Hitz Center’s Galician-Basque machine translation model
Model description
This model was trained from scratch using Marian NMT on a combination of Galician-Basque datasets totalling 13,125,745 sentence pairs. 413,057 sentence pairs were parallel data collected from the web while the remaining 12,712,688 sentence pairs were parallel synthetic data. 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: Galician
Target Language: Basque
License: apache-2.0
Intended uses and limitations
You can use this model for machine translation from Galician 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 = ["Esta é unha proba."]
model_name = "HiTZ/mt-hitz-gl-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 Galician-Basque data collected from the web was a combination of the following datasets:
Dataset
Sentences before cleaning
XLENT
236,460
KDE4
96,509
WikiMatrix
43,101
GNOME
12,759
OpenSubtitles
12,391
QED
5,524
Ubuntu
3,051
TED2020 v1
2,433
NeuLab-TedTalks
804
ELRC-wikipedia_health
25
Total
413,057
The 12,712,688 sentence pairs of synthetic parallel data were created, on the one hand, by translating into Galician a compendium of ES-EU parallel corpora of 9,692,996 sentence pairs (using the ES-GL translator of the Nos project) and, on the other hand, by adapting into Galician 3,019,692 sentences in Portuguese from a PT-EU corpus (using the rule-base translator Apertium with the support of the transliterator Port2Gal)
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 12,699,402 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
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.
