Instructions to use mrm8488/chEMBL_smiles_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/chEMBL_smiles_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mrm8488/chEMBL_smiles_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mrm8488/chEMBL_smiles_v1") model = AutoModelForMaskedLM.from_pretrained("mrm8488/chEMBL_smiles_v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a06c2f4cb43f60f3305981c5081c263450d65a4a733df759e076bc50f765c66
- Size of remote file:
- 94.5 MB
- SHA256:
- 6a9654fbb820bff55a0fadf5053c314b562891141d64d2400d5cb0fab8c22f20
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