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README.md
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---
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license:
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---
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## Projecte Aina’s Catalan-Italian machine translation model
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## Table of Contents
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- [Model Description](#model-description)
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- [Intended Uses and Limitations](#intended-use)
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- [How to Use](#how-to-use)
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- [Training](#training)
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- [Training data](#training-data)
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- [Training procedure](#training-procedure)
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- [Data Preparation](#data-preparation)
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- [Tokenization](#tokenization)
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- [Hyperparameters](#hyperparameters)
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- [Evaluation](#evaluation)
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- [Variable and Metrics](#variable-and-metrics)
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- [Evaluation Results](#evaluation-results)
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- [Additional Information](#additional-information)
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- [Author](#author)
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- [Contact Information](#contact-information)
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- [Copyright](#copyright)
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- [Licensing Information](#licensing-information)
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- [Funding](#funding)
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- [Disclaimer](#disclaimer)
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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
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## Intended uses and limitations
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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/
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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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print(tokenizer.detokenize(translated[0][0]['tokens']))
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```
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## Training
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### Training data
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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
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#### Tokenization
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Below are the evaluation results on the machine translation from Catalan to Italian compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es):
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| Test set | SoftCatalà | Google Translate |
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|----------------------|------------|------------------|---------------|
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| Flores 101 dev | 24,3 | **28,5** | 26,1 |
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| Flores 101 devtest |24,7 | **29,1** | 26,3 |
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### Author
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Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
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### Contact
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For further information, send an email to <langtech@bsc.es>
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### Copyright
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Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023)
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###
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This work is licensed under a [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]
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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. However, we are aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
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### Disclaimer
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---
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license: apache-2.0
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datasets:
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- projecte-aina/CA-IT_Parallel_Corpus
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language:
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- ca
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- it
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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-Italian 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
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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.
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## Intended uses and limitations
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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-it", 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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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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### Data preparation
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All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less
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than 0.75. This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE).
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The filtered datasets are then concatenated to form a final corpus of 9.482.927 and before training the punctuation is normalized
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using a modified version of the join-single-file.py script 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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Below are the evaluation results on the machine translation from Catalan to Italian compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es):
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| Test set | SoftCatalà | Google Translate | aina-translator-ca-it |
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|----------------------|------------|------------------|---------------|
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| Flores 101 dev | 24,3 | **28,5** | 26,1 |
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| Flores 101 devtest |24,7 | **29,1** | 26,3 |
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### Author
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Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
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### Contact
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For further information, send an email to <langtech@bsc.es>
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### Copyright
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Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023)
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### License
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This work is licensed under a [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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