Text Classification
Transformers
PyTorch
Safetensors
English
sproto
multi-label-classification
long-tail-learning
medical
clinical-nlp
interpretability
prototypical-networks
ehr
custom_code
Instructions to use DATEXIS/sproto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DATEXIS/sproto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DATEXIS/sproto", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DATEXIS/sproto", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfd126a36c66bad14fdb0838ebee9a715202668ee8bcd3b0d9974590982bf585
- Size of remote file:
- 456 MB
- SHA256:
- 9129bc3ced6a0e088a7ab7a8ab8ad508af4dd33fc5f29f64615d5116acb5f409
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