BERT.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "!pip install torchmetrics\n",
        "!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tvtvydK45KC",
        "outputId": "aa9e6294-119a-46e5-fd71-d488ea5d98f7"
      },
      "execution_count": 1,
      "outputs": [
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.38.2)\n",
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, lightning-utilities, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torchmetrics\n",
            "Successfully installed lightning-utilities-0.11.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 torchmetrics-1.3.2\n",
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            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.18.0 dill-0.3.8 multiprocess-0.70.16 xxhash-3.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Load up the libraries"
      ],
      "metadata": {
        "id": "c0OEOtEL9PFG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ogimtHo42FrF"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "from transformers import AutoTokenizer, BertModel, BertForSequenceClassification, BertConfig\n",
        "from tqdm import tqdm\n",
        "import torch\n",
        "import pickle\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "from torchmetrics import Accuracy\n",
        "from datasets import load_dataset\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## A function to pre-process each line"
      ],
      "metadata": {
        "id": "St26XviuAV9q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess(x):\n",
        "    x = re.sub('<.*?>', ' ', x)\n",
        "    x = re.sub('http\\S+', ' ', x)\n",
        "    x = re.sub('\\s+', ' ', x)\n",
        "    return x.lower().strip()"
      ],
      "metadata": {
        "id": "mDp6vcrUAViK"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Helper functions to save and load pickle files"
      ],
      "metadata": {
        "id": "6WCJSARo4rhR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def save_pickle_file(object, file_name):\n",
        "    fp = open(file_name, \"wb\")\n",
        "    pickle.dump(object, fp)\n",
        "    fp.close()\n",
        "\n",
        "\n",
        "def load_pickle_file(file_name):\n",
        "    fp = open(file_name, \"rb\")\n",
        "    data = pickle.load(fp)\n",
        "    fp.close()\n",
        "    return data"
      ],
      "metadata": {
        "id": "6TUTkbe94qsH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## This function converts input dataframe to transformer usable format"
      ],
      "metadata": {
        "id": "qCWz49-CAiFt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def pipeline(dataframe):\n",
        "    # Pre-process the sentences\n",
        "    dataframe['text'] = dataframe['text'].apply(lambda x: preprocess(x))\n",
        "\n",
        "    # Pre-pend CLS token to each sentence\n",
        "    sentences = [\"[CLS] \" + s for s in dataframe.text.values]\n",
        "\n",
        "    # Extract labels\n",
        "    labels = dataframe.label.values\n",
        "\n",
        "    # Tokenize each sentence\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\", do_lower_case=True)\n",
        "    tokenized = [tokenizer.tokenize(s) for s in tqdm(sentences)]\n",
        "\n",
        "    # Append the SEP token and also set a threshold for the number of tokens in a sentence\n",
        "    MAX_LEN_TRAIN, MAX_LEN_TEST = 140, 140\n",
        "    tokenized = [t[:(MAX_LEN_TRAIN-1)]+['SEP'] for t in tokenized]\n",
        "\n",
        "    # Generate IDs of each token and add padding to sentences smaller than given threshold\n",
        "    ids = [tokenizer.convert_tokens_to_ids(t) for t in tqdm(tokenized)]\n",
        "    ids = np.array([np.pad(i, (0, MAX_LEN_TRAIN-len(i)), mode='constant') for i in ids])\n",
        "\n",
        "    # Also generate Attention masks. An attention mask is a binary tensor\n",
        "    # that indicates the position of padded indices so that the model does not attend to them\n",
        "    amasks = np.asarray([[float(i>0) for i in seq] for seq in tqdm(ids)])\n",
        "\n",
        "    return torch.tensor(ids), torch.tensor(labels), torch.tensor(amasks)"
      ],
      "metadata": {
        "id": "SgqNBkY2Aghi"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load the training and validation datasets"
      ],
      "metadata": {
        "id": "gwBxq13-_8Dm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset('imdb')\n",
        "df_train = dataset['train'].to_pandas()\n",
        "display(df_train.head())\n",
        "\n",
        "df_val = dataset['test'].to_pandas()\n",
        "display(df_val.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "i4m2O0Gx9AdX",
        "outputId": "b288c405-e9e5-4653-b716-2f1fb82c6dc8"
      },
      "execution_count": 9,
      "outputs": [
        {
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              "        const element = document.querySelector('#df-319507c8-efe0-4c3d-973d-1f139ffefb00');\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",
              "\n",
              "\n",
              "<div id=\"df-4c711f2f-3814-4e65-8b73-598b90e7aceb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4c711f2f-3814-4e65-8b73-598b90e7aceb')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4c711f2f-3814-4e65-8b73-598b90e7aceb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df_val\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"\\\"I Am Curious: Yellow\\\" is a risible and pretentious steaming pile. It doesn't matter what one's political views are because this film can hardly be taken seriously on any level. As for the claim that frontal male nudity is an automatic NC-17, that isn't true. I've seen R-rated films with male nudity. Granted, they only offer some fleeting views, but where are the R-rated films with gaping vulvas and flapping labia? Nowhere, because they don't exist. The same goes for those crappy cable shows: schlongs swinging in the breeze but not a clitoris in sight. And those pretentious indie movies like The Brown Bunny, in which we're treated to the site of Vincent Gallo's throbbing johnson, but not a trace of pink visible on Chloe Sevigny. Before crying (or implying) \\\"double-standard\\\" in matters of nudity, the mentally obtuse should take into account one unavoidably obvious anatomical difference between men and women: there are no genitals on display when actresses appears nude, and the same cannot be said for a man. In fact, you generally won't see female genitals in an American film in anything short of porn or explicit erotica. This alleged double-standard is less a double standard than an admittedly depressing ability to come to terms culturally with the insides of women's bodies.\",\n          \"Oh, brother...after hearing about this ridiculous film for umpteen years all I can think of is that old Peggy Lee song..<br /><br />\\\"Is that all there is??\\\" ...I was just an early teen when this smoked fish hit the U.S. I was too young to get in the theater (although I did manage to sneak into \\\"Goodbye Columbus\\\"). Then a screening at a local film museum beckoned - Finally I could see this film, except now I was as old as my parents were when they schlepped to see it!!<br /><br />The ONLY reason this film was not condemned to the anonymous sands of time was because of the obscenity case sparked by its U.S. release. MILLIONS of people flocked to this stinker, thinking they were going to see a sex film...Instead, they got lots of closeups of gnarly, repulsive Swedes, on-street interviews in bland shopping malls, asinie political pretension...and feeble who-cares simulated sex scenes with saggy, pale actors.<br /><br />Cultural icon, holy grail, historic artifact..whatever this thing was, shred it, burn it, then stuff the ashes in a lead box!<br /><br />Elite esthetes still scrape to find value in its boring pseudo revolutionary political spewings..But if it weren't for the censorship scandal, it would have been ignored, then forgotten.<br /><br />Instead, the \\\"I Am Blank, Blank\\\" rhythymed title was repeated endlessly for years as a titilation for porno films (I am Curious, Lavender - for gay films, I Am Curious, Black - for blaxploitation films, etc..) and every ten years or so the thing rises from the dead, to be viewed by a new generation of suckers who want to see that \\\"naughty sex film\\\" that \\\"revolutionized the film industry\\\"...<br /><br />Yeesh, avoid like the plague..Or if you MUST see it - rent the video and fast forward to the \\\"dirty\\\" parts, just to get it over with.<br /><br />\",\n          \"If only to avoid making this type of film in the future. This film is interesting as an experiment but tells no cogent story.<br /><br />One might feel virtuous for sitting thru it because it touches on so many IMPORTANT issues but it does so without any discernable motive. The viewer comes away with no new perspectives (unless one comes up with one while one's mind wanders, as it will invariably do during this pointless film).<br /><br />One might better spend one's time staring out a window at a tree growing.<br /><br />\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "                                                text  label\n",
              "0  I love sci-fi and am willing to put up with a ...      0\n",
              "1  Worth the entertainment value of a rental, esp...      0\n",
              "2  its a totally average film with a few semi-alr...      0\n",
              "3  STAR RATING: ***** Saturday Night **** Friday ...      0\n",
              "4  First off let me say, If you haven't enjoyed a...      0"
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              "      <td>STAR RATING: ***** Saturday Night **** Friday ...</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-76a57f96-ab84-4a72-b7c7-1f4ac8ef4f1c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "    .colab-df-buttons div {\n",
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              "      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",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-76a57f96-ab84-4a72-b7c7-1f4ac8ef4f1c 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-76a57f96-ab84-4a72-b7c7-1f4ac8ef4f1c');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b7798cb8-e2e7-4077-822d-b2070cd97828 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "\n",
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              "summary": "{\n  \"name\": \"display(df_val\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Worth the entertainment value of a rental, especially if you like action movies. This one features the usual car chases, fights with the great Van Damme kick style, shooting battles with the 40 shell load shotgun, and even terrorist style bombs. All of this is entertaining and competently handled but there is nothing that really blows you away if you've seen your share before.<br /><br />The plot is made interesting by the inclusion of a rabbit, which is clever but hardly profound. Many of the characters are heavily stereotyped -- the angry veterans, the terrified illegal aliens, the crooked cops, the indifferent feds, the bitchy tough lady station head, the crooked politician, the fat federale who looks like he was typecast as the Mexican in a Hollywood movie from the 1940s. All passably acted but again nothing special.<br /><br />I thought the main villains were pretty well done and fairly well acted. By the end of the movie you certainly knew who the good guys were and weren't. There was an emotional lift as the really bad ones got their just deserts. Very simplistic, but then you weren't expecting Hamlet, right? The only thing I found really annoying was the constant cuts to VDs daughter during the last fight scene.<br /><br />Not bad. Not good. Passable 4.\",\n          \"First off let me say, If you haven't enjoyed a Van Damme movie since bloodsport, you probably will not like this movie. Most of these movies may not have the best plots or best actors but I enjoy these kinds of movies for what they are. This movie is much better than any of the movies the other action guys (Segal and Dolph) have thought about putting out the past few years. Van Damme is good in the movie, the movie is only worth watching to Van Damme fans. It is not as good as Wake of Death (which i highly recommend to anyone of likes Van Damme) or In hell but, in my opinion it's worth watching. It has the same type of feel to it as Nowhere to Run. Good fun stuff!\",\n          \"its a totally average film with a few semi-alright action sequences that make the plot seem a little better and remind the viewer of the classic van dam films. parts of the plot don't make sense and seem to be added in to use up time. the end plot is that of a very basic type that doesn't leave the viewer guessing and any twists are obvious from the beginning. the end scene with the flask backs don't make sense as they are added in and seem to have little relevance to the history of van dam's character. not really worth watching again, bit disappointed in the end production, even though it is apparent it was shot on a low budget certain shots and sections in the film are of poor directed quality\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clean the data and store in BERT usable format"
      ],
      "metadata": {
        "id": "3AA3TiVc5xt_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ids_train, labels_train, amasks_train = pipeline(df_train)\n",
        "ids_val, labels_val, amasks_val = pipeline(df_val)\n",
        "\n",
        "print(ids_train.shape, amasks_train.shape, labels_train.shape)\n",
        "print(ids_val.shape, amasks_val.shape, labels_val.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347,
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        },
        "id": "yPLTuy469KOY",
        "outputId": "7e87b2ce-6570-4d1f-fa86-8029c5224725"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "499f599db0f441669871eb3cc8a4a918"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "454c7c49075e4c17a3ee2d63ec508fca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ec60f0c0ed542668fde273db34da504"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "140e00205d4845c9b9010376bc17338f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/25000 [00:00<?, ?it/s]Token indices sequence length is longer than the specified maximum sequence length for this model (687 > 512). Running this sequence through the model will result in indexing errors\n",
            "100%|██████████| 25000/25000 [00:30<00:00, 816.64it/s]\n",
            "100%|██████████| 25000/25000 [00:01<00:00, 14438.77it/s]\n",
            "100%|██████████| 25000/25000 [00:02<00:00, 10435.68it/s]\n",
            "  0%|          | 0/25000 [00:00<?, ?it/s]Token indices sequence length is longer than the specified maximum sequence length for this model (549 > 512). Running this sequence through the model will result in indexing errors\n",
            "100%|██████████| 25000/25000 [00:27<00:00, 917.84it/s]\n",
            "100%|██████████| 25000/25000 [00:01<00:00, 14435.70it/s]\n",
            "100%|██████████| 25000/25000 [00:01<00:00, 20227.82it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([25000, 140]) torch.Size([25000, 140]) torch.Size([25000])\n",
            "torch.Size([25000, 140]) torch.Size([25000, 140]) torch.Size([25000])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generate the data loaders"
      ],
      "metadata": {
        "id": "rFsTDgX66ZAw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_set = TensorDataset(ids_train, amasks_train, labels_train)\n",
        "train_dataloader = DataLoader(train_set, batch_size=32, shuffle=True)\n",
        "\n",
        "val_set = TensorDataset(ids_val, amasks_val, labels_val)\n",
        "val_dataloader = DataLoader(val_set, batch_size=32, shuffle=False)"
      ],
      "metadata": {
        "id": "-jzT37KZ6YdX"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Now Create the model"
      ],
      "metadata": {
        "id": "dzfDy53C9lC1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# If you load a pre-trained model and train it using a small learning rate\n",
        "# then it is known as fine-tuning.\n",
        "# If you provide a config file then the BERT model is loaded without the\n",
        "# pretrained weights. Training this model is known as training from scratch\n",
        "\n",
        "# Loading your model this way loads a network without the pre-trained weights\n",
        "# config = BertConfig.from_pretrained(\"bert-base-uncased\")\n",
        "# print(config)\n",
        "# model = BertForSequenceClassification(config)\n",
        "\n",
        "# Loading your model this way loads the pre-trained network\n",
        "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "print(model)\n",
        "# You can also replace the last classification layer with your own layer\n",
        "# Based on your classification needs\n",
        "# model.classifier = torch.nn.Linear(768, 10)\n",
        "# print(\"\\n\\nNew BertModel:\\n\", model)\n",
        "model = model.to(device)\n",
        "\n",
        "# You can also freeze some of the layers in Bert\n",
        "# If you freeze all the layers except the classification layer\n",
        "# and train the model, then this is known as transfer learning\n",
        "for idx, (name, param) in enumerate(model.named_parameters()):\n",
        "    # print(idx, name)\n",
        "    if \"classifier\" in name or \"bert.encoder.layer.9\" in name or \"bert.encoder.layer.10\" in name:\n",
        "        param.requires_grad = True\n",
        "    else:\n",
        "        param.requires_grad = False\n",
        "\n",
        "total_params = 0\n",
        "for param in model.parameters():\n",
        "    if param.requires_grad:\n",
        "        total_params+= param.numel()\n",
        "print(total_params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 920,
          "referenced_widgets": [
            "54b16766b94d43c985b6322541ebf049",
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        },
        "id": "vvoPxyav6lU1",
        "outputId": "515b064c-8a82-4ba7-bc5d-621b76f6954d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "54b16766b94d43c985b6322541ebf049"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BertForSequenceClassification(\n",
            "  (bert): BertModel(\n",
            "    (embeddings): BertEmbeddings(\n",
            "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
            "      (position_embeddings): Embedding(512, 768)\n",
            "      (token_type_embeddings): Embedding(2, 768)\n",
            "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "      (dropout): Dropout(p=0.1, inplace=False)\n",
            "    )\n",
            "    (encoder): BertEncoder(\n",
            "      (layer): ModuleList(\n",
            "        (0-11): 12 x BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "            (intermediate_act_fn): GELUActivation()\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (pooler): BertPooler(\n",
            "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "      (activation): Tanh()\n",
            "    )\n",
            "  )\n",
            "  (dropout): Dropout(p=0.1, inplace=False)\n",
            "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
            ")\n",
            "14177282\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train and Test the model"
      ],
      "metadata": {
        "id": "FgPdhcUBGVZq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 10\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=0.0003, eps=1e-8)\n",
        "criterion = torch.nn.CrossEntropyLoss()\n",
        "train_acc, val_acc = Accuracy(task=\"binary\", num_classes=2).to(device), Accuracy(task=\"binary\", num_classes=2).to(device)\n",
        "\n",
        "# We will train the model for the specified number of epochs\n",
        "for epoch in range(epochs):\n",
        "    train_loss, val_loss = list(), list()\n",
        "    print(\"\\n\\nEpoch:\", epoch, \"\\n-----------------------\\n\")\n",
        "    # Make sure model is in training mode\n",
        "    model.train()\n",
        "    # For each batch of data\n",
        "    for idx, (x_ids, x_masks, x_labels) in tqdm(enumerate(train_dataloader), total=len(train_dataloader)):\n",
        "        # Clear previous gradients\n",
        "        optimizer.zero_grad()\n",
        "        # Move the batch to the GPU\n",
        "        x_ids, x_masks, x_labels = x_ids.to(device), x_masks.to(device), x_labels.to(device)\n",
        "        # Perform predictions\n",
        "        preds = model(x_ids, attention_mask=x_masks)\n",
        "        # Save the current iteration's accuracy\n",
        "        train_acc.update(torch.argmax(preds.logits, dim=1), x_labels)\n",
        "        # Get the loss\n",
        "        loss = criterion(preds.logits, x_labels)\n",
        "        train_loss.append(loss.item())\n",
        "        # Calculate the gradients\n",
        "        loss.backward()\n",
        "        # Update the parameters with the calculated gradients\n",
        "        optimizer.step()\n",
        "\n",
        "    # After each epoch, test the model\n",
        "    model.eval()\n",
        "    for idx, (x_ids, x_masks, x_labels) in tqdm(enumerate(val_dataloader), total=len(val_dataloader)):\n",
        "        x_ids, x_masks, x_labels = x_ids.to(device), x_masks.to(device), x_labels.to(device)\n",
        "        preds = model(x_ids, attention_mask=x_masks)\n",
        "        loss = criterion(preds.logits, x_labels)\n",
        "        val_loss.append(loss.item())\n",
        "        val_acc.update(torch.argmax(preds.logits, dim=1), x_labels)\n",
        "\n",
        "    # Finally print out the average train and val losses\n",
        "    print(\"Train Loss =\", sum(train_loss)/len(train_loss), \"\\tVal Loss =\", sum(val_loss)/len(val_loss))\n",
        "    # As well as the train and val accuracies\n",
        "    print(\"Train Acc =\", train_acc.compute().item(), \"\\tVal Acc =\", val_acc.compute().item())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfO-mjWk9pOm",
        "outputId": "752138bf-a980-4eb2-d7f7-d4cada4fafc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Epoch: 0 \n",
            "-----------------------\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 42%|████▏     | 326/782 [01:48<02:38,  2.88it/s]"
          ]
        }
      ]
    }
  ]
}