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
Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021) Tensorflow implementation of Bridging the Gap between Label- and Reference-ba

huangqiusheng 8 Jul 13, 2022
source code of Adversarial Feedback Loop Paper

Adversarial Feedback Loop [ArXiv] [project page] Official repository of Adversarial Feedback Loop paper Firas Shama, Roey Mechrez, Alon Shoshan, Lihi

17 Jul 20, 2022
tensorrt int8 量化yolov5 4.0 onnx模型

onnx模型转换为 int8 tensorrt引擎

123 Dec 28, 2022
[CVPR 2020] Interpreting the Latent Space of GANs for Semantic Face Editing

InterFaceGAN - Interpreting the Latent Space of GANs for Semantic Face Editing Figure: High-quality facial attributes editing results with InterFaceGA

GenForce: May Generative Force Be with You 1.3k Dec 29, 2022
OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis

OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis Overview OpenABC-D is a large-scale labeled dataset generate

NYU Machine-Learning guided Design Automation (MLDA) 31 Nov 22, 2022
A lane detection integrated Real-time Instance Segmentation based on YOLACT (You Only Look At CoefficienTs)

Real-time Instance Segmentation and Lane Detection This is a lane detection integrated Real-time Instance Segmentation based on YOLACT (You Only Look

Jin 4 Dec 30, 2022
Implementation of neural class expression synthesizers

NCES Implementation of neural class expression synthesizers (NCES) Installation Clone this repository: https://github.com/ConceptLengthLearner/NCES.gi

NeuralConceptSynthesis 0 Jan 06, 2022
Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation"

DSP Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation". Accepted by ACM Multimedia 2021. Authors

20 Oct 24, 2022
NAACL'2021: Factual Probing Is [MASK]: Learning vs. Learning to Recall

OptiPrompt This is the PyTorch implementation of the paper Factual Probing Is [MASK]: Learning vs. Learning to Recall. We propose OptiPrompt, a simple

Princeton Natural Language Processing 150 Dec 20, 2022
Official and maintained implementation of the paper "OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data" [BMVC 2021].

OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data Christoph Reich, Tim Prangemeier, Özdemir Cetin & Heinz Koeppl | Pr

Christoph Reich 23 Sep 21, 2022
Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker

Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker This repository contai

Nikita 12 Dec 14, 2022
Go from graph data to a secure and interactive visual graph app in 15 minutes. Batteries-included self-hosting of graph data apps with Streamlit, Graphistry, RAPIDS, and more!

✔️ Linux ✔️ OS X ❌ Windows (#39) Welcome to graph-app-kit Turn your graph data into a secure and interactive visual graph app in 15 minutes! Why This

Graphistry 107 Jan 02, 2023
Official Pytorch Implementation of GraphiT

GraphiT: Encoding Graph Structure in Transformers This repository implements GraphiT, described in the following paper: Grégoire Mialon*, Dexiong Chen

Inria Thoth 80 Nov 27, 2022
Official implementation of "Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection" (ICCV Workshops 2021: RSL-CV).

Official PyTorch implementation of "Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection" This is the implementation of the paper "Syn

Marcella Astrid 11 Oct 07, 2022
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022)

Pop-Out Motion Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022) Jihyun Lee*, Minhyuk Sung*, Hyunjin Kim, Tae-Ky

Jihyun Lee 88 Nov 22, 2022
BERT model training impelmentation using 1024 A100 GPUs for MLPerf Training v1.1

Pre-trained checkpoint and bert config json file Location of checkpoint and bert config json file This MLCommons members Google Drive location contain

SAIT (Samsung Advanced Institute of Technology) 12 Apr 27, 2022
1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking

Instead, two models for appearance modeling are included, together with the open-source BAGS model and the full set of code for inference. With this code, you can achieve around 79 Oct 08, 2022

FLAVR is a fast, flow-free frame interpolation method capable of single shot multi-frame prediction

FLAVR is a fast, flow-free frame interpolation method capable of single shot multi-frame prediction. It uses a customized encoder decoder architecture with spatio-temporal convolutions and channel ga

Tarun K 280 Dec 23, 2022
A Pythonic library for Nvidia Codec.

A Pythonic library for Nvidia Codec. The project is still in active development; expect breaking changes. Why another Python library for Nvidia Codec?

Zesen Qian 12 Dec 27, 2022
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning This repository is the official implementation of CARE.

ChongjianGE 89 Dec 02, 2022