https://github.com/zhuchen03/FreeLB

Adversarial Training for Natural Language Understanding
https://github.com/zhuchen03/FreeLB

adversarial freelb iclr2020 natural-language

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Adversarial Training for Natural Language Understanding

• Host: GitHub
• URL: https://github.com/zhuchen03/FreeLB
• Owner: zhuchen03
• Created: 2020-02-14T01:34:12.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-09-06T17:33:04.000Z (about 1 year ago)
• Last Synced: 2024-08-02T16:55:53.581Z (3 months ago)
• Topics: adversarial, freelb, iclr2020, natural-language
• Language: Python
• Homepage:
• Size: 5.83 MB
• Stars: 251
• Watchers: 8
• Forks: 40
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

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README

        
# Introduction

This repository contains the implementation for FreeLB on GLUE tasks based on both [fairseq](https://github.com/pytorch/fairseq) and [HuggingFace's transformers](https://github.com/huggingface/transformers) libraries, under `./fairseq-RoBERTa/` and `./huggingface-transformers/` respectively.

We also integrated our implementations of vanilla PGD, [FreeAT](https://arxiv.org/abs/1904.12843) and [YOPO](https://arxiv.org/abs/1905.00877) in our fairseq version.

FreeLB is an adversarial training approach for improving transformer-based language models on Natural Language Understanding tasks.

It accumulates the gradient in the ascent steps and updates the parameters with the accumulated gradients, which is approximately equivalent to enlarging the batch size with diversified adversarial examples within different radiuses around the clean input.

FreeLB improves the performance of BERT and RoBERTa on various Natural Language Understanding tasks including Question Answering, Natural Language Inference, and Sentiment Analysis.

For technical details and additional experimental results, please refer to our paper:

Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu. [FreeLB: Enhanced Adversarial Training for Language Understanding](https://arxiv.org/abs/1909.11764). In ICLR, 2020.

# What's New

* Feb 15, 2020: Initial release of FreeLB based on fairseq and HuggingFace's transformers. The first one contains our implementations of FreeLB, FreeAT, YOPO for [RoBERTa](https://arxiv.org/abs/1907.11692), while the latter one is FreeLB for [ALBERT](https://arxiv.org/abs/1909.11942).

* May 16, 2020: Hyperparameters for ALBERT are now available at `huggingface-transformers/launch/run_glue.sh`.

# Prerequisites

The code is compatible with PyTorch 1.4.0. 

In addition, you need to execute the followings in order, to install the prerequisites for fairseq and HuggingFace's transformers:

```

# Install apex

git clone https://github.com/NVIDIA/apex

cd apex

pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

# Configure fairseq

cd ../fairseq-RoBERTa

pip install --editable .

# Download and pre-process GLUE data

wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py

python download_glue_data.py --data_dir glue_data --tasks all

source ./examples/roberta/preprocess_GLUE_tasks.sh glue_data ALL

cd ../huggingface-transformers

pip install --editable .

mkdir logs

```

# Launch

The launch scripts are under `./fairseq-RoBERTa/launch/` and `./huggingface-transformers/launch/`, where we have included most of the running scripts for RoBERTa and ALBERT on GLUE dev sets. 

We will release more details in the future.