Hitz Center’s Spanish-Basque machine translation model
Model description
This model was trained from scratch using Marian NMT on a combination of Spanish-Basque datasets totalling 35,619,691 sentence pairs. 12,091,549 sentence pairs were parallel data collected from the web while the remaining 23,528,142 sentence pairs were parallel synthetic data created backtranslating EusCrawl Basque monolingual dataset. 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: Spanish
Target Language: Basque
License: apache-2.0
Intended uses and limitations
You can use this model for machine translation from Spanish 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 = ["Esto es una prueba."]
model_name = "HiTZ/mt-hitz-es-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 Spanish-Basque data collected from the web was a combination of the following datasets:
Dataset
Sentences before cleaning
CCMatrix
6,564,108
MultiParaCrawl
3,344,373
Paracrawl
2,410,895
TranslationMemories_EJ
1,127,141
OpenData2017 (IWSLT18)
926,941
OpenSubtitles
793,593
TranslationMemories_GD
788,776
EhuHac
609,912
OPUS-Elhuyar
642,347
EiTB-ParCC
637,182
WikiMatrix
154,281
Total
** 12,091,549 **
The 23,528,142 sentence pairs of synthetic parallel data were created by backtranslating the EusCrawl Basque monolingual dataset using a previous version (without synthetic parallel data) of the EU-ES translator from the HiTZ center.
Training Procedure
Preprocessing
After concatenation, all datasets are cleaned and deduplicated using bifixer and biclener tools (Ramírez-Sánchez et al., 2020). Any sentence pairs with a classification score of less than 0.5 is removed. The filtered corpus is composed of 30,776,776 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, TaCon and NTREX
Evaluation results
Below are the evaluation results on the machine translation from Spanish to Basque compared to Google Translate and NLLB 200 3.3B:
####BLEU scores
Test set
Google Translate
NLLB 3.3
mt-hitz-es-eu
Flores 200 devtest
13.7
11.7
13.8
TaCON
14.2
11.3
13.7
NTREX
13.9
11.3
14.3
Average
13.9
11.4
14.1
####TER scores
Test set
Google Translate
NLLB 3.3
mt-hitz-es-eu
Flores 200 devtest
70.4
74.2
71.1
TaCON
63.3
72.0
66.7
NTREX
69.5
74.3
69.7
Average
67.7
73.5
69.2
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 hitz@ehu.eus
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 with reference 2022/TL22/00215337, 2022/TL22/00215336, 2022/TL22/00215335 y 2022/TL22/00215334
Disclaimer
Click to expand
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.
