license: cc-by-4.0 language:

• cs
• pl
• sk
• sl
• en library_name: transformers tags:
• translation
• mt
• marian
• pytorch
• sentence-piece
• multilingual
• allegro
• laniqo

MultiSlav BiDi Models

Multilingual BiDi MT Models

BiDi is a collection of Encoder-Decoder vanilla transformer models trained on sentence-level Machine Translation task. Each model is supporting Bi-Directional translation.

BiDi models are 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.

Graphic above provides an example of an BiDi model - BiDi-ces-pol to translate from Polish to Czech language. BiDi-ces-pol is a bi-directional model supporting translation both form Czech to Polish and from Polish to Czech directions.

Supported languages

To use a BiDi model, you must provide the 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. Note that, for each model only two directions are available. Each of them was added as a special token to Sentence-Piece tokenizer.

Bi-Di models available

We provided 10 BiDi models, allowing to translate between 20 languages.

Use case quickstart

Example code-snippet to use model. Due to bug the MarianMTModel must be used explicitly. Remember to adjust source and target languages to your use-case.

from transformers import AutoTokenizer, MarianMTModel

source_lang = "pol"
target_lang = "ces"
first_lang, second_lang = sorted([source_lang, target_lang])
model_name = f"Allegro/BiDi-{first_lang}-{second_lang}"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)

text = f">>{target_lang}<<" + " " + "Allegro to internetowa platforma e-commerce, na której swoje produkty sprzedają średnie i małe firmy, jak również duże marki."

batch_to_translate = [text]
translations = model.generate(**tokenizer.batch_encode_plus(batch_to_translate, return_tensors="pt"))
decoded_translation = tokenizer.batch_decode(translations, skip_special_tokens=True, clean_up_tokenization_spaces=True)[0]

print(decoded_translation)

Generated Czech output:

Allegro je online e-commerce platforma, na které své výrobky prodávají střední a malé firmy, stejně jako velké značky.

Training

SentencePiece tokenizer has a vocab size 32k 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:

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. BiDi models are our baseline before expanding the data-regime by using higher-level multilinguality.

Datasets were downloaded via MT-Data library. The number of total examples post filtering and deduplication varies, depending on languages supported, see the table below.

The datasets used (only applicable to specific directions):

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. Metric used: Unbabel/wmt22-comet-da.

◊ system of 2 models Many2XXX and XXX2Many, see P5-ces2many

* results combined for all bi-directional models; each values for applicable model

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