Hitz Center’s English-Basque machine translation model
Model description
This model was trained from scratch using Marian NMT on a combination of English-Basque datasets totalling 20,523,431 sentence pairs. 9,033,998 sentence pairs were parallel data collected from the web while the remaining 11,489,433 sentence pairs were parallel synthetic data created using the Google Translate translator. 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: English
Target Language: Basque
License: apache-2.0
Intended uses and limitations
You can use this model for machine translation from English 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 = ["this is a test"]
model_name = "HiTZ/mt-hitz-en-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 English-Basque data collected from the web was a combination of the following datasets:
Dataset
Sentences before cleaning
CCMatrix v1
7,788,871
EhuHac
585,210
Ehuskaratuak
482,259
Ehuskaratuak
482,259
Elhuyar
1,176,529
HPLT
4,546,563
OpenSubtitles
805,780
PaCO_2012
109,524
PaCO_2013
48,892
WikiMatrix
119,480
Total
15,653,108
The 11,489,433 sentence pairs of synthetic parallel data were created by translating a compendium of ES-EU parallel corpora into English using the ES-EN translator from Google Translate.
Training Procedure
Preprocessing
After concatenation, all datasets are cleaned and deduplicated using bifixer(Ramírez-Sánchez et al., 2020) 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 9,033,998 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 English to Basque compared to Google Translate and NLLB 200 3.3B:
####BLEU scores
Test set
Google Translate
NLLB 3.3
mt-hitz-en-eu
Flores 200 devtest
20.5
13.3
19.2
TaCON
12.1
9.4
8.8
NTREX
15.7
8.0
14.5
Average
16.1
10.2
14.2
####TER scores
Test set
Google Translate
NLLB 3.3
mt-hitz-en-eu
Flores 200 devtest
59.5
70.4
65.0
TaCON
69.5
75.3
76.8
NTREX
65.8
81.6
66.7
Average
64.9
75.8
68.2
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.
