Instructions to use aehrc/cxrmate-rrg24 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aehrc/cxrmate-rrg24 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aehrc/cxrmate-rrg24", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aehrc/cxrmate-rrg24", trust_remote_code=True) model = AutoModel.from_pretrained("aehrc/cxrmate-rrg24", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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This is an evolution of https://huggingface.co/aehrc/cxrmate developed for the [Radiology Report Generation](https://stanford-aimi.github.io/RRG24/) task of [BioNLP @ ACL 2024](https://aclweb.org/aclwiki/BioNLP_Workshop).
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For this, we proposed EAST: Entropy-Augmented Self-critical sequence Training (EAST):
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- EAST modifies [Self-Critical Sequence Training (SCST)](https://openaccess.thecvf.com/content_cvpr_2017/papers/Rennie_Self-Critical_Sequence_Training_CVPR_2017_paper.pdf) by adding entropy regularisation.
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- Helps maintain a higher entropy in the token distribution.
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This is an evolution of https://huggingface.co/aehrc/cxrmate developed for the [Radiology Report Generation](https://stanford-aimi.github.io/RRG24/) task of [BioNLP @ ACL 2024](https://aclweb.org/aclwiki/BioNLP_Workshop).
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The leaderboard for the task can be found [here](https://vilmedic.app/misc/bionlp24/leaderboard).
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For this, we proposed EAST: Entropy-Augmented Self-critical sequence Training (EAST):
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- EAST modifies [Self-Critical Sequence Training (SCST)](https://openaccess.thecvf.com/content_cvpr_2017/papers/Rennie_Self-Critical_Sequence_Training_CVPR_2017_paper.pdf) by adding entropy regularisation.
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- Helps maintain a higher entropy in the token distribution.
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