Instructions to use fabikru/MolEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fabikru/MolEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fabikru/MolEncoder")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fabikru/MolEncoder") model = AutoModelForMaskedLM.from_pretrained("fabikru/MolEncoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43367f8ab956eaf928f5a4d46084801ce3dccb8d38e1caf7bb540cbf4ffb332f
- Size of remote file:
- 60.9 MB
- SHA256:
- 268a7d5cbc47c1f7bf7146375e091f1d6e51f6e8d244a819cf42f2aad18362c0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.