Summarization
Transformers
Safetensors
English
bart
text2text-generation
radiology
medical
healthcare
Eval Results (legacy)
Instructions to use Kumud2k16/radiology-expression-summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kumud2k16/radiology-expression-summarizer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="Kumud2k16/radiology-expression-summarizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Kumud2k16/radiology-expression-summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("Kumud2k16/radiology-expression-summarizer") - Notebooks
- Google Colab
- Kaggle
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Model tree for Kumud2k16/radiology-expression-summarizer
Base model
facebook/bart-baseEvaluation results
- ROUGE-1 F1 on Open-i NLMCXR (Indiana University Chest X-ray)self-reported0.517
- ROUGE-2 F1 on Open-i NLMCXR (Indiana University Chest X-ray)self-reported0.388
- ROUGE-L F1 on Open-i NLMCXR (Indiana University Chest X-ray)self-reported0.509