Feature Extraction
Transformers
PyTorch
English
modernbert
genomics
rna
nucleotide
sequence-modeling
biology
bioinformatics
electra
Instructions to use FreakingPotato/RNAElectra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FreakingPotato/RNAElectra with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FreakingPotato/RNAElectra")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("FreakingPotato/RNAElectra") model = AutoModel.from_pretrained("FreakingPotato/RNAElectra", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from FreakingPotato/RNAElectra: direct link, hf CLI and curl.
- Browser
- Download file 369 MB
-
https://huggingface.co/FreakingPotato/RNAElectra/resolve/refs%2Fpr%2F3/pytorch_model.bin
- Command line
-
hf download hf://FreakingPotato/RNAElectra@refs/pr/3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/FreakingPotato/RNAElectra/resolve/refs%2Fpr%2F3/pytorch_model.bin
369 MB
- Xet hash:
- 2499ff0750ab772cfe721fa7600ee07724e57fb838867fd5dc70e1e5741f797b
- Size of remote file:
- 369 MB
- SHA256:
- 1a969702e2fa804ade947ab3c221030653a0accbf7a9d1bec72585a9a0bf44e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.