Instructions to use Lianglab/PharmBERT-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lianglab/PharmBERT-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Lianglab/PharmBERT-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Lianglab/PharmBERT-cased") model = AutoModelForMaskedLM.from_pretrained("Lianglab/PharmBERT-cased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8856984181576ebd9fe40a8b7a5b6564d6785acd296a9200e5aada7095327cb
- Size of remote file:
- 433 MB
- SHA256:
- 9851d04aca24fa0e272f1ab51e24451ac99130b392afe084b87f018cedc9f91d
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