You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

Model Card for Model ID

The fine-tuned Google Pegasus model for text summarization utilizes a transformer-based encoder-decoder architecture optimized for abstractive summarization. Pre-trained using Gap-sentence Generation (GSG), the model learns to predict and generate missing sentences, enhancing its ability to understand context and importance within text. Fine-tuning involves training the pre-trained model on a specific summarization dataset to adapt it to the desired domain and style, improving its performance on task-specific summaries.

  • Developed by: [Akash Devbanshi]
  • Model type: [Text2Text Generation]
  • License: [Apache license 2.0]
  • Finetuned from model [optional]: [google/pegasus-cnn_dailymail]

Model Sources [optional]

  • Repository: [google/pegasus-cnn_dailymail]

Uses

The fine-tuned Google Pegasus model for text summarization can be used in various applications:

Automated News Summarization: It can generate concise summaries of news articles, helping readers quickly grasp the main points. Summarizing Scientific Papers: Researchers can use it to produce brief overviews of lengthy academic papers, saving time. Content Creation: Bloggers and content creators can generate summaries for their articles or videos, making content more accessible. Customer Support: Summarize long customer service interactions or emails to provide quick insights for support agents. Legal Document Summarization: Lawyers and legal professionals can use it to summarize lengthy legal documents and contracts.

Downloads last month
0
Safetensors
Model size
571M params
Tensor type
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train adevbanshi/fine_tuned_pegasus