projecte-aina/aina-translator-it-ca

Projecte Aina’s Italian-Catalan machine translation model

Model description

This model was trained from scratch using the Fairseq toolkit on a combination of Catalan-Italian datasets, which after filtering and cleaning comprised 9.482.927 sentence pairs. The model was evaluated on the Flores and NTREX evaluation datasets.

Intended uses and limitations

You can use this model for machine translation from Italian 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-it-ca", revision="main")
tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
tokenized=tokenizer.tokenize("Benvenuto al progetto 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 model was trained on a combination of the following datasets:

Dataset	Sentences	Sentences after Cleaning
CCMatrix v1	11.444.720	7.757.357
MultiCCAligned v1	1.379.251	1.010.921
WikiMatrix	316.208	271.587
GNOME	8.571	1.198
KDE4	163.907	115.027
OpenSubtitles	391.293	225.732
GlobalVoices	6.318	5.209

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 9.482.927 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 19.000 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 the Flores-101 and NTREX test sets.

Evaluation results

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

Test set	SoftCatalà	Google Translate	aina-translator-it-ca
Flores 101 dev	25,4	30,4	27,5
Flores 101 devtest	26,6	31,2	27,7
NTREX	29,3	33,5	30,7
Average	27,1	31,7	28,6

Additional information

Author

The Language Technologies Unit from Barcelona Supercomputing Center.

Contact

For further information, please send an email to [email protected].

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.