Text Classification
Transformers
Safetensors
English
proto
medical
prototypical-networks
Eval Results (legacy)
Instructions to use row56/ProtoPatient with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use row56/ProtoPatient with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="row56/ProtoPatient")# Load model directly from transformers import ProtoForMultiLabelClassification model = ProtoForMultiLabelClassification.from_pretrained("row56/ProtoPatient", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "text_config": {"pretrained_model_name_or_path": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext"}, | |
| "architectures": ["ProtoForMultiLabelClassification"], | |
| "model_type": "proto", | |
| "num_labels": 519, | |
| "reduce_hidden_size": 256, | |
| "use_attention": true, | |
| "use_sigmoid": true, | |
| "label_order_path": "label_order.json", | |
| "prototype_vector_path": "prototype_vectors", | |
| "attention_vector_path": "attention_vectors", | |
| "prototype_vector_path": "model.safetensors", | |
| "attention_vector_path": "model.safetensors" | |
| } | |