Binary_Text_Classification_using_FineTuned_BERT_Transformer_Model.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Lab-of-Infinity/Text-Classification-using-FineTuned-BERT-Transformer/blob/main/Binary_Text_Classification_using_FineTuned_BERT_Transformer_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> ## **Binary Text Classification using FineTuned BERT Transformer Model**\n",
        "\n",
        "- **Objective : To perform Text Classification on Movies Review Dataset and Classify Review as Positive or Negative Sentiment.**\n",
        "\n",
        "- **Dataset : [Large Movie Review Dataset](https://ai.stanford.edu/~amaas/data/sentiment/) From Stanford**\n",
        "\n",
        "- **Methodology: Using a pretrained BERT transformers model and fine-tune it on a classification task.**\n",
        "\n",
        "- **Implementation Framework: PyTorch**"
      ],
      "metadata": {
        "id": "J-4GbPmAKQIu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19jYlJX1JjSD",
        "outputId": "6fca22ef-5f21-4c59-b74a-fdc8d7f28696"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Binary Text Classification using Fine Tuned Bert Transformer\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/Binary Text Classification using Fine Tuned Bert Transformer"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset Information\n",
        "\n",
        "- *For this Text Classification task we are using wellknown **Large Movie Datset from Stanford**.*\n",
        "- *This is a dataset for binary sentiment classification containing a **set of 25,000 highly polar movie reviews for training, and 25,000 for testing**.*\n",
        "- *There is additional unlabeled data for use as well. Raw text and already processed bag of words formats are provided.*\n",
        "\n"
      ],
      "metadata": {
        "id": "MBDMzRKCODm7"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78UA1RvdxDTp"
      },
      "source": [
        "\n",
        "Download the *Large Movie Review Dataset* and unzip it locally.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQjZbbcgPf5J"
      },
      "source": [
        "# Download the dataset.\n",
        "!wget -q -nc http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
        "# Unzip the dataset.\n",
        "!tar -zxf aclImdb_v1.tar.gz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Installation of Transformer Library\n",
        "\n",
        "We need to download Transformer Library from HuggingFace"
      ],
      "metadata": {
        "id": "KoF1av9HQQzT"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUz_vmR8tyoc",
        "outputId": "2dd0b1d6-9860-4812-fafb-b41b25c06d5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Install transformers library.\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git\n",
        "# Install helper functions.\n",
        "!pip install -q git+https://github.com/gmihaila/ml_things.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Require Library & Packages"
      ],
      "metadata": {
        "id": "9pMt4hTqUPVK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import io # python library for different I/O\n",
        "import torch\n",
        "from tqdm.notebook import tqdm\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from ml_things import plot_dict, plot_confusion_matrix, fix_text\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "from transformers import (AutoConfig, AutoModelForSequenceClassification,\n",
        "                          AutoTokenizer, AdamW,\n",
        "                          get_linear_schedule_with_warmup,\n",
        "                          set_seed)"
      ],
      "metadata": {
        "id": "hNYcz3NIPJp2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset, DataLoader"
      ],
      "metadata": {
        "id": "RrGqnl5BXqCT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Parameter Setting"
      ],
      "metadata": {
        "id": "Coen1byDXSt1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting Random Seed value for Reproducibility\n",
        "set_seed(99)\n",
        "\n",
        "# Number of Training Epochs\n",
        "epochs = 3\n",
        "\n",
        "# length of Batch Size\n",
        "batch_size = 32\n",
        "\n",
        "# Max_ length : Pad or truncate text sequences to a specific length\n",
        "max_length = 60\n",
        "\n",
        "# GPU utilisation: Will use 'CPU' by default if no GPU found\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Transformer Model : We will use pretrained Bert-base-uncased model\n",
        "# In uncased model all text is converted to lower case\n",
        "model_name = 'bert-base-uncased'\n",
        "\n",
        "# Label Dicitonary for encoding\n",
        "labels_ids = {'neg':0, 'pos':1}\n",
        "\n",
        "# Size of classification Head : Number of target variables or Labels\n",
        "n_labels = len(labels_ids)"
      ],
      "metadata": {
        "id": "t8yoc-8gPJmj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Implementation of classes & functions required"
      ],
      "metadata": {
        "id": "CEeCJxhQb85Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **In this section we need to build few helper functions which help us to load dataset and train dataset**\n",
        "- **Following Functions we are gone implement in `Class MovieReviewsDataset` :**\n",
        "  - Data Loading using PyTorch DataLoader \n",
        "  - Training Pipeline\n",
        "  - Validation Pipeline"
      ],
      "metadata": {
        "id": "qZO7z3SlcHn_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MovieReviewsDataset(Dataset):\n",
        "  \"\"\" PyTorch Dataset class for loading data. \n",
        "  Here Data Parsing happens & text encoding happens using tokenizer\n",
        "\n",
        "  Arguments:\n",
        "    `path` : path to dataset\n",
        "    `use_tokenizer` : Transformer type tokenizer for text encoding\n",
        "    `labels_ids` = target variable encoding dictionary\n",
        "    `max_sequence_len` = maximum desired sequence to truncate or pad text sequences\n",
        "  \"\"\"\n",
        "  def __init__(self, path,use_tokenizer, labels_ids, max_sequence_len = None):\n",
        "    # Checking path validity \n",
        "    if not os.path.isdir(path):\n",
        "      # Raise error if path is Invalid\n",
        "      raise ValueError(\"Invalid 'Path' of Directory : Provide accurate path to data partition\") \n",
        "    max_sequence_len = use_tokenizer.max_len if max_sequence_len is None else max_sequence_len\n",
        "    # Creating Empty list for storing texts & label\n",
        "    texts = []\n",
        "    labels = []\n",
        "    print('Reading partitions...')\n",
        "\n",
        "    # Looping on each label folder data\n",
        "    for label, label_id in tqdm(labels_ids.items()):\n",
        "      label_path = os.path.join(path,label)\n",
        "      # fetching all files path\n",
        "      files_names = os.listdir(label_path)\n",
        "      print('Reading %s files...' % label)\n",
        "      # Loop to read content of each file\n",
        "      for file_name in tqdm(files_names):\n",
        "        file_path = os.path.join(label_path, file_name)\n",
        "        # Read content\n",
        "        content = io.open(file_path, mode = 'r', encoding='utf-8').read()\n",
        "        content = fix_text(content) # fixing unicode issue if present\n",
        "        # appending Content in texts list\n",
        "        texts.append(content)\n",
        "        # appending labels for each file\n",
        "        labels.append(label_id)\n",
        "\n",
        "    # Number of labels\n",
        "    self.n_examples = len(labels)\n",
        "    # tokenization of text\n",
        "    print(\"Tokenzation of text is in progress...\" )\n",
        "    self.inputs = use_tokenizer(texts, add_special_tokens=True, truncation=True, padding=True, return_tensors='pt',  max_length=max_sequence_len)\n",
        "    self.sequence_len = self.inputs['input_ids'].shape[-1]\n",
        "    print('Text padded or truncated to %d length !' % self.sequence_len)\n",
        "    self.inputs.update({'labels': torch.tensor(labels)})\n",
        "    print('Finished! \\n')\n",
        "    return\n",
        "\n",
        "  def __len__(self):\n",
        "    \"\"\" `len` returns the number of examples/data points\n",
        "    \"\"\"\n",
        "    return self.n_examples\n",
        "  \n",
        "  def __getitem__(self, item):\n",
        "    \"\"\" It returns the index of data points.\n",
        "    \"\"\"\n",
        "    return {key: self.inputs[key][item] for key in self.inputs.keys()}\n",
        "  "
      ],
      "metadata": {
        "id": "RCEh1WY9aCco"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(dataloader, optimizer_, scheduler_, device_):\n",
        "  \"\"\" Train pytorch model on single pass through the data loader\n",
        "  Returns: true_labels, predictions_labels, avg_epoch_loss\n",
        "  \"\"\"\n",
        "  global model \n",
        "  # Creating empty list for tracking variables\n",
        "  predictions_labels = []\n",
        "  true_labels = []\n",
        "\n",
        "  # Intialize total loss for this epoch as zero\n",
        "  total_loss = 0\n",
        "\n",
        "  # set model under training\n",
        "  model.train()\n",
        "\n",
        "  # for each batch of training data\n",
        "  for batch in tqdm(dataloader, total = len(dataloader)):\n",
        "    true_labels += batch['labels'].numpy().flatten().tolist()\n",
        "\n",
        "    # move batch to device\n",
        "    batch  = {k:v.type(torch.long).to(device_) for k,v in batch.items()}\n",
        "\n",
        "    # Clearing previously calculated gradients\n",
        "    model.zero_grad()\n",
        "\n",
        "    # Perform forward pass\n",
        "    outputs = model(**batch)\n",
        "    loss, logits = outputs[:2]\n",
        "\n",
        "    # Accumulate training loss over all batches\n",
        "    total_loss +=loss.item()  # item function returns python value from tensor\n",
        "\n",
        "    # Perform backward pass calculation of gradients\n",
        "    loss.backward()\n",
        "\n",
        "    # Clip the gradients norm to 1.0 (to prevent explosion gradient problem)\n",
        "    torch.nn.utils.clip_grad_norm(model.parameters(),1.0)\n",
        "    optimizer.step()\n",
        "    # update learning rate\n",
        "    scheduler.step()\n",
        "\n",
        "    # moving logits and labels to CPU\n",
        "    logits = logits.detach().cpu().numpy()\n",
        "\n",
        "    # get predications to list\n",
        "    predict_content = logits.argmax(axis = -1).flatten().tolist()\n",
        "\n",
        "    # update list\n",
        "    predictions_labels += predict_content\n",
        "\n",
        "  # calculate the average loss over the training data\n",
        "  avg_epoch_loss = total_loss /  len(dataloader)\n",
        "\n",
        "  # Return all true labels and predications for future evalutions\n",
        "  return true_labels, predictions_labels, avg_epoch_loss\n",
        "\n"
      ],
      "metadata": {
        "id": "6CAXFRK8x2BF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def validation(dataloader,device_):\n",
        "  \"\"\"Validation function to evaluate model performance on separate set of data\n",
        "  \"\"\"\n",
        "\n",
        "  global model\n",
        "\n",
        "  # tracking variable\n",
        "  predictions_labels = []\n",
        "  true_labels = []\n",
        "\n",
        "  # Intialise total loss\n",
        "  total_loss = 0\n",
        "\n",
        "  # Putting model in evaluation mode\n",
        "  model.eval()\n",
        "\n",
        "  # Evaluate data for one epoch\n",
        "\n",
        "  for batch in tqdm(dataloader, total = len(dataloader)):\n",
        "\n",
        "    # add original labels\n",
        "    true_labels += batch['labels'].numpy().flatten().tolist()\n",
        "\n",
        "    # move batch to device\n",
        "    batch = {k:v.type(torch.long).to(device_) for k,v in batch.items()}\n",
        "\n",
        "    # for not storing and computating gradinets\n",
        "    with torch.no_grad():\n",
        "\n",
        "      # forward pass, calculate logit predications\n",
        "      outputs = model(**batch)\n",
        "\n",
        "      # fetch loss and logit value from return tuple\n",
        "      loss, logits = outputs[:2]\n",
        "\n",
        "      # moving logits to cpu memory\n",
        "      logits = logits.detach().cpu().numpy()\n",
        "\n",
        "      # Accumlating total loss\n",
        "      total_loss += loss.item()\n",
        "\n",
        "      # get predications to list\n",
        "      predict_content = logits.argmax(axis = -1).flatten().tolist()\n",
        "\n",
        "      # update predications list\n",
        "      predictions_labels += predict_content\n",
        "\n",
        "  # calculate aveage loss over training data\n",
        "  avg_epoch_loss = total_loss / len(dataloader)\n",
        "\n",
        "  # Return all true labels and prediciton for future evaluations.\n",
        "  return true_labels, predictions_labels, avg_epoch_loss"
      ],
      "metadata": {
        "id": "GTRBKZ2Nir2_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Model and Tokenizer\n",
        "\n",
        "Loding the three esential parts of pretrained transformers: configuration, tokenizer and model. We also need to load model to the device we're planning to use (GPU / CPU)."
      ],
      "metadata": {
        "id": "n_PbPXMNJ2ue"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get model configuration.\n",
        "print('Loading configuraiton...')\n",
        "model_config = AutoConfig.from_pretrained(pretrained_model_name_or_path=model_name, \n",
        "                                          num_labels=n_labels)\n",
        "\n",
        "# Get model's tokenizer.\n",
        "print('Loading tokenizer...')\n",
        "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
        "\n",
        "# Get the actual model.\n",
        "print('Loading model...')\n",
        "model = AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path=model_name, \n",
        "                                                           config=model_config)\n",
        "\n",
        "# Load model to defined device.\n",
        "model.to(device)\n",
        "print('Model loaded to `%s`'%device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i53OLboXpFyt",
        "outputId": "5f285f5c-0ad1-4170-bbb7-bbb294d91c3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading configuraiton...\n",
            "Loading tokenizer...\n",
            "Loading model...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "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": [
            "Model loaded to `cuda`\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset and DataLoader\n",
        "\n",
        "Create the PyTorch Dataset and DataLoader objects that will be used to feed data into our model."
      ],
      "metadata": {
        "id": "wtpUgtkYKIDM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Dealing with Train...')\n",
        "# Create pytorch dataset.\n",
        "train_dataset = MovieReviewsDataset(path='aclImdb/train', \n",
        "                               use_tokenizer=tokenizer, \n",
        "                               labels_ids=labels_ids,\n",
        "                               max_sequence_len=max_length)\n",
        "print('Created `train_dataset` with %d examples!'%len(train_dataset))\n",
        "\n",
        "# Move pytorch dataset into dataloader.\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "print('Created `train_dataloader` with %d batches!'%len(train_dataloader))\n",
        "\n",
        "print()\n",
        "\n",
        "print('Dealing with ...')\n",
        "# Create pytorch dataset.\n",
        "valid_dataset =  MovieReviewsDataset(path='aclImdb/test', \n",
        "                               use_tokenizer=tokenizer, \n",
        "                               labels_ids=labels_ids,\n",
        "                               max_sequence_len=max_length)\n",
        "print('Created `valid_dataset` with %d examples!'%len(valid_dataset))\n",
        "\n",
        "# Move pytorch dataset into dataloader.\n",
        "valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)\n",
        "print('Created `eval_dataloader` with %d batches!'%len(valid_dataloader))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574,
          "referenced_widgets": [
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        },
        "id": "4OqQqvIDpFv0",
        "outputId": "36852146-63f3-4370-c07e-2cea47685899"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dealing with Train...\n",
            "Reading partitions...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e3213480c464cf584809d4c3e0b7840"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading neg files...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/12500 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "acc87b0792d940d881806bed72c147f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading pos files...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/12500 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "167289544cad4454a0f9c344422ea8af"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenzation of text is in progress...\n",
            "Text padded or truncated to 60 length !\n",
            "Finished! \n",
            "\n",
            "Created `train_dataset` with 25000 examples!\n",
            "Created `train_dataloader` with 782 batches!\n",
            "\n",
            "Dealing with ...\n",
            "Reading partitions...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d66de3f4bba47aaa71fdb90586a40c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading neg files...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/12500 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8da7427bba324732b5cdcb2b152c8993"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading pos files...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/12500 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "682037e2230a4eeeb1fb0135fe4ed127"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenzation of text is in progress...\n",
            "Text padded or truncated to 60 length !\n",
            "Finished! \n",
            "\n",
            "Created `valid_dataset` with 25000 examples!\n",
            "Created `eval_dataloader` with 782 batches!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train\n",
        "\n",
        "Create optimizer and scheduler use by PyTorch in training.\n",
        "\n",
        "Loop through the number of defined epochs and call the **train** and **validation** functions.\n"
      ],
      "metadata": {
        "id": "Z7draW60KaHm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Note: AdamW is a class from the huggingface library (as opposed to pytorch) \n",
        "# I believe the 'W' stands for 'Weight Decay fix\"\n",
        "optimizer = AdamW(model.parameters(),\n",
        "                  lr = 5e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5\n",
        "                  eps = 1e-8 # args.adam_epsilon  - default is 1e-8.\n",
        "                  )\n",
        "\n",
        "# Total number of training steps is number of batches * number of epochs.\n",
        "# `train_dataloader` contains batched data so `len(train_dataloader)` gives \n",
        "# us the number of batches.\n",
        "total_steps = len(train_dataloader) * epochs\n",
        "\n",
        "# Create the learning rate scheduler.\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, \n",
        "                                            num_warmup_steps = 0, # Default value in run_glue.py\n",
        "                                            num_training_steps = total_steps)\n",
        "\n",
        "# Store the average loss after each epoch so we can plot them.\n",
        "all_loss = {'train_loss':[], 'val_loss':[]}\n",
        "all_acc = {'train_acc':[], 'val_acc':[]}\n",
        "\n",
        "# Loop through each epoch.\n",
        "print('Epoch')\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print()\n",
        "  print('Training on batches...')\n",
        "  # Perform one full pass over the training set.\n",
        "  train_labels, train_predict, train_loss = train(train_dataloader, optimizer, scheduler, device)\n",
        "  train_acc = accuracy_score(train_labels, train_predict)\n",
        "\n",
        "  # Get prediction form model on validation data. \n",
        "  print('Validation on batches...')\n",
        "  valid_labels, valid_predict, val_loss = validation(valid_dataloader, device)\n",
        "  val_acc = accuracy_score(valid_labels, valid_predict)\n",
        "\n",
        "  # Print loss and accuracy values to see how training evolves.\n",
        "  print(\"  train_loss: %.5f - val_loss: %.5f - train_acc: %.5f - valid_acc: %.5f\"%(train_loss, val_loss, train_acc, val_acc))\n",
        "  print()\n",
        "\n",
        "  # Store the loss value for plotting the learning curve.\n",
        "  all_loss['train_loss'].append(train_loss)\n",
        "  all_loss['val_loss'].append(val_loss)\n",
        "  all_acc['train_acc'].append(train_acc)\n",
        "  all_acc['val_acc'].append(val_acc)\n",
        "\n",
        "# Plot loss curves.\n",
        "plot_dict(all_loss, use_xlabel='Epochs', use_ylabel='Value', use_linestyles=['-', '--'])\n",
        "\n",
        "# Plot accuracy curves.\n",
        "plot_dict(all_acc, use_xlabel='Epochs', use_ylabel='Value', use_linestyles=['-', '--'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "A_EFPmiFirlf",
        "outputId": "9d03e4d5-d6e6-43f1-d024-3806d8be3985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cd5642ce58674907b3e10313f3491c24"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/782 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cadaf10722a54530ac40dd512a9ae4cc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-8852c78462c6>:37: UserWarning: torch.nn.utils.clip_grad_norm is now deprecated in favor of torch.nn.utils.clip_grad_norm_.\n",
            "  torch.nn.utils.clip_grad_norm(model.parameters(),1.0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/782 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0eb87c82f44247ce870a9174ff7cffa1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 0.13693 - val_loss: 0.46121 - train_acc: 0.95192 - valid_acc: 0.82392\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/782 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d7e6853351e4abdbc89a3cfcb983f6b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-8852c78462c6>:37: UserWarning: torch.nn.utils.clip_grad_norm is now deprecated in favor of torch.nn.utils.clip_grad_norm_.\n",
            "  torch.nn.utils.clip_grad_norm(model.parameters(),1.0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/782 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c13bcfeb27e49bc856704f376df94fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 0.05530 - val_loss: 0.85041 - train_acc: 0.98416 - valid_acc: 0.82632\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/782 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6dba70a4d52b4081a82113693fc30cb7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-8852c78462c6>:37: UserWarning: torch.nn.utils.clip_grad_norm is now deprecated in favor of torch.nn.utils.clip_grad_norm_.\n",
            "  torch.nn.utils.clip_grad_norm(model.parameters(),1.0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation on batches...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/782 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c47d515ad5c4f8d91dfbf08c76cd2d8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  train_loss: 0.01888 - val_loss: 1.08609 - train_acc: 0.99536 - valid_acc: 0.82848\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/ml_things/plot_functions.py:409: DeprecationWarning: `magnify` needs to have value in [0,1]! `1.2` will be converted to `0.1` as default.\n",
            "  warnings.warn(f'`magnify` needs to have value in [0,1]! `{magnify}` will be converted to `0.1` as default.',\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/ml_things/plot_functions.py:409: DeprecationWarning: `magnify` needs to have value in [0,1]! `1.2` will be converted to `0.1` as default.\n",
            "  warnings.warn(f'`magnify` needs to have value in [0,1]! `{magnify}` will be converted to `0.1` as default.',\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluate\n",
        "\n",
        "When dealing with classification is useful to look at precision recall and F1 score.\n",
        "\n",
        "A good gauge to have when evaluating a model is the confusion matrix."
      ],
      "metadata": {
        "id": "kQ4XNs91KlTs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get prediction form model on validation data. This is where you should use\n",
        "# your test data.\n",
        "true_labels, predictions_labels, avg_epoch_loss = validation(valid_dataloader, device)\n",
        "\n",
        "# Create the evaluation report.\n",
        "evaluation_report = classification_report(true_labels, predictions_labels, labels=list(labels_ids.values()), target_names=list(labels_ids.keys()))\n",
        "# Show the evaluation report.\n",
        "print(evaluation_report)\n",
        "\n",
        "# Plot confusion matrix.\n",
        "plot_confusion_matrix(y_true=true_labels, y_pred=predictions_labels, \n",
        "                      classes=list(labels_ids.keys()), normalize=True, \n",
        "                      magnify=3,\n",
        "                      );"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659,
          "referenced_widgets": [
            "fdecd1d6b4e842d28b10ac9d46fb7e51",
            "1badbf6a51444e2ea4b99fea8b66b6db",
            "7a04e06a0cd245bd9fae74e2f34be17b",
            "9d6f4f0979bf4523af8a1fab0fdedb61",
            "28761dfa534d4b0e8d53826452edf457",
            "c1b156f78075467b881175bdd66c953b",
            "399c480cc7dd45b695cb3548daea53e7",
            "fce8d276ab754bed888e60c738776ab7",
            "7e3b7cc1f7a643fa866bf97767952638",
            "a0b390c8ae8a4f79855d8bfd5ba0f0e6",
            "428205acdc67453e83dc7555c8b0c809"
          ]
        },
        "id": "6x8h0ty8KiE7",
        "outputId": "21e2f857-e8c6-4051-e61a-0a5634d5b952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/782 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fdecd1d6b4e842d28b10ac9d46fb7e51"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         neg       0.84      0.81      0.83     12500\n",
            "         pos       0.82      0.84      0.83     12500\n",
            "\n",
            "    accuracy                           0.83     25000\n",
            "   macro avg       0.83      0.83      0.83     25000\n",
            "weighted avg       0.83      0.83      0.83     25000\n",
            "\n",
            "Normalized confusion matrix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/ml_things/plot_functions.py:676: DeprecationWarning: `magnify` needs to have value in [0,1]! `3` will be converted to `0.1` as default.\n",
            "  warnings.warn(f'`magnify` needs to have value in [0,1]! `{magnify}` will be converted to `0.1` as default.',\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1944x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}