projecte-aina/aina-translator-ca-en

Projecte Aina's English-Catalan machine translation model

Model description

This model was trained from scratch using the Fairseq toolkit on a combination of English-Catalan datasets, which after filtering and cleaning comprised 30.023.034 sentence pairs. The model was evaluated on several public datasets comprising different domains.

Intended uses and limitations

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

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

tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
tokenized=tokenizer.tokenize("Benvingut al projecte 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 several datasets, including data collected from Opus, HPLT, an internally created CA-EN Parallel Corpus, and other sources.

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 30.023.034 parallel sentences 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, using 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_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	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 for a total of 12.500 updates. Weights were saved every 1000 updates and reported results are the average of the last 6 checkpoints.

Evaluation

Variable and metrics

We use the BLEU score for evaluation on test sets: Spanish Constitution (TaCon), United Nations, AAPP, European Commission, Flores-200, Cybersecurity, wmt19 biomedical test set, wmt13 news test set.

Evaluation results

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

Test set	SoftCatalà	Google Translate	aina-translator-ca-en
Spanish Constitution	35,8	39,1	42,8
United Nations	44,4	46,9	45,9
AAPP	50,7	52,9	54
European Commission	52,0	53,7	54
Flores 200 dev	42,7	52,0	47,9
Flores 200 devtest	42,5	50,7	46,3
Cybersecurity	52,5	66,8	56,8
wmt 19 biomedical	18,3	24,4	25,2
wmt 13 news	37,8	42,5	39,4
Average	40,8	47,7	45,8

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.

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.