projecte-aina/aina-translator-gl-ca

Projecte Aina’s Galician-Catalan machine translation model

Model description

This model was trained from scratch using the Fairseq toolkit on a combination of Galician-Catalan datasets totalling 10.017.995 sentence pairs. 4.267.995 sentence pairs were parallel data collected from the web while the remaining 5.750.000 sentence pairs were parallel synthetic data created using the GL-ES translator of Proxecto Nós. The model was evaluated on the Flores, TaCon and NTREX evaluation datasets.

Intended uses and limitations

You can use this model for machine translation from Galician 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-gl-ca", revision="main")
tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
tokenized=tokenizer.tokenize("Benvido ao proxecto Ilenia.")
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 Galician-Catalan data collected from the web was a combination of the following datasets:

Dataset	Sentences before cleaning
CCMatrix v1	3.041.152
XLENT	371.377
WikiMatrix	286.446
GNOME	18
KDE4	147.182
TED2020 v1	11.041
OpenSubtitles	16.379
Covost 2	263.729
Gene-Crawling	38.320
Memories Projectes Lliures	794.631
Total	4.952.275

The datasets were concatenated before filtering to avoid intra-dataset duplicates and the final size was 4.267.995. The 5.750.000 sentence pairs of synthetic parallel data were created from a random sampling of the Projecte Aina ES-CA corpus.

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 to form a final corpus of 10.017.995 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 24.000 updates on the parallel data collected from the web. This data was then concatenated with the synthetic parallel data and training continued for a total of 34.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 4 checkpoints.

Evaluation

Variable and metrics

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

Evaluation results

Below are the evaluation results on the machine translation from Galician to Catalan compared to Google Translate, M2M100 1.2B, NLLB 200 3.3B and NLLB-200's distilled 1.3B variant:

Test set	Google Translate	M2M100 1.2B	NLLB 1.3B	NLLB 3.3	aina-translator-gl-ca
Flores 101 devtest	36,4	32,6	22,3	34,3	32,4
TaCON	48,4	56,5	32,2	54,1	58,2
NTREX	34,7	34,0	20,4	34,2	33,7
Average	39,0	41,0	25,0	40,9	41,4

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 is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project ILENIA with reference 2022/TL22/00215337, 2022/TL22/00215336, 2022/TL22/00215335 y 2022/TL22/00215334

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.