Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.

Related tags

Deep Learningwechsel
Overview

WECHSEL

Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.

arXiv: https://arxiv.org/abs/2112.06598

Models from the paper are available on the HuggingFace Hub:

Installation

We distribute a Python Package via PyPI:

pip install wechsel

Alternatively, clone the repository, install requirements.txt and run the code in wechsel/.

Example usage

Transferring English roberta-base to Swahili:

import torch
from transformers import AutoModel, AutoTokenizer
from datasets import load_dataset
from wechsel import WECHSEL, load_embeddings

source_tokenizer = AutoTokenizer.from_pretrained("roberta-base")
model = AutoModel.from_pretrained("roberta-base")

target_tokenizer = source_tokenizer.train_new_from_iterator(
    load_dataset("oscar", "unshuffled_deduplicated_sw", split="train")["text"],
    vocab_size=len(source_tokenizer)
)

wechsel = WECHSEL(
    load_embeddings("en"),
    load_embeddings("sw"),
    bilingual_dictionary="swahili"
)

target_embeddings, info = wechsel.apply(
    source_tokenizer,
    target_tokenizer,
    model.get_input_embeddings().weight.detach().numpy(),
)

model.get_input_embeddings().weight.data = torch.from_numpy(target_embeddings)

# use `model` and `target_tokenizer` to continue training in Swahili!

Bilingual dictionaries

We distribute 3276 bilingual dictionaries from English to other languages for use with WECHSEL in dicts/.

Citation

Please cite WECHSEL as

@misc{minixhofer2021wechsel,
      title={WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models}, 
      author={Benjamin Minixhofer and Fabian Paischer and Navid Rekabsaz},
      year={2021},
      eprint={2112.06598},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
Owner
Institute of Computational Perception
Johannes Kepler University
Institute of Computational Perception
Torch Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"

Photo-Realistic-Super-Resoluton Torch Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" [Paper]

Harry Yang 199 Dec 01, 2022
zeus is a Python implementation of the Ensemble Slice Sampling method.

zeus is a Python implementation of the Ensemble Slice Sampling method. Fast & Robust Bayesian Inference, Efficient Markov Chain Monte Carlo (MCMC), Bl

Minas Karamanis 197 Dec 04, 2022
Nodule Generation Algorithm Baseline and template code for node21 generation track

Nodule Generation Algorithm This codebase implements a simple baseline model, by following the main steps in the paper published by Litjens et al. for

node21challenge 10 Apr 21, 2022
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).

MixHop and N-GCN ⠀ A PyTorch implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019)

Benedek Rozemberczki 393 Dec 13, 2022
Public repository containing materials used for Feed Forward (FF) Neural Networks article.

Art041_NN_Feed_Forward Public repository containing materials used for Feed Forward (FF) Neural Networks article. -- Illustration of a very simple Fee

SolClover 2 Dec 29, 2021
ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation - SIGGRAPH 2021

ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation - SIGGRAPH 2021 Dataset Code Demos Authors: He Zhang, Yuting Ye, Tak

HE ZHANG 194 Dec 06, 2022
Cweqgen - The CW Equation Generator

The CW Equation Generator The cweqgen (pronouced like "Queck-Jen") package provi

2 Jan 15, 2022
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

Introduction This is a Python package available on PyPI for NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pyto

Artit 'Art' Wangperawong 5 Sep 29, 2021
👨‍💻 run nanosaur in simulation with Gazebo/Ingnition

🦕 👨‍💻 nanosaur_gazebo nanosaur The smallest NVIDIA Jetson dinosaur robot, open-source, fully 3D printable, based on ROS2 & Isaac ROS. Designed & ma

nanosaur 9 Jul 19, 2022
Utilities and information for the signals.numer.ai tournament

dsignals Utilities and information for the signals.numer.ai tournament using eodhistoricaldata.com eodhistoricaldata.com provides excellent historical

Degerhan Usluel 23 Dec 18, 2022
Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Zhengxia Zou 1.5k Dec 28, 2022
Near-Optimal Sparse Allreduce for Distributed Deep Learning (published in PPoPP'22)

Near-Optimal Sparse Allreduce for Distributed Deep Learning (published in PPoPP'22) Ok-Topk is a scheme for distributed training with sparse gradients

Shigang Li 9 Oct 29, 2022
NAS-HPO-Bench-II is the first benchmark dataset for joint optimization of CNN and training HPs.

NAS-HPO-Bench-II API Overview NAS-HPO-Bench-II is the first benchmark dataset for joint optimization of CNN and training HPs. It helps a fair and low-

yoichi hirose 8 Nov 21, 2022
Deep Residual Learning for Image Recognition

Deep Residual Learning for Image Recognition This is a Torch implementation of "Deep Residual Learning for Image Recognition",Kaiming He, Xiangyu Zhan

Kimmy 561 Dec 01, 2022
TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition

TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition Xue, Wenyuan, et al. "TGRNet: A Table Graph Reconstruction Network for Ta

Wenyuan 68 Jan 04, 2023
Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

Recursive-NeRF: An Efficient and Dynamically Growing NeRF This is a Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

33 Nov 30, 2022
TLDR: Twin Learning for Dimensionality Reduction

TLDR (Twin Learning for Dimensionality Reduction) is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self

NAVER 105 Dec 28, 2022
Efficiently computes derivatives of numpy code.

Note: Autograd is still being maintained but is no longer actively developed. The main developers (Dougal Maclaurin, David Duvenaud, Matt Johnson, and

Formerly: Harvard Intelligent Probabilistic Systems Group -- Now at Princeton 6.1k Jan 08, 2023
code for our ECCV 2020 paper "A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation"

Code for our ECCV (2020) paper A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation. Prerequisites: python == 3.6.8 pytorch ==1.1.0

32 Nov 27, 2022
This is the code for the paper "Motion-Focused Contrastive Learning of Video Representations" (ICCV'21).

Motion-Focused Contrastive Learning of Video Representations Introduction This is the code for the paper "Motion-Focused Contrastive Learning of Video

11 Sep 23, 2022