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
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- **metadata**
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Additional information useful for analysis and interpretability.
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## Explainability
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S-Proto provides built-in faithful explanations through its prototypical structure:
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Additional information useful for analysis and interpretability.
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## Explainability
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S-Proto provides built-in faithful explanations through its prototypical structure:
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