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Aina Project's Spanish-Catalan machine translation model

Model description

This model was trained from scratch using the Fairseq toolkit on a combination of Catalan-Spanish datasets, up to 92 million sentences. Additionally, the model is evaluated on several public datasets comprising 5 different domains (general, adminstrative, technology, biomedical, and news).

Intended uses and limitations

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

How to use


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

tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
tokenized=tokenizer.tokenize("Bienvenido al Proyecto Aina!")

translator = ctranslate2.Translator(model_dir)
translated = translator.translate_batch([tokenized[0]])

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 model was trained on a combination of several datasets, totalling around 92 million parallel sentences before filtering and cleaning. The trainig data includes corpora collected from Opus, internally created parallel datsets, and corpora from other sources.

Training procedure

Data preparation

All datasets were concatenated and filtered using the mBERT Gencata parallel filter and cleaned using the clean-corpus-n.pl script from moses, allowing sentences between 5 and 150 words.

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


All data was tokenized using sentencepiece, with 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 hyperparamenters were set on the Fairseq toolkit:

Hyperparameter Value
Architecture transformer_vaswani_wmt_en_de_bi
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 96.000
Optimizer adam
Adam betas (0.9, 0.980)
Clip norm 0.0
Learning rate 1e-3
Lr. schedurer inverse sqrt
Warmup updates 4000
Dropout 0.1
Label smoothing 0.1

The model was trained using shards of 10 million sentences, for a total of 8.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 6 checkpoints.


Variables and metrics

We use the BLEU score for evaluation on following test sets: Flores-101, TaCon, United Nations, Cybersecurity, wmt19 biomedical test set, wmt13 news test set

Evaluation results

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

Test set SoftCatalà Google Translate aina-translator-es-ca
Spanish Constitution 63,6 61,7 63,0
United Nations 73,8 74,8 74,9
Flores 101 dev 22 23,1 22,5
Flores 101 devtest 22,7 23,6 23,1
Cybersecurity 61,4 69,5 67,3
wmt 19 biomedical 60,2 59,7 60,6
wmt 13 news 21,3 22,4 22,0
Average 46,4 47,8 47,6

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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