The source code of FL-Tuning, including the model source code and some downstream tasks based on this model.
The model details of FL-Tuning are described in the paper:
FL-Tuning: Layer Tuning for Feed-Forward Network in Transformer
- We have successfully run this code on 3090, V100 and A100 respectively.
pip install nvidia-pyindex==1.0.9
conda env create -f tf1.15.yml
We use the CLUE benchmark as our downstream tasks.
The datasets can be downloaded from the CLUE benchmark repository. The datasets should be the following format.
├── clue_dataset
│ ├── afqmc
│ │ ├── dev.json
│ │ ├── test.json
│ │ └── train.json
│ ├── c3
│ │ ├── d-dev.json
│ │ ├── d-train.json
│ │ ├── m-dev.json
│ │ ├── m-train.json
│ │ ├── README.md
│ │ ├── test1.0.json
│ │ └── test1.1.json
│ ├── chid
│ │ ├── dev_answer.json
│ │ ├── ......
│ ├── ......
│
We conducted experiments on three language models RoBERTa, NEZHA, and RoFormer respectively. You can download the pretrained language models from the links below.
The directory of the model in snippets.py should be changed, for example:
config_path = '/data/PretrainedModels/chinese_roformer-char_L-12_H-768_A-12/bert_config.json'
checkpoint_path = '/data/PretrainedModels/chinese_roformer-char_L-12_H-768_A-12/bert_model.ckpt'
dict_path = '/data/PretrainedModels/chinese_roformer-char_L-12_H-768_A-12/vocab.txt'The prompt size is in the snippets.py as below.
m = build_transformer_model(
config_path,
checkpoint_path,
model=PtuningV2AddFNNPtuning,
application='unilm',
prompt_size=0,
ffn_prompt_size=160,
keep_tokens=keep_tokens,
return_keras_model=False,
)The hyperparameter ffn_prompt_size represents the prompt size of FL-Tuning, the FL-Tuning will be disabled when the ffn_prompt_size is 0.
The hyperparameter prompt_size represents the prompt size of P-tuning V2, the P-tuning V2 will be disabled when the prompt_size is 0.
For the convenience of comparison experiments, we also implemented P-Tuning V2 in this code, you can use these two tuning methods by changing the hyperparameter ffn_prompt_size and prompt_size. In most of our experiments, the size is 160 (One of the tuning methods is enabled) or 0 (The other tuning method is disabled).
You can train and test the downstream tasks directly by running the downstream task python files, for example:
python afqmc.py
python c3.py
...The code is implemented based on bert4keras and CLUE-bert4keras. Sincere thanks to the author for his selfless dedication.
@misc{https://doi.org/10.48550/arxiv.2206.15312,
doi = {10.48550/ARXIV.2206.15312},
url = {https://arxiv.org/abs/2206.15312},
author = {Liu, Jingping and Song, Yuqiu and Xue, Kui and Sun, Hongli and Wang, Chao and Chen, Lihan and Jiang, Haiyun and Liang, Jiaqing and Ruan, Tong},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {FL-Tuning: Layer Tuning for Feed-Forward Network in Transformer},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}