Instructions to use DeepChem/ChemBERTa-77M-MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepChem/ChemBERTa-77M-MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="DeepChem/ChemBERTa-77M-MLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("DeepChem/ChemBERTa-77M-MLM") model = AutoModelForMaskedLM.from_pretrained("DeepChem/ChemBERTa-77M-MLM", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 220e30a805405dcdc2a55cca7566666a02eaf1486305c2ec7d1d4a3d0e57da68
- Size of remote file:
- 13.7 MB
- SHA256:
- 95571af26b61ba8d56b9d61ff108b5c9afe96fd69ea3cbd1901d9421e3d2dd6d
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