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
Download training_progress_scores.csv from mertas7/medroberta-binary: direct link, hf CLI and curl.
- Browser
- Download file 180 Bytes
-
https://huggingface.co/mertas7/medroberta-binary/resolve/refs%2Fpr%2F1/training_progress_scores.csv
- Command line
-
hf download hf://mertas7/medroberta-binary@refs/pr/1/training_progress_scores.csv
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curl -L -o training_progress_scores.csv https://huggingface.co/mertas7/medroberta-binary/resolve/refs%2Fpr%2F1/training_progress_scores.csv
180 Bytes
| 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 | |