Instructions to use pamessina/CXRFE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pamessina/CXRFE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pamessina/CXRFE", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pamessina/CXRFE", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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license: apache-2.0
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CXRFE is a BERT-based encoder model trained to represent radiological facts from the findings and impression sections of X-ray radiological reports, as described in the article:
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Pablo Messina, Rene Vidal, Denis Parra, Alvaro Soto, and Vladimir Araujo. 2024.
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**Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation**.
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In Findings of the Association for Computational Linguistics: ACL 2024, pages 3955–3986, Bangkok, Thailand. Association for Computational Linguistics.
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https://aclanthology.org/2024.findings-acl.236/
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