Text Classification
Transformers
PyTorch
TensorBoard
English
esm
protein
classification
fluorescence
Instructions to use Old-Shatterhand/esm_fine_fluorescence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Old-Shatterhand/esm_fine_fluorescence with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Old-Shatterhand/esm_fine_fluorescence")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Old-Shatterhand/esm_fine_fluorescence") model = AutoModelForSequenceClassification.from_pretrained("Old-Shatterhand/esm_fine_fluorescence", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Old-Shatterhand/esm_fine_fluorescence: direct link, hf CLI and curl.
- Browser
- Download file 136 MB
-
https://huggingface.co/Old-Shatterhand/esm_fine_fluorescence/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Old-Shatterhand/esm_fine_fluorescence/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Old-Shatterhand/esm_fine_fluorescence/resolve/main/pytorch_model.bin
136 MB
- Xet hash:
- 195453b7758e53527992da96eaa561daaf8e21ba5e00821a43c451f44cc03797
- Size of remote file:
- 136 MB
- SHA256:
- abbe0aa80237a99ad8c5cf2915bafcc60213faaa3627bbc53147aba29157433c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.