Instructions to use AI4Protein/deep_bpe_3200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4Protein/deep_bpe_3200 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AI4Protein/deep_bpe_3200")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI4Protein/deep_bpe_3200") model = AutoModelForMaskedLM.from_pretrained("AI4Protein/deep_bpe_3200") - Notebooks
- Google Colab
- Kaggle
Add model card with metadata
Browse filesThis PR adds a model card with the necessary metadata including `library_name`, `pipeline_tag`, and `license`. It also includes links to the paper and the GitHub repository.
README.md
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---
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pipeline_tag: feature-extraction
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library_name: transformers
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license: mit
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---
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This repository contains a RoFormerForMaskedLM model for protein feature extraction, as described in [VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning](https://huggingface.co/papers/2503.15438).
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Code: https://github.com/tyang816/VenusFactory
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This model is intended for feature extraction tasks related to protein language modeling. Refer to the GitHub repository for details on usage.
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