projecte-aina / aina-translator-zh-ca

Projecte Aina’s Chinese-Catalan 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 evaluation datasets.

Intended uses and limitations

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

How to use

Usage

Required libraries:

pip install ctranslate2 pyonmttok

Translate a sentence using python

import ctranslate2
import pyonmttok
from huggingface_hub import snapshot_download
model_dir = snapshot_download(repo_id="projecte-aina/aina-translator-zh-ca", revision="main")
tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
tokenized=tokenizer.tokenize("欢迎来到 Aina 项目!")
translator = ctranslate2.Translator(model_dir)
translated = translator.translate_batch([tokenized[0]])
print(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

Training data

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

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

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. Before training the punctuation is normalized using a modified version of the join-single-file.py script from SoftCatalà .

Tokenization

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.

Hyperparameters

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

The model was trained for 24.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 8 checkpoints.

Evaluation

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 Chinese to Catalan compared to Google Translate , M2M 1.2B and NLLB-200's distilled 1.3B variant :

Additional information

Author

The Language Technologies Unit from Barcelona Supercomputing Center.

Contact

For further information, please send an email to [email protected] .

Copyright

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

License

Apache License, Version 2.0

Funding

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

Disclaimer