github: https://github.com/SujalNeupane9 linkedin: https://www.linkedin.com/in/sujal-neupane-2a9a2b210/

Summarization

This project is a machine learning pipeline for natural language processing tasks. It contains a set of scripts and modules that allow you to train and evaluate various models on your own data.

Description

This repository contains a sample code with aim to demonstrate how to train a model for text summarization. The main focus is to show a basic template on how to create a structure from which we can smoothly deploy the model as well as perform inference on the trained model.

Framework used:

• PyTorch
• Transformers

Project Structure

• pipeline This directory contains the code for the main data pipeline.

• training_pipeline.py: Code for the training pipeline.
• inference_pipeline.py: Code for the inference pipeline.

• steps This directory includes various steps involved in the data pipeline.

• evaluation.py: Code for evaluating the model.
• ingest_data.py: Code for ingesting data into the pipeline.
  • preprocess.py: Data preprocessing code.
  • model_train.py: Model training code.

• utils This directory contains utility functions used throughout the project.

  • utils.py: General utility functions.

• run_pipeline.py This script is the entry point for running the entire data pipeline.

• Dockerfile The Dockerfile for creating a Docker image for this project.

• requirements.txt List of Python packages required for running the project. Install them using:

Demo

I have already trained a t5-base model and uploaded it into HuggingFace. The streamlit demo can be accessed from following link. https://summarization-2s9wr7njxcgpeuuraprip5.streamlit.app/

License

This project is licensed under the MIT License - see the LICENSE file for details.