Instructions to use AI4Protein/deep_bpe_200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4Protein/deep_bpe_200 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AI4Protein/deep_bpe_200")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI4Protein/deep_bpe_200") model = AutoModelForMaskedLM.from_pretrained("AI4Protein/deep_bpe_200", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add model card
Verified signatureThis PR adds a model card for the RoFormer model used in the paper [VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning](https://huggingface.co/papers/2503.15438). The model card includes relevant metadata such as `pipeline_tag`, `library_name`, and license, as well as a link to the Github repository.2c3f90b
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Upload RoFormerForMaskedLM
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Upload tokenizer
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Upload config
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initial commit
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