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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.ptconvert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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