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")# 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
metadata
license: mit
library_name: transformers
pipeline_tag: feature-extraction
This repository contains the RoFormer model used in the paper VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning.
VenusFactory is a unified platform for protein engineering data retrieval and language model fine-tuning. This model is designed for feature extraction tasks related to proteins.
Github repository: https://github.com/tyang816/VenusFactory