Instructions to use AI4Protein/deep_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4Protein/deep_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AI4Protein/deep_base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI4Protein/deep_base") model = AutoModelForMaskedLM.from_pretrained("AI4Protein/deep_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| pipeline_tag: feature-extraction | |
| library_name: transformers | |
| license: mit | |
| # VenusFactory Protein Language Model | |
| This model is part of the VenusFactory platform, described in [VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning](https://huggingface.co/papers/2503.15438). VenusFactory provides a unified platform for protein engineering data retrieval and language model fine-tuning, integrating many protein-related datasets and popular PLMs. | |
| This specific model uses a masked language modeling objective for protein sequence feature extraction. | |
| Code and further details are available at [https://github.com/tyang816/VenusFactory](https://github.com/tyang816/VenusFactory). |