diff --git a/abstractive/BART_based_approaches/Quick Start for beginners/BART_fineTune.ipynb b/abstractive/BART_based_approaches/Quick Start for beginners/BART_fineTune.ipynb new file mode 100644 index 0000000..4f56e6c --- /dev/null +++ b/abstractive/BART_based_approaches/Quick Start for beginners/BART_fineTune.ipynb @@ -0,0 +1,2747 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7cbceb43", + "metadata": { + "id": "7cbceb43" + }, + "source": [ + "# Script to finetune BART model\n", + "\n", + "This script generates a fine tuned BART model from the fine tuning data" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Basic Setup" + ], + "metadata": { + "id": "IVk3Ph26uhHw" + }, + "id": "IVk3Ph26uhHw" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "qPKiTM9DMKoJ", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qPKiTM9DMKoJ", + "outputId": "d5b526af-c429-45ca-d17c-7e99cdeb88d1" + }, + "outputs": [ + { + 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/usr/local/lib/python3.10/dist-packages (from nltk) (1.4.2)\n", + "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2024.9.11)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.6)\n" + ] + } + ], + "source": [ + "# Install dependencies\n", + "!pip install pytorch-lightning\n", + "!pip install rouge-score nltk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "l0PtljwsxJOD", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "l0PtljwsxJOD", + "outputId": "9f86648b-5fed-487f-a36b-be252f071e09" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ] + } + ], + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a334485c", + "metadata": { + "id": "a334485c" + }, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../')\n", + "import transformers\n", + "import pandas as pd\n", + "import numpy as np\n", + "import glob\n", + "import math\n", + "import random\n", + "import re\n", + "import argparse\n", + "import nltk\n", + "from transformers import Trainer, TrainingArguments" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Documentation: Text Summarization with PyTorch Lightning\n", + "\n", + "## **Overview**\n", + "This code implements a framework for fine-tuning transformer models (e.g., BART) for text summarization tasks using PyTorch Lightning. Below are the key components and their functionality:\n", + "\n", + "---\n", + "\n", + "## **Imports**\n", + "- **`nltk`, `transformers`**: For text preprocessing and working with pre-trained transformer models.\n", + "- **PyTorch (`torch`, `torch.nn.functional`, `torch.utils.data`)**: Provides tools for neural network building, training, and dataset handling.\n", + "- **`pytorch_lightning`**: A wrapper for PyTorch to streamline training workflows.\n", + "- **`ModelCheckpoint`**: Saves the best model during training.\n", + "\n", + "---\n", + "\n", + "## **LitModel (LightningModule)**\n", + "This is the core model training and inference logic encapsulated in a PyTorch Lightning module.\n", + "\n", + "1. **Initialization (`__init__`)**:\n", + " - Takes inputs like the transformer model, tokenizer, and learning rate.\n", + " - Includes options to freeze encoder and embedding layers to improve efficiency.\n", + "\n", + "2. **Methods**:\n", + " - `forward`: Defines how data passes through the model.\n", + " - `configure_optimizers`: Configures the optimizer (Adam).\n", + " - `training_step`: Implements the training logic, computing loss using cross-entropy.\n", + " - `validation_step`: Similar to `training_step`, used for validation loss computation.\n", + " - `generate_text`: Generates text summaries using the model's `generate` method.\n", + "\n", + "---\n", + "\n", + "## **SummaryDataModule (LightningDataModule)**\n", + "Handles data preparation, splitting, and loading for training.\n", + "\n", + "1. **`prepare_data`**:\n", + " - Splits the data into training, validation, and test sets (60/20/20).\n", + "\n", + "2. **`setup`**:\n", + " - Encodes source and target sentences using the tokenizer.\n", + "\n", + "3. **Data Loaders**:\n", + " - `train_dataloader`: Prepares batched training data.\n", + " - `val_dataloader`: Prepares batched validation data.\n", + " - `test_dataloader`: Prepares batched test data.\n", + "\n", + "---\n", + "\n", + "## **Utility Functions**\n", + "1. **`freeze_params`**:\n", + " - Freezes parameters of specific model layers for faster training.\n", + "\n", + "2. **`shift_tokens_right`**:\n", + " - Shifts input token sequences to the right for proper alignment during training.\n", + "\n", + "3. **`encode_sentences`**:\n", + " - Tokenizes and encodes source/target sentences for training and evaluation.\n", + "\n", + "---\n", + "\n", + "## **Key Features**\n", + "- **Freezing Layers**: Freezes encoder and embeddings for efficiency.\n", + "- **Efficient Data Handling**: Uses PyTorch DataLoader for batched data processing.\n", + "- **Text Generation**: Implements summarization using beam search.\n", + "\n", + "---\n", + "\n", + "## **How to Use**\n", + "1. Load your dataset into a pandas DataFrame.\n", + "2. Initialize the tokenizer and model (e.g., from Hugging Face's Transformers library).\n", + "3. Create instances of `LitModel` and `SummaryDataModule`.\n", + "4. Use PyTorch Lightning's `Trainer` to train the model.\n" + ], + "metadata": { + "id": "L_LGmIS9mtAu" + }, + "id": "L_LGmIS9mtAu" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "JZnk7Bi4L5d4", + "metadata": { + "id": "JZnk7Bi4L5d4" + }, + "outputs": [], + "source": [ + "# Import\n", + "import glob\n", + "from nltk import tokenize\n", + "import nltk\n", + "import transformers\n", + "from torch.utils.data import DataLoader, TensorDataset, random_split, RandomSampler, Dataset\n", + "import pandas as pd\n", + "import numpy as np\n", + "import torch.nn.functional as F\n", + "import pytorch_lightning as pl\n", + "import torch\n", + "from pytorch_lightning.callbacks import ModelCheckpoint\n", + "\n", + "#Source - https://colab.research.google.com/drive/1Cy27V-7qqYatqMA7fEqG2kgMySZXw9I4?usp=sharing&pli=1\n", + "class LitModel(pl.LightningModule):\n", + " # Instantiate the model\n", + " def __init__(self, learning_rate, tokenizer, model):\n", + " super().__init__()\n", + " self.tokenizer = tokenizer\n", + " self.model = model\n", + " self.learning_rate = learning_rate\n", + " # self.freeze_encoder = freeze_encoder\n", + " # self.freeze_embeds_ = freeze_embeds\n", + "# self.hparams = argparse.Namespace()\n", + "\n", + " self.hparams.freeze_encoder = True\n", + " self.hparams.freeze_embeds = True\n", + " self.hparams.eval_beams = 4\n", + " # self.hparams = hparams\n", + "\n", + " if self.hparams.freeze_encoder:\n", + " freeze_params(self.model.get_encoder())\n", + "\n", + " if self.hparams.freeze_embeds:\n", + " self.freeze_embeds()\n", + "\n", + " def freeze_embeds(self):\n", + " ''' freeze the positional embedding parameters of the model; adapted from finetune.py '''\n", + " freeze_params(self.model.model.shared)\n", + " for d in [self.model.model.encoder, self.model.model.decoder]:\n", + " freeze_params(d.embed_positions)\n", + " freeze_params(d.embed_tokens)\n", + "\n", + " # Do a forward pass through the model\n", + " def forward(self, input_ids, **kwargs):\n", + " return self.model(input_ids, **kwargs)\n", + "\n", + " def configure_optimizers(self):\n", + " optimizer = torch.optim.Adam(self.parameters(), lr = self.learning_rate)\n", + " return optimizer\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + " # Load the data into variables\n", + " src_ids, src_mask = batch[0], batch[1]\n", + " tgt_ids = batch[2]\n", + " # Shift the decoder tokens right (but NOT the tgt_ids)\n", + " decoder_input_ids = shift_tokens_right(tgt_ids, self.tokenizer.pad_token_id)\n", + "\n", + " # Run the model and get the logits\n", + " outputs = self(src_ids, attention_mask=src_mask, decoder_input_ids=decoder_input_ids, use_cache=False)\n", + " lm_logits = outputs[0]\n", + " # Create the loss function\n", + " ce_loss_fct = torch.nn.CrossEntropyLoss(ignore_index=self.tokenizer.pad_token_id)\n", + " # Calculate the loss on the un-shifted tokens\n", + " loss = ce_loss_fct(lm_logits.view(-1, lm_logits.shape[-1]), tgt_ids.view(-1))\n", + "\n", + " # Log the validation loss\n", + " self.log('train_loss', loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)\n", + "\n", + " return {'loss':loss}\n", + "\n", + " def validation_step(self, batch, batch_idx):\n", + "\n", + " src_ids, src_mask = batch[0], batch[1]\n", + " tgt_ids = batch[2]\n", + "\n", + " decoder_input_ids = shift_tokens_right(tgt_ids, self.tokenizer.pad_token_id)\n", + "\n", + " # Run the model and get the logits\n", + " outputs = self(src_ids, attention_mask=src_mask, decoder_input_ids=decoder_input_ids, use_cache=False)\n", + " lm_logits = outputs[0]\n", + "\n", + " ce_loss_fct = torch.nn.CrossEntropyLoss(ignore_index=self.tokenizer.pad_token_id)\n", + " val_loss = ce_loss_fct(lm_logits.view(-1, lm_logits.shape[-1]), tgt_ids.view(-1))\n", + "\n", + " # Log the validation loss\n", + " self.log('val_loss', val_loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)\n", + "\n", + " return {'loss': val_loss}\n", + "\n", + " # Method that generates text using the BartForConditionalGeneration's generate() method\n", + "def generate_text(self, text, eval_beams, early_stopping = True, max_len = 1024):\n", + " ''' Function to generate text '''\n", + " generated_ids = self.model.generate(\n", + " text[\"input_ids\"],\n", + " attention_mask=text[\"attention_mask\"],\n", + " use_cache=True,\n", + " decoder_start_token_id = self.tokenizer.pad_token_id,\n", + " num_beams= eval_beams,\n", + " max_length = max_len,\n", + " early_stopping = early_stopping\n", + " )\n", + " return [self.tokenizer.decode(w, skip_special_tokens=True, clean_up_tokenization_spaces=True) for w in generated_ids]\n", + "\n", + "def freeze_params(model):\n", + " ''' Function that takes a model as input (or part of a model) and freezes the layers for faster training\n", + " adapted from finetune.py '''\n", + " for layer in model.parameters():\n", + " layer.requires_grade = False\n", + "\n", + "\n", + "# Create a dataloading module as per the PyTorch Lightning Docs\n", + "class SummaryDataModule(pl.LightningDataModule):\n", + " def __init__(self, tokenizer, df, batch_size):\n", + " super().__init__()\n", + " self.tokenizer = tokenizer\n", + " self.batch_size = batch_size\n", + " self.data = df\n", + "\n", + " # Loads and splits the data into training, validation and test sets with a 60/20/20 split\n", + " def prepare_data(self):\n", + " self.train, self.validate, self.test = np.split(self.data.sample(frac=1), [int(.6*len(self.data)), int(.8*len(self.data))])\n", + "\n", + " # encode the sentences using the tokenizer\n", + " def setup(self, stage):\n", + " self.train = encode_sentences(self.tokenizer, self.train['source'], self.train['target'])\n", + " self.validate = encode_sentences(self.tokenizer, self.validate['source'], self.validate['target'])\n", + " self.test = encode_sentences(self.tokenizer, self.test['source'], self.test['target'])\n", + "\n", + " # Load the training, validation and test sets in Pytorch Dataset objects\n", + " def train_dataloader(self):\n", + " dataset = TensorDataset(self.train['input_ids'], self.train['attention_mask'], self.train['labels'])\n", + " train_data = DataLoader(dataset, sampler = RandomSampler(dataset), batch_size = self.batch_size)\n", + " return train_data\n", + "\n", + " def val_dataloader(self):\n", + " dataset = TensorDataset(self.validate['input_ids'], self.validate['attention_mask'], self.validate['labels'])\n", + " val_data = DataLoader(dataset, batch_size = self.batch_size)\n", + " return val_data\n", + "\n", + " def test_dataloader(self):\n", + " dataset = TensorDataset(self.test['input_ids'], self.test['attention_mask'], self.test['labels'])\n", + " test_data = DataLoader(dataset, batch_size = self.batch_size)\n", + " return test_data\n", + "\n", + "\n", + "\n", + "def shift_tokens_right(input_ids, pad_token_id):\n", + " \"\"\" Shift input ids one token to the right, and wrap the last non pad token (usually ).\n", + " This is taken directly from modeling_bart.py\n", + " \"\"\"\n", + " prev_output_tokens = input_ids.clone()\n", + " index_of_eos = (input_ids.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)\n", + " prev_output_tokens[:, 0] = input_ids.gather(1, index_of_eos).squeeze()\n", + " prev_output_tokens[:, 1:] = input_ids[:, :-1]\n", + " return prev_output_tokens\n", + "\n", + "def encode_sentences(tokenizer, source_sentences, target_sentences, max_length=1024, min_length = 512, pad_to_max_length=True, return_tensors=\"pt\"):\n", + " ''' Function that tokenizes a sentence\n", + " Args: tokenizer - the BART tokenizer; source and target sentences are the source and target sentences\n", + " Returns: Dictionary with keys: input_ids, attention_mask, target_ids\n", + " '''\n", + "\n", + " input_ids = []\n", + " attention_masks = []\n", + " target_ids = []\n", + " tokenized_sentences = {}\n", + "\n", + " for sentence in source_sentences:\n", + " encoded_dict = tokenizer(\n", + " sentence,\n", + " max_length=max_length,\n", + " padding=\"max_length\" if pad_to_max_length else None,\n", + " truncation=True,\n", + " return_tensors=return_tensors,\n", + " add_prefix_space = True\n", + " )\n", + "\n", + " input_ids.append(encoded_dict['input_ids'])\n", + " attention_masks.append(encoded_dict['attention_mask'])\n", + "\n", + " input_ids = torch.cat(input_ids, dim = 0)\n", + " attention_masks = torch.cat(attention_masks, dim = 0)\n", + "\n", + " for sentence in target_sentences:\n", + " encoded_dict = tokenizer(\n", + " sentence,\n", + " max_length=min_length,\n", + " padding=\"max_length\" if pad_to_max_length else None,\n", + " truncation=True,\n", + " return_tensors=return_tensors,\n", + " add_prefix_space = True\n", + " )\n", + " # Shift the target ids to the right\n", + " # shifted_target_ids = shift_tokens_right(encoded_dict['input_ids'], tokenizer.pad_token_id)\n", + " target_ids.append(encoded_dict['input_ids'])\n", + "\n", + " target_ids = torch.cat(target_ids, dim = 0)\n", + "\n", + "\n", + " batch = {\n", + " \"input_ids\": input_ids,\n", + " \"attention_mask\": attention_masks,\n", + " \"labels\": target_ids,\n", + " }\n", + "\n", + " return batch" + ] + }, + { + "cell_type": "markdown", + "id": "47a51716", + "metadata": { + "id": "47a51716", + "outputId": "8fba864b-33d0-4e26-a454-dec8f9a9939b" + }, + "source": [ + "## **Documentation: Data Loading Function**\n", + "\n", + "### **Overview**\n", + "The `load_data` function loads text data for training and testing from specific directories containing case files and corresponding summaries. It retrieves data from the specified paths for both the training and testing datasets, and structures it into a format suitable for training a model.\n", + "\n", + "### **Function: `load_data(data)`**\n", + "\n", + "This function loads the source (case details) and target (summaries) text files into a list of dictionaries. The function expects a list, `data`, which will be populated with dictionaries where each dictionary contains a \"source\" and \"target\" key.\n", + "\n", + "---\n", + "\n", + "### **Steps in the Function**\n", + "\n", + "1. **Loading Training Data**:\n", + " - **Path**: `/content/drive/MyDrive/IN-Abs/train-data/summary`\n", + " - Loads all `.txt` files containing summaries into the `summary` list.\n", + " - **Path**: `/content/drive/MyDrive/IN-Abs/train-data/judgement`\n", + " - Loads all `.txt` files containing case details into the `source` list.\n", + " - Pairs the case details (`source`) with their corresponding summaries (`target`) and appends them to the `data` list in dictionary format.\n", + "\n", + "2. **Loading Testing Data**:\n", + " - **Path**: `/content/drive/MyDrive/IN-Abs/test-data/summary`\n", + " - Loads all `.txt` files containing test summaries into the `summary` list.\n", + " - **Path**: `/content/drive/MyDrive/IN-Abs/test-data/judgement`\n", + " - Loads all `.txt` files containing test case details into the `source` list.\n", + " - Pairs the test case details (`source`) with their corresponding summaries (`target`) and appends them to the `data` list in dictionary format.\n", + "\n", + "3. **Returning the Data**:\n", + " - The function returns the populated `data` list, which contains the source-summary pairs for both training and testing.\n", + "\n", + "---\n", + "\n", + "### **Parameters**\n", + "- `data`: A list to store the source-summary pairs. This list is populated by the function.\n", + "\n", + "### **Returns**\n", + "- `data`: A list of dictionaries where each dictionary has:\n", + " - `\"source\"`: The case details (text from `.txt` files in the \"judgement\" directories).\n", + " - `\"target\"`: The corresponding summary (text from `.txt` files in the \"summary\" directories).\n", + "\n", + "---\n", + "\n", + "### **Example Usage**\n", + "```python\n", + "data = []\n", + "data = load_data(data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "q0j32cytMmYD", + "metadata": { + "id": "q0j32cytMmYD" + }, + "outputs": [], + "source": [ + "import glob\n", + "\n", + "def load_data(data):\n", + " #Path to the summary\n", + " path = \"/content/drive/MyDrive/IN-Abs/train-data/summary\"\n", + "\n", + " #Get all the txt files with summaries\n", + " all_files = glob.glob(path + \"/*.txt\")\n", + "\n", + " summary = []\n", + "\n", + " #Get the summaries\n", + " for filename in all_files:\n", + " with open(filename, 'r') as f:\n", + " a = f.read()\n", + " summary.append(a)\n", + "\n", + " #Path to the case files\n", + " path = \"/content/drive/MyDrive/IN-Abs/train-data/judgement\"\n", + "\n", + " #Get all the txt files with case details\n", + " all_files = glob.glob(path + \"/*.txt\")\n", + " print(all_files)\n", + " source = []\n", + "\n", + " for filename in all_files:\n", + " with open(filename, 'r') as f:\n", + " a = f.read()\n", + " source.append(a)\n", + "\n", + " #Prepare the dataset\n", + "\n", + "\n", + " for src,summ in zip(source,summary):\n", + " data.append({\"source\" : src , \"target\" : summ})\n", + "\n", + " #Path to the summary\n", + " path = \"/content/drive/MyDrive/IN-Abs/test-data/summary\"\n", + "\n", + " #Get all the txt files with summaries\n", + " all_files = glob.glob(path + \"/*.txt\")\n", + "\n", + " summary = []\n", + "\n", + " #Get the summaries\n", + " for filename in all_files:\n", + " with open(filename, 'r') as f:\n", + " a = f.read()\n", + " summary.append(a)\n", + "\n", + " #Path to the case files\n", + " path = \"/content/drive/MyDrive/IN-Abs/test-data/judgement\"\n", + "\n", + " #Get all the txt files with case details\n", + " all_files = glob.glob(path + \"/*.txt\")\n", + " print(all_files)\n", + " source = []\n", + "\n", + " for filename in all_files:\n", + " with open(filename, 'r') as f:\n", + " a = f.read()\n", + " source.append(a)\n", + " #Prepare the dataset\n", + "\n", + " for src,summ in zip(source,summary):\n", + " data.append({\"source\" : src , \"target\" : summ})\n", + "\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "CLqiPjWzNhBg", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CLqiPjWzNhBg", + "outputId": "47133292-1900-4169-d87d-a9797a8129a0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['/content/drive/MyDrive/IN-Abs/train-data/judgement/676.txt', 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'/content/drive/MyDrive/IN-Abs/train-data/judgement/1803.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/157.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1507.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1727.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1840.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1674.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/170.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/160.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1734.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1510.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1545.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1701.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1671.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/174.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1484.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1791.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/181.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1559.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1774.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1782.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1699.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1613.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1720.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1678.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1527.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1848.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1780.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1714.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1495.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1669.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1816.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1758.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1683.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1587.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1685.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1521.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1809.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1715.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1640.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1592.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1494.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1506.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1768.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1769.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1856.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1752.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/15.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1558.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1647.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1657.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1539.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/179.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1524.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1667.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1604.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1664.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/18.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1793.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1847.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1487.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1718.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1692.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1566.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1738.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/17.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1564.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/176.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1814.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1703.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1641.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1586.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1852.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1696.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/183.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1820.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1805.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1624.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1631.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1761.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1681.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1596.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1766.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1490.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1833.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/153.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/156.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/163.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1574.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1749.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1536.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1594.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1775.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1606.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1627.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1497.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1688.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1580.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1621.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1649.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1654.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1859.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1746.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1504.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1719.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1863.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1503.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1571.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/162.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/167.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1826.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1796.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1698.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1535.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1588.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1578.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/2126.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/10.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/6618.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/381.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/2828.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1001.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/3494.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/3370.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/4148.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/3204.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/5838.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/3608.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/4145.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/2391.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/3622.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/390.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1965.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1961.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/3930.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1000.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/899.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1171.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/5927.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/466.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/1.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/6958.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/100.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/2965.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/3781.txt', '/content/drive/MyDrive/IN-Abs/train-data/judgement/6328.txt']\n", + "['/content/drive/MyDrive/IN-Abs/test-data/judgement/3210.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/4963.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/3292.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5861.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/3356.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/3542.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/362.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2913.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2657.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/3844.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2796.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/1195.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6668.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/3602.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6270.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/1181.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5266.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/1778.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6276.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/1329.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/4316.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5141.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5597.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5994.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/4917.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/4641.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/3531.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5364.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5397.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2593.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2727.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6118.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/4568.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/1974.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/652.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/415.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/78.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6778.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2207.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/266.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2124.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/314.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6852.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5248.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2649.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5707.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/3893.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6521.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2392.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/4782.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6647.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6413.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/715.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/3924.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2609.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/1697.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6728.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2035.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6622.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/1762.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/3436.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/4480.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/3019.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/380.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2065.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2248.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/784.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2052.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/4451.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/1531.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2627.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/660.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2256.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5937.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5142.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/4860.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2440.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5538.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/1406.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/690.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/4807.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6157.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2122.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6881.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/4938.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/1378.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/1789.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/496.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/3168.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/3442.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6003.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/4071.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5471.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/232.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/1522.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/7130.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/7109.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/2304.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/5888.txt', '/content/drive/MyDrive/IN-Abs/test-data/judgement/6245.txt']\n" + ] + } + ], + "source": [ + "#Load the data\n", + "data=list()\n", + "data=load_data(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "jccjTXZ5R5T3", + "metadata": { + "id": "jccjTXZ5R5T3" + }, + "outputs": [], + "source": [ + "# Covert to appropriate format\n", + "data_pd=pd.DataFrame(data)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Load the BART Model, Tokenizer and Dataset" + ], + "metadata": { + "id": "vN3nua1ZvfSG" + }, + "id": "vN3nua1ZvfSG" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "625b9f64", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "625b9f64", + "outputId": "b2d0dac7-3a81-4260-ba3c-607addf2776f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# Loading Model and tokenizer\n", + "from transformers import BartTokenizer, BartForConditionalGeneration, AdamW, BartConfig\n", + "\n", + "tokenizer = BartTokenizer.from_pretrained('facebook/bart-large', add_prefix_space=True)\n", + "\n", + "bart_model = BartForConditionalGeneration.from_pretrained(\"facebook/bart-large\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37f25fA65aSB", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "37f25fA65aSB", + "outputId": "53633b3c-4b70-4ffa-e484-7169ce5a5726" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "The new embeddings will be initialized from a multivariate normal distribution that has old embeddings' mean and covariance. As described in this article: https://nlp.stanford.edu/~johnhew/vocab-expansion.html. To disable this, use `mean_resizing=False`\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "BartScaledWordEmbedding(50272, 1024, padding_idx=1)" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ], + "source": [ + "# Add special tokens if required\n", + "\n", + "new_tokens = ['', '', '', '', '

', '', '']\n", + "\n", + "special_tokens_dict = {'additional_special_tokens': new_tokens}\n", + "num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)\n", + "bart_model.resize_token_embeddings(len(tokenizer))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0707c7d9", + "metadata": { + "id": "0707c7d9" + }, + "outputs": [], + "source": [ + "summary_data = SummaryDataModule(tokenizer, data_pd, batch_size = 1)\n", + "model = LitModel(learning_rate = 2e-5, tokenizer = tokenizer, model = bart_model)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Train the Model\n", + "**Explanation:**\n", + "\n", + "1. **Install PyTorch Lightning:**\n", + " - `!pip install pytorch-lightning --upgrade`: This line ensures that the latest version of PyTorch Lightning is installed.\n", + "\n", + "2. **Import Necessary Modules:**\n", + " - `import pytorch_lightning as pl`: Imports the main PyTorch Lightning library.\n", + " - `from pytorch_lightning.callbacks import TQDMProgressBar`: Imports the `TQDMProgressBar` callback for displaying a progress bar during training.\n", + "\n", + "3. **Configure the Trainer:**\n", + " - `trainer = pl.Trainer(...)`: Creates a `Trainer` object, which is the central component of PyTorch Lightning for managing the training process.\n", + " - **`accelerator=\"gpu\"`:** Specifies that the training should be performed on a GPU.\n", + " - **`devices=1\"`:** Indicates that one GPU should be used.\n", + " - **`max_epochs=3\"`:** Sets the maximum number of training epochs.\n", + " - **`min_epochs=2\"`:** Sets the minimum number of training epochs.\n", + " - **`callbacks=[TQDMProgressBar(refresh_rate=5)]`:** Adds a progress bar with a refresh rate of 5 to the training process.\n", + " - **`precision=16\"`:** Enables mixed precision training with 16-bit floating-point numbers.\n", + "\n", + "\n" + ], + "metadata": { + "id": "FjDaZH9hwX0p" + }, + "id": "FjDaZH9hwX0p" + }, + { + "source": [ + "!pip install pytorch-lightning --upgrade # Ensure PyTorch Lightning is up-to-date\n", + "\n", + "import pytorch_lightning as pl\n", + "from pytorch_lightning.callbacks import TQDMProgressBar\n", + "\n", + "\n", + "# Use TQDMProgressBar to control the refresh rate\n", + "trainer = pl.Trainer(accelerator=\"gpu\", # Use \"gpu\" for GPU training\n", + " devices=1, # Specify the number of GPUs to use\n", + " max_epochs=3,\n", + " min_epochs=2,\n", + " callbacks=[TQDMProgressBar(refresh_rate=5)], # Set refresh rate here\n", + " precision=16)" + ], + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7zON_-sD0cjP", + "outputId": "e170696e-3759-4ce8-bd8c-df5b8517da49" + }, + "id": "7zON_-sD0cjP", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pytorch-lightning in /usr/local/lib/python3.10/dist-packages (2.4.0)\n", + "Requirement already satisfied: torch>=2.1.0 in 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already satisfied: idna>=2.0 in /usr/local/lib/python3.10/dist-packages (from yarl<2.0,>=1.17.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=2022.5.0->pytorch-lightning) (3.10)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/lightning_fabric/connector.py:571: `precision=16` is supported for historical reasons but its usage is discouraged. Please set your precision to 16-mixed instead!\n", + "INFO:pytorch_lightning.utilities.rank_zero:Using 16bit Automatic Mixed Precision (AMP)\n", + "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:HPU available: False, using: 0 HPUs\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "trainer.fit(model, summary_data)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 329, + "referenced_widgets": [ + "e25511a7254a4c8790d11bbb7cdb8d83", + "7658a3df0df748b5ba8a8a541219c006", + "96bd23d1f8f64c4ea353f29bdee4befb", + "4ccff8b34ea44ea6beea7c8446dd7d6a", + "a3be0ac9473d420ab7d3177e7af468cd", + "26b8aeb89f544fe08a46ab9e726cf40e", + "b9bc02cc06564b3f8e6cec8d54436b32", + "dae7589638304f2d9f9d3a95bcb82646", + "3ae9a5e1d4344505bcc4ade46742b61b", + "592079402a8c41109e47d16f2ee456e1", + "c353b0a4ade04d0fbb26eca355f04cae", + "b795e8ba9bfe45b2be01fada33e5630e", + "3abfb854c33f4a1a86c0c42450e848e3", + "b643ca98fdbc4c62bf41cffc3456d6ba", + "395dc7d5d5d94ab8b61dac62ffcda795", + "321c71fff85f4ed69d6690617e5219f1", + "85edb70fc3de4ee586364debd459bb18", + "f4b8c334c4bd4a5e89e178db254296a5", + "93adb824e5af4e3c94b8889365deec67", + "6afe982e765443258ed599c4dbd8a267", + "7c035b9f986d44a69e9579864b2b7c6e", + "445c810208e94c669146b4b25c3d40cc", + "aedb03657aaa415780e936ec14a96af2", + "f45c2d928b254b409f3c7b96f9ed5156", + "e54dc098113247e5ad6f6094abe12aa4", + "bc8d39afc0664d7a895e289b6751686a", + "8b521deb10c94a4eaedecd226d383669", + "9e61e0b3be5e48f69439cb03dc1f4933", + "d32127e8f4a44ad680daa9457a01b23d", + "24e5010e7ef54cac807034da4e0a235e", + "82e545db34a44c50844b7893d021d03e", + "7030f1a406c44ed28a8ab673c4ddc078", + "f2af49da3e0744e8aaf4a449ad7905e2", + "2a8ac5127fbd4c7bac6408cc1897974d", + "3f90d2ccbbb4461c9aef9781fd832f73", + "bf0572366509491595c57efd6d4a6c46", + "b60f00c303b14382adea32e486654843", + "2abb91b91e344eb6bb6bc41574da4f99", + "a6f3969b2e3c48c2b7b230cd670e9bd4", + "5cafb5c9f28b4caaa6dd095aa6dac4d4", + "e4474c9ef955443bb607e7219a635be7", + "1600e2363adc4546978811f625ac225e", + "4617b840926748ecba5f237657441b0c", + "fa231bedddfd4c7b9da7cbff82631ebb", + "71abde9076894585bba21918b903adca", + "8e322c2730db4e94ad487bab3ef962a2", + "32ac56818d6d4f43afa31f5fce048960", + "69322d1cf65540b19aa7981d3ad769d4", + "89f9ae5bc0de4954951959eb1e76333b", + "8c3d40e306f34e8aa0f9158862bdacfe", + "c63c1d670f014cc896b233c8ae11b4df", + "6f1d0748241740b680fd72d1d57e5c29", + "0b04bb4ef56842e38ca8795ff7d8dfa7", + "ce6aa78d0d654b5d98fc90c86f9af889", + "b7ee18d7b4e44dec95a8f4f054687dfa" + ] + }, + "id": "hiat4SGB0DAy", + "outputId": "85b0dce6-1b4d-4f8b-ddf5-5638501af364" + }, + "id": "hiat4SGB0DAy", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", + " return bound(*args, **kwds)\n", + "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "INFO:pytorch_lightning.callbacks.model_summary:\n", + " | Name | Type | Params | Mode\n", + "--------------------------------------------------------------\n", + "0 | model | BartForConditionalGeneration | 406 M | eval\n", + "--------------------------------------------------------------\n", + "406 M Trainable params\n", + "0 Non-trainable params\n", + "406 M Total params\n", + "1,625.194 Total estimated model params size (MB)\n", + "0 Modules in train mode\n", + "348 Modules in eval mode\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Sanity Checking: | | 0/? [00:00).\n", + " This is taken directly from modeling_bart.py\n", + " \"\"\"\n", + " prev_output_tokens = input_ids.clone()\n", + " index_of_eos = (input_ids.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)\n", + " prev_output_tokens[:, 0] = input_ids.gather(1, index_of_eos).squeeze()\n", + " prev_output_tokens[:, 1:] = input_ids[:, :-1]\n", + " return prev_output_tokens\n", + "\n", + "def encode_sentences(tokenizer, source_sentences, target_sentences, max_length=1024, min_length = 512, pad_to_max_length=True, return_tensors=\"pt\"):\n", + " ''' Function that tokenizes a sentence\n", + " Args: tokenizer - the BART tokenizer; source and target sentences are the source and target sentences\n", + " Returns: Dictionary with keys: input_ids, attention_mask, target_ids\n", + " '''\n", + "\n", + " input_ids = []\n", + " attention_masks = []\n", + " target_ids = []\n", + " tokenized_sentences = {}\n", + "\n", + " for sentence in source_sentences:\n", + " encoded_dict = tokenizer(\n", + " sentence,\n", + " max_length=max_length,\n", + " padding=\"max_length\" if pad_to_max_length else None,\n", + " truncation=True,\n", + " return_tensors=return_tensors,\n", + " add_prefix_space = True\n", + " )\n", + "\n", + " input_ids.append(encoded_dict['input_ids'])\n", + " attention_masks.append(encoded_dict['attention_mask'])\n", + "\n", + " input_ids = torch.cat(input_ids, dim = 0)\n", + " attention_masks = torch.cat(attention_masks, dim = 0)\n", + "\n", + " for sentence in target_sentences:\n", + " encoded_dict = tokenizer(\n", + " sentence,\n", + " max_length=min_length,\n", + " padding=\"max_length\" if pad_to_max_length else None,\n", + " truncation=True,\n", + " return_tensors=return_tensors,\n", + " add_prefix_space = True\n", + " )\n", + " # Shift the target ids to the right\n", + " # shifted_target_ids = shift_tokens_right(encoded_dict['input_ids'], tokenizer.pad_token_id)\n", + " target_ids.append(encoded_dict['input_ids'])\n", + "\n", + " target_ids = torch.cat(target_ids, dim = 0)\n", + "\n", + "\n", + " batch = {\n", + " \"input_ids\": input_ids,\n", + " \"attention_mask\": attention_masks,\n", + " \"labels\": target_ids,\n", + " }\n", + "\n", + " return batch" + ] + }, + { + "cell_type": "markdown", + "id": "xz-1TZA8kJ1S", + "metadata": { + "id": "xz-1TZA8kJ1S" + }, + "source": [ + "### Function Documentation\n", + "\n", + "1. **get_summary_data(dataset, train)** \n", + " Retrieves names, documents, and summaries from two directories: one for judgment documents and the other for their corresponding summaries. \n", + " - **Parameters**: \n", + " - `dataset`: Dataset to be processed. \n", + " - `train`: Indicates whether the dataset is used for training.\n", + " - **Returns**: \n", + " - `names`: A list of file names. \n", + " - `data_source`: A list containing the content of judgment documents. \n", + " - `data_summary`: A list containing the summary for each judgment document. \n", + "\n", + " - **Usage**: \n", + " Change the `path` variable to point to the location of your dataset, which is expected to contain text files for judgment documents and summaries.\n", + "\n", + "2. **get_req_len_dict(dataset, istrain)** \n", + " Reads a file containing statistics (such as document lengths) and creates a dictionary mapping document names to required lengths. \n", + " - **Parameters**: \n", + " - `dataset`: The dataset being used. \n", + " - `istrain`: Boolean indicating whether the dataset is for training. \n", + " - **Returns**: \n", + " - A dictionary mapping document names to required lengths. \n", + " - **Usage**: \n", + " The function reads from a file located at `/content/drive/MyDrive/IN-Abs/test-data/stats-IN-test.txt` and processes its content into a dictionary.\n", + "\n", + "3. **nest_sentencesV2(document, chunk_length)** \n", + " Splits a document into chunks, where each chunk is a list of sentences. Each chunk's length does not exceed the specified `chunk_length`. \n", + " - **Parameters**: \n", + " - `document`: The input document to be chunked. \n", + " - `chunk_length`: The maximum length of each chunk, in terms of the number of words. \n", + " - **Returns**: \n", + " - A list of chunks, where each chunk is a list of sentences. \n", + "\n", + " - **Usage**: \n", + " This function helps in breaking down long documents into smaller, manageable pieces that can be processed more efficiently by the model.\n", + "\n", + "4. **nest_sentences(document, chunk_length)** \n", + " Similar to `nest_sentencesV2`, but instead of returning a list of sentences for each chunk, it joins sentences into a single string per chunk. \n", + " - **Parameters**: \n", + " - `document`: The input document to be chunked. \n", + " - `chunk_length`: The maximum length of each chunk, in terms of the number of words. \n", + " - **Returns**: \n", + " - A list of chunks, where each chunk is a string containing a sequence of sentences. \n", + "\n", + " - **Usage**: \n", + " This function is useful when you need each chunk as a single text block, rather than a list of individual sentences, for further processing or summarization tasks.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d6YdeZKYkcB", + "metadata": { + "id": "2d6YdeZKYkcB" + }, + "outputs": [], + "source": [ + "import glob\n", + "from nltk import tokenize\n", + "import nltk\n", + "import transformers\n", + "from torch.utils.data import DataLoader, TensorDataset, random_split, RandomSampler, Dataset\n", + "import pandas as pd\n", + "import numpy as np\n", + "import torch.nn.functional as F\n", + "import torch\n", + "\n", + "def get_summary_data(dataset, train):\n", + " '''\n", + " function to get names, documents, and summaries\n", + "\n", + " change the path variable to the path of the dataset\n", + " '''\n", + " path = \"/content/drive/MyDrive/IN-Abs/test-data/judgement\"\n", + " all_files = glob.glob(path + \"/*.txt\")\n", + " data_source = []\n", + " names = []\n", + " for filename in all_files:\n", + " with open(filename, 'r') as f:\n", + " p = filename.rfind(\"/\")\n", + " names.append(filename[p+1:])\n", + " a = f.read()\n", + " data_source.append(a)\n", + " path = \"/content/drive/MyDrive/IN-Abs/test-data/summary\"\n", + " all_files = glob.glob(path + \"/*.txt\")\n", + " data_summary = []\n", + " for filename in all_files:\n", + " with open(filename, 'r') as f:\n", + " a = f.read()\n", + " l = len(a)\n", + " data_summary.append(a)\n", + "\n", + " return names, data_source, data_summary\n", + "\n", + "def get_req_len_dict(dataset, istrain):\n", + " f = open(\"/content/drive/MyDrive/IN-Abs/test-data/stats-IN-test.txt\", \"r\")\n", + " a = (f.read())\n", + " print(\"File Content:\", a) # Debug: Print file content\n", + " a = a.split(\"\\n\")\n", + " dict_names = {}\n", + " for i in a:\n", + " b = i.split(\"\t\")\n", + " print(\"Split Line:\", b) # Debug: Print split line\n", + " try:\n", + " tp = int(b[2])\n", + " dict_names[b[0]] = tp\n", + " except Exception as e:\n", + " print(\"Error processing line:\", i, \"Error:\", e) # Debug: Print errors\n", + " return dict_names\n", + "\n", + "def nest_sentencesV2(document,chunk_length):\n", + " '''\n", + " function to chunk a document\n", + " input: document - Input document\n", + " chunk_length - chunk length\n", + " output: list of chunks. Each chunk is a list of sentences.\n", + " '''\n", + " nested = []\n", + " sent = []\n", + " length = 0\n", + " for sentence in nltk.sent_tokenize(document):\n", + " length += len(sentence.split(\" \"))\n", + " if length < chunk_length:\n", + " sent.append(sentence)\n", + " else:\n", + " nested.append(sent)\n", + " sent = []\n", + " sent.append(sentence)\n", + " length = 0\n", + " if len(sent)>0:\n", + " nested.append(sent)\n", + " return nested\n", + "\n", + "def nest_sentences(document,chunk_length):\n", + " '''\n", + " function to chunk a document\n", + " input: document - Input document\n", + " chunk_length - chunk length\n", + " output: list of chunks. Each chunk is a string.\n", + " '''\n", + " nested = []\n", + " sent = []\n", + " length = 0\n", + " for sentence in nltk.sent_tokenize(document):\n", + " length += len(sentence.split(\" \"))\n", + " if length < chunk_length:\n", + " sent.append(sentence)\n", + " else:\n", + " nested.append(\" \".join(sent))\n", + " sent = []\n", + " sent.append(sentence)\n", + " length = 0\n", + " if len(sent)>0:\n", + " nested.append(\" \".join(sent))\n", + " return nested\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "###OPTIONAL\n", + "Automate the process to format 'stats' file so that it can be parse easily (do if necessary)" + ], + "metadata": { + "id": "7gB9hx3Jpber" + }, + "id": "7gB9hx3Jpber" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad7a3c99", + "metadata": { + "id": "ad7a3c99" + }, + "outputs": [], + "source": [ + "import sys\n", + "import transformers\n", + "import pandas as pd\n", + "import numpy as np\n", + "import glob\n", + "import nltk\n", + "import torch\n", + "import math\n", + "import random\n", + "import re\n", + "import argparse\n", + "import os\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "BOIsVnamep1h", + "metadata": { + "id": "BOIsVnamep1h" + }, + "outputs": [], + "source": [ + "import re\n", + "\n", + "def generate_modified_stats_file(input_file_path, output_file_path):\n", + " with open(input_file_path, 'r') as infile, open(output_file_path, 'w') as outfile:\n", + " for line in infile:\n", + " # Split the line by one or more occurrences of tabs or spaces\n", + " parts = re.split(r'[\\t ]+', line.strip())\n", + " # Check if the line has at least 3 parts (filename, {some_value}, required_length)\n", + " if len(parts) >= 3:\n", + " try:\n", + " # Attempt to convert the last part to an integer\n", + " required_length = int(parts[-1])\n", + " # Join the parts back together with tabs, ensuring the last part is an integer\n", + " modified_line = \"\\t\".join(parts[:-1]) + \"\\t\" + str(required_length) + \"\\n\"\n", + " outfile.write(modified_line)\n", + " except ValueError:\n", + " print(f\"Skipping line: {line.strip()} - Could not convert {parts[-1]} to integer\")\n", + "\n", + "# Call the function to generate the modified file\n", + "input_file_path = '/content/drive/MyDrive/IN-Abs/test-data/stats-IN-test.txt'\n", + "output_file_path = '/content/drive/MyDrive/IN-Abs/test-data/modified_stats-IN-test.txt' # Choose your desired output path\n", + "generate_modified_stats_file(input_file_path, output_file_path)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "###Load the Fine tuned Model, Tokenizer and Dataset\n" + ], + "metadata": { + "id": "HH_VWL4Lptb3" + }, + "id": "HH_VWL4Lptb3" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "383b92c3", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "383b92c3", + "outputId": "76095c85-f799-43da-d14b-f12376100c78" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "100\n", + "100\n", + "100\n", + "File Content: 4963.txt\t1326\t596\t0.45\t40\n", + "5994.txt\t6170\t1048\t0.17\t198\n", + "7130.txt\t6356\t1374\t0.22\t186\n", + "6118.txt\t2216\t569\t0.26\t95\n", + "4860.txt\t2440\t826\t0.34\t51\n", + "660.txt\t14141\t507\t0.04\t444\n", + "3436.txt\t1748\t322\t0.18\t57\n", + "6276.txt\t3699\t599\t0.16\t105\n", + "2727.txt\t1525\t316\t0.21\t52\n", + "3356.txt\t1957\t354\t0.18\t83\n", + "1531.txt\t3138\t514\t0.16\t94\n", + "2609.txt\t2380\t353\t0.15\t88\n", + "1522.txt\t3210\t272\t0.08\t93\n", + "2392.txt\t2554\t480\t0.19\t88\n", + "1378.txt\t2281\t205\t0.09\t78\n", + "4071.txt\t2508\t662\t0.26\t75\n", + "3168.txt\t3271\t736\t0.23\t91\n", + "232.txt\t29144\t1872\t0.06\t770\n", + "4568.txt\t9353\t978\t0.1\t278\n", + "2440.txt\t5231\t1166\t0.22\t163\n", + "2913.txt\t2037\t488\t0.24\t88\n", + "1697.txt\t12801\t1389\t0.11\t405\n", + "3844.txt\t3379\t1448\t0.43\t103\n", + "5364.txt\t15416\t3302\t0.21\t409\n", + "3542.txt\t2344\t266\t0.11\t91\n", + "266.txt\t6510\t410\t0.06\t123\n", + "3210.txt\t3780\t603\t0.16\t132\n", + "1974.txt\t2768\t529\t0.19\t82\n", + "6003.txt\t6554\t1571\t0.24\t161\n", + "5141.txt\t3595\t919\t0.26\t108\n", + "2207.txt\t2309\t717\t0.31\t74\n", + "2593.txt\t2836\t630\t0.22\t80\n", + "1195.txt\t4389\t531\t0.12\t155\n", + "1406.txt\t2165\t410\t0.19\t68\n", + "2627.txt\t2894\t752\t0.26\t72\n", + "6270.txt\t2090\t421\t0.2\t61\n", + "3924.txt\t2858\t769\t0.27\t134\n", + "6778.txt\t39753\t10008\t0.25\t1166\n", + "2124.txt\t2637\t163\t0.06\t109\n", + "78.txt\t2101\t398\t0.19\t64\n", + "4316.txt\t7014\t1720\t0.25\t251\n", + "1778.txt\t1515\t468\t0.31\t48\n", + "314.txt\t2914\t258\t0.09\t84\n", + "690.txt\t4136\t1066\t0.26\t124\n", + "1789.txt\t2149\t346\t0.16\t73\n", + "2122.txt\t13858\t3873\t0.28\t482\n", + "2796.txt\t1334\t479\t0.36\t58\n", + "6852.txt\t1784\t669\t0.38\t68\n", + "2649.txt\t3139\t466\t0.15\t99\n", + "6728.txt\t863\t436\t0.51\t33\n", + "2052.txt\t3630\t746\t0.21\t215\n", + "5266.txt\t19110\t6079\t0.32\t680\n", + "5248.txt\t3982\t1089\t0.27\t133\n", + "2304.txt\t4379\t855\t0.2\t176\n", + "6881.txt\t3067\t854\t0.28\t68\n", + "6668.txt\t1447\t471\t0.33\t55\n", + "3531.txt\t2933\t624\t0.21\t85\n", + "415.txt\t3431\t305\t0.09\t107\n", + "3019.txt\t1555\t274\t0.18\t57\n", + "2248.txt\t3904\t569\t0.15\t138\n", + "715.txt\t1378\t249\t0.18\t39\n", + "5142.txt\t2337\t600\t0.26\t108\n", + "4938.txt\t1730\t901\t0.52\t60\n", + "2065.txt\t5521\t616\t0.11\t167\n", + "4641.txt\t7113\t1457\t0.2\t176\n", + "380.txt\t1891\t303\t0.16\t56\n", + "7109.txt\t2093\t623\t0.3\t61\n", + "5707.txt\t3980\t1160\t0.29\t102\n", + "5538.txt\t4481\t776\t0.17\t144\n", + "1762.txt\t4953\t552\t0.11\t134\n", + "652.txt\t6270\t246\t0.04\t181\n", + "496.txt\t4930\t429\t0.09\t199\n", + "5397.txt\t2232\t752\t0.34\t79\n", + "4782.txt\t3527\t726\t0.21\t113\n", + "2657.txt\t2456\t432\t0.18\t77\n", + "5937.txt\t512\t241\t0.47\t13\n", + "4807.txt\t1001\t548\t0.55\t37\n", + "4451.txt\t1575\t578\t0.37\t58\n", + "1181.txt\t3510\t321\t0.09\t119\n", + "6245.txt\t649\t212\t0.33\t25\n", + "3292.txt\t1665\t577\t0.35\t53\n", + "4917.txt\t10828\t3299\t0.3\t278\n", + "6622.txt\t8196\t1169\t0.14\t209\n", + "1329.txt\t3083\t343\t0.11\t91\n", + "3893.txt\t2103\t953\t0.45\t86\n", + "5888.txt\t575\t226\t0.39\t26\n", + "6521.txt\t10411\t1646\t0.16\t314\n", + "3602.txt\t2063\t421\t0.2\t83\n", + "6647.txt\t6023\t1274\t0.21\t136\n", + "6157.txt\t8483\t1906\t0.22\t262\n", + "6413.txt\t1432\t586\t0.41\t61\n", + "5471.txt\t3911\t1078\t0.28\t137\n", + "3442.txt\t5358\t666\t0.12\t197\n", + "362.txt\t6457\t1038\t0.16\t173\n", + "2035.txt\t4802\t553\t0.12\t177\n", + "5597.txt\t3650\t323\t0.09\t67\n", + "2256.txt\t6713\t674\t0.1\t190\n", + "5861.txt\t5550\t1062\t0.19\t238\n", + "4480.txt\t7320\t2288\t0.31\t219\n", + "784.txt\t3834\t463\t0.12\t122\n", + "\n", + "Split Line: ['4963.txt', '1326', '596', '0.45', '40']\n", + "Split Line: ['5994.txt', '6170', '1048', '0.17', '198']\n", + "Split Line: ['7130.txt', '6356', '1374', '0.22', '186']\n", + "Split Line: ['6118.txt', '2216', '569', '0.26', '95']\n", + "Split Line: ['4860.txt', '2440', '826', '0.34', '51']\n", + "Split Line: ['660.txt', '14141', '507', '0.04', '444']\n", + "Split Line: ['3436.txt', '1748', '322', '0.18', '57']\n", + "Split Line: ['6276.txt', '3699', '599', '0.16', '105']\n", + "Split Line: ['2727.txt', '1525', '316', '0.21', '52']\n", + "Split Line: ['3356.txt', '1957', '354', '0.18', '83']\n", + "Split Line: ['1531.txt', '3138', '514', '0.16', '94']\n", + "Split Line: ['2609.txt', '2380', '353', '0.15', '88']\n", + "Split Line: ['1522.txt', '3210', '272', '0.08', '93']\n", + "Split Line: ['2392.txt', '2554', '480', '0.19', '88']\n", + "Split Line: ['1378.txt', '2281', '205', '0.09', '78']\n", + "Split Line: ['4071.txt', '2508', '662', '0.26', '75']\n", + "Split Line: ['3168.txt', '3271', '736', '0.23', '91']\n", + "Split Line: ['232.txt', '29144', '1872', '0.06', '770']\n", + "Split Line: ['4568.txt', '9353', '978', '0.1', '278']\n", + "Split Line: ['2440.txt', '5231', '1166', '0.22', '163']\n", + "Split Line: ['2913.txt', '2037', '488', '0.24', '88']\n", + "Split Line: ['1697.txt', '12801', '1389', '0.11', '405']\n", + "Split Line: ['3844.txt', '3379', '1448', '0.43', '103']\n", + "Split Line: ['5364.txt', '15416', '3302', '0.21', '409']\n", + "Split Line: ['3542.txt', '2344', '266', '0.11', '91']\n", + "Split Line: ['266.txt', '6510', '410', '0.06', '123']\n", + "Split Line: ['3210.txt', '3780', '603', '0.16', '132']\n", + "Split Line: ['1974.txt', '2768', '529', '0.19', '82']\n", + "Split Line: ['6003.txt', '6554', '1571', '0.24', '161']\n", + "Split Line: ['5141.txt', '3595', '919', '0.26', '108']\n", + "Split Line: ['2207.txt', '2309', '717', '0.31', '74']\n", + "Split Line: ['2593.txt', '2836', '630', '0.22', '80']\n", + "Split Line: ['1195.txt', '4389', '531', '0.12', '155']\n", + "Split Line: ['1406.txt', '2165', '410', '0.19', '68']\n", + "Split Line: ['2627.txt', '2894', '752', '0.26', '72']\n", + "Split Line: ['6270.txt', '2090', '421', '0.2', '61']\n", + "Split Line: ['3924.txt', '2858', '769', '0.27', '134']\n", + "Split Line: ['6778.txt', '39753', '10008', '0.25', '1166']\n", + "Split Line: ['2124.txt', '2637', '163', '0.06', '109']\n", + "Split Line: ['78.txt', '2101', '398', '0.19', '64']\n", + "Split Line: ['4316.txt', '7014', '1720', '0.25', '251']\n", + "Split Line: ['1778.txt', '1515', '468', '0.31', '48']\n", + "Split Line: ['314.txt', '2914', '258', '0.09', '84']\n", + "Split Line: ['690.txt', '4136', '1066', '0.26', '124']\n", + "Split Line: ['1789.txt', '2149', '346', '0.16', '73']\n", + "Split Line: ['2122.txt', '13858', '3873', '0.28', '482']\n", + "Split Line: ['2796.txt', '1334', '479', '0.36', '58']\n", + "Split Line: ['6852.txt', '1784', '669', '0.38', '68']\n", + "Split Line: ['2649.txt', '3139', '466', '0.15', '99']\n", + "Split Line: ['6728.txt', '863', '436', '0.51', '33']\n", + "Split Line: ['2052.txt', '3630', '746', '0.21', '215']\n", + "Split Line: ['5266.txt', '19110', '6079', '0.32', '680']\n", + "Split Line: ['5248.txt', '3982', '1089', '0.27', '133']\n", + "Split Line: ['2304.txt', '4379', '855', '0.2', '176']\n", + "Split Line: ['6881.txt', '3067', '854', '0.28', '68']\n", + "Split Line: ['6668.txt', '1447', '471', '0.33', '55']\n", + "Split Line: ['3531.txt', '2933', '624', '0.21', '85']\n", + "Split Line: ['415.txt', '3431', '305', '0.09', '107']\n", + "Split Line: ['3019.txt', '1555', '274', '0.18', '57']\n", + "Split Line: ['2248.txt', '3904', '569', '0.15', '138']\n", + "Split Line: ['715.txt', '1378', '249', '0.18', '39']\n", + "Split Line: ['5142.txt', '2337', '600', '0.26', '108']\n", + "Split Line: ['4938.txt', '1730', '901', '0.52', '60']\n", + "Split Line: ['2065.txt', '5521', '616', '0.11', '167']\n", + "Split Line: ['4641.txt', '7113', '1457', '0.2', '176']\n", + "Split Line: ['380.txt', '1891', '303', '0.16', '56']\n", + "Split Line: ['7109.txt', '2093', '623', '0.3', '61']\n", + "Split Line: ['5707.txt', '3980', '1160', '0.29', '102']\n", + "Split Line: ['5538.txt', '4481', '776', '0.17', '144']\n", + "Split Line: ['1762.txt', '4953', '552', '0.11', '134']\n", + "Split Line: ['652.txt', '6270', '246', '0.04', '181']\n", + "Split Line: ['496.txt', '4930', '429', '0.09', '199']\n", + "Split Line: ['5397.txt', '2232', '752', '0.34', '79']\n", + "Split Line: ['4782.txt', '3527', '726', '0.21', '113']\n", + "Split Line: ['2657.txt', '2456', '432', '0.18', '77']\n", + "Split Line: ['5937.txt', '512', '241', '0.47', '13']\n", + "Split Line: ['4807.txt', '1001', '548', '0.55', '37']\n", + "Split Line: ['4451.txt', '1575', '578', '0.37', '58']\n", + "Split Line: ['1181.txt', '3510', '321', '0.09', '119']\n", + "Split Line: ['6245.txt', '649', '212', '0.33', '25']\n", + "Split Line: ['3292.txt', '1665', '577', '0.35', '53']\n", + "Split Line: ['4917.txt', '10828', '3299', '0.3', '278']\n", + "Split Line: ['6622.txt', '8196', '1169', '0.14', '209']\n", + "Split Line: ['1329.txt', '3083', '343', '0.11', '91']\n", + "Split Line: ['3893.txt', '2103', '953', '0.45', '86']\n", + "Split Line: ['5888.txt', '575', '226', '0.39', '26']\n", + "Split Line: ['6521.txt', '10411', '1646', '0.16', '314']\n", + "Split Line: ['3602.txt', '2063', '421', '0.2', '83']\n", + "Split Line: ['6647.txt', '6023', '1274', '0.21', '136']\n", + "Split Line: ['6157.txt', '8483', '1906', '0.22', '262']\n", + "Split Line: ['6413.txt', '1432', '586', '0.41', '61']\n", + "Split Line: ['5471.txt', '3911', '1078', '0.28', '137']\n", + "Split Line: ['3442.txt', '5358', '666', '0.12', '197']\n", + "Split Line: ['362.txt', '6457', '1038', '0.16', '173']\n", + "Split Line: ['2035.txt', '4802', '553', '0.12', '177']\n", + "Split Line: ['5597.txt', '3650', '323', '0.09', '67']\n", + "Split Line: ['2256.txt', '6713', '674', '0.1', '190']\n", + "Split Line: ['5861.txt', '5550', '1062', '0.19', '238']\n", + "Split Line: ['4480.txt', '7320', '2288', '0.31', '219']\n", + "Split Line: ['784.txt', '3834', '463', '0.12', '122']\n", + "Split Line: ['']\n", + "Error processing line: Error: list index out of range\n", + "{'4963.txt': 596, '5994.txt': 1048, '7130.txt': 1374, '6118.txt': 569, '4860.txt': 826, '660.txt': 507, '3436.txt': 322, '6276.txt': 599, '2727.txt': 316, '3356.txt': 354, '1531.txt': 514, '2609.txt': 353, '1522.txt': 272, '2392.txt': 480, '1378.txt': 205, '4071.txt': 662, '3168.txt': 736, '232.txt': 1872, '4568.txt': 978, '2440.txt': 1166, '2913.txt': 488, '1697.txt': 1389, '3844.txt': 1448, '5364.txt': 3302, '3542.txt': 266, '266.txt': 410, '3210.txt': 603, '1974.txt': 529, '6003.txt': 1571, '5141.txt': 919, '2207.txt': 717, '2593.txt': 630, '1195.txt': 531, '1406.txt': 410, '2627.txt': 752, '6270.txt': 421, '3924.txt': 769, '6778.txt': 10008, '2124.txt': 163, '78.txt': 398, '4316.txt': 1720, '1778.txt': 468, '314.txt': 258, '690.txt': 1066, '1789.txt': 346, '2122.txt': 3873, '2796.txt': 479, '6852.txt': 669, '2649.txt': 466, '6728.txt': 436, '2052.txt': 746, '5266.txt': 6079, '5248.txt': 1089, '2304.txt': 855, '6881.txt': 854, '6668.txt': 471, '3531.txt': 624, '415.txt': 305, '3019.txt': 274, '2248.txt': 569, '715.txt': 249, '5142.txt': 600, '4938.txt': 901, '2065.txt': 616, '4641.txt': 1457, '380.txt': 303, '7109.txt': 623, '5707.txt': 1160, '5538.txt': 776, '1762.txt': 552, '652.txt': 246, '496.txt': 429, '5397.txt': 752, '4782.txt': 726, '2657.txt': 432, '5937.txt': 241, '4807.txt': 548, '4451.txt': 578, '1181.txt': 321, '6245.txt': 212, '3292.txt': 577, '4917.txt': 3299, '6622.txt': 1169, '1329.txt': 343, '3893.txt': 953, '5888.txt': 226, '6521.txt': 1646, '3602.txt': 421, '6647.txt': 1274, '6157.txt': 1906, '6413.txt': 586, '5471.txt': 1078, '3442.txt': 666, '362.txt': 1038, '2035.txt': 553, '5597.txt': 323, '2256.txt': 674, '5861.txt': 1062, '4480.txt': 2288, '784.txt': 463}\n" + ] + } + ], + "source": [ + "#Reading the test documents\n", + "names, data_source, data_summary = get_summary_data(dataset, \"test\")\n", + "print(len(names))\n", + "print(len(data_source))\n", + "print(len(data_summary))\n", + "dict_names = get_req_len_dict(dataset, \"test\")\n", + "print(dict_names)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea21bb4b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 333, + "referenced_widgets": [ + "8c3808aa043a4a119a3b8ff6d4c652a0", + "1c01e5e3541e42fc924d56c850c1fd7a", + "98f54d388e0049c799abab8086379184", + "964b0af8ada24428be7cbc2f47d2daf2", + "2dd4ff85bede4919bfb81d5c7d8deade", + "7782d4103d214ccfb151d4d24bcbd5e0", + "14b46b68ec4d484db790558be29a43dc", + "eb6634f0ace6428ab72395971721396c", + "1d1c140fb4ac43f0adc8a7a474a49df7", + "366868c16c234100821cdf0b759b4622", + "52d42a6610da461aa2ac14b36366d72a", + "6e98d99396a2409e86a8a81a683722c0", + "0f740c465bde401e8f1800523800cffa", + "fc4b4e5a14354d1ba1f73aabddcd8ef1", + "0ab716a8d16446ca87a092ad88159f56", + "169ca247ffdb41d8addac097eec53f97", + "dc662f7657d54865aeaec4e0c8df90f2", + "52352fb165d8415ca5ee90ab5d5f2889", + "0a60f92104624565b9ab89b900bf3778", + "e0216ae8d59141abbf6b0b8082b45822", + "435ec7a51e354cdc893b595398d7d0c6", + "c8066e01d8834c78aa113c9e3d7bb65f", + "822d3c927d5945f1bb9b8042612ef6db", + "e83d4b01d01b499b9b630145ed76dbb7", + "6c4a55f1c63648ed942c0c4c27f90227", + "4cdadd2b55024946937d6cbe3d996a8c", + "55ff5f00dff0418c8f26cdc08a103fb8", + "7042ee1e945740faafb2c2467fbb6a8a", + "07a5b88ed952416f9aa5f3d65d5ee177", + "bb7f5b5206904d9bbdc0b02287644ddb", + "2f865fc0fd444113a4bbe0b398ddd697", + "348e4d318c7348f384d5832865e9038e", + "8f2bdb1c1d3441fe9b5978200f43c2c3", + "2ba4765ae1d2447eb616bfaa34136333", + "806a523f405b4e849318c53e3b95ba10", + "7d87bbbc868047b1b1ccd5180ed88b58", + "8f7ca2139e9d42d08620c7e4d7b9683b", + "4c5d112f264245b6ab3144c5b79f5c08", + "b8f1d8fe294b480e93b277a0de88dcd0", + "d20e6f48e433475486d2f1d78e33c7df", + "80b8f38d87b94ef4b789c151a2d6e042", + "ce5c2cb8f0a34e67a97847240e86a069", + "c917d17a398e40dfb69b9552f1d4f8b0", + "6cea93d648df48c78a8d1079483dc8dc", + "66e02b56bc3e4287bc67a1c115af13c9", + "33aa019516bd485cb6a20b4807dc5f11", + "ea239fe94880454f9b803919f10511b8", + "369382fbba664f958bc481d5a2789cdd", + "ff2f439657894f49bfef54a3c97a9ea3", + "82319e90610f45cc9b60a4068f3dd841", + "7f71ed4786244b28b2d5087c5b1625ec", + "88208e5851b744aa80f1d0db85bd4e85", + "7ea059c260dc4cc895ab4b631fb8c0b1", + "7f0e45a4339e4b268f9c7de602811ba1", + "882d87b846c24ba5ae5140cd2a023b83", + "babeb7aabe714d9890d16db5ca6cde6d", + "9c1a7250449b4bae9bd292fcdd960e39", + "0a8f4966c9ab41d399c12e4475c40d85", + "ec3cafb48f644dcfa98b13ec5c6d4424", + "64cdb8c336194bacb1d3d99984b3eef6", + "df767a02face424aa0934178ac00a01e", + "dc35273ad9674af08309cb664c3a7f86", + "8f866d54e2354b4a9dec7920f46d0720", + "bee05404381b4fb4a0f744f2e822fba1", + "3c40a25ad8f34ae0bdeb417b1b0d91e2", + "cb02e11094d641bdbe67a57146421625" + ] + }, + "id": "ea21bb4b", + "outputId": "6142dd15-629f-45bd-a9d7-9591f0e2d8e3" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8c3808aa043a4a119a3b8ff6d4c652a0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/26.0 [00:00', '', '', '', '

', '', '']\n", + "\n", + "special_tokens_dict = {'additional_special_tokens': new_tokens}\n", + "num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)\n", + "model.resize_token_embeddings(len(tokenizer))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98713de3", + "metadata": { + "id": "98713de3" + }, + "outputs": [], + "source": [ + "#bart_model = LitModel(learning_rate = 2e-5, tokenizer = tokenizer, model = model)\n", + "\n", + "bart_model = LitModel.load_from_checkpoint(\"/content/drive/MyDrive/IN-Abs/model/output.ckpt\",\n", + " learning_rate = 2e-5, tokenizer = tokenizer, model = model)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Define the generate summary function" + ], + "metadata": { + "id": "Z5xc-Ayf18ny" + }, + "id": "Z5xc-Ayf18ny" + }, + { + "cell_type": "markdown", + "source": [ + "### Function Documentation\n", + "\n", + " - **generate_summary_gpu(nested_sentences, p=0.2)** \n", + "\n", + "---\n", + "\n", + "\n", + "\n", + "\n", + " This function generates summaries from a list of document chunks using a pre-trained BART model. The function computes the summary based on the number of words in the document. The summary's length is determined by a proportion (`p`) of the document's word count.\n", + " - **Parameters**: \n", + " - `nested_sentences`: A list of chunks (sentences or phrases) from the document to be summarized.\n", + " - `p`: A proportion that defines the number of words in the summary compared to the number of words in the document. The default value is 0.2 (20% of the document's length).\n", + " - **Returns**: \n", + " - A list of summarized sentences. Each sentence is generated by the BART model and is decoded from token IDs into readable text. \n", + " \n", + " - **Usage**: \n", + " The function uses a GPU for inference (defaulted in the code, can be changed to CPU) and employs the BART model to generate summaries for each chunk in the document. The `p` parameter allows flexibility in controlling the summary's length based on the original document's word count.\n", + "\n", + " - **Process**: \n", + " 1. For each chunk of the document, calculate the summary length based on the given proportion `p`. \n", + " 2. Tokenize the chunk and move it to the selected device (GPU). \n", + " 3. Generate the summary using the BART model's `generate()` method, adjusting the summary's length. \n", + " 4. Decode the generated token IDs into human-readable text. \n", + " 5. Return a list of all the summarized sentences.\n" + ], + "metadata": { + "id": "HhHLIgTCq7ff" + }, + "id": "HhHLIgTCq7ff" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39eae844", + "metadata": { + "id": "39eae844" + }, + "outputs": [], + "source": [ + "def generate_summary_gpu(nested_sentences,p=0.2):\n", + " '''\n", + " Function to generate summaries from the list containing chunks of the document\n", + " input: nested_sentences - chunks\n", + " p - Number of words in summaries per word in the document\n", + " output: document summary\n", + " '''\n", + " device = 'gpu' # you can select cpu\n", + " summaries = []\n", + " for nested in nested_sentences:\n", + " l = int(p * len(nested.split(\" \")))\n", + " input_tokenized = tokenizer.encode(nested, truncation=True, return_tensors='pt')\n", + " input_tokenized = input_tokenized.to(device)\n", + " summary_ids = bart_model.model.to(device).generate(input_tokenized,\n", + " length_penalty=0.01,\n", + " min_length=l-5,\n", + " max_length=l+5)\n", + " output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids]\n", + " summaries.append(output)\n", + " summaries = [sentence for sublist in summaries for sentence in sublist]\n", + " return summaries" + ] + }, + { + "cell_type": "markdown", + "source": [ + "##Generating the summaries" + ], + "metadata": { + "id": "MtjByC7l1gM3" + }, + "id": "MtjByC7l1gM3" + }, + { + "cell_type": "markdown", + "source": [ + "### Code Documentation\n", + "\n", + "1. **Installing and Importing NLTK** \n", + " - **`!pip install nltk`**: This command installs the `nltk` library, which is used for natural language processing tasks such as tokenization.\n", + " - **`import nltk`**: This imports the `nltk` library, enabling its functionality for text processing.\n", + "\n", + "2. **Downloading NLTK Resources** \n", + " - **`nltk.download('punkt_tab')`**: This command downloads the `punkt_tab` resource, which is used by NLTK for tokenization (splitting text into sentences or words).\n", + "\n", + "3. **Main Loop for Summary Generation** \n", + " The loop processes a dataset to generate summaries for each document. Below is a breakdown of the steps:\n", + " - **Iterating through documents**: \n", + " The loop processes documents from `data_source`, starting from index 16. For each document:\n", + " - The **name** and **document content** (`doc`) are extracted.\n", + " - The **word count** of the document (`input_len`) is calculated by splitting the text into words.\n", + " - The **required summary length** (`req_len`) for each document is retrieved from `dict_names`.\n", + " - The function prints information about the current document (name, word count, required length).\n", + " \n", + " - **Chunking the document**: \n", + " The `nest_sentences` function is used to divide the document into smaller chunks of sentences, each with a length of 1024 tokens.\n", + "\n", + " - **Summary Length Calculation**: \n", + " - **`l`** is calculated as the proportion of the required length per chunk.\n", + " - **`p`** represents the proportion of the document's word count that will be included in the summary.\n", + "\n", + " - **Summary Generation**: \n", + " - The `generate_summary_gpu` function is called with the chunked document and the proportion `p` to generate the summary.\n", + " - The resulting summary (`abs_summ`) is combined into a single string.\n", + "\n", + " - **Truncating the Summary**: \n", + " If the length of the generated summary exceeds the required length, it is truncated to `req_len` words to meet the summary length constraints.\n", + "\n", + " - **Saving the Summary**: \n", + " The summary is saved to a file. Each document's summary is written to a file named after the document (`name`), and the file is saved to `output_path`.\n", + "\n", + " - **Appending to `predicted_summ`**: \n", + " The summary is added to the list `predicted_summ` for further analysis or evaluation.\n", + "\n", + " - **Print Statements**: \n", + " The loop prints:\n", + " - The document index, name, word count, and required summary length.\n", + " - The proportion `p` and the final length of the summary.\n", + "\n", + "### Summary of Key Variables and Functions\n", + "\n", + "- **`names`**: A list of document names.\n", + "- **`data_source`**: A list of document content.\n", + "- **`dict_names`**: A dictionary containing the required summary length for each document.\n", + "- **`output_path`**: Path where the generated summaries are saved.\n", + "- **`generate_summary_gpu(nested_sentences, p)`**: Function that generates summaries using GPU, based on the chunks and the word proportion `p`.\n", + "- **`nest_sentences(doc, 1024)`**: Function that splits a document into smaller chunks of sentences, each having a maximum length of 1024 tokens.\n" + ], + "metadata": { + "id": "dpmYMF_9sDmC" + }, + "id": "dpmYMF_9sDmC" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bdf4344", + "metadata": { + "colab": { + "background_save": true, + "base_uri": "https://localhost:8080/" + }, + "id": "2bdf4344", + "outputId": "b7a9ebb1-3037-462f-f811-fdcd2eb90972" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.9.1)\n", + "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.7)\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.4.2)\n", + "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2024.9.11)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.6)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n", + "[nltk_data] Unzipping tokenizers/punkt_tab.zip.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16: 5266.txt - 18433 : 6079 ,0.32978896544241304\n", + "5247\n", + "17: 1778.txt - 1463 : 468 ,0.3198906356801094\n", + "427\n", + "18: 6276.txt - 3590 : 599 ,0.16685236768802228\n", + "533\n", + "19: 1329.txt - 2990 : 343 ,0.11471571906354515\n", + "309\n", + "20: 4316.txt - 6756 : 1720 ,0.2545885139135583\n", + "1482\n", + "21: 5141.txt - 3505 : 919 ,0.2621968616262482\n", + "782\n", + "22: 5597.txt - 3583 : 323 ,0.09014792073681273\n", + "283\n", + "23: 5994.txt - 5963 : 1048 ,0.17575046117725976\n", + "874\n", + "24: 4917.txt - 10531 : 3299 ,0.3132655968094198\n", + "2866\n", + "25: 4641.txt - 6934 : 1457 ,0.21012402653591\n" + ] + } + ], + "source": [ + "!pip install nltk\n", + "import nltk\n", + "\n", + "# Download the 'punkt_tab' resource\n", + "nltk.download('punkt_tab')\n", + "\n", + "\n", + "predicted_summ=[]\n", + "# main loop to generate and save summaries of each document in the test dataset\n", + "for i in range(16,len(data_source)):\n", + " name = names[i]\n", + " doc = data_source[i]\n", + " wc = doc.split(\" \")\n", + " input_len = len(wc)\n", + " req_len = dict_names[name]\n", + " print(str(i) + \": \" + name + \" - \" + str(input_len) + \" : \" + str(req_len), end = \" ,\")\n", + "\n", + " nested = nest_sentences(doc,1024)\n", + " l = int(req_len/len(nested))\n", + " p = float(req_len/input_len)\n", + " print(p)\n", + "\n", + " abs_summ = generate_summary_gpu(nested,p)\n", + "# print(abs_summ)\n", + "# break\n", + " abs_summ = \" \".join(abs_summ)\n", + " if len(abs_summ.split(\" \")) > req_len:\n", + " abs_summ = abs_summ.split(\" \")\n", + " abs_summ = abs_summ[:req_len]\n", + " abs_summ = \" \".join(abs_summ)\n", + "# print(abs_summ)\n", + "# break\n", + " print(len((abs_summ.split(\" \"))))\n", + "\n", + " path = output_path + name\n", + " file = open(path,'w')\n", + " file.write(abs_summ)\n", + " file.close()\n", + " predicted_summ.append(abs_summ)\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Load the summaries generated by the model to use for generating ROUGE metrics" + ], + "metadata": { + "id": "F-MSU44tsODA" + }, + "id": "F-MSU44tsODA" + }, + { + "cell_type": "code", + "source": [ + " import glob\n", + " path = output_path\n", + " #Get all the txt files with summaries\n", + " all_files = glob.glob(path + \"/*.txt\")\n", + "\n", + " predicted_summary = []\n", + "\n", + " #Get the summaries\n", + " for filename in all_files:\n", + " with open(filename, 'r') as f:\n", + " a = f.read()\n", + " predicted_summary.append(a)\n" + ], + "metadata": { + "id": "FUI6Q1ujxIpn" + }, + "id": "FUI6Q1ujxIpn", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### ROUGE Evaluation Metrics\n", + "\n", + "1) **ROUGE[n]-recall** = \n", + "\n", + " $$\\frac{\\text{# n-grams that appear in both R and C}}{\\text{number of n-grams in R}}$$\n", + "\n", + "\n", + "2) **ROUGE[n]-precision** = \n", + "\n", + " $$\\frac{\\text{# n-grams that appear in both R and C}}{\\text{number of n-grams in C}}$$\n", + "\n", + "\n", + "3) The **ROUGE[n]-F1 score** is then defined as:\n", + "\n", + "$$\n", + "\\text{ROUGE[n]-F1} = \\frac{2 \\times \\text{ROUGE[n]-recall} \\times \\text{ROUGE[n]-precision}}{\\text{ROUGE[n]-recall} + \\text{ROUGE[n]-precision}}\n", + "$$\n", + "\n", + "4) While **ROUGE-N** is based on the overlap of n-consecutive words in both the reference and the automatically produced text, **ROUGE-L** considers the longest common subsequence of words (LCS) — even if they aren’t consecutive, but still in order.\n", + "\n", + "5) **ROUGE-Lsum** first splits the summaries into sentences, then performs ROUGE-L calculations for each sentence individually.\n", + "\n", + "**Reference**: [Mastering ROUGE Matrix: Your Guide to Large Language Model Evaluation for Summarization with Examples](https://dev.to/aws-builders/mastering-rouge-matrix-your-guide-to-large-language-model-evaluation-for-summarization-with-examples-jjg)\n" + ], + "metadata": { + "id": "p_APF8eOtEVi" + }, + "id": "p_APF8eOtEVi" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "BsUYSV76hFS-", + "metadata": { + "id": "BsUYSV76hFS-", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "56e0a8ca-8b5f-4efa-d4e5-40c10867feda" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: rouge-score==0.1.2 in /usr/local/lib/python3.10/dist-packages (0.1.2)\n", + "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from rouge-score==0.1.2) (1.4.0)\n", + "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from rouge-score==0.1.2) (3.9.1)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rouge-score==0.1.2) (1.26.4)\n", + "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from rouge-score==0.1.2) (1.16.0)\n", + "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score==0.1.2) (8.1.7)\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score==0.1.2) (1.4.2)\n", + "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score==0.1.2) (2024.9.11)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk->rouge-score==0.1.2) (4.66.6)\n", + "{'rouge1': 0.3652336980728314, 'rouge2': 0.10150773473356164, 'rougeL': 0.18457206231026227, 'rougeLsum': 0.34250539252031587}\n" + ] + } + ], + "source": [ + "!pip install rouge-score==0.1.2 # Install the rouge-score library\n", + "\n", + "from rouge_score import rouge_scorer\n", + "\n", + "def calculate_rouge_scores(predicted_summaries, reference_summaries):\n", + " \"\"\"Calculates all types of ROUGE scores.\n", + "\n", + " Args:\n", + " predicted_summaries: A list of predicted summaries.\n", + " reference_summaries: A list of reference summaries.\n", + "\n", + " Returns:\n", + " A dictionary containing the average ROUGE scores for all types.\n", + " \"\"\"\n", + "\n", + " scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL', 'rougeLsum'], use_stemmer=True)\n", + " all_scores = {'rouge1': [], 'rouge2': [], 'rougeL': [], 'rougeLsum': []}\n", + "\n", + " for predicted, reference in zip(predicted_summaries, reference_summaries):\n", + " scores = scorer.score(reference, predicted) # Note: reference first, then predicted\n", + " for rouge_type in all_scores:\n", + " all_scores[rouge_type].append(scores[rouge_type].fmeasure) # Store F1-scores\n", + "\n", + " # Calculate average scores\n", + " average_scores = {rouge_type: sum(scores) / len(scores) for rouge_type, scores in all_scores.items()}\n", + "\n", + " return average_scores\n", + "\n", + "# Example usage (assuming you have your summaries loaded):\n", + "predicted_summaries = predicted_summary\n", + "reference_summaries = data_summary[:len(predicted_summary)]\n", + "\n", + "rouge_scores = calculate_rouge_scores(predicted_summaries, reference_summaries)\n", + "print(rouge_scores) # Print the average ROUGE scores" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### ROUGE Scores\n", + "\n", + "The ROUGE scores for the model are as follows:\n", + "\n", + "- **ROUGE-1**: 0.3652\n", + "- **ROUGE-2**: 0.1015\n", + "- **ROUGE-L**: 0.1846\n", + "- **ROUGE-Lsum**: 0.3425\n" + ], + "metadata": { + "id": "8yPEu26qtbqu" + }, + "id": "8yPEu26qtbqu" + } + ], + "metadata": { + "colab": { + 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