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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Lianglab/PharmBERT-cased") model = AutoModelForMaskedLM.from_pretrained("Lianglab/PharmBERT-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
92cffec
-
Update README.md
91242e8
-
Update README.md
eb50294 -
Update README.md
f0a7d46 -
Create README.md
e646740
-
Upload 6 files
cd0bcdd -
initial commit
e8826ee