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
| { | |
| "auto_map": { | |
| "AutoConfig": "configuration_sproto.SprotoConfig", | |
| "AutoModel": "modeling_sproto.SprotoModel" | |
| }, | |
| "attention_probs_dropout_prob": 0.1, | |
| "dot_product": false, | |
| "final_layer": false, | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "label_order_path": null, | |
| "loss": "BCE", | |
| "max_position_embeddings": 512, | |
| "model_type": "sproto", | |
| "normalize": null, | |
| "num_classes": 1643, | |
| "num_prototypes_per_class": 5, | |
| "pretrained_model": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext", | |
| "reduce_hidden_size": 256, | |
| "seed": 7, | |
| "transformers_version": "4.25.1", | |
| "use_attention": true, | |
| "use_global_attention": false, | |
| "use_prototype_loss": false, | |
| "use_sigmoid": false, | |
| "vocab_size": 28996 | |
| } | |