Text Summarization Model using Langchain and LLAMA2 Welcome to the Text Summarization Model repository! This project leverages the power of Langchain and LLAMA2 to create an efficient and effective text summarization solution. This README file provides an overview of the project, installation instructions, and usage guidelines.

Table of Contents Introduction Installation Usage Contributing

Introduction Text summarization is the process of distilling the main ideas and important information from a piece of text, allowing users to quickly grasp its key points. This project combines the capabilities of Langchain, a language modeling tool, and LLAMA2, a summarization framework, to build a text summarization model that can handle a variety of input texts and generate concise and coherent summaries.

Installation To get started with using the text summarization model, follow these steps:

Clone the Repository

Follow these steps to use the text summarization model:

Input Data: Prepare the text data you want to summarize. This could be a single document, a paragraph, or a series of sentences.

Run the Model: Use the provided scripts to run the text summarization model. The specifics will depend on the implementation details of Langchain and LLAMA2. Generally, you will need to load the pre-trained models, process the input text, and generate a summary.

Output: The model will generate a summarized version of the input text. This summary should capture the essential information and key points from the original text.

Contributing We welcome contributions to enhance the capabilities of this text summarization model. If you'd like to contribute, please follow these steps:

Fork the repository. Create a new branch for your feature or bug fix: git checkout -b feature-name. Make your modifications and commit changes: git commit -am 'Add feature'. Push to the branch: git push origin feature-name. Create a pull request outlining your changes.

special thanks to Rithesh for the concepts.