coronavirus_tweet_nlp.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rybread1/coronavirus-tweet-nlp/blob/master/coronavirus_tweet_nlp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8KM9EjsHZ7jX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "36fa51a4-f58c-4893-c4b3-4537c66b3072"
      },
      "source": [
        "pip install transformers"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ae/05/c8c55b600308dc04e95100dc8ad8a244dd800fe75dfafcf1d6348c6f6209/transformers-3.1.0-py3-none-any.whl (884kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 6.6MB/s \n",
            "\u001b[?25hCollecting sentencepiece!=0.1.92\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/a4/d0a884c4300004a78cca907a6ff9a5e9fe4f090f5d95ab341c53d28cbc58/sentencepiece-0.1.91-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1MB 33.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.7)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
            "Collecting tokenizers==0.8.1.rc2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/80/83/8b9fccb9e48eeb575ee19179e2bdde0ee9a1904f97de5f02d19016b8804f/tokenizers-0.8.1rc2-cp36-cp36m-manylinux1_x86_64.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0MB 39.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers) (1.18.5)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 35.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.6.20)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (1.15.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (0.16.0)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893257 sha256=b89054f8e349b59ba7e8c28349020829f658bdf462907b93c46c32e8d33fd43f\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sentencepiece, tokenizers, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.43 sentencepiece-0.1.91 tokenizers-0.8.1rc2 transformers-3.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h78UkLjkaBbM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "66b56f4c-52af-4316-d170-525aed038f78"
      },
      "source": [
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import cv2\n",
        "import os\n",
        "import math\n",
        "import seaborn as sn\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "from bs4 import BeautifulSoup\n",
        "from sklearn import metrics as sk_metrics\n",
        "import nltk\n",
        "import re"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LqrLQyTUaCRE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "424170a9-6a4a-4559-89ed-ead386e15935"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive/')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0ddrG6EaDON",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "folder = '/content/gdrive/My Drive/corona-tweet-sent'\n",
        "train_f = 'Corona_NLP_train.csv'\n",
        "test_f = 'Corona_NLP_test.csv'\n",
        "\n",
        "df_train = pd.read_csv(os.path.join(folder, train_f), engine='python')\n",
        "df_test = pd.read_csv(os.path.join(folder, test_f), engine='python')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pklk6KusbGf9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "220576fe-67ba-4672-91d2-88d008b67183"
      },
      "source": [
        "df_train.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 41157 entries, 0 to 41156\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   UserName       41157 non-null  int64 \n",
            " 1   ScreenName     41157 non-null  int64 \n",
            " 2   Location       32567 non-null  object\n",
            " 3   TweetAt        41157 non-null  object\n",
            " 4   OriginalTweet  41157 non-null  object\n",
            " 5   Sentiment      41157 non-null  object\n",
            "dtypes: int64(2), object(4)\n",
            "memory usage: 1.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhF9mMVKbnBN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e5c178c2-87b6-413a-83e7-1f012178e515"
      },
      "source": [
        "df_train.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>UserName</th>\n",
              "      <th>ScreenName</th>\n",
              "      <th>Location</th>\n",
              "      <th>TweetAt</th>\n",
              "      <th>OriginalTweet</th>\n",
              "      <th>Sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3799</td>\n",
              "      <td>48751</td>\n",
              "      <td>London</td>\n",
              "      <td>16-03-2020</td>\n",
              "      <td>@MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3800</td>\n",
              "      <td>48752</td>\n",
              "      <td>UK</td>\n",
              "      <td>16-03-2020</td>\n",
              "      <td>advice Talk to your neighbours family to excha...</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3801</td>\n",
              "      <td>48753</td>\n",
              "      <td>Vagabonds</td>\n",
              "      <td>16-03-2020</td>\n",
              "      <td>Coronavirus Australia: Woolworths to give elde...</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3802</td>\n",
              "      <td>48754</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16-03-2020</td>\n",
              "      <td>My food stock is not the only one which is emp...</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3803</td>\n",
              "      <td>48755</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16-03-2020</td>\n",
              "      <td>Me, ready to go at supermarket during the #COV...</td>\n",
              "      <td>Extremely Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   UserName  ...           Sentiment\n",
              "0      3799  ...             Neutral\n",
              "1      3800  ...            Positive\n",
              "2      3801  ...            Positive\n",
              "3      3802  ...            Positive\n",
              "4      3803  ...  Extremely Negative\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1UXvi9kQve9y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "58e2911f-5313-4bca-f824-6656c8bebefc"
      },
      "source": [
        "df_train['OriginalTweet'].iloc[0]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'@MeNyrbie @Phil_Gahan @Chrisitv https://t.co/iFz9FAn2Pa and https://t.co/xX6ghGFzCC and https://t.co/I2NlzdxNo8'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbVsoiYsbuYF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_map = {\n",
        "    'Positive': 0, \n",
        "    'Negative': 1, \n",
        "    'Neutral': 2, \n",
        "    'Extremely Positive': 3, \n",
        "    'Extremely Negative': 4\n",
        "}\n",
        "\n",
        "df_train['Sentiment_Cat'] = df_train['Sentiment'].map(y_map)\n",
        "df_test['Sentiment_Cat'] = df_test['Sentiment'].map(y_map)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-mUdhBwNbwmn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "bf3e8bf2-8182-497c-d3f4-c6700aaf4597"
      },
      "source": [
        "nltk.download('stopwords')\n",
        "stop_words = nltk.corpus.stopwords.words('english')\n",
        "\n",
        "def clean_text(data):\n",
        "    data = data.lower()\n",
        "    data = re.sub(r'https?:\\/\\/(www\\.)?[-a-zA-Z0-9@:%._\\+~#=]{1,256}\\.[a-zA-Z0-9()]{1,6}\\b([-a-zA-Z0-9()@:%_\\+.~#?&//=]*)', '', data)\n",
        "    data = re.sub(r'([^a-zA-Z\\s])', '', data)\n",
        "    data = data.split()\n",
        "    temp = []\n",
        "    for i in data:\n",
        "        if i not in stop_words:\n",
        "            temp.append(i)\n",
        "    data = ' '.join(temp)\n",
        "    return data\n",
        "\n",
        "df_train['tweet_cleaned'] = df_train['OriginalTweet'].apply(clean_text)\n",
        "df_test['tweet_cleaned'] = df_test['OriginalTweet'].apply(clean_text)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzhyEzc2dIsv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, y_train = df_train['tweet_cleaned'].values, np.array(df_train['Sentiment_Cat'], dtype=np.int64)\n",
        "x_test, y_test = df_test['tweet_cleaned'].values, np.array(df_test['Sentiment_Cat'], dtype=np.int64)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfVEmb5Idiv5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tokenizers import BertWordPieceTokenizer\n",
        "import transformers"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8o7uh0Gbdlmf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "ed2075f984db4cd09f2fe41bdceb3ad3",
            "35eacc08b8e54cc1b9d578a93a6380e6",
            "615a79add0e04dfabfc3e7645c070831",
            "44ca68d9a5f34a3280b905a509e912d9",
            "88b9b05012a44a60a3874d55ab0510f3",
            "18cb559761bc4fd6adb4b767b57d3100",
            "889ee304fb5740369dbd96a45b8ce681",
            "f4433f03ea744f73bc0f557391de1f0f"
          ]
        },
        "outputId": "767581f7-b0eb-490c-f21c-6933ff37a067"
      },
      "source": [
        "# First load the real tokenizer\n",
        "# tokenizer = transformers.DistilBertTokenizer.from_pretrained('distilbert-base-uncased' , lower=True)\n",
        "tokenizer = transformers.BertTokenizerFast.from_pretrained('bert-base-uncased', lower=True)\n",
        "\n",
        "# Save the loaded tokenizer locally\n",
        "tokenizer.save_pretrained('.')\n",
        "\n",
        "# Reload it with the huggingface tokenizers library\n",
        "fast_tokenizer = BertWordPieceTokenizer('vocab.txt', lowercase=True)\n",
        "fast_tokenizer"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ed2075f984db4cd09f2fe41bdceb3ad3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tokenizer(vocabulary_size=30522, model=BertWordPiece, unk_token=[UNK], sep_token=[SEP], cls_token=[CLS], pad_token=[PAD], mask_token=[MASK], clean_text=True, handle_chinese_chars=True, strip_accents=None, lowercase=True, wordpieces_prefix=##)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vvy3OyJddodD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fast_encode(texts, tokenizer, chunk_size=256, maxlen=200):\n",
        "    tokenizer.enable_truncation(max_length=maxlen)\n",
        "    tokenizer.enable_padding(length=maxlen)\n",
        "    all_ids = []\n",
        "    \n",
        "    for i in range(0, len(texts), chunk_size):\n",
        "        text_chunk = texts[i:i+chunk_size].tolist()\n",
        "        encs = tokenizer.encode_batch(text_chunk)\n",
        "        all_ids.extend([enc.ids for enc in encs])\n",
        "    \n",
        "    return np.array(all_ids)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9LJvgi0d5i-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "max_len=100\n",
        "\n",
        "x_train_enc = fast_encode(x_train, fast_tokenizer, maxlen=max_len)\n",
        "x_test_enc = fast_encode(x_test, fast_tokenizer, maxlen=max_len)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcBnfPChd9PO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223,
          "referenced_widgets": [
            "33153a2672ce494eb2604de81c2aa07a",
            "7a999e62d56e423caf1b5fe2e6f5199a",
            "f12e3a53398d41efa9b087091b1e1203",
            "b0c721c3cb3c498ab77bc848ccdcadcb",
            "c57d306f68174eca87bbab848495638b",
            "7ef3228cbe4749ff8a2bb5b83391808c",
            "47b1b0ef384846c3a7b662ff98c5a05f",
            "9a3680cb39c94a50bff85f6eda084757",
            "07185e4479e947249f38f08516377bd4",
            "5185c7b892ff47188f657a9fa9c73c0e",
            "789098a8a6f642ec864fe3304fc5abcc",
            "32f6632cdf5c46b68363db833ef8a87c",
            "3c838f98570f433caef3401b71d35e54",
            "411cd6fa98974f5487284327588b0ff1",
            "037433a271794a79b3692511c1a238ee",
            "3d47ed49579947e49767e32c7a5ea4bd"
          ]
        },
        "outputId": "c109e147-5fb5-4bff-c3c0-dad86aae1a91"
      },
      "source": [
        "NUM_CLASSES = df_train['Sentiment_Cat'].nunique()\n",
        "\n",
        "# bert_transformer = transformers.TFDistilBertModel.from_pretrained('distilbert-base-uncased')\n",
        "bert_transformer = transformers.TFBertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=NUM_CLASSES)\n",
        "\n",
        "input = tf.keras.layers.Input(shape=(max_len,), dtype=tf.int32)\n",
        "sequence_output = bert_transformer(input)[0]\n",
        "# cls_token = sequence_output[:, 0, :]\n",
        "# output = tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')(cls_token)\n",
        "\n",
        "model = tf.keras.Model(inputs=input, outputs=sequence_output)\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(1e-5), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "33153a2672ce494eb2604de81c2aa07a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "07185e4479e947249f38f08516377bd4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=536063208.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing TFBertForSequenceClassification: ['nsp___cls', 'mlm___cls']\n",
            "- This IS expected if you are initializing TFBertForSequenceClassification 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 TFBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['dropout_37', 'classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0bliWbCjeRLV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "ec6d1955-9649-4521-e24e-3ece11419cc1"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 100)]             0         \n",
            "_________________________________________________________________\n",
            "tf_bert_for_sequence_classif ((None, 5),)              109486085 \n",
            "=================================================================\n",
            "Total params: 109,486,085\n",
            "Trainable params: 109,486,085\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJRsiDYFevgr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "ce4ff816-8f56-4987-c120-eb2bfe639194"
      },
      "source": [
        "# call backs\n",
        "early_stop_callback = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_loss', \n",
        "    patience=3, \n",
        "    restore_best_weights=True\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    x_train_enc,\n",
        "    y_train,\n",
        "    batch_size=16,\n",
        "    validation_data=(x_test_enc, y_test),\n",
        "    epochs=50,\n",
        "    callbacks=[early_stop_callback])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "2573/2573 [==============================] - 1749s 680ms/step - loss: 0.9260 - accuracy: 0.6323 - val_loss: 0.7608 - val_accuracy: 0.7299\n",
            "Epoch 2/50\n",
            "2573/2573 [==============================] - 1741s 677ms/step - loss: 0.6310 - accuracy: 0.7778 - val_loss: 0.7289 - val_accuracy: 0.7446\n",
            "Epoch 3/50\n",
            "2573/2573 [==============================] - 1734s 674ms/step - loss: 0.5329 - accuracy: 0.8173 - val_loss: 0.6981 - val_accuracy: 0.7567\n",
            "Epoch 4/50\n",
            "2573/2573 [==============================] - 1730s 672ms/step - loss: 0.4509 - accuracy: 0.8486 - val_loss: 0.7833 - val_accuracy: 0.7341\n",
            "Epoch 5/50\n",
            "2573/2573 [==============================] - 1726s 671ms/step - loss: 0.3748 - accuracy: 0.8790 - val_loss: 0.7402 - val_accuracy: 0.7609\n",
            "Epoch 6/50\n",
            "2573/2573 [==============================] - 1725s 670ms/step - loss: 0.3131 - accuracy: 0.9009 - val_loss: 0.8165 - val_accuracy: 0.7314\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZxKEI6KIt19",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7accaec6-b6b1-45df-95bb-470c543c718d"
      },
      "source": [
        "np.argmax(ypred, axis=1)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 3, 3, ..., 2, 4, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zY9uNgVAe0MP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ypred = model.predict(x_test_enc)\n",
        "# df_test['ypred'] = ypred\n",
        "# df_test['ypred_cat'] = np.where(df_test['ypred']>=0.5, 1, 0)\n",
        "df_test['ypred_cat'] = np.argmax(ypred, axis=1)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3XLO099856Xm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "7ef0f45b-4436-42fa-f7b5-6ed0c8f93e0b"
      },
      "source": [
        "y_map_inv = {\n",
        "    0: 'Positive',\n",
        "    1: 'Negative',\n",
        "    2: 'Neutral',\n",
        "    3: 'Extremely Positive',\n",
        "    4: 'Extremely Negative',\n",
        "}\n",
        "\n",
        "sns.heatmap(\n",
        "    pd.crosstab(df_test['ypred_cat'].map(y_map_inv), df_test['Sentiment_Cat'].map(y_map_inv)),\n",
        "    annot=True,\n",
        "    fmt='0.0f',\n",
        "    cbar=False,\n",
        "    linewidth=2,\n",
        "    cmap='coolwarm'     \n",
        ")\n",
        "\n",
        "plt.xlabel('True')\n",
        "plt.ylabel('Predicted')\n",
        "plt.title('Confusion Matrix');"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LECQAio-74E2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}