Instructions to use mertas7/medroberta-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mertas7/medroberta-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mertas7/medroberta-binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mertas7/medroberta-binary") model = AutoModelForSequenceClassification.from_pretrained("mertas7/medroberta-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 180 Bytes
212b4bb | 1 2 3 | global_step,train_loss,mcc,tp,tn,fp,fn,auroc,auprc,eval_loss
900,0.4150235652923584,0.4405866080236367,989,18041,2521,191,0.9338443755883405,0.46705859825493107,0.3807122678076797
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