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-DAPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Derify/ModChemBERT-MLM-DAPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Derify/ModChemBERT-MLM-DAPT", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Derify/ModChemBERT-MLM-DAPT", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51c9c619a59179ff09cce6f4274ed9b6e50934ec5cb2bfea4e3078496d6d344c
- Size of remote file:
- 231 MB
- SHA256:
- 3efb42279804940552d9af7328ae9b96d8a8b6a147609ed30aa2dfb21d95fff1
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