Research code for the paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models"

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

git clone [email protected]:Adapter-Hub/hgiyt.git

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

git submodule update --init --recursive

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

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" ./

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

pip install gdown

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

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, [email protected]

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.