Instructions to use nidhinthomas/esm2_t12_35M_UR50D-finetuned-secondary-structure with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nidhinthomas/esm2_t12_35M_UR50D-finetuned-secondary-structure with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nidhinthomas/esm2_t12_35M_UR50D-finetuned-secondary-structure")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("nidhinthomas/esm2_t12_35M_UR50D-finetuned-secondary-structure") model = AutoModelForTokenClassification.from_pretrained("nidhinthomas/esm2_t12_35M_UR50D-finetuned-secondary-structure", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nidhinthomas/esm2_t12_35M_UR50D-finetuned-secondary-structure: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/nidhinthomas/esm2_t12_35M_UR50D-finetuned-secondary-structure/resolve/main/training_args.bin
- Command line
-
hf download hf://nidhinthomas/esm2_t12_35M_UR50D-finetuned-secondary-structure/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nidhinthomas/esm2_t12_35M_UR50D-finetuned-secondary-structure/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 8e4e319e83571b01a6005e190c187b8208a439bf6b77ecc554cb52389222f6f4
- Size of remote file:
- 4.73 kB
- SHA256:
- d058694f9e4e7005504de596b905584e40f95cd24060942522f69091ad5b5ddd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.