Model: chrisvoncsefalvay/CRAG-dual-encoder-mimicause
Author: chrisvoncsefalvay
Pipeline: text-classification
Library: pytorch
Tags: pytorch, onnx, medical, biomedical, drug-safety, adverse-drug-events, pharmacovigilance, relation-extraction, dual-encoder, clinical-nlp, text-classification, en, dataset:ade_corpus_v2, dataset:bigbio/mimicause, base_model:michiyasunaga/BioLinkBERT-base, base_model:quantized:michiyasunaga/BioLinkBERT-base, doi:10.57967/hf/7298, license:apache-2.0, model-index, region:us
Base models (quantized): id: michiyasunaga/BioLinkBERT-base
Card metadata: language: en
license: apache-2.0
tags: medical, biomedical, drug-safety, adverse-drug-events, pharmacovigilance, relation-extraction, dual-encoder, clinical-nlp
datasets: ade_corpus_v2, bigbio/mimicause
metrics: f1, precision, recall, roc_auc
pipeline_tag: text-classification
library_name: pytorch
base_model: michiyasunaga/BioLinkBERT-base
model-index: name: CRAG-dual-encoder-mimicause
results: task: type: text-classification
name: Adverse Drug Event Extraction
dataset: name: ADE Corpus + MIMICause (Combined Test Set)
type: custom
config: combined_test
split: test
metrics: type: f1
value: 0.937
name: F1 Score
verified: no, type: precision
value: 0.9336
name: Precision
verified: no, type: recall
value: 0.9405
name: Recall
verified: no, type: roc_auc
value: 0.9735
name: AUC-ROC
verified: no, type: accuracy
value: 0.938
name: Accuracy
verified: no
Likes: 2 | All-time downloads: 36
Created: 2025-12-19 13:19:35