Feature Extraction
Transformers
Safetensors
bert
chemistry
cheminformatics
materials-science
astra
property-prediction
regression
Instructions to use wkdghdus23/astra-predictor-initial-lumo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wkdghdus23/astra-predictor-initial-lumo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="wkdghdus23/astra-predictor-initial-lumo")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("wkdghdus23/astra-predictor-initial-lumo") model = AutoModel.from_pretrained("wkdghdus23/astra-predictor-initial-lumo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfd50911b9050a630254fe6fbb96c92761552c5a4fb0ac98dad95391ff394040
- Size of remote file:
- 354 MB
- SHA256:
- 973ae94cb694e9fb963c1a6f5e724183b4ab98698f3fb2f2b2fe0161532df7e8
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