Projecte Aina’s Catalan-French machine translation model
Model description
This model was trained from scratch using the Fairseq toolkit on a combination of datasets comprising both Catalan-French data sourced from Opus, and additional datasets where synthetic Catalan was generated from the Spanish side of Spanish-French corpora using Projecte Aina’s Spanish-Catalan model. This gave a total of approximately 100 million sentence pairs. The model is evaluated on the Flores, NTEU and NTREX evaluation sets.
Intended uses and limitations
You can use this model for machine translation from Catalan to French.
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 model was trained on a combination of the following datasets for a total of 100 million sentence pairs:
Datasets
DGT
EU Bookshop
Europarl
Global Voices
GNOME
KDE 4
Multi CCAligned
Multi Paracrawl
Multi UN
NLLB
NTEU
Open Subtitles
UNPC
WikiMatrix
All data was sourced from OPUS and ELRC. After all Catalan-French data had
been collected, Spanish-French data was collected and the Spanish data translated to Catalan using Projecte Aina’s Spanish-Catalan model.
Training procedure
Data preparation
All datasets are deduplicated, filtered for language identification, 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 the final corpus.
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 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 11.000 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-101, NTREX, and
NTEU (unpublished evaluation corpus)
Evaluation results
Below are the evaluation results on the machine translation from Catalan to French compared to Softcatalà
and Google Translate:
Test set
SoftCatalà
Google Translate
aina-translator-ca-fr
Flores 101 dev
37,2
43,6
42,3
Flores 101 devtest
36,9
42,8
41,6
NTEU
43.8
46,7
53,4
NTREX
27,9
31,5
30,2
Average
36,5
41,1
41,8
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.
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.
