Applying "Load What You Need: Smaller Versions of Multilingual BERT" to LaBSE

Last update: Sep 02, 2022

Overview

smaller-LaBSE

LaBSE(Language-agnostic BERT Sentence Embedding) is a very good method to get sentence embeddings across languages. But it is hard to fine-tune due to the parameter size(~=471M) of this model. For instance, if I fine-tune this model with Adam optimizer, I need the GPU that has VRAM at least 7.5GB = 471M * (parameters 4 bytes + gradients 4 bytes + momentums 4 bytes + variances 4 bytes). So I applied "Load What You Need: Smaller Multilingual Transformers" method to LaBSE to reduce parameter size since most of this model's parameter is the word embedding table(~=385M).

The smaller version of LaBSE is evaluated for 14 languages using tatoeba dataset. It shows we can reduce LaBSE's parameters to 47% without a big performance drop.

If you need the PyTorch version, see https://github.com/Geotrend-research/smaller-transformers. I followed most of the steps in the paper.

Model	#param(transformer)	#param(word embedding)	#param(model)	vocab size
tfhub_LaBSE	85.1M	384.9M	470.9M	501,153
15lang_LaBSE	85.1M	133.1M	219.2M	173,347

Used Languages

English (en or eng)
French (fr or fra)
Spanish (es or spa)
German (de or deu)
Chinese (zh, zh_classical or cmn)
Arabic (ar or ara)
Italian (it or ita)
Japanese (ja or jpn)
Korean (ko or kor)
Dutch (nl or nld)
Polish (pl or pol)
Portuguese (pt or por)
Thai (th or tha)
Turkish (tr or tur)
Russian (ru or rus)

I selected the languages multilingual-USE supports.

Scripts

A smaller version of the vocab was constructed based on the frequency of tokens using Wikipedia dump data. I followed most of the algorithms in the paper to extract proper vocab for each language and rewrite it for TensorFlow.

Convert weight

mkdir -p downloads/labse-2
curl -L https://tfhub.dev/google/LaBSE/2?tf-hub-format=compressed -o downloads/labse-2.tar.gz
tar -xf downloads/labse-2.tar.gz -C downloads/labse-2/
python save_as_weight_from_saved_model.py

Select vocabs

./download_dataset.sh
python select_vocab.py

Make smaller LaBSE

./make_smaller_labse.py

Evaluate tatoeba

./download_tatoeba_dataset.sh
# evaluate TFHub LaBSE
./evaluate_tatoeba.sh
# evaluate the smaller LaBSE
./evaluate_tatoeba.sh \
    --model models/LaBSE_en-fr-es-de-zh-ar-zh_classical-it-ja-ko-nl-pl-pt-th-tr-ru/1/ \
    --preprocess models/LaBSE_en-fr-es-de-zh-ar-zh_classical-it-ja-ko-nl-pl-pt-th-tr-ru_preprocess/1/

Results

Tatoeba

Model	fr	es	de	zh	ar	it	ja	ko	nl	pl	pt	th	tr	ru	avg
tfHub_LaBSE(en→xx)	95.90	98.10	99.30	96.10	90.70	95.30	96.40	94.10	97.50	97.90	95.70	82.85	98.30	95.30	95.25
tfHub_LaBSE(xx→en)	96.00	98.80	99.40	96.30	91.20	94.00	96.50	92.90	97.00	97.80	95.40	83.58	98.50	95.30	95.19
15lang_LaBSE(en→xx)	95.20	98.00	99.20	96.10	90.50	95.20	96.30	93.50	97.50	97.90	95.80	82.85	98.30	95.40	95.13
15lang_LaBSE(xx→en)	95.40	98.70	99.40	96.30	91.10	94.00	96.30	92.70	96.70	97.80	95.40	83.58	98.50	95.20	95.08

Accuracy(%) of the Tatoeba datasets.
If the strategy to select vocabs is changed or the corpus used in the selection step is changed to the corpus similar to the evaluation dataset, it is expected to reduce the performance drop.

References

Load What You Need: Smaller Versions of Multilingual BERT (Paper: https://arxiv.org/abs/2010.05609, GitHub: https://github.com/Geotrend-research/smaller-transformers)
Language-agnostic BERT Sentence Embedding: https://arxiv.org/abs/2007.01852
TFHub - LaBSE: https://tfhub.dev/google/LaBSE/2
LaBSE blog post: https://ai.googleblog.com/2020/08/language-agnostic-bert-sentence.html
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond: https://arxiv.org/abs/1812.10464

Comments

Training time and Machine configuration

Hi, thanks for your sharing model. I want to make a smaller model, just contains two languages(en, zh). And I want to know the kind of machine GPU and how long does it need to cost?

opened by QzzIsCoding 2
Publish model to HuggingFace Model Hub?

I migrated the full LaBSE model from TF to PyTorch and uploaded them to the HuggingFace model hub. I saw this model on the TF hub and started migrating it for uploading to the HF Hub. I realized then that this wasn't published by Google but by @jeongukjae, so wanted to check with you before uploading it.

I have exported the model locally. I'm happy to check the changes in and upload the exported model if that's fine for you :).

opened by setu4993 2

Applying "Load What You Need: Smaller Versions of Multilingual BERT" to LaBSE

Related tags

Overview

smaller-LaBSE

Used Languages

Scripts

Convert weight

Select vocabs

Make smaller LaBSE

Evaluate tatoeba

Results

Tatoeba

References

You might also like...

Comments

Training time and Machine configuration

Publish model to HuggingFace Model Hub?

Releases(15lang-1)

15lang-1(Sep 19, 2021)

Owner

Jeong Ukjae

Neural network sequence labeling model

Graph4nlp is the library for the easy use of Graph Neural Networks for NLP

This repository contains Python scripts for extracting linguistic features from Filipino texts.

This repository is home to the Optimus data transformation plugins for various data processing needs.

YACLC - Yet Another Chinese Learner Corpus

Mastering Transformers, published by Packt

NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles

This repository contains the code for EMNLP-2021 paper "Word-Level Coreference Resolution"

Toward a Visual Concept Vocabulary for GAN Latent Space, ICCV 2021

Learn meanings behind words is a key element in NLP. This project concentrates on the disambiguation of preposition senses. Therefore, we train a bert-transformer model and surpass the state-of-the-art.

All the code I wrote for Overwatch-related projects that I still own the rights to.

justCTF [*] 2020 challenges sources

What are the best Systems? New Perspectives on NLP Benchmarking

The official implementation of "BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?, ACL 2021 main conference"

Korea Spell Checker

💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

TTS is a library for advanced Text-to-Speech generation.

An Explainable Leaderboard for NLP

BiNE: Bipartite Network Embedding