harshrao-dev/text-summarizer-t5

T5-Small Text Summarizer

A fine-tuned T5-small model for abstractive text summarization. The model generates concise summaries from long-form text while preserving the most important information.

Model Details

Model Description

This model is a fine-tuned version of google-t5/t5-small for abstractive text summarization. It is designed to generate concise and meaningful summaries from long input texts while retaining the most important information. The model can be used for summarizing articles, documents, blogs, and other long-form content.

• Developed by: [Harsh Rao]
• Funded by [optional]: [Self-funded]
• Shared by [optional]: [Harsh Rao]
• Model type: [T5 (Text-to-Text Transfer Transformer)]
• Language(s) (NLP): [English]
• License: [Apache-2.0]
• Finetuned from model [optional]: [google-t5/t5-small]

Model Sources [optional]

• Repository: https://github.com/harshrao-dot/Breifly-AI

Uses

Direct Use

This model can be used to generate concise summaries from long conversations and text documents. It is suitable for dialogue summarization, content condensation, and quick information extraction.

Downstream Use [optional]

This model can be integrated into NLP applications, chat assistants, content management systems, and document processing pipelines where automatic text summarization is required.

Out-of-Scope Use

This model is not intended for factual verification, legal advice, medical advice, financial decision-making, or any other high-stakes applications where accuracy is critical.

Bias, Risks, and Limitations

The model may generate incomplete summaries, omit important details, or occasionally produce factually inaccurate information. Performance may vary depending on the domain and quality of the input text.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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