| | --- |
| | license: apache-2.0 |
| | datasets: |
| | - Samsung/samsum |
| | language: |
| | - en |
| | metrics: |
| | - bleu |
| | library_name: transformers |
| | pipeline_tag: summarization |
| | tags: |
| | - code |
| | --- |
| | |
| | # Model Card for Model ID |
| |
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| | <!-- Provide a quick summary of what the model is/does. --> |
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| | 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. |
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| | - **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] |
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| | ## Uses |
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| | The fine-tuned Google Pegasus model for text summarization can be used in various applications: |
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| | 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. |
