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:
- 36be45e059e6905268d5c909338cd8756599663afb96d58cfc62f76af80f3153
- Size of remote file:
- 2.27 kB
- SHA256:
- c35bd60b030915e7dbf3ee5ce1474929ca5d971d823c17dcc84abe318860fc17
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