| --- |
| library_name: transformers |
| tags: |
| - summarization |
| - text-generation |
| - nlp |
| - t5 |
| - t5-small |
| - transformers |
| - pytorch |
| license: apache-2.0 |
| datasets: |
| - knkarthick/samsum |
| language: |
| - en |
| metrics: |
| - rouge |
| base_model: |
| - google-t5/t5-small |
| pipeline_tag: summarization |
| --- |
| |
| # T5-Small Text Summarizer |
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| 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. |
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| ## Model Details |
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| ### Model Description |
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| 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. |
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| - **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] |
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| ### Model Sources [optional] |
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| <!-- Provide the basic links for the model. --> |
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| - **Repository:** https://github.com/harshrao-dot/Breifly-AI |
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| ## Uses |
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| <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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| ### Direct Use |
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| 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. |
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| ### Downstream Use [optional] |
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| This model can be integrated into NLP applications, chat assistants, content management systems, and document processing pipelines where automatic text summarization is required. |
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| ### Out-of-Scope Use |
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| 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. |
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| ## Bias, Risks, and Limitations |
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| 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. |
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| ### Recommendations |
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| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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| ## How to Get Started with the Model |
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| Use the code below to get started with the model. |
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| [More Information Needed] |
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| ## Training Details |
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| ### Training Data |
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| <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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| [More Information Needed] |
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| ### Training Procedure |
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| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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| #### Preprocessing [optional] |
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| [More Information Needed] |
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| #### Training Hyperparameters |
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| - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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| #### Speeds, Sizes, Times [optional] |
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| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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| [More Information Needed] |
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| ## Evaluation |
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| <!-- This section describes the evaluation protocols and provides the results. --> |
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| ### Testing Data, Factors & Metrics |
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| #### Testing Data |
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| <!-- This should link to a Dataset Card if possible. --> |
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| [More Information Needed] |
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| #### Factors |
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| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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| [More Information Needed] |
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| #### Metrics |
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| <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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| [More Information Needed] |
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| ### Results |
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| [More Information Needed] |
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| #### Summary |
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| ## Model Examination [optional] |
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| <!-- Relevant interpretability work for the model goes here --> |
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| [More Information Needed] |
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| ## Environmental Impact |
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| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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| Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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| - **Hardware Type:** [More Information Needed] |
| - **Hours used:** [More Information Needed] |
| - **Cloud Provider:** [More Information Needed] |
| - **Compute Region:** [More Information Needed] |
| - **Carbon Emitted:** [More Information Needed] |
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| ## Technical Specifications [optional] |
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| ### Model Architecture and Objective |
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| [More Information Needed] |
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| ### Compute Infrastructure |
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| [More Information Needed] |
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| #### Hardware |
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| [More Information Needed] |
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| #### Software |
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| [More Information Needed] |
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| ## Citation [optional] |
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| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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| **BibTeX:** |
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| [More Information Needed] |
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| **APA:** |
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| [More Information Needed] |
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| ## Glossary [optional] |
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| <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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| [More Information Needed] |
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| ## More Information [optional] |
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| [More Information Needed] |
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| ## Model Card Authors [optional] |
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| [More Information Needed] |
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| ## Model Card Contact |
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| [More Information Needed] |
