Instructions to use alothomas/radbert-rad-verifier-context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alothomas/radbert-rad-verifier-context with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alothomas/radbert-rad-verifier-context")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alothomas/radbert-rad-verifier-context") model = AutoModelForSequenceClassification.from_pretrained("alothomas/radbert-rad-verifier-context", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval/metrics.csv from alothomas/radbert-rad-verifier-context: direct link, hf CLI and curl.
- Browser
- Download file 779 Bytes
-
https://huggingface.co/alothomas/radbert-rad-verifier-context/resolve/main/eval/metrics.csv
- Command line
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hf download hf://alothomas/radbert-rad-verifier-context/eval/metrics.csv
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curl -L -o metrics.csv https://huggingface.co/alothomas/radbert-rad-verifier-context/resolve/main/eval/metrics.csv
779 Bytes
| variant,point_accuracy,point_f1_macro,point_f1_micro,point_mcc,point_auroc,point_auprc,accuracy_mean,accuracy_lo95,accuracy_hi95,f1_macro_mean,f1_macro_lo95,f1_macro_hi95,f1_micro_mean,f1_micro_lo95,f1_micro_hi95,mcc_mean,mcc_lo95,mcc_hi95,auroc_mean,auroc_lo95,auroc_hi95,auprc_mean,auprc_lo95,auprc_hi95 | |
| radbert-context,0.7453924914675768,0.7445923138152308,0.7453924914675769,0.48920206483916717,0.8410444162863455,0.8332411502680421,0.7451904436860068,0.7228668941979522,0.7658873720136519,0.744252850770218,0.7221360509606009,0.7652412333705655,0.7451904436860068,0.7228668941979522,0.7658873720136519,0.4887779708542386,0.44464981927790764,0.5307318636740038,0.8409501250339281,0.8209986723787389,0.8599001867358658,0.8331465353921191,0.8058492140998792,0.8584640324187138 | |