Feature Extraction
Transformers
PyTorch
Safetensors
English
modernbert
genomics
nucleotide
dna
sequence-modeling
biology
bioinformatics
Instructions to use FreakingPotato/NucEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FreakingPotato/NucEL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FreakingPotato/NucEL")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("FreakingPotato/NucEL") model = AutoModel.from_pretrained("FreakingPotato/NucEL") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d888b7afec2331df6670e2f4c4af0b6599003e0d96deb243deba0fa3d97d810
- Size of remote file:
- 369 MB
- SHA256:
- fc5f5df02d1961293f62f8a0753440bdbe9e360e7680409f6ebcf34b2589b2bf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.