The Large-Scale Pre-Trained Model for RNA
Project description
title: README authors: - Zhiyuan Chen date: 2023-06-06
README
本项目将一个UniRNA Checkpoint转换成一个HuggingFace Transformers兼容的Pretrained。
安装
pip install .
转换
python -m unirna.convert unirna_L16_E1024_DPRNA500M_STEP400K.pt
对于预训练的Checkpoint,在本例中使用unirna_L16_E1024_DPRNA500M_STEP400K.pt
。
convert
将会自动识别模型结构参数,生成恰当的配置文件,并转换模型结构。
最终结果将保存在同名(但没有扩展名)的目录中,本例为unirna_L16_E1024_DPRNA500M_STEP400K
。
使用
DeepProtein
在DeepProtein训练时,请在--sequence.pretrained
指定转换后的文件路径,建议指定绝对路径。
python -m deepprotein.train --sequence.pretrained /path/to/unirna_L16_E1024_DPRNA500M_STEP400K
Transformers
在通过transformers使用转换后的Pretrained时,请务必import unirna
来确保配置、模型和令牌器被正确注册。
import unirna # import的顺序不重要
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("unirna_L16_E1024_DPRNA500M_STEP400K")
model = AutoModel.from_pretrained("unirna_L16_E1024_DPRNA500M_STEP400K")
文件结构
- {unirna}
- |- convert.py
- |- config.py
- |- model.py
- |- tokenizer.py
- |- template
- |- vocab.txt
- |- tokenizer_config.json
- |- special_tokens_map.json
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