MultiSlav
Collection
Multilingual open-source Slavic models
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Updated
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MultiSlav MultiSlav-4slav
Multilingual Many2Many MT Model
MultiSlav-4slav is an Encoder-Decoder vanilla transformer model trained on sentence-level Machine Translation task. Model is supporting translation between 4 languages: Czech, Polish, Slovak, Slovene. This model is part of the MultiSlav collection. More information will be available soon in our upcoming MultiSlav paper.
Experiments were conducted under research project by Machine Learning Research lab for Allegro.com. Big thanks to laniqo.com for cooperation in the research.
MultiSlav-4slav - translates directly between 4 supported Slavic languages using single Many2Many model as seen on the diagram above.
Model description
- Model name: MultiSlav-4slav
- Source Languages: Czech, Polish, Slovak, Slovene
- Target Languages: Czech, Polish, Slovak, Slovene
- Model Collection: MultiSlav
- Model type: MarianMTModel Encoder-Decoder
- License: CC BY 4.0 (commercial use allowed)
- Developed by: MLR @ Allegro & Laniqo.com
Supported languages
Using model you must specify target language for translation. Target language tokens are represented as 3-letter ISO 639-3 language codes embedded in a format >>xxx<<. All accepted directions and their respective tokens are listed below. Each of them was added as a special token to Sentence-Piece tokenizer.
| Target Language | First token |
|---|---|
| Czech | >>ces<< |
| Polish | >>pol<< |
| Slovak | >>slk<< |
| Slovene | >>slv<< |
Use case quickstart
Example code-snippet to use model. Due to bug the MarianMTModel must be used explicitly.
from transformers import AutoTokenizer, MarianMTModel
model_name = "Allegro/MultiSlav-4slav"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
text = "Allegro to internetowa platforma e-commerce, na której swoje produkty sprzedają średnie i małe firmy, jak również duże marki."
target_languages = ["ces", "slk", "slv"]
batch_to_translate = [
f">>{lang}<<" + " " + text for lang in target_languages
]
translations = model.generate(**tokenizer.batch_encode_plus(batch_to_translate, return_tensors="pt"))
decoded_translations = tokenizer.batch_decode(translations, skip_special_tokens=True, clean_up_tokenization_spaces=True)
for trans in decoded_translations:
print(trans)
Generated outputs:
Czech output:
Allegro je on-line e-commerce platforma, na které prodávají své produkty střední a malé firmy, stejně jako velké značky.
Slovak output:
Allegro je internetová e-commerce platforma, na ktorej svoje produkty predávajú stredné a malé podniky, ako aj veľké značky.
Slovene output:
Allegro je spletna platforma za e-trgovino, na kateri srednje velika in mala podjetja ter velike blagovne znamke prodajajo svoje izdelke.
The model is also capable of translating into Polish language, following the same pattern:
text = ">>pol<<" + " " + "Allegro je on-line e-commerce platforma, na které své produkty prodávají střední a malé firmy, stejně jako velké značky."
translation = model.generate(**tokenizer.batch_encode_plus([text], return_tensors="pt"))
decoded_translation = tokenizer.batch_decode(translation, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(decoded_translation[0])
Generated Polish output:
Allegro to internetowa platforma e-commerce, na której średnie i małe firmy, a także duże marki sprzedają swoje produkty.
Training
SentencePiece tokenizer has a vocab size 64k in total (16k per language). Tokenizer was trained on randomly sampled part of the training corpus. During the training we used the MarianNMT framework. Base marian configuration used: transfromer-big. All training parameters are listed in table below.
Training hyperparameters:
| Hyperparameter | Value |
|---|---|
| Total Parameter Size | 242M |
| Training Examples | 185M |
| Vocab Size | 64k |
| Base Parameters | Marian transfromer-big |
| Number of Encoding Layers | 6 |
| Number of Decoding Layers | 6 |
| Model Dimension | 1024 |
| FF Dimension | 4096 |
| Heads | 16 |
| Dropout | 0.1 |
| Batch Size | mini batch fit to VRAM |
| Training Accelerators | 4x A100 40GB |
| Max Length | 100 tokens |
| Optimizer | Adam |
| Warmup steps | 8000 |
| Context | Sentence-level MT |
| Source Languages Supported | Czech, Polish, Slovak, Slovene |
| Target Languages Supported | Czech, Polish, Slovak, Slovene |
| Precision | float16 |
| Validation Freq | 3000 steps |
| Stop Metric | ChrF |
| Stop Criterion | 20 Validation steps |
Training corpora
The main research question was: "How does adding additional, related languages impact the quality of the model?" - we explored it in the Slavic language family. In this model we experimented by using parallel corpora from all languages of interest to further increase open-source data-regime. We found that additional data clearly improved performance compared to the bi-directional baseline models, and compared to pivot models in the most of the directions. For example in translation from Polish to Czech, this allowed us to expand training data-size from 63M to 185M examples, and from 18M to 185M for Slovak to Slovene translation.
We only used explicitly open-source data to ensure open-source license of our model. Datasets were downloaded via MT-Data library. Number of total examples post filtering and deduplication: 185M.
The datasets used:
| Corpus |
|---|
| paracrawl |
| opensubtitles |
| multiparacrawl |
| dgt |
| elrc |
| xlent |
| wikititles |
| wmt |
| wikimatrix |
| dcep |
| ELRC |
| tildemodel |
| europarl |
| eesc |
| eubookshop |
| emea |
| jrc_acquis |
| ema |
| qed |
| elitr_eca |
| EU-dcep |
| rapid |
| ecb |
| kde4 |
| news_commentary |
| kde |
| bible_uedin |
| europat |
| elra |
| wikipedia |
| wikimedia |
| tatoeba |
| globalvoices |
| euconst |
| ubuntu |
| php |
| ecdc |
| eac |
| eac_reference |
| gnome |
| EU-eac |
| books |
| EU-ecdc |
| newsdev |
| khresmoi_summary |
| czechtourism |
| khresmoi_summary_dev |
| worldbank |
Evaluation
Evaluation of the models was performed on Flores200 dataset. The table below compares performance of the open-source models and all applicable models from our collection. Metrics BLEU, ChrF2, and Unbabel/wmt22-comet-da.
Translation results on translation from Polish to Czech (Slavic direction with the highest data-regime):
| Model | Comet22 | BLEU | ChrF | Model Size |
|---|---|---|---|---|
| M2M−100 | 89.6 | 19.8 | 47.7 | 1.2B |
| NLLB−200 | 89.4 | 19.2 | 46.7 | 1.3B |
| Opus Sla-Sla | 82.9 | 14.6 | 42.6 | 64M |
| BiDi-ces-pol (baseline) | 90.0 | 20.3 | 48.5 | 209M |
| P4-pol ◊ | 90.2 | 20.2 | 48.5 | 2x 242M |
| P5-eng ◊ | 89.0 | 19.9 | 48.3 | 2x 258M |
| P5-ces ◊ | 90.3 | 20.2 | 48.6 | 2x 258M |
| MultiSlav-4slav * | 90.2 | 20.6 | 48.7 | 242M |
| MultiSlav-5lang | 90.4 | 20.7 | 48.9 | 258M |
Translation results on translation from Slovak to Slovene (Slavic direction with the lowest data-regime):
| Model | Comet22 | BLEU | ChrF | Model Size |
|---|---|---|---|---|
| M2M−100 | 89.6 | 26.6 | 55.0 | 1.2B |
| NLLB−200 | 88.8 | 23.3 | 42.0 | 1.3B |
| BiDi-slk-slv (baseline) | 89.4 | 26.6 | 55.4 | 209M |
| P4-pol ◊ | 88.4 | 24.8 | 53.2 | 2x 242M |
| P5-eng ◊ | 88.5 | 25.6 | 54.6 | 2x 258M |
| P5-ces ◊ | 89.8 | 26.6 | 55.3 | 2x 258M |
| MultiSlav-4slav * | 90.1 | 27.1 | 55.7 | 242M |
| MultiSlav-5lang | 90.2 | 27.1 | 55.7 | 258M |
* this model
◊ system of 2 models Many2XXX and XXX2Many, see P5-ces2many
Limitations and Biases
We did not evaluate inherent bias contained in training datasets. It is advised to validate bias of our models in perspective domain. This might be especially problematic in translation from English to Slavic languages, which require explicitly indicated gender and might hallucinate based on bias present in training data.
License
The model is licensed under CC BY 4.0, which allows for commercial use.
Citation
TO BE UPDATED SOON 🤗
Contact Options
Authors:
- MLR @ Allegro: Artur Kot, Mikołaj Koszowski, Wojciech Chojnowski, Mieszko Rutkowski
- Laniqo.com: Artur Nowakowski, Kamil Guttmann, Mikołaj Pokrywka
Please don't hesitate to contact authors if you have any questions or suggestions:
- e-mail: artur.kot@allegro.com or mikolaj.koszowski@allegro.com
- LinkedIn: Artur Kot or Mikołaj Koszowski
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