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Projecte Aina’s Catalan-Chinese machine translation model

Model description

This model was trained from scratch using the Fairseq toolkit on a combination of Catalan-Chinese datasets totalling 6.833.114 sentence pairs. 174.507 sentence pairs were parallel data collected from the web while the remaining 6.658.607 sentence pairs were parallel synthetic data created using the ES-CA translator of PlanTL. The model was evaluated on the Flores and NTREX evaluation datasets.

Intended uses and limitations

You can use this model for machine translation from Catalan to simplified Chinese.

How to use


Required libraries:

pip install ctranslate2 pyonmttok

Translate a sentence using python

import ctranslate2
import pyonmttok
import re

def remove_jieba(text):
        preserve_spaces = re.sub(r'(?<=[\x00-\x7F])\s(?=[\x00-\x7F])', '@@', text)
        quit_jieba = re.sub(r'\s', '', preserve_spaces)
        replace_spaces = re.sub(r'@@', ' ', quit_jieba)
        return replace_spaces

from huggingface_hub import snapshot_download
model_dir = snapshot_download(repo_id="projecte-aina/aina-translator-ca-zh", revision="main")
tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
tokenized=tokenizer.tokenize("Benvingut al projecte Aina!")
translator = ctranslate2.Translator(model_dir)
translated = translator.translate_batch([tokenized[0]], beam_size=10)
translation = tokenizer.detokenize(translated[0][0]['tokens'])

Limitations and bias

At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.


Training data

The Catalan-Chinese data collected from the web was a combination of the following datasets:

Dataset Sentences before cleaning
WikiMatrix 90.643
XLENT 535.803
QED 3.677
TED2020 v1 56.269
OpenSubtitles 139.300
Total 882.039

The 6.658.607 sentence pairs of synthetic parallel data were created from the following Spanish-Chinese datasets:

Dataset Sentences before cleaning
UNPC 17.599.223
CCMatrix 24.051.233
MultiParacrawl 3.410.087
Total 45.060.543

Training procedure

Data preparation

The Chinese side of all datasets are passed through the fastlangid language detector and any sentences which are not identified as simplified Chinese are discarded. The datasets are then also deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75. This is done using sentence embeddings calculated using LaBSE. The filtered datasets are then concatenated to form a final corpus of 6.833.114. The Chinese side of the dataset is tokenized using Jieba and before training the punctuation is normalized using a modified version of the join-single-file.py script from SoftCatalà.


All data is tokenized using sentencepiece, with a 50 thousand token sentencepiece model learned from the combination of all filtered training data. This model is included.


The model is based on the Transformer-XLarge proposed by Subramanian et al. The following hyperparameters were set on the Fairseq toolkit:

Hyperparameter Value
Architecture transformer_vaswani_wmt_en_de_big
Embedding size 1024
Feedforward size 4096
Number of heads 16
Encoder layers 24
Decoder layers 6
Normalize before attention True
--share-decoder-input-output-embed True
--share-all-embeddings True
Effective batch size 48.000
Optimizer adam
Adam betas (0.9, 0.980)
Clip norm 0.0
Learning rate 5e-4
Lr. schedurer inverse sqrt
Warmup updates 8000
Dropout 0.1
Label smoothing 0.1

The model was trained for 17.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 4 checkpoints.


Variable and metrics

We use the BLEU score for evaluation on the Flores-200 and NTREX test sets.

Evaluation results

Below are the evaluation results on the machine translation from Catalan to Chinese compared to Google Translate, M2M 1.2B and NLLB-200's distilled 1.3B variant:

Test set Google Translate M2M 1.2B NLLB 1.3B aina-translator-ca-zh
Flores Dev 42,6 27,8 18,9 31,4
Flores Devtest 43,7 28,4 18,4 32,6
NTREX 36,3 24,4 14,2 26,6
Average 41,0 26,9 17,0 30,2

Additional information


The Language Technologies Unit from Barcelona Supercomputing Center.


For further information, please send an email to langtech@bsc.es.


Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.


Apache License, Version 2.0


This work has been promoted and financed by the Generalitat de Catalunya through the Aina project.


Click to expand

The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0.

Be aware that the model may have biases and/or any other undesirable distortions.

When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.

In no event shall the owner and creator of the model (Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties.

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