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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## PharmBERT cased
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A domain-specific BERT model for drug labels
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Please refer to the PharmBERT
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https://github.com/TahaAslani/PharmBERT
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## Citation
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## PharmBERT cased
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A domain-specific BERT model for drug labels
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Please refer to the PharmBERT paper for the model description:
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https://doi.org/10.1093/bib/bbad226
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For pre-training and fine-tuning codes, please refer to the PharmBERT Github:
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https://github.com/TahaAslani/PharmBERT
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## Citation
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