BERT_finetune_v2.ipynb

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      "source": [
        "#from sklearn.datasets import fetch_20newsgroups #encountered issues SSLCertificationError with this \n",
        "from datasets import load_dataset, load_metric\n",
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "from bs4 import BeautifulSoup\n",
        "from transformers import BertTokenizer, BertForSequenceClassification, TrainingArguments, Trainer, DataCollatorWithPadding,get_scheduler\n",
        "from torch.optim import AdamW\n",
        "\n",
        "from spacy import tokenizer\n",
        "from spacy.lang.en import English\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "from utils import RoBERT_Model,ToBERT_Model\n",
        "import json\n",
        "import matplotlib.pyplot as plt"
      ]
    },
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      "source": [
        "accuracy = load_metric('accuracy'); f1_score = load_metric('f1'); perplexity = load_metric('perplexity')"
      ]
    },
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      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Reusing dataset hyperpartisan_news_detection (/Users/max/.cache/huggingface/datasets/hyperpartisan_news_detection/bypublisher/1.0.0/7f4215b0474950ddf516e806400ab81d098b3da3b3a919a13cd1a4cf2c677012)\n",
            "100%|██████████| 2/2 [00:00<00:00, 22.00it/s]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "dict_keys(['train', 'validation'])"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hyperpartisan_dataset = load_dataset(\"hyperpartisan_news_detection\", \"bypublisher\") #Not stripped of headers and footers\n",
        "hyperpartisan_dataset.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "distribution_name = \"hyperpartisan_distribution\"\n",
        "\n",
        "with open(distribution_name, 'r') as ins:\n",
        "    distribution= json.load(ins)\n",
        "\n",
        "seed = distribution['seed']\n",
        "\n",
        "train_index = distribution['train']\n",
        "eval_index = distribution['eval']\n",
        "test_index = distribution['test']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "VFvt489BJc_y"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Loading cached shuffled indices for dataset at /Users/max/.cache/huggingface/datasets/hyperpartisan_news_detection/bypublisher/1.0.0/7f4215b0474950ddf516e806400ab81d098b3da3b3a919a13cd1a4cf2c677012/cache-66d6117f38d3c868.arrow\n",
            "Loading cached shuffled indices for dataset at /Users/max/.cache/huggingface/datasets/hyperpartisan_news_detection/bypublisher/1.0.0/7f4215b0474950ddf516e806400ab81d098b3da3b3a919a13cd1a4cf2c677012/cache-31fcbf3d65e8fea2.arrow\n"
          ]
        }
      ],
      "source": [
        "#change to bypublisher because byarticle has no test set.\n",
        "hyperpartisan_train = hyperpartisan_dataset['train']\n",
        "hyperpartisan_valid = hyperpartisan_dataset['validation']\n",
        "\n",
        "hyperpartisan_train = hyperpartisan_train.shuffle(seed = seed)\n",
        "hyperpartisan_valid = hyperpartisan_valid.shuffle(seed = seed)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9JVba8JK21SQ"
      },
      "source": [
        "With huggingface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uI-H-gJQ6qeL",
        "outputId": "585fd2ab-f489-455a-890f-32a0e99d05b3"
      },
      "outputs": [],
      "source": [
        "eval_test_split = hyperpartisan_valid.train_test_split(test_size = 0.5)\n",
        "\n",
        "hyperpartisan_eval = eval_test_split['train']\n",
        "hyperpartisan_test = eval_test_split['test']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "d0dxLYSQHgWS"
      },
      "outputs": [],
      "source": [
        "int_labels_train = {i: 1 if x == True else 0 for i,x in enumerate(hyperpartisan_train['hyperpartisan'])}\n",
        "int_labels_eval = {i: 1 if x == True else 0 for i,x in enumerate(hyperpartisan_eval['hyperpartisan'])}\n",
        "int_labels_test = {i: 1 if x == True else 0 for i,x in enumerate(hyperpartisan_test['hyperpartisan'])}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "DOCUMENTS LESS THAN 300"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lyLArTmX21SQ"
      },
      "outputs": [],
      "source": [
        "#Should work once then modularize\n",
        "\n",
        "max_input_length = 300 #Vary after obtaining end to end result\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels = 2, max_length = max_input_length)\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer) #to pad sequences\n",
        "\n",
        "\n",
        "def tokenize(batch):\n",
        "    return tokenizer(batch['text'], truncation=True, max_length=max_input_length)\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, label = eval_pred\n",
        "    predictions = np.argmax(predictions, axis = 1)\n",
        "    return f1_score.compute(predictions = predictions, references = label)\n",
        "\n",
        "def hp_space(trial):\n",
        "\n",
        "  return {\"per_device_train_batch_size\": trial.suggest_discrete_uniform(\"per_device_train_batch_size\", 8,32,8)\n",
        "        ,\"learning_rate\": trial.suggest_float(\"learning_rate\", 0.0001,0.0005, log = True)\n",
        "        ,\"num_train_epochs\": trial.suggest_int(\"num_train_epochs\",1,10)\n",
        "  }\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8-Mr65oT4VVW"
      },
      "outputs": [],
      "source": [
        "hyperpartisan_train = hyperpartisan_train.select(train_index['Short'])\n",
        "hyperpartisan_eval = hyperpartisan_eval.select(eval_index['Short'])\n",
        "hyperpartisan_test = hyperpartisan_test.select(test_index['Short'])\n",
        "\n",
        "hyperpartisan_train = hyperpartisan_train.map(tokenize)\n",
        "hyperpartisan_eval = hyperpartisan_eval.map(tokenize)\n",
        "hyperpartisan_valid = hyperpartisan_valid.map(tokenize)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1uqGTYZKD7W"
      },
      "source": [
        "EFFECT OF FINETUNING (with varying number of training files)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gvQk3nYxDazf"
      },
      "outputs": [],
      "source": [
        "#Problems with finetuning with limited number of training files, is training set is unbalanced\n",
        "#Less knowledge for neural network to learn from \n",
        "#Why not investigate the definition of what a chunk is, and use different pretraining objective e.g BERT,Roberta\n",
        "#Problem with finetuning with long document is document input are limited in length, therefore learning across text documents is unbalanced\n",
        "\n",
        "#Only way around finetuning on text document is to carefully analyse the training set and long documents."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "results = {}\n",
        "a_dataset = hyperpartisan_train.select(range(2000))\n",
        "b_dataset = hyperpartisan_valid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Insight into the distribution of the dataset\n",
        "plt.histplot(a_dataset['labels'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "0iyEz4og21SR",
        "outputId": "17ec51e8-6877-4368-cb4b-15b490e5211f"
      },
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-37a6dc4c3a48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mresults\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[0;32m---> 10\u001b[0;31m \u001b[0ma_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhyperpartisan_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2000\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[0m\u001b[1;32m     11\u001b[0m \u001b[0mb_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhyperpartisan_valid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'select'"
          ]
        }
      ],
      "source": [
        "#for multi-class classification\n",
        "#labels = hyperpartisan_train[\"label\"]\n",
        "#num_labels = len(labels)\n",
        "#labels2id, id2labels = dict(),dict()\n",
        "#for i,label in enumerate(labels):\n",
        "    #labels2id[label] = i\n",
        "    #id2labels[i] = label\n",
        "\n",
        "def bert_init():\n",
        "  return (BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels = 2, max_length = max_input_length))\n",
        "\n",
        "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels = 2, max_length = max_input_length)\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer) #to pad sequences\n",
        "\n",
        "#default training arguments \n",
        "training_args = TrainingArguments(output_dir=\"./results\", per_device_train_batch_size=16, \n",
        "                                  evaluation_strategy= \"epoch\", per_device_eval_batch_size=16, num_train_epochs=5,weight_decay=0.01)\n",
        "\n",
        "#fine-tuning model\n",
        "\n",
        "trainer = Trainer(model_init = bert_init, \n",
        "              model = model,\n",
        "              args = training_args,\n",
        "              train_dataset= a_dataset,\n",
        "              eval_dataset= b_dataset,\n",
        "              tokenizer= tokenizer,\n",
        "              data_collator=data_collator,\n",
        "              compute_metrics= compute_metrics)\n",
        "\n",
        "#searching for best hyperparameters\n",
        "best_run = trainer.hyperparameter_search(n_trials = 10, direction = \"maximize\", hp_space = hp_space)\n",
        "\n",
        "#update hyperparameters \n",
        "for k,v in best_run.hyperparameters.items():\n",
        "  setattr(training_args, k, v)\n",
        "\n",
        "best_trainer = Trainer(model = model,\n",
        "                       args = training_args,\n",
        "                       train_dataset = a_dataset,\n",
        "                       eval_dataset = b_dataset,\n",
        "                       compute_metrics = compute_metrics,\n",
        "                       data_collator = data_collator)\n",
        "\n",
        "best_trainer.train()"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jP6nKqSLq5d4"
      },
      "outputs": [],
      "source": [
        "best_trainer.evaluate()"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "sFSTq4rZGLpf",
        "outputId": "fb4b2a84-ecfd-4668-922e-79c5bcc08f79"
      },
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          "execution_count": 10,
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      ],
      "source": [
        "#display result \n",
        "#display confusion matrix\n"
      ]
    }
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