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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lauraha/peptide_encoder_2") model = AutoModelForMaskedLM.from_pretrained("lauraha/peptide_encoder_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 427b0b0b76a06a28c7eb7345b0ef4814fb698f3e427ac51615aa3d7df72ce3f9
- Size of remote file:
- 595 MB
- SHA256:
- 6a14fb0080ab8ec08fbb1bdca413a7af923014c2dff43b16129e3fe542b3fa41
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.