Instructions to use AI4Protein/deep_bpe_1600 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4Protein/deep_bpe_1600 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AI4Protein/deep_bpe_1600")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI4Protein/deep_bpe_1600") model = AutoModelForMaskedLM.from_pretrained("AI4Protein/deep_bpe_1600", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AI4Protein/deep_bpe_1600: direct link, hf CLI and curl.
- Browser
- Download file 591 Bytes
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https://huggingface.co/AI4Protein/deep_bpe_1600/resolve/main/README.md
- Command line
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hf download hf://AI4Protein/deep_bpe_1600/README.md
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curl -L -o README.md https://huggingface.co/AI4Protein/deep_bpe_1600/resolve/main/README.md
591 Bytes
metadata
pipeline_tag: feature-extraction
library_name: transformers
license: mit
VenusFactory RoFormer Model
This model is a RoFormer-based protein language model used within the VenusFactory platform, as described in VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning. It's designed for feature extraction tasks related to protein sequences.
For more details on VenusFactory and its capabilities, please refer to the project's GitHub repository.