Fill-Mask
Transformers
Safetensors
modchembert
modernbert
ModChemBERT
cheminformatics
chemical-language-model
molecular-property-prediction
mergekit
Merge
custom_code
Eval Results (legacy)
Instructions to use Derify/ModChemBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Derify/ModChemBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Derify/ModChemBERT", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Derify/ModChemBERT", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95090f6d5bfd55e0d84ddf43bc06b9dbcc5ec16201bd58ae3a31ee02016dbe4c
- Size of remote file:
- 460 MB
- SHA256:
- de6926433af59f86e8cdb62ccb573fb160752386612a3e9c36860d4a0e2f48c8
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