Instructions to use entropy/roberta_zinc_480m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use entropy/roberta_zinc_480m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="entropy/roberta_zinc_480m")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("entropy/roberta_zinc_480m") model = AutoModelForMaskedLM.from_pretrained("entropy/roberta_zinc_480m") - Notebooks
- Google Colab
- Kaggle
Roberta Zinc 480m
This is a Roberta style masked language model trained on ~480m SMILES strings from the ZINC database. The model has ~102m parameters and was trained for 150000 iterations with a batch size of 4096 to a validation loss of ~0.122. This model is useful for generating embeddings from SMILES strings.