Subjectivity_Detection_Model.ipynb

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        "<a href=\"https://colab.research.google.com/github/Confirmation-Bias-Analyser/Confirmation-Bias-Model/blob/main/Subjectivity_Detection_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "%%capture\n",
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "CJjKKLkkW8Bg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ee3b855-7bd2-48aa-f5b5-2ec6d53d56c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from transformers import BertTokenizer, TFBertForSequenceClassification, InputExample, InputFeatures\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "from google.colab import files, drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# The shutil module offers a number of high-level \n",
        "# operations on files and collections of files.\n",
        "import os\n",
        "import shutil"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gjFWhREjcSP7"
      },
      "source": [
        "## Obtain BERT Model"
      ]
    },
    {
      "cell_type": "code",
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        {
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          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
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              "model_id": "e041c09217ee497d9e03915473bbbea1"
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      ],
      "source": [
        "model = TFBertForSequenceClassification.from_pretrained(\"bert-base-uncased\")\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9tQSJ7QYQcSW",
        "outputId": "e6781fb5-0cc3-490a-f01f-f26d4174b23b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"tf_bert_for_sequence_classification\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " bert (TFBertMainLayer)      multiple                  109482240 \n",
            "                                                                 \n",
            " dropout_37 (Dropout)        multiple                  0         \n",
            "                                                                 \n",
            " classifier (Dense)          multiple                  1538      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 109,483,778\n",
            "Trainable params: 109,483,778\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vynyZBe4cQon"
      },
      "source": [
        "## Obtain Dataset\n",
        "Based on this dataset: https://www.cs.cornell.edu/people/pabo/movie-review-data/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2YLkJCcYqQg",
        "outputId": "3d86959b-3ee9-433e-b4de-d3ead8905888"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from http://www.cs.cornell.edu/people/pabo/movie-review-data/rotten_imdb.tar.gz\n",
            "524288/519599 [==============================] - 0s 0us/step\n",
            "532480/519599 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "URL = \"http://www.cs.cornell.edu/people/pabo/movie-review-data/rotten_imdb.tar.gz\"\n",
        "\n",
        "dataset = tf.keras.utils.get_file(fname=\"rotten_imdb.tar.gz\", \n",
        "                                  origin=URL,\n",
        "                                  untar=True,\n",
        "                                  cache_dir='.',\n",
        "                                  cache_subdir='')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E15aY7Cicax_"
      },
      "source": [
        "## Reorganise Datasets\n",
        "Removing unlabelled data, obtain the directory of train dataset and get the details of train and test sets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "RJdb3YHxQpAs"
      },
      "outputs": [],
      "source": [
        "with open('/content/plot.tok.gt9.5000', 'r') as f_o:\n",
        "  objective_data = f_o.read()\n",
        "  f_o.close()\n",
        "\n",
        "objective_data = objective_data.split(' \\n')\n",
        "\n",
        "with open('/content/quote.tok.gt9.5000', 'r', encoding=\"ISO-8859-1\") as f_s:\n",
        "  subjective_data = f_s.read()\n",
        "  f_s.close()\n",
        "\n",
        "subjective_data = subjective_data.split(' \\n')"
      ]
    },
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          "output_type": "execute_result",
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              "                                         DATA_COLUMN  LABEL_COLUMN\n",
              "0  the movie begins in the past where a young boy...             0\n",
              "1  emerging from the human psyche and showing cha...             0\n",
              "2  spurning her mother's insistence that she get ...             0\n",
              "3  amitabh can't believe the board of directors a...             0\n",
              "4  she , among others excentricities , talks to a...             0"
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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      "source": [
        "objective_from_dict = {'DATA_COLUMN': objective_data}\n",
        "objective_df = pd.DataFrame.from_dict(objective_from_dict)\n",
        "objective_df['LABEL_COLUMN'] = 0\n",
        "\n",
        "subjective_from_dict = {'DATA_COLUMN': subjective_data}\n",
        "subjective_df = pd.DataFrame.from_dict(subjective_from_dict)\n",
        "subjective_df['LABEL_COLUMN'] = 1\n",
        "\n",
        "df = pd.concat([objective_df, subjective_df])\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x3louHzdtWbC"
      },
      "source": [
        "### Train-Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "GjAVW9yMYqjg"
      },
      "outputs": [],
      "source": [
        "# We create a training dataset and a validation \n",
        "# dataset from our \"aclImdb/train\" directory with a 80/20 split.\n",
        "\n",
        "train, test, y_train, y_test = train_test_split(df, df['LABEL_COLUMN'], test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NR2t6hJEZrD4"
      },
      "source": [
        "## View Train and Test Datasets\n",
        "Using the Pandas library to view the datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
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            "7883\n"
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              "                                            DATA_COLUMN  LABEL_COLUMN\n",
              "4923  in 1975 , as the vietnam war was ending , thou...             0\n",
              "1745  at the last minute a cyber-friend arrives to j...             0\n",
              "430   it's endearing to hear madame d . refer to her...             1\n",
              "1452  70 years later , these appliances were condemn...             0\n",
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              "      <th>1452</th>\n",
              "      <td>70 years later , these appliances were condemn...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>arjun ( akshay kumar ) is a high profile vigil...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7de578a-1967-4a10-9216-d7d5ef2a25ee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e7de578a-1967-4a10-9216-d7d5ef2a25ee button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e7de578a-1967-4a10-9216-d7d5ef2a25ee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "print(len(train))\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vFhq5Wafs-U7"
      },
      "source": [
        "## Data Processing\n",
        "Using the Input Examples function of Transformers to process the data\n",
        "\n",
        "Create two main functions:\n",
        "\n",
        "1 — `convert_data_to_examples`: This will accept our train and test datasets and convert each row into an InputExample object.\n",
        "\n",
        "2 — `convert_examples_to_tf_dataset`: This function will tokenize the InputExample objects, then create the required input format with the tokenized objects, finally, create an input dataset that we can feed to the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41AXAXnuYqrb",
        "outputId": "6d801967-c595-4992-9718-11f88ea67a98"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InputExample(guid=None, text_a='Hello, world', text_b=None, label=1)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "InputExample(guid=None,\n",
        "             text_a = \"Hello, world\",\n",
        "             text_b = None,\n",
        "             label = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "JhVxMJwaY0lG"
      },
      "outputs": [],
      "source": [
        "def convert_data_to_examples(train, test, DATA_COLUMN, LABEL_COLUMN): \n",
        "  train_InputExamples = train.apply(lambda x: InputExample(guid=None, # Globally unique ID for bookkeeping, unused in this case\n",
        "                                                          text_a = x[DATA_COLUMN], \n",
        "                                                          text_b = None,\n",
        "                                                          label = x[LABEL_COLUMN]), axis = 1)\n",
        "\n",
        "  validation_InputExamples = test.apply(lambda x: InputExample(guid=None, # Globally unique ID for bookkeeping, unused in this case\n",
        "                                                          text_a = x[DATA_COLUMN], \n",
        "                                                          text_b = None,\n",
        "                                                          label = x[LABEL_COLUMN]), axis = 1)\n",
        "  \n",
        "  return train_InputExamples, validation_InputExamples\n",
        "  \n",
        "def convert_examples_to_tf_dataset(examples, tokenizer, max_length=128):\n",
        "    features = [] # -> will hold InputFeatures to be converted later\n",
        "\n",
        "    for e in examples:\n",
        "        # Documentation is really strong for this method, so please take a look at it\n",
        "        input_dict = tokenizer.encode_plus(\n",
        "            e.text_a,\n",
        "            add_special_tokens=True,\n",
        "            max_length=max_length, # truncates if len(s) > max_length\n",
        "            return_token_type_ids=True,\n",
        "            return_attention_mask=True,\n",
        "            pad_to_max_length=True, # pads to the right by default # CHECK THIS for pad_to_max_length\n",
        "            truncation=True\n",
        "        )\n",
        "\n",
        "        input_ids, token_type_ids, attention_mask = (input_dict[\"input_ids\"],\n",
        "            input_dict[\"token_type_ids\"], input_dict['attention_mask'])\n",
        "\n",
        "        features.append(\n",
        "            InputFeatures(\n",
        "                input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, label=e.label\n",
        "            )\n",
        "        )\n",
        "\n",
        "    def gen():\n",
        "        for f in features:\n",
        "            yield (\n",
        "                {\n",
        "                    \"input_ids\": f.input_ids,\n",
        "                    \"attention_mask\": f.attention_mask,\n",
        "                    \"token_type_ids\": f.token_type_ids,\n",
        "                },\n",
        "                f.label,\n",
        "            )\n",
        "\n",
        "    return tf.data.Dataset.from_generator(\n",
        "        gen,\n",
        "        ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32, \"token_type_ids\": tf.int32}, tf.int64),\n",
        "        (\n",
        "            {\n",
        "                \"input_ids\": tf.TensorShape([None]),\n",
        "                \"attention_mask\": tf.TensorShape([None]),\n",
        "                \"token_type_ids\": tf.TensorShape([None]),\n",
        "            },\n",
        "            tf.TensorShape([]),\n",
        "        ),\n",
        "    )\n",
        "\n",
        "DATA_COLUMN = 'DATA_COLUMN'\n",
        "LABEL_COLUMN = 'LABEL_COLUMN'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "njDc4l6kZ9lu",
        "outputId": "b2b9ed55-a0b8-4454-e7d5-7f9ccc9b4cb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2291: 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",
            "  FutureWarning,\n"
          ]
        }
      ],
      "source": [
        "# train_InputExamples, validation_InputExamples refers to the train and test data respectively\n",
        "train_InputExamples, validation_InputExamples = convert_data_to_examples(train, test, DATA_COLUMN, LABEL_COLUMN)\n",
        "\n",
        "train_data = convert_examples_to_tf_dataset(list(train_InputExamples), tokenizer)\n",
        "train_data = train_data.shuffle(100).batch(32).repeat(2)\n",
        "\n",
        "validation_data = convert_examples_to_tf_dataset(list(validation_InputExamples), tokenizer)\n",
        "validation_data = validation_data.batch(32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mp_dMWPiWveS"
      },
      "source": [
        "## Model Training\n",
        "Training for 3 epochs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fuz6IHb3Z_i1",
        "outputId": "cb87c140-883e-4549-a668-c92b1d4aee55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "494/494 [==============================] - 445s 856ms/step - loss: 0.1059 - accuracy: 0.9627 - val_loss: 0.1033 - val_accuracy: 0.9726\n",
            "Epoch 2/3\n",
            "494/494 [==============================] - 423s 855ms/step - loss: 0.0162 - accuracy: 0.9950 - val_loss: 0.1467 - val_accuracy: 0.9670\n",
            "Epoch 3/3\n",
            "494/494 [==============================] - 422s 854ms/step - loss: 0.0062 - accuracy: 0.9980 - val_loss: 0.1695 - val_accuracy: 0.9706\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f8969c9ec50>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0), \n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), \n",
        "              metrics=[tf.keras.metrics.SparseCategoricalAccuracy('accuracy')])\n",
        "\n",
        "model.fit(train_data, epochs=3, validation_data=validation_data, verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yh4Tfpt70YdA",
        "outputId": "8dfb1d73-dfe0-47df-a1e6-da0d0ccf7c92"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/drive/MyDrive/Final Year Project/Key Notebooks/Confirmation Bias Analyser/subjectivity_tokenizer/tokenizer_config.json',\n",
              " '/content/drive/MyDrive/Final Year Project/Key Notebooks/Confirmation Bias Analyser/subjectivity_tokenizer/special_tokens_map.json',\n",
              " '/content/drive/MyDrive/Final Year Project/Key Notebooks/Confirmation Bias Analyser/subjectivity_tokenizer/vocab.txt',\n",
              " '/content/drive/MyDrive/Final Year Project/Key Notebooks/Confirmation Bias Analyser/subjectivity_tokenizer/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "saved_path = '/content/drive/MyDrive/Final Year Project/Key Notebooks/Confirmation Bias Analyser/'\n",
        "\n",
        "model.save_pretrained(saved_path + 'saved_subjectivity_model')\n",
        "tokenizer.save_pretrained(saved_path + 'subjectivity_tokenizer')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vU_qCwkU4_rQ",
        "outputId": "abda272a-47e7-4e1d-ad6d-8d26645c7a5f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at /content/drive/MyDrive/Final Year Project/Key Notebooks/Confirmation Bias Analyser/saved_subjectivity_model were not used when initializing TFBertForSequenceClassification: ['dropout_37']\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",
            "All the layers of TFBertForSequenceClassification were initialized from the model checkpoint at /content/drive/MyDrive/Final Year Project/Key Notebooks/Confirmation Bias Analyser/saved_subjectivity_model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"tf_bert_for_sequence_classification_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " bert (TFBertMainLayer)      multiple                  109482240 \n",
            "                                                                 \n",
            " dropout_75 (Dropout)        multiple                  0         \n",
            "                                                                 \n",
            " classifier (Dense)          multiple                  1538      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 109,483,778\n",
            "Trainable params: 109,483,778\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "new_model = TFBertForSequenceClassification.from_pretrained(saved_path + 'saved_subjectivity_model')\n",
        "new_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "t92j6FEowa6b"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
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