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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b57d9509da8e1e7a0a6ce44ee9469d2b6baabc67d8f3cd496da6086d361a662
- Size of remote file:
- 558 MB
- SHA256:
- 0a2ea52d98d9d97d2af74b683ed8a200e454711677693e16300cc9149d256396
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