BERT_finetune.ipynb

{
  "cells": [
    {
      "cell_type": "code",
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kj-orIcs25Ol",
        "outputId": "986848cf-0e05-4f4c-8cb6-21dbfda8bad3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 22.2 is available.\n",
            "You should consider upgrading via the '/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
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            "ERROR: unknown command \"transformers\"\n",
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            "You should consider upgrading via the '/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
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            "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 22.2 is available.\n",
            "You should consider upgrading via the '/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
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            "zsh:1: parse error near `|'\n",
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            "You should consider upgrading via the '/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
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            "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 22.2 is available.\n",
            "You should consider upgrading via the '/Users/max/Desktop/Dissertation/implementations/Disso-COLD/proj_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install sklearn\n",
        "%pip transformers\n",
        "%pip install huggingface\n",
        "%pip install torch \n",
        "%pip install pandas\n",
        "%pip install sklearn|\n",
        "%pip install datasets\n",
        "%pip install transformers\n",
        "%pip install optuna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "w528_kXC21SI"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "yrv_tqi3SMch",
        "outputId": "80a42bc1-8654-47cc-c6ca-4fb390eafa94"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading builder script: 8.36kB [00:00, 1.90MB/s]                   \n"
          ]
        }
      ],
      "source": [
        "accuracy = load_metric('accuracy'); f1_score = load_metric('f1'); perplexity = load_metric('perplexity')"
      ]
    },
    {
      "cell_type": "code",
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            "a1299669035f47afae6159719f0a4300"
          ]
        },
        "id": "EsMBWtPr21SQ",
        "outputId": "a28807d5-fbef-44b6-efa9-a97fd9c8592f"
      },
      "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, 21.19it/s]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "dict_keys(['train', 'validation'])"
            ]
          },
          "execution_count": 5,
          "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": 20,
      "metadata": {},
      "outputs": [],
      "source": [
        "distribution_name = \"hyperpartisan_distribution\"\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": 19,
      "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": 23,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_keys(['Too Long', 'Long', 'BERT', 'Short'])"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Documents that are <300 are categorised as short\n",
        "#Documents that are >300 are categorised as BERT\n",
        "#Documents that are >512 but <4096 are categorised long \n",
        "\n",
        "hyperpartisan_train"
      ]
    },
    {
      "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",
        "hyperpartisan_train.select(train_index['short'])\n",
        "\n",
        "hyperpartisan_train = hyperpartisan_train.map(tokenize)\n",
        "hyperpartisan_valid = hyperpartisan_valid.map(tokenize)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "new_model = RoBERT_Model(,num_classes=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GkYOy-ofKLqb"
      },
      "source": [
        "NO FINETUNING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F0fWzuwZKOsZ"
      },
      "outputs": [],
      "source": [
        "#with torch.no_grad():\n",
        "#Obtain results of methods without finetuning\n",
        "#obtain best parameter for this models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sb9U9fuX_JGa"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1uqGTYZKD7W"
      },
      "source": [
        "EFFECT OF FINETUNING (with varying number of training files)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8-Mr65oT4VVW"
      },
      "outputs": [],
      "source": [
        "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",
        "  }"
      ]
    },
    {
      "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": {
        "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",
        "results = {}\n",
        "a_dataset = hyperpartisan_train.select(range(2000))\n",
        "b_dataset = hyperpartisan_valid\n",
        "\n",
        "#BERT models setup\n",
        "max_input_length = 300 #Vary after obtaining end to end result\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jP6nKqSLq5d4"
      },
      "outputs": [],
      "source": [
        "best_trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sFSTq4rZGLpf",
        "outputId": "fb4b2a84-ecfd-4668-922e-79c5bcc08f79"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Value(dtype='int64', id=None)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#display result \n",
        "#display confusion matrix\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Dd71DqE21SR"
      },
      "source": [
        "With pytorch "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0eUBJo7M21SR"
      },
      "outputs": [],
      "source": [
        "def select_files_to_dataloader(num,):\n",
        "    \n",
        "    \"\"\"Num is the number of files to be finetuned\"\"\"\n",
        "    \n",
        "    small_train_dataset = hyperpartisan_train.shuffle(seed = 31).select(range(num))\n",
        "    small_eval_dataset = hyperpartisan_valid.shuffle(seed = 31).select(range(num))\n",
        "\n",
        "    train_dataloader = DataLoader(small_train_dataset, shuffle = True, batch_size = 8)\n",
        "    eval_dataloader = DataLoader(small_eval_dataset, shuffle = True, batch_size= 8)\n",
        "\n",
        "    return train_dataloader, eval_dataloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wvhj2WNV21SS",
        "outputId": "04b4662a-c2f7-496b-b831-20e6abea957e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tT2LTPUq21SS"
      },
      "outputs": [],
      "source": [
        "train_dataloader, eval_dataloader = select_files_to_dataloader(100)\n",
        "\n",
        "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels = 2)\n",
        "optimizer = AdamW(model.parameters(), lr = 5e-5)\n",
        "\n",
        "num_epochs = 5\n",
        "num_training_steps = num_epochs * len(train_dataloader)\n",
        "lr_scheduler = get_scheduler(\"linear\", optimizer = optimizer, num_warmup_steps= 0, num_training_steps= num_training_steps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zg5cR-Dy21SS"
      },
      "outputs": [],
      "source": [
        "from tqdm.auto import tqdm\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "model.train()\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    for batch in train_dataloader:\n",
        "        batch = {k: v.to(device) for k, v in batch.items()}\n",
        "        outputs = model(**batch)\n",
        "        loss = outputs.loss()\n",
        "        \n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad() #prevents accumulation of gradients, probably useful in RNNs\n",
        "\n",
        "        progress_bar.update(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gqTf9nCgKJXi"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4soaanUM21SL"
      },
      "source": [
        "LOADING AND PREPROCESSING"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RxrcZMEy21SM"
      },
      "source": [
        "20 Newsgroup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "dtlfQfez21SM",
        "outputId": "e4137d5b-be99-44a4-d1dd-ff419b972d59"
      },
      "outputs": [
        {
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-ea02dc0afbfb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnewsgroup_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"20news-bydate\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0melement\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewsgroup_path\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      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mnewsgroup_train_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnewsgroup_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mnewsgroup_test_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnewsgroup_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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: '20news-bydate'"
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      ],
      "source": [
        "newsgroup_path = \"20news-bydate\"\n",
        "element = os.listdir(newsgroup_path)\n",
        "\n",
        "newsgroup_train_path = newsgroup_path + '/' + element[1]\n",
        "newsgroup_test_path = newsgroup_path + '/' + element[0]\n",
        "\n",
        "newsgroup_train = prepare_corpus(newsgroup_train_path)\n",
        "newsgroup_test = prepare_corpus(newsgroup_test_path)\n",
        "\n",
        "X_train_newsgroup_text = preproc(newsgroup_train)\n",
        "X_test_newsgroup_text = preproc(newsgroup_test)"
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              "dict_keys(['talk.politics.mideast', 'rec.autos', 'comp.sys.mac.hardware', 'alt.atheism', 'rec.sport.baseball', 'comp.os.ms-windows.misc', 'rec.sport.hockey', 'sci.crypt', 'sci.med', 'talk.politics.misc', 'rec.motorcycles', 'comp.windows.x', 'comp.graphics', 'comp.sys.ibm.pc.hardware', 'sci.electronics', 'talk.politics.guns', 'sci.space', 'soc.religion.christian', 'misc.forsale', 'talk.religion.misc'])"
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