transformers safetensors marian text2text-generation translation mt pytorch sentence-piece many2one multilingual pivot allegro laniqo cs en pl sk sl license:cc-by-4.0 autotrain_compatible endpoints_compatible region:us
---
language:
- cs
- en
- pl
- sk
- sl
library_name: transformers
license: cc-by-4.0
tags:
- translation
- mt
- marian
- pytorch
- sentence-piece
- many2one
- multilingual
- pivot
- allegro
- laniqo
---

# MultiSlav P5-many2ces

<p align="center">
  <a href="https://ml.allegro.tech/"><img src="allegro-title.svg" alt="MLR @ Allegro.com"></a>
</p>

## Multilingual Many-to-Czech MT Model

___P5-many2ces___ is an Encoder-Decoder vanilla transformer model trained on sentence-level Machine Translation task. 
Model is supporting translation from 4 languages: English, Polish, Slovak, and Slovene to Czech.
This model is part of the [___MultiSlav___ collection](https://huggingface.co/collections/allegro/multislav-6793d6b6419e5963e759a683). 
More information will be available soon in our upcoming MultiSlav paper.

Experiments were conducted under research project by [Machine Learning Research](https://ml.allegro.tech/) lab for [Allegro.com](https://ml.allegro.tech/).
Big thanks to [laniqo.com](laniqo.com) for cooperation in the research. 

<p align="center">
  <img src="p5-ces.svg">
</p>

___P5-many2ces___ - _5_-language _Many-to-Czech_ model translating from all applicable languages to Czech.
This model and [_P5-ces2many_](https://huggingface.co/allegro/P5-ces2many) combine into ___P5-ces___ pivot system translating between _5_ languages. 
_P5-ces_ translates all supported languages using Many2One model to Czech bridge sentence 
and next using the One2Many model from Czech bridge sentence to target language.  

### Model description

* **Model name:** P5-many2ces
* **Source Languages:** English, Polish, Slovak, Slovene
* **Target Language:** Czech
* **Model Collection:** [MultiSlav](https://huggingface.co/collections/allegro/multislav-6793d6b6419e5963e759a683)
* **Model type:** MarianMTModel Encoder-Decoder
* **License:** CC BY 4.0 (commercial use allowed)
* **Developed by:** [MLR @ Allegro](https://ml.allegro.tech/) & [Laniqo.com](https://laniqo.com/)

### Supported languages

Using model you must specify source language for translation.
Source 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.

| **Source Language** | **First token** |
|---------------------|-----------------|
| English             | `>>eng<<`       |
| Polish              | `>>pol<<`       |
| Slovak              | `>>slk<<`       |
| Slovene             | `>>slv<<`       |


## Use case quickstart

Example code-snippet to use model. Due to bug the `MarianMTModel` must be used explicitly.

```python
from transformers import AutoTokenizer, MarianMTModel

m2o_model_name = "Allegro/P5-many2ces"

m2o_tokenizer = AutoTokenizer.from_pretrained(m2o_model_name)
m2o_model = MarianMTModel.from_pretrained(m2o_model_name)

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

translations = m2o_model.generate(**m2o_tokenizer.batch_encode_plus([text], return_tensors="pt"))
bridge_translation = m2o_tokenizer.batch_decode(translations, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(bridge_translation[0])
```

Generated _bridge_ Czech output:
> Allegro je online e-commerce platforma, na které své produkty prodávají střední a malé firmy, stejně jako velké značky.

To pivot-translate to other languages via _bridge_ Czech sentence, we need One2Many model.
One2Many model requires explicit target language token as well:

```python

o2m_model_name = "Allegro/P5-ces2many"

o2m_tokenizer = AutoTokenizer.from_pretrained(o2m_model_name)
o2m_model = MarianMTModel.from_pretrained(o2m_model_name)

texts_to_translate = [
    ">>eng<<" + bridge_translation[0],
    ">>slk<<" + bridge_translation[0],
    ">>slv<<" + bridge_translation[0]
]
translation = o2m_model.generate(**o2m_tokenizer.batch_encode_plus(texts_to_translate, return_tensors="pt"))
decoded_translations = o2m_tokenizer.batch_decode(translation, skip_special_tokens=True, clean_up_tokenization_spaces=True)

for trans in decoded_translations:
    print(trans)
```

Generated Polish to English pivot translation via Czech:
> Allegro is an online e-commerce platform on which medium and small businesses as well as large brands sell their products.

Generated Polish to Slovak pivot translation via Czech:
> Allegro je online e-commerce platforma, na ktorej svoje produkty predávajú stredné a malé firmy, rovnako ako veľké značky.

Generated Polish to Slovene pivot translation via Czech:
> Allegro je spletna e-poslovanje platforma, na kateri prodajajo svoje izdelke srednje velika in mala podjetja ter velike blagovne znamke.

## Training

[SentencePiece](https://github.com/google/sentencepiece) tokenizer has a vocab size 80k in total (16k per language). Tokenizer was trained on randomly sampled part of the training corpus. 
During the training we used the [MarianNMT](https://marian-nmt.github.io/) framework.
Base marian configuration used: [transfromer-big](https://github.com/marian-nmt/marian-dev/blob/master/src/common/aliases.cpp#L113).
All training parameters are listed in table below.

### Training hyperparameters:

| **Hyperparameter**          | **Value**                                                                                                  |
|-----------------------------|------------------------------------------------------------------------------------------------------------|
| Total Parameter Size        | 258M                                                                                                       |
| Training Examples           | 269M                                                                                                       |
| Vocab Size                  | 80k                                                                                                        |
| Base Parameters             | [Marian transfromer-big](https://github.com/marian-nmt/marian-dev/blob/master/src/common/aliases.cpp#L113) |
| 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  | English, Polish, Slovak, Slovene                                                                           |
| Target Language Supported   | Czech                                                                                                      |
| Precision                   | float16                                                                                                    |
| Validation Freq             | 3000 steps                                                                                                 |
| Stop Metric                 | ChrF                                                                                                       |
| Stop Criterion              | 20 Validation steps                                                                                        |


## Training corpora

<p align="center">
  <img src="pivot-data-many2ces.svg">
</p>

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 with expanding data-regime by using data from multiple source language and expanding language-pool by adding English. 
We found that additional fluency data clearly improved performance compared to the bi-directional baseline models.
For example in translation from Polish to Czech, this allowed us to expand training data-size from 63M to 269M examples, and from 25M to 269M for Slovene to Czech translation.
We only used explicitly open-source data to ensure open-source license of our model.

Datasets were downloaded via [MT-Data](https://pypi.org/project/mtdata/0.2.10/) library. Number of total examples post filtering and deduplication: __269M__.

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](https://huggingface.co/datasets/facebook/flores) 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 <span style="color:red;">◊</span>              |    90.2     |   20.2   |   48.5   |        2x 242M |
| P5-eng <span style="color:red;">◊</span>              |    89.0     |   19.9   |   48.3   |        2x 258M |
| ___P5-many2ces___ <span style="color:green;">*</span> |    90.3     |   20.2   |   48.6   |           258M |
| MultiSlav-4slav                                       |    90.2     |   20.6   |   48.7   |           242M |
| MultiSlav-5lang                                       |  __90.4__   | __20.7__ | __48.9__ |           258M |

Translation results on translation from Slovene to Czech (direction to Czech with the __lowest__ data-regime):

| **Model**                                             | **Comet22** | **BLEU** | **ChrF** | **Model Size** |
|-------------------------------------------------------|:-----------:|:--------:|:--------:|---------------:|
| M2M−100                                               |    90.3     |   24.3   |   51.6   |           1.2B |
| NLLB−200                                              |    90.0     |   22.5   |   49.9   |           1.3B |
| Opus Sla-Sla                                          |    83.5     |   17.4   |   46.0   |           1.3B |
| BiDi-ces-slv (baseline)                               |    90.0     |   24.4   |   52.0   |           209M |
| P4-pol <span style="color:red;">◊</span>              |    89.3     |   22.7   |   50.4   |        2x 242M |
| P5-eng <span style="color:red;">◊</span>              |    89.6     |   24.7   |   52.4   |        2x 258M |
| ___P5-many2ces___ <span style="color:green;">*</span> |    90.3     |   24.9   |   52.4   |           258M |
| MultiSlav-4slav                                       |  __90.6__   | __25.3__ | __52.7__ |           242M |
| MultiSlav-5lang                                       |  __90.6__   |   25.2   |   52.5   |           258M |


<span style="color:green;">*</span> this model is Many2One part of P5-ces pivot system.

<span style="color:red;">◊</span> system of 2 models *Many2XXX* and *XXX2Many*.

## 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](https://linkedin.com/in/arturkot), [Mikołaj Koszowski](https://linkedin.com/in/mkoszowski), [Wojciech Chojnowski](https://linkedin.com/in/wojciech-chojnowski-744702348), [Mieszko Rutkowski](https://linkedin.com/in/mieszko-rutkowski)
- Laniqo.com: [Artur Nowakowski](https://linkedin.com/in/artur-nowakowski-mt), [Kamil Guttmann](https://linkedin.com/in/kamil-guttmann), [Mikołaj Pokrywka](https://linkedin.com/in/mikolaj-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](https://linkedin.com/in/arturkot) or [Mikołaj Koszowski](https://linkedin.com/in/mkoszowski)