Instructions to use DeepChem/ChemBERTa-100M-MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepChem/ChemBERTa-100M-MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="DeepChem/ChemBERTa-100M-MLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("DeepChem/ChemBERTa-100M-MLM") model = AutoModelForMaskedLM.from_pretrained("DeepChem/ChemBERTa-100M-MLM") - Inference
- Notebooks
- Google Colab
- Kaggle
ChemBERTa-100M-MLM
ChemBERTa model pretrained on a subset of 100M molecules from ZINC20 dataset using masked language modeling (MLM).
Usage
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("DeepChem/ChemBERTa-100M-MLM")
model = AutoModelForMaskedLM.from_pretrained("DeepChem/ChemBERTa-100M-MLM")
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