Create README.md

README.md

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-license: mit
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+# 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.
+
+Saving and Uploading the Model
+After training, save your model and tokenizer using:
+
+# Save the model and tokenizer
+save_directory = "./custom_bart_model"
+custom_model.model.save_pretrained(save_directory)
+tokenizer.save_pretrained(save_directory)
+Upload the saved model to Hugging Face Hub:
+
+
+from huggingface_hub import notebook_login, create_repo, upload_folder
+
+# Log in to Hugging Face
+notebook_login()
+
+# Upload to Hugging Face Hub
+upload_folder(
+    folder_path=save_directory,
+    path_in_repo="",
+    repo_id="your_hf_username/custom-bart-finetuned",
+    repo_type="model",
+)
+Generating Summaries
+The model can be used to generate summaries by feeding it input text. Adjust the parameters in the generate_summary() function to tweak the length, beam size, and other settings.
+
+Contributing
+Contributions are welcome! If you have any suggestions or improvements, please submit a pull request.
+
+License
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+
+### Key Points:
+- **Overview**: Describes what the project is about.
+- **Installation**: Lists dependencies and how to install them.
+- **Usage**: Provides instructions on how to load and use the model.
+- **Training, Saving, and Uploading**: Steps to train, save, and upload the model.
+- **Contributing and License**: Information on contributing and licensing.
+
+Feel free to modify any section to better fit your project’s needs!