Instructions to use DeepChem/ChemBERTa-5M-MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepChem/ChemBERTa-5M-MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="DeepChem/ChemBERTa-5M-MLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("DeepChem/ChemBERTa-5M-MLM") model = AutoModelForMaskedLM.from_pretrained("DeepChem/ChemBERTa-5M-MLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 976537934ea454f553e74749359655c265631e0925e17309950c0aed3c5a199b
- Size of remote file:
- 13.7 MB
- SHA256:
- 2eb1f1c94c3a27d842746ec4e82c9a9e8ea506d7f53fa4ff7ced7ebae5d02474
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