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--- |
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license: apache-2.0 |
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datasets: |
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- projecte-aina/CA-FR_Parallel_Corpus |
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language: |
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- ca |
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- fr |
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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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## Projecte Aina’s Catalan-French 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-French datasets, |
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which after filtering and cleaning comprised 18.634.844 sentence pairs. The model is evaluated on the Flores and NTREX evaluation sets. |
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## Intended uses and limitations |
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You can use this model for machine translation from Catalan to French. |
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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-fr", 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 the following datasets: |
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| Dataset | Sentences | Sentences after Cleaning | |
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|-------------------|----------------|-------------------| |
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| CCMatrix |24.386.198 | 16.305.758 | |
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| Multi CCAligned | 1.954.475 | 1.442.584 | |
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| WikiMatrix | 490.871 | 437.665 | |
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| GNOME | 12.962 | 1.686 | |
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|KDE 4 | 163.143 | 111.750 | |
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| QED | 65.336 | 52.797 | |
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| TED 2020 | 51.833 | 44.101 | |
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| Open Subtitles | 392.159 | 225.786 | |
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| **Total** | **27.532.050** | **18.634.844** | |
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### Training procedure |
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### Data preparation |
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All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75. |
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This is done using sentence embeddings calculated using LaBSE. The filtered datasets are then concatenated to form the final corpus |
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and before training the punctuation is normalized using a modified version of the join-single-file.py script from SoftCatalà |
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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 11.000 updates. |
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Weights were saved every 1000 updates and reported results are the average of the last 4 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: [Flores-101](https://github.com/facebookresearch/flores), [NTREX](https://github.com/MicrosoftTranslator/NTREX) |
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### Evaluation results |
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Below are the evaluation results on the machine translation from Catalan to French 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-fr | |
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|----------------------|------------|------------------|---------------| |
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| Flores 101 dev | 34,6 | **43,4** | 38,2 | |
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| Flores 101 devtest | 35,3 | **43,4** | 38,2 | |
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| NTREX | 25,3 | **31,5** | 27,7 | |
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| Average | 31,7 | **39,4** | 34,7 | |
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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> |
