Instructions to use lauraha/peptide_encoder_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lauraha/peptide_encoder_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lauraha/peptide_encoder_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lauraha/peptide_encoder_2") model = AutoModelForMaskedLM.from_pretrained("lauraha/peptide_encoder_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from lauraha/peptide_encoder_2: direct link, hf CLI and curl.
- Browser
- Download file 125 Bytes
-
https://huggingface.co/lauraha/peptide_encoder_2/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://lauraha/peptide_encoder_2/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/lauraha/peptide_encoder_2/resolve/main/special_tokens_map.json
125 Bytes
| { | |
| "cls_token": "<cls>", | |
| "eos_token": "<eos>", | |
| "mask_token": "<mask>", | |
| "pad_token": "<pad>", | |
| "unk_token": "<unk>" | |
| } | |