Fairseq
Catalan
English
File size: 8,858 Bytes
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---
license: cc-by-4.0
---
## Aina Project's Catalan-English  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](https://fairseq.readthedocs.io/en/latest/) on a combination of Catalan-English datasets, up to 11  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 Catalan to English. 

## 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-ca-en", revision="main")

tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
tokenized=tokenizer.tokenize("Benvingut al projecte 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      |
|--------------------|----------------|
| Global Voices      | 21.342         |
| Memories Lluires   | 1.173.055      |
| Wikimatrix         | 1.205.908      |
| TED Talks          | 50.979         |
| Tatoeba            | 5.500          |
| CoVost 2 ca-en     | 79.633         |
| CoVost 2 en-ca     | 263.891        |
| Europarl           | 1.965.734      |
| jw300              | 97.081         |
| Crawled Generalitat| 38.595         |
| Opus Books         | 4.580          |
| CC Aligned         | 5.787.682      |
| COVID_Wikipedia    | 1.531          |
| EuroBooks          | 3.746          |
| Gnome              | 2.183          |
| KDE 4              | 144.153        |
| OpenSubtitles      | 427.913        |
| QED                | 69.823         |
| Ubuntu             | 6.781          |
| Wikimedia          | 208.073        |
|--------------------|----------------|
| **Total**          | **11.558.183** |

### Training procedure

### Data preparation

 All datasets are concatenated and filtered using the [mBERT Gencata parallel filter](https://huggingface.co/projecte-aina/mbert-base-gencata). Before training, the punctuation is normalized 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)


#### Tokenization

 All data is tokenized using sentencepiece, using 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_big |
| 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 for a total of 35.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 16 checkpoints. 

## Evaluation

### Variable and metrics

We use the BLEU score for evaluation on test sets: [Flores-101](https://github.com/facebookresearch/flores), [TaCon](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/), [United Nations](https://zenodo.org/record/3888414#.Y33-_tLMIW0), [Cybersecurity](https://elrc-share.eu/repository/browse/cyber-mt-test-set/2bd93faab98c11ec9c1a00155d026706b96a490ed3e140f0a29a80a08c46e91e/), [wmt19 biomedical test set](), [wmt13 news test set](https://elrc-share.eu/repository/browse/catalan-wmt2013-machine-translation-shared-task-test-set/84a96139b98611ec9c1a00155d0267061a0aa1b62e2248e89aab4952f3c230fc/), [aina aapp]()

### Evaluation results

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


| Test set             | SoftCatalà | Google Translate | mt-aina-ca-en |
|----------------------|------------|------------------|---------------|
| Spanish Constitution | 35,8       | **43,2**         | 40,3          |
| United Nations       | 44,4       | **47,4**         | 44,8          |
| aina_aapp            | 48,8       | **53,0**         | 51,5          |
| european_comission   | 52,0       | **53,7**         | 53,1          |
| Flores 101 dev       | 42,7       | **47,5**         | 46,1          |
| Flores 101 devtest   | 42,5       | **46,9**         | 45,2          |
| Cybersecurity        | 52,5       | **58,0**         | 54,2          |
| wmt 19 biomedical    | 18,3       | **23,4**         | 21,6          |
| wmt 13 news          | 37,8       | **39,8**         | 39,3          |
| Average              | 39,2       | **45,0**         | 41,6          |


## 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 aina@bsc.es

### Copyright
Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center 


### Licensing Information
[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](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).


## Disclaimer
<details>
<summary>Click to expand</summary>

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.