This repository contains a summarizer model for the TURB Model that uses the FLAN-T5 transformer model and fine-tunes with the Multi-News dataset. The aim of the summarizer is to condense a large text into a brief summary without losing its key points.
To start using the summarizer model, you can refer to the Python notebook provided in the repository. The notebook contains detailed instructions on how to set up the environment, install the required libraries, and run the model.
The notebook also includes configuration options that allow you to customize the summarizer's performance to suit your needs. You can adjust the number of summaries generated, the length of each summary, and the threshold for summary relevance.
The model is fine-tuned using the Multi-News dataset, which contains news articles and their corresponding summaries. The dataset is already included in the repository, and you can use it to train the model further or test its performance.
Additionally, the notebook includes instructions on how to upload the fine-tuned summarizer model to Hugging Face's model hub. This will allow others to easily use and integrate the model into their own projects. Follow the provided steps to package the model, upload it to Hugging Face, and make it publicly available. Sharing your fine-tuned model on Hugging Face's model hub is a great way to contribute to the NLP community and help others with their summarization tasks.