Fairseq
Catalan
German
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Projecte Aina’s Catalan-German machine translation model

Model description

This model was trained from scratch using the Fairseq toolkit on a combination of Catalan-German datasets, totalling 100.000.000 sentence pairs. 6.258.272 sentence pairs were parallel data collected from the web while the remaining 93.741.728 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 German.

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-ca-de", revision="main")

tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.50k.model")
tokenized=tokenizer.tokenize("Benvingut al projecte Aina!")

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-German data collected from the web was a combination of the following datasets:

Dataset Sentences Sentences after Cleaning
Multi CCAligned 1.478.152 1.027.481
WikiMatrix 180.322 125.811
GNOME 12.333 1.241
KDE4 165.439 105.098
QED 63.041 49.181
TED2020 v1 46.680 38.428
OpenSubtitles 303.329 171.376
GlobalVoices 4.636 3.578
Tatoeba 732 655
Books 4.445 2049
Europarl 1.734.643 1.734.643
Tilde 3.434.091 3.434.091
Total 7.427.843 6.258.272

All corpora except Europarl and Tilde were collected from Opus. The Europarl and Tilde corpora are synthetic parallel corpora created from the original Spanish-German corpora by SoftCatalà.

The 93.741.728 sentence pairs of synthetic parallel data were created from the following Spanish-German datasets:

Dataset Sentences before cleaning
globalvoices_es-de_20230901 70.097
multiparacrawl_es-de_20230901 56.873.541
dgt_es-de_20240129 4.899.734
eubookshop_es-de_20240129 4.750.170
nllb_es-de_20240129 112.444.838
opensubtitles_es-de_20240129 18.951.214
Total 197.989.594

Training procedure

Data preparation

All datasets are 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 and 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:

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 a total of 22.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 3 checkpoints.

Evaluation

Variable and metrics

We use the BLEU score for evaluation on the Flores-101 and NTREX test sets.

Evaluation results

Below are the evaluation results on the machine translation from Catalan to German compared to Softcatalà and Google Translate:

Test set SoftCatalà Google Translate aina-translator-ca-de
Flores 101 dev 26,2 34,8 34,1
Flores 101 devtest 26,3 34,0 33,3
NTREX 21,7 28,8 27,8
Average 24,7 32,5 31,7

Additional information

Author

The Language Technologies Unit from Barcelona Supercomputing Center.

Contact

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

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

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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