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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04dc14e699636541e9505368622de31a29e6a1703db3c4cbb37cfecab733fd78
- Size of remote file:
- 2.27 kB
- SHA256:
- fb3cd4b5c339b2edf74ad481afd3998060d65255a5db7e15b762927767db280e
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