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_ci.csv from alothomas/radbert-rad-verifier-context: direct link, hf CLI and curl.
- Browser
- Download file 407 Bytes
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https://huggingface.co/alothomas/radbert-rad-verifier-context/resolve/main/eval/metrics_ci.csv
- Command line
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hf download hf://alothomas/radbert-rad-verifier-context/eval/metrics_ci.csv
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curl -L -o metrics_ci.csv https://huggingface.co/alothomas/radbert-rad-verifier-context/resolve/main/eval/metrics_ci.csv
407 Bytes
| metric,mean,lo95,hi95 | |
| accuracy,0.7451904436860068,0.7228668941979522,0.7658873720136519 | |
| f1_macro,0.744252850770218,0.7221360509606009,0.7652412333705655 | |
| f1_micro,0.7451904436860068,0.7228668941979522,0.7658873720136519 | |
| mcc,0.4887779708542386,0.44464981927790764,0.5307318636740038 | |
| auroc,0.8409501250339281,0.8209986723787389,0.8599001867358658 | |
| auprc,0.8331465353921191,0.8058492140998792,0.8584640324187138 | |