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--- |
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license: apache-2.0 |
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language: |
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- ca |
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- es |
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metrics: |
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- bleu |
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library_name: fairseq |
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--- |
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## Aina Project's Catalan-Spanish machine translation model |
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## Model description |
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This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Catalan-Spanish datasets, |
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up to 92 million sentences before cleaning and filtering. Additionally, the model is evaluated on several public datasets comprising 5 different domains |
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(general, adminstrative, technology, biomedical, and news). |
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## Intended uses and limitations |
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You can use this model for machine translation from Catalan to Spanish. |
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## How to use |
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### Usage |
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Required libraries: |
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```bash |
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pip install ctranslate2 pyonmttok |
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``` |
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Translate a sentence using python |
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```python |
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import ctranslate2 |
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import pyonmttok |
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from huggingface_hub import snapshot_download |
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model_dir = snapshot_download(repo_id="projecte-aina/aina-translator-ca-es", revision="main") |
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tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model") |
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tokenized=tokenizer.tokenize("Benvingut al projecte Aina!") |
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translator = ctranslate2.Translator(model_dir) |
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translated = translator.translate_batch([tokenized[0]]) |
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print(tokenizer.detokenize(translated[0][0]['tokens'])) |
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``` |
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## Limitations and bias |
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At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. |
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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. |
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## Training |
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### Training data |
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The model was trained on a combination of several datasets, totalling around 92 million parallel sentences before filtering and cleaning. |
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The trainig data includes corpora collected from [Opus](https://opus.nlpl.eu/), internally created parallel datsets, and corpora from other sources. |
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### Training procedure |
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### Data preparation |
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All datasets are concatenated and filtered using the [mBERT Gencata parallel filter](https://huggingface.co/projecte-aina/mbert-base-gencata) and |
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cleaned using the clean-corpus-n.pl script from [moses](https://github.com/moses-smt/mosesdecoder), allowing sentences between 5 and 150 words. |
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Before training, the punctuation is normalized using a modified version of the join-single-file.py script |
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from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py). |
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#### Tokenization |
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All data is tokenized using sentencepiece, with 50 thousand token sentencepiece model learned from the combination of all filtered training data. |
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This model is included. |
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#### Hyperparameters |
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The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf) |
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The following hyperparamenters were set on the Fairseq toolkit: |
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| Hyperparameter | Value | |
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|------------------------------------|----------------------------------| |
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| Architecture | transformer_vaswani_wmt_en_de_bi | |
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| Embedding size | 1024 | |
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| Feedforward size | 4096 | |
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| Number of heads | 16 | |
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| Encoder layers | 24 | |
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| Decoder layers | 6 | |
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| Normalize before attention | True | |
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| --share-decoder-input-output-embed | True | |
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| --share-all-embeddings | True | |
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| Effective batch size | 96.000 | |
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| Optimizer | adam | |
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| Adam betas | (0.9, 0.980) | |
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| Clip norm | 0.0 | |
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| Learning rate | 1e-3 | |
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| Lr. schedurer | inverse sqrt | |
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| Warmup updates | 4000 | |
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| Dropout | 0.1 | |
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| Label smoothing | 0.1 | |
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The model was trained using shards of 10 million sentences, for a total of 13.000 updates. |
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Weights were saved every 1000 updates and reported results are the average of the last 6 checkpoints. |
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## Evaluation |
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### Variable and metrics |
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We use the BLEU score for evaluation on test sets: |
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[Flores-101](https://github.com/facebookresearch/flores), |
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[TaCon](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/), |
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[United Nations](https://zenodo.org/record/3888414#.Y33-_tLMIW0), |
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[Cybersecurity](https://elrc-share.eu/repository/browse/cyber-mt-test-set/2bd93faab98c11ec9c1a00155d026706b96a490ed3e140f0a29a80a08c46e91e/), |
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[wmt19 biomedical test set](http://www.statmt.org/wmt19/biomedical-translation-task.html), |
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[wmt13 news test set](https://elrc-share.eu/repository/browse/catalan-wmt2013-machine-translation-shared-task-test-set/84a96139b98611ec9c1a00155d0267061a0aa1b62e2248e89aab4952f3c230fc/) |
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### Evaluation results |
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Below are the evaluation results on the machine translation from Catalan to Spanish compared to [Softcatalà](https://www.softcatala.org/) |
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and [Google Translate](https://translate.google.es/?hl=es): |
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| Test set | SoftCatalà | Google Translate | aina-translator-ca-es | |
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|----------------------|------------|------------------|---------------| |
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| Spanish Constitution | 70,7 | 77,1 | **88,3** | |
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| United Nations | 78,1 | 84,3 | **87,3** | |
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| Flores 101 dev | 23,5 | 24 | **24,2** | |
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| Flores 101 devtest | 24,1 | 24,2 | **24,7** | |
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| Cybersecurity | 67,3 | 76,9 | **78,2** | |
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| wmt 19 biomedical | 60,4 | 62,7 | **64,1** | |
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| wmt 13 news | 22,5 | 23,1 | **23,7** | |
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| Average | 53,4 | 53,2 | **55,1** | |
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## Additional information |
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### Author |
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The Language Technologies Unit from Barcelona Supercomputing Center. |
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### Contact |
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For further information, please send an email to <langtech@bsc.es>. |
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### Copyright |
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Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center. |
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### License |
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[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
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### Funding |
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This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/). |
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### Disclaimer |
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<details> |
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<summary>Click to expand</summary> |
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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. |
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Be aware that the model may have biases and/or any other undesirable distortions. |
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When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) |
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or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, |
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in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. |
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In no event shall the owner and creator of the model (Barcelona Supercomputing Center) |
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be liable for any results arising from the use made by third parties. |
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</details> |
