training.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbTzZn4yBHu8"
      },
      "source": [
        "# Importing Libraries and Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L6f-hCIr3fxf"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install transformers\n",
        "!pip install pytorch-lightning\n",
        "\n",
        "\n",
        "from google.colab import drive\n",
        "import re\n",
        "\n",
        "\n",
        "from transformers import AutoTokenizer, AutoModel, AdamW, get_cosine_schedule_with_warmup\n",
        "from torchmetrics.functional.classification import auroc\n",
        "import torch.nn.functional as F\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.nn as nn\n",
        "import pytorch_lightning as pl\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import math\n",
        "\n",
        "import gc\n",
        "\n",
        "gc.collect()\n",
        "\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "gZ7gyBaU5X-c",
        "outputId": "258009f4-6c76-4d4b-9e74-44d11f873d95"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-416abad5780e>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0marticles_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'(/content/drive/MyDrive/combined_data.csv)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlabeled_articles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marticles_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1736\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    854\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    857\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '(/content/drive/MyDrive/combined_data.csv)'"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive')\n",
        "articles_path = '(/content/drive/MyDrive/combined_data.csv)'\n",
        "labeled_articles = pd.read_csv(articles_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S32A6akjBVf2"
      },
      "source": [
        "# EDA and Data Pre-Processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKqmvcXB4ah1",
        "outputId": "baa36445-8737-419f-d4a6-b4735da7dab1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "shape: (37554, 2)\n",
            "columns: ['content_original', 'bias']\n",
            "counts:\n",
            "\n",
            "2    13734\n",
            "0    13005\n",
            "1    10815\n",
            "Name: bias, dtype: int64\n",
            "\n",
            "percentages:\n",
            "\n",
            "2    0.365713\n",
            "0    0.346301\n",
            "1    0.287985\n",
            "Name: bias, dtype: float64\n",
            "\n"
          ]
        }
      ],
      "source": [
        "## gut check - should be about 37k articles and 3 columns\n",
        "\n",
        "labeled_articles = labeled_articles[['content_original', 'bias', 'title', 'source']]\n",
        "\n",
        "# b = labeled_articles.tail()\n",
        "# b.to_csv(\"pre-cleaning.csv\", encoding='utf-8', index=False)\n",
        "\n",
        "def clean_column(df, column_name):\n",
        "    terms_to_remove = {\n",
        "        'source': [\"Web News\", \"Online\", \"Opinion\", \"News\"],\n",
        "        'content_original': [\"JUST WATCHED\", \"MUST WATCH\", \"Replay More Videos ...\", \"Advertisement:\", \"SHARE THIS ARTICLE\",\n",
        "                             \"LISTEN TO ARTICLE\", \"Share Tweet Post Email\", \"Story Continued Below\", \"ADVERTISEMENT\"]\n",
        "    }\n",
        "\n",
        "\n",
        "    if column_name not in terms_to_remove:\n",
        "        return df\n",
        "\n",
        "\n",
        "    for term in terms_to_remove[column_name]:\n",
        "        df[column_name] = df[column_name].str.replace(f\"(?i){re.escape(term)}\", \"\", regex=True)\n",
        "\n",
        "    if column_name == 'source':\n",
        "        df[column_name] = df[column_name].str.replace(\" - \", \" \", regex=False)\n",
        "        df[column_name] = df[column_name].str.replace(\"-\", \" \", regex=False)\n",
        "\n",
        "    df[column_name] = df[column_name].str.strip()\n",
        "\n",
        "    return df\n",
        "\n",
        "## CLEANING DATA\n",
        "labeled_articles = clean_column(labeled_articles, 'source')\n",
        "labeled_articles = clean_column(labeled_articles, 'content_original')\n",
        "labeled_articles['content_original'] = labeled_articles['title'] + \". \" + labeled_articles['content_original']\n",
        "labeled_articles['content_original'] = labeled_articles.apply(lambda row: row['content_original'].replace(row['source'], ''), axis=1)\n",
        "\n",
        "\n",
        "\n",
        "# a = labeled_articles.tail()\n",
        "# a.to_csv(\"post-cleaning.csv\", encoding='utf-8', index=False)\n",
        "\n",
        "\n",
        "labeled_articles = labeled_articles[['content_original', 'bias']]\n",
        "\n",
        "print(f\"shape: {labeled_articles.shape}\")\n",
        "print(f\"columns: {list(labeled_articles.columns)}\")\n",
        "\n",
        "## relevant columns: source, bias, content, title\n",
        "\n",
        "print(f\"counts:\\n\\n{labeled_articles['bias'].value_counts()}\\n\")\n",
        "print(f\"percentages:\\n\\n{labeled_articles['bias'].value_counts(normalize=True)}\\n\")\n",
        "\n",
        "\n",
        "# base model: always predict conservative - most common class: should have 36.5% accuracy (easy to beat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xtN7PA2CWVI"
      },
      "source": [
        "Big problem: RoBERTa can only take in so many tokens and the average political article is way too big for the encoder. This is where we could do more pre-processing (i.e. breaking down the article to its \"essential components)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FDl-0m1WGU6t"
      },
      "source": [
        "Split articles into train, validation, test, remove souc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Op-Xp4LT9vt8",
        "outputId": "09c748fc-5b82-4389-eff9-88017b5be19b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "shape train: (34000, 2)\n",
            "shape val: (1500, 2)\n",
            "shape test: (1500, 2)\n",
            "columns: ['content_original', 'bias']\n",
            "training counts:\n",
            "\n",
            "2    12430\n",
            "0    11747\n",
            "1     9823\n",
            "Name: bias, dtype: int64\n",
            "\n",
            "training percents:\n",
            "\n",
            "2    0.365588\n",
            "0    0.345500\n",
            "1    0.288912\n",
            "Name: bias, dtype: float64\n",
            "\n",
            "validation counts:\n",
            "\n",
            "2    540\n",
            "0    503\n",
            "1    457\n",
            "Name: bias, dtype: int64\n",
            "\n",
            "validation percents:\n",
            "\n",
            "2    0.360000\n",
            "0    0.335333\n",
            "1    0.304667\n",
            "Name: bias, dtype: float64\n",
            "\n",
            "test counts:\n",
            "\n",
            "2    566\n",
            "0    550\n",
            "1    384\n",
            "Name: bias, dtype: int64\n",
            "\n",
            "test percents:\n",
            "\n",
            "2    0.377333\n",
            "0    0.366667\n",
            "1    0.256000\n",
            "Name: bias, dtype: float64\n",
            "\n"
          ]
        }
      ],
      "source": [
        "## split into training, validation, and test - APPEND title and REMOVE source\n",
        "\n",
        "\n",
        "NUM_NOT_TEST = 35500\n",
        "NUM_TEST = 1500\n",
        "\n",
        "NUM_TRAIN = 34000\n",
        "NUM_VAL = 1500\n",
        "\n",
        "not_test_df, test_df = train_test_split(labeled_articles, train_size=NUM_NOT_TEST, test_size=NUM_TEST, random_state=42)\n",
        "train_df, val_df = train_test_split(not_test_df, train_size=NUM_TRAIN, test_size=NUM_VAL, random_state = 38)\n",
        "\n",
        "print(f\"shape train: {train_df.shape}\")\n",
        "print(f\"shape val: {val_df.shape}\")\n",
        "print(f\"shape test: {test_df.shape}\")\n",
        "\n",
        "print(f\"columns: {list(train_df.columns)}\")\n",
        "\n",
        "print(f\"training counts:\\n\\n{train_df['bias'].value_counts()}\\n\")\n",
        "print(f\"training percents:\\n\\n{train_df['bias'].value_counts(normalize=True)}\\n\")\n",
        "\n",
        "print(f\"validation counts:\\n\\n{val_df['bias'].value_counts()}\\n\")\n",
        "print(f\"validation percents:\\n\\n{val_df['bias'].value_counts(normalize=True)}\\n\")\n",
        "\n",
        "print(f\"test counts:\\n\\n{test_df['bias'].value_counts()}\\n\")\n",
        "print(f\"test percents:\\n\\n{test_df['bias'].value_counts(normalize=True)}\\n\")\n",
        "\n",
        "test_df.to_csv('/content/drive/MyDrive/test.csv', encoding='utf-8', index=False)\n",
        "train_df.to_csv('/content/drive/MyDrive/train.csv', encoding='utf-8', index=False)\n",
        "val_df.to_csv('/content/drive/MyDrive/val.csv', encoding='utf-8', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jA5DIu0M4iC"
      },
      "source": [
        "# Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tlIwAq6SNHfV"
      },
      "outputs": [],
      "source": [
        "class ArticleDataSet(Dataset):\n",
        "  def __init__(self, article_df, label_name, tokenizer, max_token_len: int = 512):\n",
        "    '''\n",
        "    takes feature dataframe (article text + title) and the label dataframe (numeric political bias)\n",
        "    training, validation, test splits must be done BEFORE this step\n",
        "    '''\n",
        "    self.article_df = article_df\n",
        "    self.tokenizer = tokenizer\n",
        "    self.label_name = label_name\n",
        "    self.max_token_len = max_token_len\n",
        "\n",
        "\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.article_df)\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    item = self.article_df.iloc[index]\n",
        "    label = item['bias']\n",
        "\n",
        "    text_content = str(item.content_original)\n",
        "\n",
        "\n",
        "    ## vectorize / encode\n",
        "\n",
        "    tokens = self.tokenizer.encode_plus(text_content,\n",
        "                                        add_special_tokens=True,\n",
        "                                        return_tensors='pt',\n",
        "                                        truncation=True,\n",
        "                                        max_length=self.max_token_len,\n",
        "                                        padding='max_length',\n",
        "                                        return_attention_mask=True)\n",
        "\n",
        "    return {'input_ids': tokens.input_ids.flatten(),\n",
        "            'attention_mask': tokens.attention_mask.flatten(),\n",
        "            'label': label}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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          ]
        },
        "id": "930q-w0MQjKS",
        "outputId": "9398d7f2-f470-4a64-f45d-129831972932"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5a8048a0491c421998ae7c2baa3ad613",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/481 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ed575eaeed6e4ccdb0a6c7721ab26da8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "55d0cc50f9564feb8f56b8f1aacebe7d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b203ce0681d94d34b3315be032d57933",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model_name = 'roberta-base'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "features = labeled_articles[['content_original']]\n",
        "labels = 'bias'\n",
        "\n",
        "train_ds = ArticleDataSet(train_df, labels, tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OKnWT8p_pbNC",
        "outputId": "653dd681-671e-44a1-9d40-78e1a5626c24"
      },
      "outputs": [
        {
          "data": {
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              "             4,    22,  1121,     5,   251,   422,    24,    40,    45,   913,\n",
              "            14,     2]),\n",
              " 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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              "         1, 1, 1, 1, 1, 1, 1, 1]),\n",
              " 'label': 1}"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_ds.__getitem__(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pven-eRUbW3S"
      },
      "source": [
        "# Data Module\n",
        "\n",
        "The purpose of a PyTorch data module is to separate data-related code from model-related code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UDGSuBeCdCVc"
      },
      "outputs": [],
      "source": [
        "class Article_Data_Module(pl.LightningDataModule):\n",
        "\n",
        "  def __init__(self, train_ds, val_ds, test_ds, labels, batch_size: int = 16, max_token_len=512, model_name = 'roberta-base'):\n",
        "    '''\n",
        "    train_ds, val_ds, labels should all be data frames\n",
        "    '''\n",
        "    super().__init__()\n",
        "\n",
        "    self.train_ds = train_ds\n",
        "    self.val_ds = val_ds\n",
        "    self.test_ds = test_ds\n",
        "    self.labels = labels\n",
        "    self.batch_size = batch_size\n",
        "    self.max_token_len = max_token_len\n",
        "    self.model_name = model_name\n",
        "    self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)\n",
        "\n",
        "\n",
        "\n",
        "  def setup(self, stage=None):\n",
        "    if stage in (None, \"fit\"):\n",
        "      self.train_dataset = ArticleDataSet(self.train_ds, self.labels, self.tokenizer)\n",
        "      self.val_dataset = ArticleDataSet(self.val_ds, self.labels, self.tokenizer)\n",
        "    if stage in (None, \"predict\", \"test\"):\n",
        "      self.test_dataset = ArticleDataSet(self.test_ds, self.labels, self.tokenizer)\n",
        "\n",
        "\n",
        "\n",
        "  def train_dataloader(self):\n",
        "    return DataLoader(self.train_dataset, batch_size = self.batch_size, num_workers=4, shuffle=True)\n",
        "\n",
        "\n",
        "  def val_dataloader(self):\n",
        "    return DataLoader(self.val_dataset, batch_size = self.batch_size, num_workers=4, shuffle=False)\n",
        "\n",
        "\n",
        "  def predict_dataloader(self):\n",
        "    return DataLoader(self.test_dataset, batch_size = self.batch_size, num_workers=4, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W7Va3KrtgZi4"
      },
      "outputs": [],
      "source": [
        "article_dm = Article_Data_Module(train_df, val_df, test_df, labels)\n",
        "# note - this is not the actual data module we use"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qlbG0yT2ljBJ"
      },
      "outputs": [],
      "source": [
        "article_dm.setup()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zll6gBBLpDmp",
        "outputId": "216f37ae-1538-48de-9ac6-e1c4f91d44c3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        }
      ],
      "source": [
        "article_dl = article_dm.train_dataloader()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nr6MVXvOpJsO",
        "outputId": "fcc59250-5aeb-41ed-b039-ae74740146e8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2125"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(article_dl)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "shIHHd-Zpo0R"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CWv4_WSLw7JH"
      },
      "outputs": [],
      "source": [
        "class Article_Classifier(pl.LightningModule):\n",
        "  def __init__(self, config: dict):\n",
        "    super().__init__()\n",
        "    self.config = config\n",
        "    self.pretrained_model = AutoModel.from_pretrained(config['model_name'], return_dict = True)\n",
        "\n",
        "    ## add in hidden layer and final layer\n",
        "\n",
        "    self.hidden = nn.Linear(self.pretrained_model.config.hidden_size, self.pretrained_model.config.hidden_size)\n",
        "    torch.nn.init.xavier_uniform_(self.hidden.weight)\n",
        "    self.hidden.bias.data.fill_(0)\n",
        "\n",
        "    self.classifier = nn.Linear(self.pretrained_model.config.hidden_size, self.config['n_labels'])\n",
        "    torch.nn.init.xavier_uniform_(self.classifier.weight)\n",
        "    self.classifier.bias.data.fill_(0)\n",
        "\n",
        "    self.loss_func = nn.CrossEntropyLoss()\n",
        "    self.dropout = nn.Dropout()\n",
        "\n",
        "\n",
        "  def forward(self, input_ids, attention_mask, label=None):\n",
        "    '''\n",
        "    label is None during prediction\n",
        "    '''\n",
        "\n",
        "    # roberta model\n",
        "    output = self.pretrained_model(input_ids=input_ids, attention_mask=attention_mask)\n",
        "    pooled_output = torch.mean(output.last_hidden_state, 1)\n",
        "\n",
        "    # neural network classification layers\n",
        "\n",
        "    pooled_output = self.hidden(pooled_output)\n",
        "    pooled_output = self.dropout(pooled_output)\n",
        "    pooled_output = F.relu(pooled_output)\n",
        "    pooled_output = self.dropout(pooled_output)\n",
        "    logits = self.classifier(pooled_output)\n",
        "\n",
        "\n",
        "\n",
        "    loss = 0\n",
        "    if label is not None:\n",
        "      loss = self.loss_func(logits, label)\n",
        "    return loss, logits\n",
        "\n",
        "  def training_step(self, batch, batch_index):\n",
        "    loss, logits = self(**batch)  # unpack - will call forward pass\n",
        "\n",
        "    self.log(\"train_loss\", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)\n",
        "    return {\"loss\": loss, \"predictions\": logits, \"labels\": batch['label']}\n",
        "\n",
        "\n",
        "  def validation_step(self, batch, batch_index):\n",
        "    loss, logits = self(**batch)\n",
        "\n",
        "    self.log(\"val_loss\", loss, on_epoch=True, prog_bar=True, logger=True)\n",
        "    return {\"val_loss\": loss, \"predictions\": logits, \"labels\": batch['label']}\n",
        "\n",
        "\n",
        "  def predict_step(self, batch, batch_index):\n",
        "    _, logits = self(**batch)\n",
        "    return logits\n",
        "\n",
        "  def configure_optimizers(self):\n",
        "    optimizer = AdamW(self.parameters(), lr=self.config['lr'], weight_decay=self.config['w_decay'])\n",
        "    total_steps = self.config['train_size'] / self.config['batch_size']\n",
        "    warmup_steps = math.floor(total_steps * self.config['warmup'])\n",
        "    scheduler = get_cosine_schedule_with_warmup(optimizer, warmup_steps, total_steps)\n",
        "    return [optimizer], [scheduler]\n",
        "\n",
        "  def on_save_checkpoint(self, checkpoint):\n",
        "    print(\"Saving checkpoint!\\n\")\n",
        "\n",
        "    super().on_save_checkpoint(checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        },
        "id": "-Yc_yyqR23BB",
        "outputId": "11318a8f-0e42-478d-bf3f-648cbe0b78b5"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dd2c40f88e8a4266bf484a0977413e1a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/480 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "063591a55aea4afeaaed50827e0a62b0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/331M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# could just use regular roberta\n",
        "config = {\n",
        "  'model_name' : 'distilroberta-base',\n",
        "  'n_labels': 3,\n",
        "  'batch_size': 32,\n",
        "  'lr': 1.5e-6,\n",
        "  'warmup': 0.2,\n",
        "  'train_size': len(article_dl),\n",
        "  'w_decay': 0.001,\n",
        "  'n_epochs': 15\n",
        "}\n",
        "\n",
        "model = Article_Classifier(config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1wNffjW5kxh",
        "outputId": "74abd48f-ffbe-4d80-a80b-a0f72b2323da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor(0.7829, grad_fn=<NllLossBackward0>)\n"
          ]
        }
      ],
      "source": [
        "## test single input\n",
        "\n",
        "idx = 0\n",
        "input_ids = train_ds.__getitem__(idx)['input_ids']\n",
        "am = train_ds.__getitem__(idx)['attention_mask']\n",
        "lbl_value = train_ds.__getitem__(idx)['label'].item()\n",
        "lbl_tensor = torch.tensor([lbl_value], dtype=torch.long)\n",
        "\n",
        "loss, output = model(input_ids.unsqueeze(dim=0), am.unsqueeze(dim=0), lbl_tensor)\n",
        "print(loss)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOYtPtLD5nbg"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "WARNING:pytorch_lightning.loggers.tensorboard:Missing logger folder: /content/lightning_logs\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py:639: Checkpoint directory /content/drive/MyDrive exists and is not empty.\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name             | Type             | Params\n",
            "------------------------------------------------------\n",
            "0 | pretrained_model | RobertaModel     | 82.1 M\n",
            "1 | hidden           | Linear           | 590 K \n",
            "2 | classifier       | Linear           | 2.3 K \n",
            "3 | loss_func        | CrossEntropyLoss | 0     \n",
            "4 | dropout          | Dropout          | 0     \n",
            "------------------------------------------------------\n",
            "82.7 M    Trainable params\n",
            "0         Non-trainable params\n",
            "82.7 M    Total params\n",
            "330.845   Total estimated model params size (MB)\n"
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=15` reached.\n"
          ]
        }
      ],
      "source": [
        "# data module\n",
        "from pytorch_lightning.callbacks import ModelCheckpoint\n",
        "\n",
        "## checkpointing due to connectivity concerns\n",
        "checkpoint_callback = ModelCheckpoint(\n",
        "    dirpath='/content/drive/MyDrive',\n",
        "    filename='{epoch}-{val_loss:.2f}',\n",
        "    monitor='val_loss',\n",
        "    save_top_k=1,\n",
        "    every_n_epochs=1,\n",
        "    mode='min'\n",
        ")\n",
        "\n",
        "article_dm = Article_Data_Module(train_df, val_df, test_df, labels, batch_size = config['batch_size'])\n",
        "\n",
        "article_dm.setup()\n",
        "\n",
        "\n",
        "# model\n",
        "\n",
        "model = Article_Classifier(config)\n",
        "\n",
        "\n",
        "# train\n",
        "\n",
        "trainer = pl.Trainer(max_epochs=config['n_epochs'], accelerator=\"auto\", num_sanity_val_steps=4, callbacks=[checkpoint_callback])\n",
        "\n",
        "trainer.fit(model, article_dm)"
      ]
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        },
        "id": "YxIZsVpSWHAC",
        "outputId": "9fd1dd33-4779-4e63-9887-3e2f7e930233"
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            "application/javascript": [
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              "        (async () => {\n",
              "            const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n",
              "            url.searchParams.set('tensorboardColab', 'true');\n",
              "            const iframe = document.createElement('iframe');\n",
              "            iframe.src = url;\n",
              "            iframe.setAttribute('width', '100%');\n",
              "            iframe.setAttribute('height', '800');\n",
              "            iframe.setAttribute('frameborder', 0);\n",
              "            document.body.appendChild(iframe);\n",
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              "    "
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        "%load_ext tensorboard\n",
        "%tensorboard --logdir ./lightning_logs/"
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      "metadata": {
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