PlanTL-GOB-ES/mt-plantl-es-ca

PlanTL Project's Spanish-Catalan machine translation model

Model Description
Intended Uses and Limitations
How to Use
Training
- Training data
- Training procedure
Evaluation
- Variable and Metrics
- Evaluation Results
Additional Information

Model description

This model was trained from scratch using the Fairseq toolkit on a combination of Spanish-Catalan datasets, up to 92 million sentences. Additionally, the model is evaluated on several public datasecomprising 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

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="PlanTL-GOB-ES/mt-plantl-es-ca", revision="main")

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

translator = ctranslate2.Translator(model_dir)
translated = translator.translate_batch([tokenized[0]])
print(tokenizer.detokenize(translated[0][0]['tokens']))

Training

Training data

The model was trained on a combination of the following datasets:

Dataset	Sentences	Tokens
DOCG v2	8.472.786	188.929.206
El Periodico	6.483.106	145.591.906
EuroParl	1.876.669	49.212.670
WikiMatrix	1.421.077	34.902.039
Wikimedia	335.955	8.682.025
QED	71.867	1.079.705
TED2020 v1	52.177	836.882
CCMatrix v1	56.103.820	1.064.182.320
MultiCCAligned v1	2.433.418	48.294.144
ParaCrawl	15.327.808	334.199.408
Total	92.578.683	1.875.910.305

Training procedure

Data preparation

All datasets are 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 is normalized using a modified version of the join-single-file.py script from SoftCatalà

Tokenization

All data is tokenized using sentencepiece, with 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 8.000 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: 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	mt-plantl-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

Author

Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)

Contact information

For further information, send an email to plantl-gob-es@bsc.es

Copyright

Licensing information

This work is licensed under a Apache License, Version 2.0

Funding

This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)

Disclaimer

Click to expand

The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.

When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.

In no event shall the owner of the models (SEDIA – State Secretariat for Digitalization and Artificial Intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.

Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.

Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.

En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.

PlanTL-GOB-ES
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mt-plantl-es-ca