File size: 7,096 Bytes

---
license: apache-2.0
language:
- ca
- eu
metrics:
- BLEU
- TER
---
## Hitz Center’s Catalan-Basque machine translation model

## Model description

This model was trained from scratch using [Marian NMT](https://marian-nmt.github.io/) on a combination of Catalan-Basque datasets totalling 11,224,976 sentence pairs. 1,531,980 sentence pairs were parallel data collected from the web while the remaining 9,692,996 sentence pairs were parallel synthetic data created using the [ES-CA translator from Aina project](https://huggingface.proxy.nlp.skieer.com/projecte-aina/aina-translator-eu-ca). The model was evaluated on the Flores, TaCon and NTREX evaluation datasets.

- **Developed by:** HiTZ Research Center & IXA Research group (University of the Basque Country UPV/EHU)
- **Model type:** traslation
- **Source Language:** Catalan
- **Target Language:** Basque
- **License:** apache-2.0

## Intended uses and limitations

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

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.

## How to Get Started with the Model

Use the code below to get started with the model.

```
from transformers import MarianMTModel, MarianTokenizer
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM

src_text = ["això és una prova"]

model_name = "HiTZ/mt-hitz-ca-eu"
tokenizer = MarianTokenizer.from_pretrained(model_name)

model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=T
rue))
print([tokenizer.decode(t, skip_special_tokens=True) for t in translated])`
```

The recommended environments include the following transfomer versions: 4.12.3 , 4.15.0 , 4.26.1
## Training Details

### Training Data

The Catalan-Basque data collected from the web was a combination of the following datasets:

| Dataset       	| Sentences before cleaning	|
|-----------------|--------------------------:|
| CCMatrix  v1  	| 1,083,677  	              |
| XLENT	          | 219,566	                  |
| WikiMatrix  	  | 77,233	                  |
| GNOME	          | 14,828                    |
| KDE4    	      | 93,787 	                  |
| QED           	| 6,554    	                |
| TED2020 v1    	| 4,469 	                  |
| OpenSubtitles	  | 29,114                    |
| Ubuntu          | 2,752 	                  |
| **Total**     	| **1.531.980**             |

The 9,692,996 sentence pairs of synthetic parallel data were created by translating a compendium of ES-EU parallel corpora into Catalan using the [ES-CA translator from the Aina project](https://huggingface.proxy.nlp.skieer.com/projecte-aina/aina-translator-es-ca).

### Training Procedure

#### Preprocessing

After concatenation, all datasets are cleaned and deduplicated using [bifixer](https://github.com/bitextor/bifixer) [(Ramírez-Sánchez et al., 2020)](https://aclanthology.org/2020.eamt-1.31/) for identifying repetions and cleaning encoding problems and LaBSE embeddings to filter missaligned sentences. Any sentence pairs with a LaBSE similarity score of less than 0.5 is removed. The filtered corpus is composed of 10,582,279 parallel sentences.

#### Tokenization
All data is tokenized using sentencepiece, with a 32,000 token sentencepiece model learned from the combination of all filtered training data. This model is included.

## Evaluation
### Variable and metrics
We use the BLEU and TER scores for evaluation on test sets: [Flores-200](https://github.com/facebookresearch/flores/tree/main/flores200), [TaCon](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/) and [NTREX](https://github.com/MicrosoftTranslator/NTREX)

### Evaluation results
Below are the evaluation results on the machine translation from Catalan to Basque compared to [Google Translate](https://translate.google.com/), [NLLB 200 3.3B](https://huggingface.co/facebook/nllb-200-3.3B) and [ NLLB-200's distilled 1.3B variant](https://huggingface.co/facebook/nllb-200-distilled-1.3B):

####BLEU scores

| Test set         	   |Google Translate | NLLB 1.3B | NLLB 3.3 |mt-hitz-ca-eu|
|----------------------|-----------------|-----------|----------|-------------|
| Flores 200 devtest   |**18.0**         | 13.2      | 12.9     |   17.2      |
| TaCON                |  13.2           | 11.8      | 11.2     | **14.0**    |
| NTREX                |  13.8           | 11.1      | 10.5     | **14.0**    |
| Average          	   |  15.0           | 12.0      | 11.5     | **15.1**    |

####TER scores

| Test set         	   |Google Translate | NLLB 1.3B | NLLB 3.3 |mt-hitz-ca-eu|
|----------------------|-----------------|-----------|----------|-------------|
| Flores 200 devtest   |**63.1**         | 76.5      | 70.8     |   65.0      |
| TaCON                |**65.0**         | 76.5      | 72.1     | **48.4**    |
| NTREX                |**69.4**         | 79.4      | 75.5     |   69.7      |
| Average          	   |**65.8**         | 77.5      | 72.8     | **61.0**    |


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## Additional information
### Author
HiTZ Research Center & IXA Research group (University of the Basque Country UPV/EHU)
### Contact information
For further information, send an email to <[email protected]>
### Licensing information
This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the [project ILENIA](https://proyectoilenia.es/) with reference 2022/TL22/00215337, 2022/TL22/00215336, 2022/TL22/00215335 y 2022/TL22/00215334
### 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 (HiTZ Research Center) be liable for any results arising from the use made by third parties of these models.
</details>