Fill-Mask
Transformers
Safetensors
modchembert
modernbert
ModChemBERT
cheminformatics
chemical-language-model
molecular-property-prediction
custom_code
Eval Results (legacy)
Instructions to use Derify/ModChemBERT-MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Derify/ModChemBERT-MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Derify/ModChemBERT-MLM", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Derify/ModChemBERT-MLM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce59c3d2e9d8ff1c0c6bd33b909a38544c2400639454cf0c610576475d004f8b
- Size of remote file:
- 451 MB
- SHA256:
- 4639c891069a720d370c6be880d7dbc6db2a27d394601501fe2e8d0e933c54e3
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