dxzz8WsWbMiImL27NnZeVVTL5oDAFu/Z555JmpqamKXXXaJ3/3ud3HIIYfkPP+DH/wgbrnllnjooYciYs0WB7feemvcfvvt69RauXJlXHvttVmooKioKH7605/G6aefntPv/PPPj7Fjx8aVV14Zq1evjpqamvjJT34SX/ziFxsMLNRd/bNXr17xu9/9br0rKr3++uvxyCOP5P8h1FFaWhrPPPNMROSuRHXCCScktzLdECeffHIWLHj//ffj//7v/+LAAw9sdFzdm3KOPvroBj+HOXPmxHXXXZeFCjp37hy/+c1vok+fPuv0veSSS2Lo0KHx7rvvRmVlZfzoRz+K0aNHR+vWrTf0rQGfQYIFAGz1ampqomvXrvGnP/0p2rVrt87zRUVFccMNN8TEiROzu++ffPLJBoMFo0aNijfffDNr33HHHckUdNu2beP222+P+fPnx6RJk6KysjJ++9vfxl133bVO3+nTp8cTTzyRtU844YT48Y9/3GDdfffdN37/+9/HwIED17sUXF1HHnlkfOUrX2m039577x1/+MMf4uSTT45PPvkkysvL429/+1ucc845eb3OpjZw4MAsWDBt2rQoKytLBjPWqvvFqF27dg2+708//TR+/etfZ+1jjz027rrrruS2DwMHDozy8vLsbzJ8+PA499xz1wlcAABb1rYSIN0c1l6ovvvuu+Oee+6JiIgvfOELMWLEiA2qN2LEiGjZsmXccsstjS4hHLHmbrshQ4bEt771reTWDtddd1384Q9/iDvuuCNqa2tj1qxZce+99+a9RRcAsO2oqamJFi1axLBhw+Kggw5a5/mWLVvGT37yk1iwYEF2HjN69Oj4/ve/v8451F/+8peYMWNG1r7mmmvWCRWsdcIJJ8QvfvGL+OEPfxgRa7azvO++++L666/P6VdRUZGtSBkR8dOf/rTRbZp69uwZPXv2XG+f5nbUUUfFzjvvHAsXLoyINZ9pY8GCGTNm5HwWqW0QfvnLX8bSpUsjIqJ9+/bx4IMPZuHW+vbYY4/44x//GCeddFIsXrw4ZsyYEX/961/ju9/97oa8LeAzquGIPwBsZa688soGQwVrtWzZMk455ZSsXTc8sFZtbW0MHz48aw8YMCAZKlirqKgofvSjH2Xt5557LhYtWrROv7rp56KiorjuuuvWW3ePPfaI//zP/1xvn7patWqVd9+dd94556T/xRdfzHvsptavX7+cH+/rhgZS6gY0TjzxxAa3LBg1alSsWrUqIiJat24dP//5z5OhgrVOP/307Ivu6tWr47HHHsvrPQAAm0/dAGlDP26vDZDWvQD65JNPNliroQDpmWee2eDqBmsDpGvv1lobIN3W3XTTTXmFCiLW3PF3+eWXJ0MFEWs+/+9///sxdOjQ7LFHHnkkZ0ljAOCz47TTTmswVLBWQUFBXHfddTkraTa0IsDDDz+cHXfv3j0GDRq03tc96aST4ogjjsjaf//736O8vDynz6effppt/xmxZlWqz4KioqL42te+lrWffPLJqKmpWe+YutfO2rRp02Dodu7cuTF27NisfdlllyVDBWvtsssuMWTIkKy9NtwLsJZgAQBbvTZt2sQJJ5zQaL+6CeSZM2dGZWVlzvNTp06N999/P2uv3QqgMQceeGB87nOfi4g1F53X7v1W10svvZQdH3HEEbHbbrs1WvfUU0/N6/U3RN++fbPjfPbO3VxatGiRs8fbk08+GbW1tcn+b775Zs72EKnEdd0fFE466aRGE+pr1d1WYeLEiXmNAQA2r609QLqtOPjgg3M+p8Y0Jbh6/vnnR0lJSUSsuYuw7rZVAMBnR2pVgbq6du0aRx11VNZ+/vnnc57/8MMP48MPP8za3/zmN5PbWNX17W9/OzteunRpzh35EbHOkvyvv/56ozW3FXWvf82fPz9efvnl9fave+PO8ccf3+CWV08//XS2BUJJSUl885vfzGsu3/jGN7LjsrKy+OSTT/IaB2wfBAsA2OodeOCB0aJF47v3dO7cOTuura2NZcuW5Tw/efLk7Lhdu3bRq1evvOdQd1+5t956K+e58vLymD59etY+9NBD86rZrVu35B66G6vuPnSLFy+O1atXb5bXyUfdH/Nnz54dkyZNSvZdu6dcRESXLl3isMMOW6fP8uXLs72WI9asipCv9f0dAYAtb1sIkG4r6l4E3tRat26d8zdozuAqALB5dOzYcb2rFdRVN1gwY8aMWLJkSdauHwLN97pNv379clajrF+nffv20bVr16x97bXXNvoD/LbikEMOyVmBoe71sfreeeednOuQqZty6l4HPfzww/MOlXbq1Cnnc3b9DKhLsACArV7dH8nXp35yee1S+WuVlZVlx3vttVdeaem1dtppp+x47ty5Oc/NnTs3SwBHROy77755121K34g1ywVPnDgxbrrppvj2t78dRx99dPTs2TN69OiR81/9C/T1QxZbUq9evWKPPfbI2qntEKqrq+Mf//hH1v7617/e4N/o3XffzVkSrimfYd2/46JFi9b5UQIA2LK29gDptmRz7x9c9zxq3rx5m/W1AIAtb23YMh/77bdfTrvuCgV1V6Js1apV3lsWtGnTJuf6Ud2aa51xxhnZ8Zw5c2Lw4MHx1a9+NX75y1/G+PHjm/X618aqGxAYO3Zs8iahutfVdtlll5xVS+uqex10n332adJc6l6Ldd4H1NX4t3cAaGZNWaa1rvpL7i9evDg7fuutt6JHjx4bVLf+l5SlS5fmtNu3b593rab0ffPNN+PHP/5xTJ06Ne8xazXnigURa74c/e53v4uINUux3XDDDdGyZcucPhMmTIiFCxdm7borHdRV9+8YsSaAsKGWLVuW9zYKAMCmt7UHSLclje2Zm7Jw4cJ48sknY9KkSTFt2rT49NNPY8WKFTnB2fq25Yv2AEDD6p4TNab+tZS6KxbUPe7QoUOTzss6deoUH3/8cUSse70tIuLcc8+N1157LcaPH5899v7778f7778fDzzwQBQWFsZBBx0Uxx57bJxyyimx66675v3aze0b3/hG3H333RGxZrXO559/PgYMGJDTp6amJmd70K997WtRVFTUYL26188eeOCBeOCBBzZoXg39HYDtl2ABANuNTXUBtP6F7Pp3vRcXF+ddK9++L7/8cpx//vlRXl6+znNt2rSJNm3aRKtWrbIl46qrq2PWrFlZn/ohiy1t4MCBWbBgyZIl8cILL0T//v1z+jzxxBPZcffu3WP//fdvsNamvJBd/28JAGxZW3uAdFvSpk2bJvWvqKiIe+65J/7whz80eRWnioqKJvUHALZ+TTkvqx/6XLlyZXZc91pL/X5NqVu35lotWrSIe++9Nx588MG4//77Y/78+TnP19TUxJtvvhlvvvlm/Pa3v40zzzwzrrrqqg0+59yS9thjj+jVq1dMmTIlItasTFA/WPDKK6/krCCQuiknYk04YVNo6FoksP0SLABgu7HDDjvkHNddUrcp6o9r27ZtTruhLz4pK1asaLRPeXl5XHvttdmJfHFxcZx55plx/PHHx4EHHrjO60es2d+u/g/3zWnvvfeOgw46KN5+++2IWPPlqO78ysvL45lnnsnaqf3hItb9Utq1a9dkOrsx+Sy9DABs/TZXgHRb0pTzmurq6rjkkkvi+eefX+e5oqKi6NixY7Rq1Sqn5qJFi7Jz1+YOrQIAm15TVrusf85UUlLS4HFTz63q9q9bp66ioqIYPHhwfPe7342XX345XnrppZg0aVK8/fbbOSsuVVZWxogRI2Lq1Knxhz/8YZ2VM7dGJ598chYsGD9+fCxZsiQ6dOiQPV93G4R99tknDjrooGStHXbYIQsXdOzYsUmrpta1oeOAzyZX0wHYbnTs2DE7PvDAA+Ohhx7aJHV33HHHnHZTltDNZ5+ycePGxezZsyMiorCwMO6///7k/mlrbY132w0cODALFjz//POxfPnyLBTx3HPPZReqCwoK4qSTTkrWqft3jIj405/+tMFL/wIAnw2bK0D6WfXwww/nhAr233//OOuss+Lwww9Phjavueaa+Pvf/74FZwkAbEmLFi3Ku+8nn3yS067743fd4yVLlkRNTU3e2yHUrdvYD9pFRUVx5JFHxpFHHhkRa27e+fe//x1PPvlkjBkzJgsZvPrqq/G///u/8Z//+Z95zaE5ffWrX41bbrklKisro7KyMsaMGROnn356RKxZMWrs2LFZ3/XdlBOx5vrZ2mDBt7/97bjssss227yB7YdgAQDbjb333js7zucH/Xx17tw5dtxxx/j0008jIuKdd97Ja1xFRUW89957jfb797//nR0fddRRjYYKIiJmzpyZ1xy2pK9//etx2223RXV1daxevTrGjh0bp556akREPP7441m/Pn36xG677ZasU/fvGLHmbylYAADbt80VII2IbKupptqal40dPnx4dnzkkUfGfffd1+hdfPbXBYDPtnyuUa317rvv5rT32muv7LjuNZrVq1fHBx98EPvuu2+jNVesWBEzZsxosGY+2rRpE8cdd1wcd9xx8dZbb8WgQYOyFRCeeOKJbSJY0KlTpzj66KOzAOjo0aOzYME///nPnPOxxoIFe++9d3Z9cFNeBwW2b/nFxADgM+Cwww7LjmfOnBlz5szZZLW/8IUvZMfPPfdcXsvD/utf/8prmbm6+8Xtv//+ec3n5ZdfzqtfRO6yuTU1NXmPa6qdd945JxSxdvm2xYsXx4svvpg93tgXo5133jknXPDKK69s4pkCANuazRUgjVh3G6Z8AwNNuetvS5o3b158+OGHWfuyyy7La2ngrTG4CgBsOosXL85WmmzMSy+9lB1369YtZ5WCutfIIiLnms/6vPjiiznX0w455JC8xjXk4IMPzn6Qj4iYPn36BteK2HLXziJyr4u9+uqr2fXLJ554Inu8V69esccee6y3Tt3roJMmTdrEswS2V4IFAGw3Dj744OjatWvWHjly5Car/fWvfz07njt3bs4d+Cm///3v86pd90tVPkGEZcuWxWOPPZZX7YjcPevWbkewuQwcODA7/ve//x3z58+Pp59+OiorKyMiori4OAYMGNBonbp9Ro0aFRUVFZt+sgDANmNzBkjXbt20Vt3QZ0pVVVXeF+a35IXqiHWDF/kEVz/55JMm3cUIAGybHnnkkUb7zJkzJydYcMwxx+Q8v+eee+aEPh955JG8znEefvjh7LhDhw7Rq1evPGacVnfFg+rq6o2qVffa2drtBTaX4447Ltq0aRMRa64JPvHEE7Fs2bKcbawauyknIuKEE07ItqD4+OOPY/z48ZtnwsB2RbAAgO1GUVFRnHvuuVn7z3/+c5Pvdk/9sH/CCSfELrvskrVvueWWnDvB6rvvvvti8uTJeb3mrrvumh3/61//avTL2I033hjLli3Lq3ZE5Gw7MG3atLzHbYj+/ftnd/3V1NTEU089la1cEBHx5S9/OSflnjJ48OBsL+U5c+bEzTff3OS55BPSAAC2DZszQNqyZcuc2m+88UajY8aOHRsrV67Mq/6WvFDdkHzOiR566KEtEnoAAJrXqFGjGt3i85ZbbsluEImI+OY3v7lOnzPOOCM7njZtWowYMWK9NZ966qmYMGFC1j7llFOy6z5rLV68uEnnSnWvca1vy8181B1ffxuITW2HHXaI448/PmuPHj06xowZk91UU1xcHF/96lcbrbPXXnvl1PnpT38aCxcubNJcXDsD6hMsAGC7cvrpp0fPnj0jIqKysjK+//3vx4MPPpjzhaghH374Ydx9993rpLDX2mGHHeLaa6/N2p9++mmcccYZMWrUqJyLyh988EFcd9118etf/zoKCwtzEtwpRx55ZM74W265pcGk9fLly+O6666L0aNHZ4nkfNRdou7hhx+OsrKyvMc21dr97tYaMWJEvPbaa1m77ooG69OpU6e46qqrsvZf/vKXuOSSSxpd+nj58uXx+OOPxxlnnBFPPvlkE2cPAGytNmeANCL3fOmxxx6LqqqqZN9ly5bF7bffnvfr1g0tfPTRR5t9JaYuXbrktP/5z3+ut39ZWVn8z//8z2acEQCwNSgsLIyqqqq44IILGlx5qaKiIm6++eYYM2ZM9tjJJ58cPXr0WKfvmWeembNU/2233RajRo1q8HXHjRsX1113Xdbu2LFjnH/++ev0Kysri2OPPTZ+85vfrPdmnoiIZ555Jmf1hWOPPXa9/RtT91zw448/juHDh6/3fHBj1b0+VlZWFvfdd1/WPvroo6NTp0551bnmmmuiY8eOERExe/bs+Na3vpWz2kRDampqYvLkyXH99dfHhRde2PTJA59pLRrvAgCfHcXFxXHnnXfGt7/97Zg9e3aUl5fHz372s7j33nujX79+0aNHj2jfvn1UVFTEkiVL4r333ou33norPvjgg0Zrn3TSSfHaa6/FQw89FBFrktQ33HBD/PSnP42ddtopysvLY8mSJVn/8847LxYsWJDVToUB+vfvH3vttVf2pWn48OExYcKEOPHEE6Nr165RXl4eZWVlMXbs2Pj0008jIuLiiy+Ou+66K6/P5JRTTom//OUvEbFmadyBAwdG586do2PHjjlzasr2CuszcODAbF+4unv1tmvXLr7yla/kXeess86Kd955Jx599NGIiBgzZkw899xz0bdv3+jZs2fssssuUVRUFMuWLYuZM2fG1KlT4/XXX89CJHXT8wDAtu/000+Pxx9/PPv//fe///24+uqr4/TTT4/i4uLkuA8//DBGjx4dDz30UEycOLHBPl//+tfjqaeeiog1Qc9bb701fvSjH61z/jZz5sy45JJLYtasWVFQUJCzpVXKwQcfnPVdtWpV3HnnnXHJJZdEq1atmvDu89e5c+fYb7/9srvtbrvttjjggANiv/32W6fvxIkT44orrojVq1dHYWGhVQsA4DPs+OOPj3fffTfef//9OOOMM+LYY4+NL37xi9GmTZuYMWNGPPXUUzFjxoysf2lpac5NNnW1bt06br311jj77LOjoqIiqqur44YbboiHH344jjvuuOjcuXN8+umn8cILL+SEQQsLC+NnP/tZ7Lzzzg3WXbJkSQwbNiyGDRsW++23X/Ts2TP23nvv6NChQ9TW1sacOXNiwoQJMWXKlGxMp06d4rzzztuoz2bt66y9hvfzn/88fvOb38Ruu+2Ws63VJZdcknNDzYbq27dv7LLLLrFgwYKIWBNmWCufbRDW6tq1a9xxxx1x4YUXxurVq2P27NlxzjnnxH777RdHHnlkdOvWLUpKSmLVqlWxaNGiKCsri9dffz1b2aDudmMAEYIFAGyHunTpEqNGjYqLL744+6KxYMGC+Otf/9ro2MZWAvjJT34S7du3j/vvvz9bVaCqqirnTvqCgoIYMmRIXHLJJXHllVdmj9ffv3etFi1axJ133hmDBg2KpUuXRkTEe++91+A+twUFBTF06ND4xje+kXewoHfv3jFkyJAYNmzY/9fevUdVXeX/H39xFQkUBYJI8hqgjaWmgJrXLk6WJszkyqGaMptpJpmyb6NpU2NqjmnN6KQ1U+aaYNlN8jKWmRPalIXgJUkdFe+KCmLcFFBAzu8PF5/f+XDOBw7IxcvzsZarsz9n7/3Zn/2hG/u939u4durUKZfOD26IgQMHKjAwUD/99JPp+ogRI+Tt7V2vvmbPnq0bbrhBb731lqqqqlRRUaFvvvlG33zzTZ1t65PVAQAAXP6aMoB02LBh6tGjh5EaODk5Wenp6br33nsVEhKiM2fOKDMzU1999ZXKy8sVERGhzp07m3b0WQkJCdHAgQO1ceNGSdLixYuVnJysG2+80fTfRg899JDGjRvXwNkxmzBhgqZMmSJJOn36tOLj43XPPfeod+/eat26tU6dOqXvvvtOmzdvliRFRESoS5cuWrt2baPcHwAAXH58fX21YMECjR8/Xnl5eVq3bp3WrVvntG5gYKCWLFliGQAgSX379tU//vEPTZw40cjmuXPnTqfZEKSL/y33l7/8RSNGjHBpvPv27avzWILg4GAtXrxY7dq1c6lPK25ubpo7d66efPJJFRYWSpJKS0sdfjdnv6HoUri7u+u+++7Tv/71L9P1mplAXXHHHXcoKSlJEydONAIVXJm76nEAgD0CCwAA16SgoCB98MEHWrNmjd57771az49zd3fXLbfcouHDh2vMmDG19uvm5qZJkybp3nvv1bJly/Tdd98pNzdX7u7uCg0NVb9+/TR27Fj16NFDkvl/OPz9/S37jYqKUkpKil555RXLlGVRUVF67rnnNGTIEFMmAFdMmjRJgwYN0qeffqoff/xROTk5Ki0tbZJdaZ6enho5cqTD+Xr1ibi2l5iYqHvvvVeLFi3S+vXrde7cOcu6oaGhuuOOOzRq1CjFxMQ06H4AAODy1VQBpB4eHnrttdf06KOPGhmisrKyTGf3VuvYsaPeeustLVq0yOVxT58+XY8++qhOnDgh6eKxDAcPHjTVqe+ZuLUZM2aMMjIyjMxP5eXl+uyzz4ysUvbCw8O1cOFCvf322412fwAAcHmKiIjQihUrNHPmTKWmpjqk+/f09NSIESP04osvKjAwsM7+Bg4cqLVr1+qNN97Q2rVrnR495eXlpSFDhuj555+v9cjQHj16aNq0adqwYYO2b9+usrIyy7oBAQF64IEH9PTTT6tt27Z1jtMVt956q1avXq1PPvlE33//vQ4dOqQzZ87UebxqQ40ePdohsODuu++Wj49Pvfvq1auX1q1bp6SkJH300Uc6efKkZV0fHx/17dtXI0aM0MiRI+t9LwBXNzebK3n5AAC4yuXl5emHH35QXl6eiouL5e3trbZt26pTp06KiIhQmzZtmuS+gwcPNrIZvPbaa3UGLkjSsWPHtHXrVp06dUpeXl4KDg5WVFSUunXr1iRjvJKUl5dr27Ztys7OVkFBgaqqquTn56ewsDB169bNdL4fAABoGS+88IJWrFghSYqLi9OcOXPqbJOdnW3anZWamqoOHTpY1q+qqmpQAGlYWFit4zh06JBmzpzpNNCzVatWGj16tF544QX5+fnV+zmLi4uVkpKib775Rvv371dxcbHpl+8TJ05UYmKiUU5PT9ejjz5qlPfu3Vtr/zXZbDa9//77evvtt42dd/Z8fX01atQoTZ48uV7P8+abb2rhwoWSLqbPrRlMCgAALh+1/fv99OnT2rp1q06ePKkLFy4oNDRUsbGxLgUUOFNaWqrNmzfrxIkTKioqkr+/v0JCQhQdHV3v37tVVFRo3759Onz4sE6dOqXS0lLjd3mRkZGKioqqd1bMa8mBAwf0v//9T/n5+SopKZGvr68CAwPVpUsX3XzzzcwdAEsEFgAA0EKysrJMu/RXr16tiIiIFhwRAADA1acpAkiPHTumLVu2KC8vT61atVJYWJiio6MbbUdcczp//ry2bt2q/fv3q7S0VO3atVNoaKiio6PVunXrlh4eAABoQg0J+AQAXLs4CgEAgBby7rvvGp9vuOEGMg4AAAA0geDgYN1zzz2N2md4ePhVkwmpVatWGjBggAYMGNDSQwEAAAAAXMasDxAEAAD19uOPP8qVZEAffPCB/v3vfxvl+Pj4Ws/1BQAAAAAAAAAAaClkLAAAoBHNmjVLZWVlio+P1+DBg9WlSxe5ublJunj+W2ZmppKSkvTll18abUJCQvT444+31JABAAAAAAAAAABqRWABAACNLCsrS3PmzNGcOXPk4+OjgIAAXbhwQYWFhaqoqDD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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 693,
              "width": 1035
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = sns.countplot(x='sentiment', data = df)\n",
        "plt.xlabel('review sentiment')\n",
        "ax.set_xticklabels(class_names);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "E7Mj-0ne--5t"
      },
      "outputs": [],
      "source": [
        "PRE_TRAINED_MODEL_NAME = 'bert-base-uncased'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "2e15c2c51718422380fc7b92fb97e0dd",
            "76ae107162d84ea28d7647617ed0c44d",
            "502cbf9b14a7476ba8457004ce314297",
            "7a2b0bbb203b4d8a9d18846ae9bdd6af",
            "aa82e83397fb40e8b77018eeb8bbac05",
            "6a19133fc0a3430dae23a88bcbe7a744",
            "22a8ef650465406a88102a06b864b573",
            "c23bcd9121d7435891c865a4fa89996d",
            "d1c837fdfdd34a34a229975f62d3004d",
            "5bb3f0485df84af9a5d44fabfed04fc5",
            "f99c8511f1644f47b334383027362cbc",
            "2298eacb2714492d8168c5033bbcb2e8",
            "686f8b2460014c898a3bcfaa4d1b879f",
            "c15d6d1c33044ffe8c807df0a3744bcd",
            "99bb4a6fa5c84f83a776e77b1f118b0f",
            "fd84c2c5e21a4e7bbf961c4dac1f479f",
            "00448734e39c462992d80da50414189d",
            "86baa2d45c1a4708a4ed861ad924592e",
            "b059bf1d9bb14fb3bd97a42370960ba8",
            "8d0a65af4a2f4da090e7a16d914f0c45",
            "3d30c172ab3e4d9888b999ec7a8c81cb",
            "c9e397df991b4841a2c53f3078227f9a",
            "03534280de7443bba82d5d320327c878",
            "1768b6685f074ab8b19898e60e00d562",
            "7e0f34f9984245f1a552d507117d012f",
            "924e4abf94f641e2bbce7a69f7101b24",
            "ef2cb4e51b174807a013d73140e717fb",
            "655356acdbea4278a5b9ce172c78528f",
            "901d950176e046909f1a900d2af69f76",
            "5c1aebd8ca1a46eba3d3b11be04de590",
            "e666459f7dbc41f8b7bf8877930b0321",
            "08796ccc414b4089ae7e6e3929faded6",
            "6093701f1d624d0b86985f6caf86714c"
          ]
        },
        "id": "H3AfJSZ8NNLF",
        "outputId": "6c208c34-0c10-4e41-96fa-2ecd0c6249e0"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2e15c2c51718422380fc7b92fb97e0dd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2298eacb2714492d8168c5033bbcb2e8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "03534280de7443bba82d5d320327c878",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "HZMitwrqm2eb"
      },
      "outputs": [],
      "source": [
        "sample_txt = 'When was I last outside? I am stuck at home for 2 weeks.'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iTFhpHpsoWO7",
        "outputId": "db2fe6b2-8747-422a-e77f-134c22f13c41"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Sentence: When was I last outside? I am stuck at home for 2 weeks.\n",
            "   Tokens: ['When', 'was', 'I', 'last', 'outside', '?', 'I', 'am', 'stuck', 'at', 'home', 'for', '2', 'weeks', '.']\n",
            "Token IDs: [1332, 1108, 146, 1314, 1796, 136, 146, 1821, 5342, 1120, 1313, 1111, 123, 2277, 119]\n"
          ]
        }
      ],
      "source": [
        "tokens = tokenizer.tokenize(sample_txt)\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "print(f' Sentence: {sample_txt}')\n",
        "print(f'   Tokens: {tokens}')\n",
        "print(f'Token IDs: {token_ids}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXwz47bQvCbc",
        "outputId": "b6d10d59-3d46-4c23-9b2a-42bf5fbadee3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('[SEP]', 102)"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.sep_token, tokenizer.sep_token_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6K4it5HwE6l",
        "outputId": "19e6528f-c5f7-4b4a-fc87-b0806622b174"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('[CLS]', 101)"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.cls_token, tokenizer.cls_token_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vx7gD5xf1AFK",
        "outputId": "1e36bdcf-50a9-489f-cc5c-1273a554db8e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('[PAD]', 0)"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.pad_token, tokenizer.pad_token_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4cmfFsbEKQDT",
        "outputId": "b6f45f25-6af5-4f98-9cd0-d780ba7d7644"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('[UNK]', 100)"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.unk_token, tokenizer.unk_token_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vea9edaaxSPO",
        "outputId": "ed32ee22-19af-4753-b339-1e57d586b253"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask'])"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "encoding = tokenizer.encode_plus(\n",
        "  sample_txt,\n",
        "  max_length=32,\n",
        "  add_special_tokens=True, # Add '[CLS]' and '[SEP]'\n",
        "  return_token_type_ids=False,\n",
        "  pad_to_max_length=True,\n",
        "  return_attention_mask=True,\n",
        "  return_tensors='pt',  # Return PyTorch tensors\n",
        ")\n",
        "\n",
        "encoding.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzBmcOla0yQR",
        "outputId": "806a9ec3-1ef5-4f27-dff8-e04ad7401718"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "32\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "tensor([ 101, 1332, 1108,  146, 1314, 1796,  136,  146, 1821, 5342, 1120, 1313,\n",
              "        1111,  123, 2277,  119,  102,    0,    0,    0,    0,    0,    0,    0,\n",
              "           0,    0,    0,    0,    0,    0,    0,    0])"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(len(encoding['input_ids'][0]))\n",
        "encoding['input_ids'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wiv5LLiw03Ox",
        "outputId": "2e014a18-de95-4015-d37f-4c43ef047a13"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "32\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0]])"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(len(encoding['attention_mask'][0]))\n",
        "encoding['attention_mask']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IagGoafKLUwW",
        "outputId": "803a1b24-0746-42e0-b317-1616bbdc1c7f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['[CLS]',\n",
              " 'When',\n",
              " 'was',\n",
              " 'I',\n",
              " 'last',\n",
              " 'outside',\n",
              " '?',\n",
              " 'I',\n",
              " 'am',\n",
              " 'stuck',\n",
              " 'at',\n",
              " 'home',\n",
              " 'for',\n",
              " '2',\n",
              " 'weeks',\n",
              " '.',\n",
              " '[SEP]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]',\n",
              " '[PAD]']"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.convert_ids_to_tokens(encoding['input_ids'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "BUnE5CT9hbeZ"
      },
      "outputs": [],
      "source": [
        "token_lens = []\n",
        "\n",
        "for txt in df.content:\n",
        "  tokens = tokenizer.encode(txt, max_length=512)\n",
        "  token_lens.append(len(tokens))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "id": "SzE1j4jxmUtd",
        "outputId": "02bffe00-4c6e-4841-ef02-ad064fe84ff1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-31-4dbe9bd8b56e>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(token_lens)\n"
          ]
        },
        {
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            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 693,
              "width": 1052
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.distplot(token_lens)\n",
        "plt.xlim([0, 256]);\n",
        "plt.xlabel('Token count');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "t7xSmJtLuoxW"
      },
      "outputs": [],
      "source": [
        "MAX_LEN = 160"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "E2BPgRJ7YBK0"
      },
      "outputs": [],
      "source": [
        "class GPReviewDataset(Dataset):\n",
        "\n",
        "  def __init__(self, reviews, targets, tokenizer, max_len):\n",
        "    self.reviews = reviews\n",
        "    self.targets = targets\n",
        "    self.tokenizer = tokenizer\n",
        "    self.max_len = max_len\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.reviews)\n",
        "\n",
        "  def __getitem__(self, item):\n",
        "    review = str(self.reviews[item])\n",
        "    target = self.targets[item]\n",
        "\n",
        "    encoding = self.tokenizer.encode_plus(\n",
        "      review,\n",
        "      add_special_tokens=True,\n",
        "      max_length=self.max_len,\n",
        "      return_token_type_ids=False,\n",
        "      pad_to_max_length=True,\n",
        "      return_attention_mask=True,\n",
        "      return_tensors='pt',\n",
        "    )\n",
        "\n",
        "    return {\n",
        "      'review_text': review,\n",
        "      'input_ids': encoding['input_ids'].flatten(),\n",
        "      'attention_mask': encoding['attention_mask'].flatten(),\n",
        "      'targets': torch.tensor(target, dtype=torch.long)\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "B-vWzoo81dvO"
      },
      "outputs": [],
      "source": [
        "df_train, df_test = train_test_split(df, test_size=0.1, random_state=RANDOM_SEED)\n",
        "df_val, df_test = train_test_split(df_test, test_size=0.5, random_state=RANDOM_SEED)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xz3ZOQXVPCwh",
        "outputId": "1f4248ea-cd5d-43c9-efaa-274b599b7516"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((14171, 12), (787, 12), (788, 12))"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train.shape, df_val.shape, df_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "KEGqcvkuOuTX"
      },
      "outputs": [],
      "source": [
        "def create_data_loader(df, tokenizer, max_len, batch_size):\n",
        "  ds = GPReviewDataset(\n",
        "    reviews=df.content.to_numpy(),\n",
        "    targets=df.sentiment.to_numpy(),\n",
        "    tokenizer=tokenizer,\n",
        "    max_len=max_len\n",
        "  )\n",
        "\n",
        "  return DataLoader(\n",
        "    ds,\n",
        "    batch_size=batch_size,\n",
        "    num_workers=4\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vODDxMKsPHqI",
        "outputId": "499a66de-300e-4f86-d70e-f72af3c858c0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        }
      ],
      "source": [
        "BATCH_SIZE = 16\n",
        "\n",
        "train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)\n",
        "test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y93ldSN47FeT",
        "outputId": "d655c411-911f-484d-e7a1-bd827008ca12"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "dict_keys(['review_text', 'input_ids', 'attention_mask', 'targets'])"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = next(iter(train_data_loader))\n",
        "data.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IdU4YVqb7N8M",
        "outputId": "5101006f-581b-4ba1-dc4f-4ea9285410a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([16, 160])\n",
            "torch.Size([16, 160])\n",
            "torch.Size([16])\n"
          ]
        }
      ],
      "source": [
        "print(data['input_ids'].shape)\n",
        "print(data['attention_mask'].shape)\n",
        "print(data['targets'].shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121,
          "referenced_widgets": [
            "32dd91ff9b8e4fe4b26e6a0479c7a4b0",
            "82e276121f2a44ed918d66b7d6f2b8a5",
            "ddf3e39876c44794841bb2b1dbb84248",
            "b730eb1ea0b04c02bd9e797983c58678",
            "076de68d4b554f339cc765189322f53b",
            "3408c041956e4ae790d447999aa1822c",
            "ad76c188d61640f78390be8539cca640",
            "acf8466d71aa4afc9613b8e09191527c",
            "7d586e10bed94d15990e43c7929b3770",
            "fb93d21fde7046bbab2b12852949264e",
            "18130019a4be495b9aeaa9de1f32133b"
          ]
        },
        "id": "0P41FayISNRI",
        "outputId": "11d1d439-e45f-4bc9-f277-3f5078503b4b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "32dd91ff9b8e4fe4b26e6a0479c7a4b0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "s1aoFxbQSn15"
      },
      "outputs": [],
      "source": [
        "last_hidden_state, pooled_output = bert_model(\n",
        "  input_ids=encoding['input_ids'],\n",
        "  attention_mask=encoding['attention_mask'],\n",
        "  return_dict = False\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mUJHXNpIbcci",
        "outputId": "db1d0de4-be7f-4916-edf1-89aec33f4750"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 32, 768])"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "last_hidden_state.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nsxB7Qy7b5YN",
        "outputId": "7aaa06bd-2734-4b80-b0c5-83d164808fa5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "768"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bert_model.config.hidden_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jIAtRhaSz9c",
        "outputId": "581c504d-b1b5-4125-dd9e-f8b19b12509a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 768])"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pooled_output.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "m_mRflxPl32F"
      },
      "outputs": [],
      "source": [
        "class SentimentClassifier(nn.Module):\n",
        "\n",
        "  def __init__(self, n_classes):\n",
        "    super(SentimentClassifier, self).__init__()\n",
        "    self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)\n",
        "    self.drop = nn.Dropout(p=0.3)\n",
        "    self.out = nn.Linear(self.bert.config.hidden_size, n_classes)\n",
        "\n",
        "  def forward(self, input_ids, attention_mask):\n",
        "    returned = self.bert(\n",
        "      input_ids=input_ids,\n",
        "      attention_mask=attention_mask\n",
        "    )\n",
        "    pooled_output = returned[\"pooler_output\"]\n",
        "    output = self.drop(pooled_output)\n",
        "    return self.out(output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i0yQnuSFsjDp",
        "outputId": "07763801-2637-4a0f-e55a-79ffedd85747"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "model = SentimentClassifier(len(class_names))\n",
        "model = model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mz7p__CqdaMO",
        "outputId": "a869ec4f-a186-47ae-f710-85ca02a5f414"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([16, 160])\n",
            "torch.Size([16, 160])\n"
          ]
        }
      ],
      "source": [
        "input_ids = data['input_ids'].to(device)\n",
        "attention_mask = data['attention_mask'].to(device)\n",
        "\n",
        "print(input_ids.shape) # batch size x seq length\n",
        "print(attention_mask.shape) # batch size x seq length"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2rTCj46Zamry",
        "outputId": "43658e42-0dfd-444d-a3f2-3388f5d7a2ca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[0.2332, 0.4717, 0.2951],\n",
              "        [0.2219, 0.3612, 0.4168],\n",
              "        [0.3589, 0.2427, 0.3984],\n",
              "        [0.2221, 0.3218, 0.4561],\n",
              "        [0.5762, 0.2056, 0.2181],\n",
              "        [0.2249, 0.4422, 0.3329],\n",
              "        [0.2750, 0.3181, 0.4069],\n",
              "        [0.3732, 0.2454, 0.3813],\n",
              "        [0.3927, 0.1892, 0.4181],\n",
              "        [0.3349, 0.1948, 0.4703],\n",
              "        [0.3043, 0.2263, 0.4695],\n",
              "        [0.3710, 0.1894, 0.4396],\n",
              "        [0.2090, 0.4449, 0.3462],\n",
              "        [0.3396, 0.2594, 0.4010],\n",
              "        [0.3456, 0.2260, 0.4284],\n",
              "        [0.1500, 0.3302, 0.5198]], device='cuda:0', grad_fn=<SoftmaxBackward0>)"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "F.softmax(model(input_ids, attention_mask), dim=1)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "g9xikRdtRN1N"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5v-ArJ2fCCcU",
        "outputId": "089bc1c6-c804-4f56-da81-d962d82b19d0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: 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",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "EPOCHS = 6\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)\n",
        "total_steps = len(train_data_loader) * EPOCHS\n",
        "\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "  optimizer,\n",
        "  num_warmup_steps=0,\n",
        "  num_training_steps=total_steps\n",
        ")\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss().to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "bzl9UhuNx1_Q"
      },
      "outputs": [],
      "source": [
        "def train_epoch(\n",
        "  model,\n",
        "  data_loader,\n",
        "  loss_fn,\n",
        "  optimizer,\n",
        "  device,\n",
        "  scheduler,\n",
        "  n_examples\n",
        "):\n",
        "  model = model.train()\n",
        "\n",
        "  losses = []\n",
        "  correct_predictions = 0\n",
        "\n",
        "  for d in data_loader:\n",
        "    input_ids = d[\"input_ids\"].to(device)\n",
        "    attention_mask = d[\"attention_mask\"].to(device)\n",
        "    targets = d[\"targets\"].to(device)\n",
        "\n",
        "    outputs = model(\n",
        "      input_ids=input_ids,\n",
        "      attention_mask=attention_mask\n",
        "    )\n",
        "\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    loss = loss_fn(outputs, targets)\n",
        "\n",
        "    correct_predictions += torch.sum(preds == targets)\n",
        "    losses.append(loss.item())\n",
        "\n",
        "    loss.backward()\n",
        "    nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
        "    optimizer.step()\n",
        "    scheduler.step()\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "CXeRorVGIKre"
      },
      "outputs": [],
      "source": [
        "def eval_model(model, data_loader, loss_fn, device, n_examples):\n",
        "  model = model.eval()\n",
        "\n",
        "  losses = []\n",
        "  correct_predictions = 0\n",
        "\n",
        "  with torch.no_grad():\n",
        "    for d in data_loader:\n",
        "      input_ids = d[\"input_ids\"].to(device)\n",
        "      attention_mask = d[\"attention_mask\"].to(device)\n",
        "      targets = d[\"targets\"].to(device)\n",
        "\n",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "      loss = loss_fn(outputs, targets)\n",
        "\n",
        "      correct_predictions += torch.sum(preds == targets)\n",
        "      losses.append(loss.item())\n",
        "\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1zhHoFNsxufs",
        "outputId": "efeb26f8-ade3-49d1-bcb9-08b137b95aed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/6\n",
            "----------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train loss 0.7357264192392272 accuracy 0.6690424105567709\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Val   loss 0.559217081964016 accuracy 0.7789072426937739\n",
            "\n",
            "Epoch 2/6\n",
            "----------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train loss 0.42908171780878346 accuracy 0.8358619716322067\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Val   loss 0.5046901363134384 accuracy 0.8373570520965693\n",
            "\n",
            "Epoch 3/6\n",
            "----------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train loss 0.2530873617710247 accuracy 0.9162373862112766\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Val   loss 0.6362524032965302 accuracy 0.8398983481575604\n",
            "\n",
            "Epoch 4/6\n",
            "----------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train loss 0.16919020543878258 accuracy 0.9513090113612307\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Val   loss 0.6386195559287444 accuracy 0.8640406607369759\n",
            "\n",
            "Epoch 5/6\n",
            "----------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train loss 0.11795092619438897 accuracy 0.9676099075576883\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Val   loss 0.7068283748999238 accuracy 0.866581956797967\n",
            "\n",
            "Epoch 6/6\n",
            "----------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train loss 0.08801285981362382 accuracy 0.9757956389810176\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Val   loss 0.7396833367331419 accuracy 0.866581956797967\n",
            "\n",
            "CPU times: user 34min 55s, sys: 21.7 s, total: 35min 17s\n",
            "Wall time: 36min 48s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "history = defaultdict(list)\n",
        "best_accuracy = 0\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "\n",
        "  print(f'Epoch {epoch + 1}/{EPOCHS}')\n",
        "  print('-' * 10)\n",
        "\n",
        "  train_acc, train_loss = train_epoch(\n",
        "    model,\n",
        "    train_data_loader,\n",
        "    loss_fn,\n",
        "    optimizer,\n",
        "    device,\n",
        "    scheduler,\n",
        "    len(df_train)\n",
        "  )\n",
        "\n",
        "  print(f'Train loss {train_loss} accuracy {train_acc}')\n",
        "\n",
        "  val_acc, val_loss = eval_model(\n",
        "    model,\n",
        "    val_data_loader,\n",
        "    loss_fn,\n",
        "    device,\n",
        "    len(df_val)\n",
        "  )\n",
        "\n",
        "  print(f'Val   loss {val_loss} accuracy {val_acc}')\n",
        "  print()\n",
        "\n",
        "  history['train_acc'].append(train_acc)\n",
        "  history['train_loss'].append(train_loss)\n",
        "  history['val_acc'].append(val_acc)\n",
        "  history['val_loss'].append(val_loss)\n",
        "\n",
        "  if val_acc > best_accuracy:\n",
        "    torch.save(model.state_dict(), 'best_model_state.bin')\n",
        "    best_accuracy = val_acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DX8VC8xiKaX-",
        "outputId": "0fc1be5c-7a39-4bcb-e91e-011c220c5df2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[tensor(0.6690, device='cuda:0', dtype=torch.float64), tensor(0.8359, device='cuda:0', dtype=torch.float64), tensor(0.9162, device='cuda:0', dtype=torch.float64), tensor(0.9513, device='cuda:0', dtype=torch.float64), tensor(0.9676, device='cuda:0', dtype=torch.float64), tensor(0.9758, device='cuda:0', dtype=torch.float64)]\n"
          ]
        }
      ],
      "source": [
        "print(history['train_acc'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t9hOVE-XO74-",
        "outputId": "0998fb43-84f4-4313-e139-aba439dd3fde"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[array(0.66904241),\n",
              " array(0.83586197),\n",
              " array(0.91623739),\n",
              " array(0.95130901),\n",
              " array(0.96760991),\n",
              " array(0.97579564)]"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_of_train_accuracy= [t.cpu().numpy() for t in history['train_acc']]\n",
        "list_of_train_accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZPHZf3gPccd",
        "outputId": "d129148c-9861-4765-8d78-94d1a34f7f0d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[tensor(0.7789, device='cuda:0', dtype=torch.float64), tensor(0.8374, device='cuda:0', dtype=torch.float64), tensor(0.8399, device='cuda:0', dtype=torch.float64), tensor(0.8640, device='cuda:0', dtype=torch.float64), tensor(0.8666, device='cuda:0', dtype=torch.float64), tensor(0.8666, device='cuda:0', dtype=torch.float64)]\n"
          ]
        }
      ],
      "source": [
        "print(history['val_acc'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8HIB1IaPP3x",
        "outputId": "ef1c2648-f0a1-4a42-fae3-293dbe63c1d4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[array(0.77890724),\n",
              " array(0.83735705),\n",
              " array(0.83989835),\n",
              " array(0.86404066),\n",
              " array(0.86658196),\n",
              " array(0.86658196)]"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list_of_val_accuracy= [t.cpu().numpy() for t in history['val_acc']]\n",
        "list_of_val_accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "id": "-FWG7kBm372V",
        "outputId": "f6f73ad9-86a3-4a8b-84e7-46c728522e16"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 717,
              "width": 1017
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(list_of_train_accuracy, label='train accuracy')\n",
        "plt.plot(list_of_val_accuracy, label='validation accuracy')\n",
        "\n",
        "plt.title('Training history')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.ylim([0, 1]);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "zoGUH8VZ-pPQ"
      },
      "outputs": [],
      "source": [
        "# !gdown --id 1V8itWtowCYnb2Bc9KlK9SxGff9WwmogA\n",
        "\n",
        "# model = SentimentClassifier(len(class_names))\n",
        "# model.load_state_dict(torch.load('best_model_state.bin'))\n",
        "# model = model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jS3gJ_qBEljD",
        "outputId": "3b4d44f6-bf12-492a-e505-9764738a5eff"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test Accuracy :  0.8781725888324873\n"
          ]
        }
      ],
      "source": [
        "test_acc, _ = eval_model(\n",
        "  model,\n",
        "  test_data_loader,\n",
        "  loss_fn,\n",
        "  device,\n",
        "  len(df_test)\n",
        ")\n",
        "\n",
        "print(('\\n'))\n",
        "print('Test Accuracy : ', test_acc.item())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "EgR6MuNS8jr_"
      },
      "outputs": [],
      "source": [
        "def get_predictions(model, data_loader):\n",
        "  model = model.eval()\n",
        "\n",
        "  review_texts = []\n",
        "  predictions = []\n",
        "  prediction_probs = []\n",
        "  real_values = []\n",
        "\n",
        "  with torch.no_grad():\n",
        "    for d in data_loader:\n",
        "\n",
        "      texts = d[\"review_text\"]\n",
        "      input_ids = d[\"input_ids\"].to(device)\n",
        "      attention_mask = d[\"attention_mask\"].to(device)\n",
        "      targets = d[\"targets\"].to(device)\n",
        "\n",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "      probs = F.softmax(outputs, dim=1)\n",
        "\n",
        "      review_texts.extend(texts)\n",
        "      predictions.extend(preds)\n",
        "      prediction_probs.extend(probs)\n",
        "      real_values.extend(targets)\n",
        "\n",
        "  predictions = torch.stack(predictions).cpu()\n",
        "  prediction_probs = torch.stack(prediction_probs).cpu()\n",
        "  real_values = torch.stack(real_values).cpu()\n",
        "  return review_texts, predictions, prediction_probs, real_values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zHdPZr60-0c_",
        "outputId": "5ad6ff28-5ddf-4623-b0ad-ac8f49af4174"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "y_review_texts, y_pred, y_pred_probs, y_test = get_predictions(\n",
        "  model,\n",
        "  test_data_loader\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L8a9_8-ND3Is",
        "outputId": "b7cdd023-1ebd-449e-aa3e-f90b7ad166c3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.92      0.84      0.88       245\n",
            "     neutral       0.82      0.87      0.84       254\n",
            "    positive       0.91      0.91      0.91       289\n",
            "\n",
            "    accuracy                           0.88       788\n",
            "   macro avg       0.88      0.88      0.88       788\n",
            "weighted avg       0.88      0.88      0.88       788\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred, target_names=class_names))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "6d1qxsc__DTh",
        "outputId": "22944493-116a-41d1-cd91-613cbd314ba8"
      },
      "outputs": [
        {
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",
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 730,
              "width": 1008
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "def show_confusion_matrix(confusion_matrix):\n",
        "  hmap = sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "  hmap.yaxis.set_ticklabels(hmap.yaxis.get_ticklabels(), rotation=0, ha='right')\n",
        "  hmap.xaxis.set_ticklabels(hmap.xaxis.get_ticklabels(), rotation=30, ha='right')\n",
        "  plt.ylabel('True sentiment')\n",
        "  plt.xlabel('Predicted sentiment');\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "df_cm = pd.DataFrame(cm, index=class_names, columns=class_names)\n",
        "show_confusion_matrix(df_cm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "iANBiY3sLo-K"
      },
      "outputs": [],
      "source": [
        "idx = 2\n",
        "\n",
        "review_text = y_review_texts[idx]\n",
        "true_sentiment = y_test[idx]\n",
        "pred_df = pd.DataFrame({\n",
        "  'class_names': class_names,\n",
        "  'values': y_pred_probs[idx]\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-8D0rb1yfnv4",
        "outputId": "3ba7a204-3185-41a6-ceda-5a4832c21b47"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "I used to use Habitica, and I must say this is a great step up. I'd\n",
            "like to see more social features, such as sharing tasks - only one\n",
            "person has to perform said task for it to be checked off, but only\n",
            "giving that person the experience and gold. Otherwise, the price for\n",
            "subscription is too steep, thus resulting in a sub-perfect score. I\n",
            "could easily justify $0.99/month or eternal subscription for $15. If\n",
            "that price could be met, as well as fine tuning, this would be easily\n",
            "worth 5 stars.\n",
            "\n",
            "True sentiment: neutral\n"
          ]
        }
      ],
      "source": [
        "print(\"\\n\".join(wrap(review_text)))\n",
        "print()\n",
        "print(f'True sentiment: {class_names[true_sentiment]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "qj4d8lZyMkhf",
        "outputId": "72454231-de53-4130-9748-e3b3174c786b"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 693,
              "width": 1083
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.barplot(x='values', y='class_names', data=pred_df, orient='h')\n",
        "plt.ylabel('sentiment')\n",
        "plt.xlabel('probability')\n",
        "plt.xlim([0, 1]);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QEPi7zQRsDhH",
        "outputId": "d79b8fd0-fe67-4e4a-d381-1bc835a90417"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter a comment for sentiment analysis: I hate you\n"
          ]
        }
      ],
      "source": [
        "review_text = input(\"Enter a comment for sentiment analysis: \")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "zA5Or4D2sLc9"
      },
      "outputs": [],
      "source": [
        "encoded_review = tokenizer.encode_plus(\n",
        "  review_text,\n",
        "  max_length=MAX_LEN,\n",
        "  add_special_tokens=True,\n",
        "  return_token_type_ids=False,\n",
        "  pad_to_max_length=True,\n",
        "  return_attention_mask=True,\n",
        "  return_tensors='pt',\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qr_t3rUksumr",
        "outputId": "616e6865-57af-4dc2-a864-550ef211fc3e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Review text: I hate you\n",
            "Sentiment  : positive\n"
          ]
        }
      ],
      "source": [
        "input_ids = encoded_review['input_ids'].to(device)\n",
        "attention_mask = encoded_review['attention_mask'].to(device)\n",
        "\n",
        "output = model(input_ids, attention_mask)\n",
        "_, prediction = torch.max(output, dim=1)\n",
        "\n",
        "print(f'Review text: {review_text}')\n",
        "print(f'Sentiment  : {class_names[prediction]}')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Wf39tauBa2V2"
      },
      "source": [
        "## References\n",
        "\n",
        "- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)\n",
        "- [L11 Language Models - Alec Radford (OpenAI)](https://www.youtube.com/watch?v=BnpB3GrpsfM)\n",
        "- [The Illustrated BERT, ELMo, and co.](https://jalammar.github.io/illustrated-bert/)\n",
        "- [BERT Fine-Tuning Tutorial with PyTorch](https://mccormickml.com/2019/07/22/BERT-fine-tuning/)\n",
        "- [How to Fine-Tune BERT for Text Classification?](https://arxiv.org/pdf/1905.05583.pdf)\n",
        "- [Huggingface Transformers](https://huggingface.co/transformers/)\n",
        "- [BERT Explained: State of the art language model for NLP](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270)"
      ]
    }
  ],
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