Projecte Aina’s German-Catalan machine translation model
Model description
This model was trained from scratch using the Fairseq toolkit on a combination of datasets comprising both Catalan-German data
sourced from Opus, and additional datasets where synthetic Catalan was generated from the Spanish side of Spanish-Germancorpora 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 German to Catalan.
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:
Datasets
Multi CCAligned
WikiMatrix
GNOME
KDE4
OpenSubtitles
GlobalVoices
Tatoeba
Books
Europarl
Tilde
Multi-Paracawl
DGT
EU Bookshop
NLLB
OpenSubtitles
All corpora except Europarl and Tilde were collected from Opus.
The Europarl and Tilde corpora are a synthetic parallel corpus created from the original Spanish-Catalan corpora by SoftCatalà.
Where a Spanish-German corpus was used, synthetic Catalan was generated from the Spanish side 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 a final corpus of 6.258.272 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 29.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 2 checkpoints.
Evaluation
Variable and metrics
We use the BLEU score for evaluation on the Flores-101, NTEU (unpublished) and NTREX test sets.
Evaluation results
Below are the evaluation results on the machine translation from German to Catalan compared to Softcatalà and
Google Translate:
Test set
SoftCatalà
Google Translate
aina-translator-de-ca
Flores 101 dev
28,9
35,1
33,1
Flores 101 devtest
29,2
35,9
33,2
NTEU
38,9
39,1
42,9
NTREX
25,7
31,2
29,1
Average
30,7
35,3
34,3
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.
