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:
- cb429f5f72027a6994f7db7bedb6bf6dbeab8f6d43d95c0cda0a47fa6154b24a
- Size of remote file:
- 3.39 kB
- SHA256:
- 0ea15d6303962ae9b87417fbe4aab77643595fe4a029f0cd5fa0e02e1b735d4b
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