Instructions to use cnut1648/biolinkbert-mednli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cnut1648/biolinkbert-mednli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cnut1648/biolinkbert-mednli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cnut1648/biolinkbert-mednli") model = AutoModelForSequenceClassification.from_pretrained("cnut1648/biolinkbert-mednli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8550c7fedeef87722bb3cb22902f0ce134df7077ffa39849fac68c6effe09a60
- Size of remote file:
- 1.33 GB
- SHA256:
- 5fadc0dafd7509bdd583d0015a2ab49c65d8d5d9193cbb49ed46c88c313bbd55
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