Instructions to use griffin/clinical-led-summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use griffin/clinical-led-summarizer with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("griffin/clinical-led-summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("griffin/clinical-led-summarizer") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
clinical-led-summarizer
HuggingFace Model Weights for the LongFormer Hospital-Course Summarization model trained on Revised References, as described in Findings of EMNLP 2022 Paper "Learning to Revise References for Faithful Summarization"
language: - en tags: - summarization license: apache-2.0 datasets: - MIMIC-III metrics: - rouge - bertscore
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