| # Custom BART Model for Text Summarization | |
| This project involves fine-tuning a BART model for text summarization tasks. The model was trained on custom data, and the resulting model is saved locally and uploaded to Hugging Face for further use. | |
| ## Table of Contents | |
| - [Overview](#overview) | |
| - [Installation](#installation) | |
| - [Usage](#usage) | |
| - [Training the Model](#training-the-model) | |
| - [Saving and Uploading the Model](#saving-and-uploading-the-model) | |
| - [Generating Summaries](#generating-summaries) | |
| - [Contributing](#contributing) | |
| - [License](#license) | |
| ## Overview | |
| This project fine-tunes a BART model (`facebook/bart-base`) on custom summarization tasks. After training, the model can generate summaries for input text, which can be used for various applications like news article summarization, report generation, etc. | |
| ## Installation | |
| To get started, ensure you have Python installed (preferably Python 3.8 or above). Install the required dependencies using the following command: | |
| ```bash | |
| pip install transformers torch huggingface_hub | |
| Usage | |
| Loading the Model and Tokenizer | |
| Ensure you have saved your trained model and tokenizer in the ./custom_bart_model directory. The code snippet below demonstrates how to load the model and generate summaries based on user input. | |
| from transformers import BartTokenizer, BartForConditionalGeneration | |
| import torch | |
| # Load the model and tokenizer | |
| model = "rohansb10/summary" | |
| tokenizer = BartTokenizer.from_pretrained(model) | |
| model = BartForConditionalGeneration.from_pretrained(model) | |
| # Move model to the appropriate device | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| model.eval() | |
| def generate_summary(input_text): | |
| inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding="max_length", max_length=512).to(device) | |
| with torch.no_grad(): | |
| summary_ids = model.generate(inputs["input_ids"], max_length=128, num_beams=4, early_stopping=True) | |
| output_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| return output_text | |
| user_input = input("Enter your text: ") | |
| output = generate_summary(user_input) | |
| print("\nModel Output:") | |
| print(output) | |
| Training the Model | |
| The training process involves loading the pre-trained BART model and tokenizer, preparing a custom dataset, and training the model using the PyTorch DataLoader. Refer to the train_model() and evaluate_model() functions in the code for the detailed implementation. | |
| Feel free to modify any section to better fit your project’s needs! |