---
license: apache-2.0
datasets:
- Samsung/samsum
language:
- en
metrics:
- bleu
library_name: transformers
pipeline_tag: summarization
tags:
- code
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

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]

<!-- Provide the basic links for the model. -->

- **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.