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

Related tags

Deep Learninghgiyt
Overview

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

General  
  1. Clone this repo
git clone [email protected]:Adapter-Hub/hgiyt.git
  1. 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
  1. Initialize the submodules
git submodule update --init --recursive
  1. Install the adapter-transformer library and dependencies
pip install lib/adapter-transformers
pip install -r requirements.txt
Pretraining  
  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" ./
  1. Install wiki-bert-pipeline dependencies
pip install -r lib/wiki-bert-pipeline/requirements.txt
Language-specific prerequisites  

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

  1. 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
  1. 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, [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.

A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

Graph Wavelet Neural Network ⠀⠀ A PyTorch implementation of Graph Wavelet Neural Network (ICLR 2019). Abstract We present graph wavelet neural network

Benedek Rozemberczki 490 Dec 16, 2022
Official implementation of "One-Shot Voice Conversion with Weight Adaptive Instance Normalization".

One-Shot Voice Conversion with Weight Adaptive Instance Normalization By Shengjie Huang, Yanyan Xu*, Dengfeng Ke*, Mingjie Chen, Thomas Hain. This rep

31 Dec 07, 2022
The description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts.

FMFCC-A This project is the description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts. The FMFCC-A dataset is shared through BaiduCl

18 Dec 24, 2022
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes

Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes

111 Dec 29, 2022
A hue shift helper for OBS

obs-hue-shift A hue shift helper for OBS This is a repo based on the really nice script Hegemege made. The original script can be found https://gist.g

Alexis Tyler 1 Jan 10, 2022
For AILAB: Cross Lingual Retrieval on Yelp Search Engine

Cross-lingual Information Retrieval Model for Document Search Train Phase CUDA_VISIBLE_DEVICES="0,1,2,3" \ python -m torch.distributed.launch --nproc_

Chilia Waterhouse 104 Nov 12, 2022
Unofficial Implement PU-Transformer

PU-Transformer-pytorch Pytorch unofficial implementation of PU-Transformer (PU-Transformer: Point Cloud Upsampling Transformer) https://arxiv.org/abs/

Lee Hyung Jun 7 Sep 21, 2022
Efficient Training of Visual Transformers with Small Datasets

Official codes for "Efficient Training of Visual Transformers with Small Datasets", NerIPS 2021.

Yahui Liu 112 Dec 25, 2022
PyKaldi GOP-DNN on Epa-DB

PyKaldi GOP-DNN on Epa-DB This repository has the tools to run a PyKaldi GOP-DNN algorithm on Epa-DB, a database of non-native English speech by Spani

18 Dec 14, 2022
List of all dependencies affected by node-ipc malicious commit

node-ipc-dependencies-list List of all dependencies affected by node-ipc malicious commit as of 17/3/2022 - 19/3/2022 (timestamp) Please improve upon

99 Oct 15, 2022
A crash course in six episodes for software developers who want to become machine learning practitioners.

Featured code sample tensorflow-planespotting Code from the Google Cloud NEXT 2018 session "Tensorflow, deep learning and modern convnets, without a P

Google Cloud Platform 2.6k Jan 08, 2023
This dlib-based facial login system

Facial-Login-System This dlib-based facial login system is a technology capable of matching a human face from a digital webcam frame capture against a

Mushahid Ali 3 Apr 23, 2022
[CVPR 2022] TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing

TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing (CVPR 2022) This repository provides the official PyTorch impleme

Billy XU 128 Jan 03, 2023
OverFeat is a Convolutional Network-based image classifier and feature extractor.

OverFeat OverFeat is a Convolutional Network-based image classifier and feature extractor. OverFeat was trained on the ImageNet dataset and participat

593 Dec 08, 2022
Patch-Based Deep Autoencoder for Point Cloud Geometry Compression

Patch-Based Deep Autoencoder for Point Cloud Geometry Compression Overview The ever-increasing 3D application makes the point cloud compression unprec

17 Dec 05, 2022
Laplacian Score-regularized Concrete Autoencoders

Laplacian Score-regularized Concrete Autoencoders Requirements: torch = 1.9 scikit-learn = 0.24 omegaconf = 2.0.6 scipy = 1.6.0 matplotlib How to

JS 6 Dec 07, 2022
Bottom-up Human Pose Estimation

Introduction This is the official code of Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation. This paper has been accepted to CVPR2

108 Dec 01, 2022
[CVPR 2021] Teachers Do More Than Teach: Compressing Image-to-Image Models (CAT)

CAT arXiv Pytorch implementation of our method for compressing image-to-image models. Teachers Do More Than Teach: Compressing Image-to-Image Models Q

Snap Research 160 Dec 09, 2022
Playing around with FastAPI and streamlit to create a YoloV5 object detector

FastAPI-Streamlit-based-YoloV5-detector Playing around with FastAPI and streamlit to create a YoloV5 object detector It turns out that a User Interfac

2 Jan 20, 2022
This repository contains a PyTorch implementation of the paper Learning to Assimilate in Chaotic Dynamical Systems.

Amortized Assimilation This repository contains a PyTorch implementation of the paper Learning to Assimilate in Chaotic Dynamical Systems. Abstract: T

4 Aug 16, 2022