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-MLM-TAFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Derify/ModChemBERT-MLM-TAFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Derify/ModChemBERT-MLM-TAFT", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Derify/ModChemBERT-MLM-TAFT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8a8370cab65610c99faedd1f10403f8b88e901078c0c9bdf9322b5bfff37fa4
- Size of remote file:
- 460 MB
- SHA256:
- 8656f01d9edb002cf882df1e554a0a10e57065c97d4c08a1abfe34bad98da87f
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