README.md

---
license:  apache-2.0
---
## Projecte Aina’s French-Catalan  machine translation model

## Table of Contents
- [Model Description](#model-description)
- [Intended Uses and Limitations](#intended-use)
- [How to Use](#how-to-use)
- [Training](#training)
  - [Training data](#training-data)
  - [Training procedure](#training-procedure)
	- [Data Preparation](#data-preparation)
	- [Tokenization](#tokenization)
	- [Hyperparameters](#hyperparameters)
- [Evaluation](#evaluation)
   - [Variable and Metrics](#variable-and-metrics)
   - [Evaluation Results](#evaluation-results)
- [Additional Information](#additional-information)
  - [Author](#author)
  - [Contact Information](#contact-information)
  - [Copyright](#copyright)
  - [Licensing Information](#licensing-information)
  - [Funding](#funding)
  - [Disclaimer](#disclaimer)
 
## Model description

This model was trained from scratch using the Fairseq toolkit on a combination of Catalan-French datasets,which after filtering and cleaning comprised 18.634.844 sentence pairs. The model is evaluated on the Flores (general) and NTREX (news) evaluation sets. 

## Intended uses and limitations

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

## How to use

### Usage
Required libraries:

```bash
pip install ctranslate2 pyonmttok
```

Translate a sentence using python
```python
import ctranslate2
import pyonmttok
from huggingface_hub import snapshot_download
model_dir = snapshot_download(repo_id="projecte-aina/mt-aina-fr-ca", revision="main")

tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
tokenized=tokenizer.tokenize("Bienvenue au projet Aina!")

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  	| Sentences after Cleaning        	|
|-------------------|----------------|-------------------|
| CCMatrix      	|24.386.198  	| 16.305.758   	|
| Multi CCAligned | 1.954.475 | 1.442.584 |
| WikiMatrix | 490.871 | 437.665 |
| GNOME | 12.962 | 1.686 |
|KDE 4 | 163.143 | 111.750 |
| QED | 65.336 | 52.797 |
| TED 2020 | 51.833 | 44.101 |
| Open Subtitles | 392.159 | 225.786 |
| **Total**     	| **27.532.050** | **18.634.844** |

### 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 the final corpus 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, 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.](https://aclanthology.org/2021.wmt-1.18.pdf)
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 8.548 updates. Weights were saved every 1000 updates and reported results are the average of the last 8 checkpoints.

## Evaluation

### Variable and metrics

We use the BLEU score for evaluation on test sets: [Flores-101](https://github.com/facebookresearch/flores), [NTREX](https://github.com/MicrosoftTranslator/NTREX) 

### Evaluation results

Below are the evaluation results on the machine translation from French to Catalan compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es):

| Test set         	| SoftCatalà | Google Translate |mt-aina-fr-ca|
|----------------------|------------|------------------|---------------|
| Flores 101 dev   	| 30,9    	| **37,0**     	| 33,0      	|
| Flores 101 devtest   | 31,3   	| **37,1**     	| 34,4      	|
| NTREX    	| 24,5   	| **30,5**     	| 27,0      	|
| Average          	| 28,9  	| **34,9**     	| 31,5      	|

## Additional information

### Author
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center

### Contact information
For further information, send an email to <langtech@bsc.es>

### Copyright
Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023)

### Licensing information
This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)

### Funding
This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.

### Disclaimer

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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 and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
</details>