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--- |
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library_name: transformers |
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language: |
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- en |
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metrics: |
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- rouge |
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- meteor |
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base_model: |
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- google-t5/t5-base |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** Gowni Bhavishya,Dr.Shib Shankar Sahu |
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- **Model type:** T5 (Text-To-Text Transfer Transformer) fine-tuned for scientific summarization, with SciBERT-based abstract representations. |
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- **Language(s) (NLP):** English (Scientific domain) |
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- **Finetuned from model [optional]:** t5-base |
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## Uses |
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Researchers in biomedical and scientific fields |
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Academic publishers and editors |
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Developers building scientific summarization tools |
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NLP practitioners working on domain-specific summarization |
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### Direct Use |
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Generate highlights or concise summaries of scientific abstracts (especially biomedical, life sciences, or clinical research) |
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### Out-of-Scope Use |
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1. Not suitable for general news summarization, social media content, or informal language. |
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2. Should not be used for critical medical decision-making or clinical diagnostics. |
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3. Not designed for creative writing, dialogue generation, or question answering. |
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4. Avoid using this model for non-English abstracts or multilingual input—it was trained on English biomedical text only. |
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## Bias, Risks, and Limitations |
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While BART performs well on biomedical abstracts, it inherits limitations from both: |
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1. Pretrained BART model biases (from general corpora like Wikipedia and Books) |
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2. Training dataset distribution biases (e.g., if your abstracts are from PubMed or a niche field) Known Limitations: |
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3. May generate generic summaries if abstracts are vague or long. |
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4. Struggles with mathematical, chemical, or symbolic notation. |
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5. Output may appear plausible but factually incorrect. |
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6. Does not provide citations or references for claims. |
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### Recommendations |
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1. Always validate generated summaries against the full abstract or ground truth highlights. |
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2. Preferably use in human-in-the-loop systems where an expert reviews the output. |
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3. Fine-tune further or filter input for domain-specific tasks (e.g., cardiology vs oncology). 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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## Training Details |
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### Training Data |
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1.Fine-tuned on a dataset of scientific abstracts and their corresponding highlights. |
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The training dataset was split into train (10k), validation (2k), and test (1.8k) sets. Input: Abstract column Target: Highlights column (only in train/val) |
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#### Training Hyperparameters |
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Model architecture: facebook/bart-large |
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Batch size: 4 (per device) |
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Epochs: 5 |
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Learning rate: 2e-5 |
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## Evaluation |
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Rouge1,Rouge2,RougeL,Meteor. |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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The test set consists of 1,840 scientific abstracts without ground-truth highlights. |
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#### Metrics |
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ROUGE-1: Measures unigram overlap (precision & recall) |
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ROUGE-2: Measures bigram overlap |
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ROUGE-L: Measures longest common subsequence |
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METEOR: Incorporates synonymy, stemming, and word order |
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### Results |
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#### Summary |
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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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## More Information [optional] |
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SVNIT CSE |
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## Model Card Authors [optional] |
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Gowni Bhavishya,Dr.Shib Sankar Sahu |
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## Model Card Contact |
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[More Information Needed] |
