# opus-mt-tc-big-itc-eu

## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)

## Model Details

Neural machine translation model for translating from Italic languages (itc) to Basque (eu).

This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2022-07-23
- **License:** CC-BY-4.0
- **Language(s):**  
  - Source Language(s): fra ita spa
  - Target Language(s): eus
  - Language Pair(s): spa-eus
  - Valid Target Language Labels: 
- **Original Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.zip)
- **Resources for more information:**
  - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
  - More information about released models for this language pair: [OPUS-MT itc-eus README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/itc-eus/README.md)
  - [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
  - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/

## Uses

This model can be used for translation and text-to-text generation.

## Risks, Limitations and Biases

**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**

Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).

## How to Get Started With the Model

A short example code:

```python
from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Il est riche.",
    "¿Correcto?"
]

model_name = "pytorch-models/opus-mt-tc-big-itc-eu"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))

for t in translated:
    print( tokenizer.decode(t, skip_special_tokens=True) )

# expected output:
#     Aberatsa da.
#     Zuzena?
```

You can also use OPUS-MT models with the transformers pipelines, for example:

```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-itc-eu")
print(pipe("Il est riche."))

# expected output: Aberatsa da.
```

## Training

- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:**  transformer-big
- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)

## Evaluation

* test set translations: [opusTCv20210807_transformer-big_2022-07-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-07-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)

| langpair | testset | chr-F | BLEU  | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| spa-eus | tatoeba-test-v2021-08-07 | 0.60699 | 32.4 | 1850 | 10945 |

## Citation Information

* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)

```
@inproceedings{tiedemann-thottingal-2020-opus,
    title = "{OPUS}-{MT} {--} Building open translation services for the World",
    author = {Tiedemann, J{\"o}rg  and Thottingal, Santhosh},
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2020.eamt-1.61",
    pages = "479--480",
}

@inproceedings{tiedemann-2020-tatoeba,
    title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.wmt-1.139",
    pages = "1174--1182",
}
```

## Acknowledgements

The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.

## Model conversion info

* transformers version: 4.16.2
* OPUS-MT git hash: 8b9f0b0
* port time: Sat Aug 13 00:08:07 EEST 2022
* port machine: LM0-400-22516.local

{"language": ["es", "eu"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-itc-eu", "results": [{"task": {"type": "translation", "name": "Translation spa-eus"}, "dataset": {"name": "tatoeba-test-v2021-08-07", "type": "tatoeba_mt", "args": "spa-eus"}, "metrics": [{"type": "bleu", "value": 32.4, "name": "BLEU"}, {"type": "chrf", "value": 0.60699, "name": "chr-F"}]}]}]}
transformers pytorch tf safetensors marian text2text-generation translation opus-mt-tc es eu license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #es #eu #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

opus-mt-tc-big-itc-eu
=====================


Table of Contents
-----------------


* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements


Model Details
-------------


Neural machine translation model for translating from Italic languages (itc) to Basque (eu).


This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from OPUS and training pipelines use the procedures of OPUS-MT-train.
Model Description:


* Developed by: Language Technology Research Group at the University of Helsinki
* Model Type: Translation (transformer-big)
* Release: 2022-07-23
* License: CC-BY-4.0
* Language(s):
	+ Source Language(s): fra ita spa
	+ Target Language(s): eus
	+ Language Pair(s): spa-eus
	+ Valid Target Language Labels:
* Original Model: opusTCv20210807\_transformer-big\_2022-URL
* Resources for more information:
	+ OPUS-MT-train GitHub Repo
	+ More information about released models for this language pair: OPUS-MT itc-eus README
	+ More information about MarianNMT models in the transformers library
	+ Tatoeba Translation Challenge](URL and Bender et al. (2021)).


How to Get Started With the Model
---------------------------------


A short example code:


You can also use OPUS-MT models with the transformers pipelines, for example:


Training
--------


* Data: opusTCv20210807 (source)
* Pre-processing: SentencePiece (spm32k,spm32k)
* Model Type: transformer-big
* Original MarianNMT Model: opusTCv20210807\_transformer-big\_2022-URL
* Training Scripts: GitHub Repo


Evaluation
----------


* test set translations: opusTCv20210807\_transformer-big\_2022-URL
* test set scores: opusTCv20210807\_transformer-big\_2022-URL
* benchmark results: benchmark\_results.txt
* benchmark output: benchmark\_translations.zip



* Publications: OPUS-MT – Building open translation services for the World and The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT (Please, cite if you use this model.)


Acknowledgements
----------------


The work is supported by the European Language Grid as pilot project 2866, by the FoTran project, funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the MeMAD project, funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by CSC -- IT Center for Science, Finland.


Model conversion info
---------------------


* transformers version: 4.16.2
* OPUS-MT git hash: 8b9f0b0
* port time: Sat Aug 13 00:08:07 EEST 2022
* port machine: URL



TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #es #eu #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #es #eu #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
