Awesome

Introduction

This repository contains research code for the ACL 2021 paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models". Feel free to use this code to re-run our experiments or run new experiments on your own data.

Setup

1. Clone this repo

git clone git@github.com:Adapter-Hub/hgiyt.git

2. Install PyTorch (we used v1.7.1 - code may not work as expected for older or newer versions) in a new Python (>=3.6) virtual environment

pip install torch===1.7.1+cu110 -f https://download.pytorch.org/whl/torch_stable.html

3. Initialize the submodules

git submodule update --init --recursive

4. Install the adapter-transformer library and dependencies

pip install lib/adapter-transformers
pip install -r requirements.txt

1. Install Nvidia Apex for automatic mixed-precision (amp / fp16) training

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

2. Install wiki-bert-pipeline dependencies

pip install -r lib/wiki-bert-pipeline/requirements.txt

To use the Japanese monolingual model, install the morphological parser MeCab with the mecab-ipadic-20070801 dictionary:

0. Install gdown for easy downloads from Google Drive

pip install gdown

1. Download and install MeCab

gdown https://drive.google.com/uc?id=0B4y35FiV1wh7cENtOXlicTFaRUE
tar -xvzf mecab-0.996.tar.gz
cd mecab-0.996
./configure 
make
make check
sudo make install

2. Download and install the mecab-ipadic-20070801 dictionary

gdown https://drive.google.com/uc?id=0B4y35FiV1wh7MWVlSDBCSXZMTXM
tar -xvzf mecab-ipadic-2.7.0-20070801.tar.gz
cd mecab-ipadic-2.7.0-20070801
./configure --with-charset=utf8
make
sudo make install

Data

We unfortunately cannot host the datasets used in our paper in this repo. However, we provide download links (wherever possible) and instructions or scripts to preprocess the data for finetuning and for pretraining.

Experiments

Our scripts are largely borrowed from the transformers and adapter-transformers libraries. For pretrained models and adapters we rely on the ModelHub and AdapterHub. However, even if you haven't used them before, running our scripts should be pretty straightforward :).

We provide instructions on how to execute our finetuning scripts here and our pretraining script here.

Models

Our pretrained models are also available in the ModelHub: https://huggingface.co/hgiyt. Feel free to finetune them with our scripts or use them in your own code.

Citation & Authors

@inproceedings{rust-etal-2021-good,
      title     = {How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models}, 
      author    = {Phillip Rust and Jonas Pfeiffer and Ivan Vuli{\'c} and Sebastian Ruder and Iryna Gurevych},
      year      = {2021},
      booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational
                  Linguistics, {ACL} 2021, Online, August 1-6, 2021},
      url       = {https://arxiv.org/abs/2012.15613},
      pages     = {3118--3135}
}

Contact Person: Phillip Rust, plip.rust@gmail.com

Don't hesitate to send us an e-mail or report an issue if something is broken (and it shouldn't be) or if you have further questions.

This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.