Feature Extraction
Transformers
PyTorch
Safetensors
English
modernbert
genomics
nucleotide
dna
sequence-modeling
biology
bioinformatics
electra
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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("FreakingPotato/NucEL") model = AutoModel.from_pretrained("FreakingPotato/NucEL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from FreakingPotato/NucEL: direct link, hf CLI and curl.
- Browser
- Download file 369 MB
-
https://huggingface.co/FreakingPotato/NucEL/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://FreakingPotato/NucEL/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/FreakingPotato/NucEL/resolve/main/pytorch_model.bin
369 MB
- Xet hash:
- 4d888b7afec2331df6670e2f4c4af0b6599003e0d96deb243deba0fa3d97d810
- Size of remote file:
- 369 MB
- SHA256:
- fc5f5df02d1961293f62f8a0753440bdbe9e360e7680409f6ebcf34b2589b2bf
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