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
File size: 298 Bytes
127b6bf | 1 2 3 4 5 6 7 8 | from .configuration_sproto import SprotoConfig
from .modeling_sproto import SprotoModel, SprotoOutput
from transformers import AutoConfig, AutoModel
AutoConfig.register("sproto", SprotoConfig)
AutoModel.register(SprotoConfig, SprotoModel)
__all__ = ["SprotoConfig", "SprotoModel", "SprotoOutput"] |