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--- |
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language: en |
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tags: |
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- summarization |
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- bart |
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- medical question answering |
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- question understanding |
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- consumer health question |
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- prompt engineering |
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license: apache-2.0 |
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datasets: |
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- samsum |
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widget: |
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- text: ' |
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SUBJECT: high inner eye pressure above 21 possible glaucoma |
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MESSAGE: have seen inner eye pressure increase as I have begin taking |
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Rizatriptan. I understand the med narrows blood vessels. Can this med. cause |
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or effect the closed or wide angle issues with the eyelense/glacoma.' |
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model-index: |
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- name: medqsum-bart-large-xsum-meqsum |
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results: |
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- task: |
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type: summarization |
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name: Summarization |
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dataset: |
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name: 'SAMSum Corpus: Dataset for medical question summarization' |
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type: bigbio/meqsum |
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split: valid |
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metrics: |
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- type: rogue-1 |
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value: 54.32 |
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name: Validation ROGUE-1 |
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- type: rogue-2 |
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value: 38.08 |
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name: Validation ROGUE-2 |
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- type: rogue-l |
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value: 51.98 |
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name: Validation ROGUE-L |
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- type: rogue-l-sum |
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value: 51.99 |
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name: Validation ROGUE-L-SUM |
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library_name: transformers |
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--- |
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|
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## Bart Xsum model finetuned on MeqSum |
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medqsum-bart-large-xsum-meqsum is the best fine-tuned model in the paper [Enhancing Large Language Models' Utility for Medical Question-Answering: A Patient Health Question Summarization Approach](), which introduces a solution to get the most out of LLMs, when answering health-related questions. We address the challenge of crafting accurate prompts by summarizing consumer health questions (CHQs) to generate clear and concise medical questions. Our approach involves fine-tuning Transformer-based models, including Flan-T5 in resource-constrained environments and three medical question summarization datasets. |
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## Hyperparameters |
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```json |
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{ |
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"dataset_name": "MeQSum", |
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"learning_rate": 3e-05, |
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"model_name_or_path": "facebook/bart-large-xsum", |
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"num_train_epochs": 4, |
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"per_device_eval_batch_size": 4, |
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"per_device_train_batch_size": 4, |
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"predict_with_generate": true, |
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"seed": 7 |
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} |
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``` |
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## Usage |
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```python |
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from transformers import pipeline |
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summarizer = pipeline("summarization", model="NouRed/medqsum-bart-large-xsum-meqsum") |
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chq = '''SUBJECT: high inner eye pressure above 21 possible glaucoma |
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MESSAGE: have seen inner eye pressure increase as I have begin taking |
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Rizatriptan. I understand the med narrows blood vessels. Can this med. |
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cause or effect the closed or wide angle issues with the eyelense/glacoma. |
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''' |
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summarizer(chq) |
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``` |
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## Results |
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|
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| key | value | |
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| --- | ----- | |
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| eval_rouge1 | 54.32 | |
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| eval_rouge2 | 38.08 | |
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| eval_rougeL | 51.98 | |
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| eval_rougeLsum | 51.99 | |