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
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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).
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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]
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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