Feature Extraction
Transformers
PyTorch
English
fukui_net
chemistry
biology
DFT
molecular-property-prediction
graph-neural-networks
fukui-indices
reactivity
custom_code
Instructions to use Nikolenko-Sergei/FukuiNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nikolenko-Sergei/FukuiNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Nikolenko-Sergei/FukuiNet", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nikolenko-Sergei/FukuiNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tests/data/test_molecules.csv from Nikolenko-Sergei/FukuiNet: direct link, hf CLI and curl.
- Browser
- Download file 74 Bytes
-
https://huggingface.co/Nikolenko-Sergei/FukuiNet/resolve/main/tests/data/test_molecules.csv
- Command line
-
hf download hf://Nikolenko-Sergei/FukuiNet/tests/data/test_molecules.csv
-
curl -L -o test_molecules.csv https://huggingface.co/Nikolenko-Sergei/FukuiNet/resolve/main/tests/data/test_molecules.csv
74 Bytes
| smiles,name | |
| CCO,Ethanol | |
| c1ccccc1,Benzene | |
| CC(=O)OC1=CC=CC=C1C(=O)O,Aspirin | |