Summarization
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
medical
clinical
Radiology
Radiology Reports
Instructions to use Mbilal755/Radiology_Bart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mbilal755/Radiology_Bart 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="Mbilal755/Radiology_Bart")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Mbilal755/Radiology_Bart") model = AutoModelForSeq2SeqLM.from_pretrained("Mbilal755/Radiology_Bart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0dfd40a6e47c953869a19b0e73803796608b267af37ac903d71cc049f41d049
- Size of remote file:
- 1.77 GB
- SHA256:
- a36d96a8736db60d0f6d53adf99ac4e9e500befc74c78fbdbd987d64d8f7a9c8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.