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
File size: 534 Bytes
327c1ca 40828aa 242206b 40828aa 5d2a35c 40828aa ea54ef8 40828aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"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"
}
|