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--- |
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language: |
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- bg |
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- bs |
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- en |
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- hr |
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- mk |
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- sh |
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- sl |
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- sr |
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- zls |
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tags: |
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- translation |
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- opus-mt-tc |
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license: cc-by-4.0 |
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model-index: |
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- name: opus-mt-tc-big-zls-en |
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results: |
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- task: |
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name: Translation bul-eng |
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type: translation |
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args: bul-eng |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: bul eng devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 42.0 |
|
- task: |
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name: Translation hrv-eng |
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type: translation |
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args: hrv-eng |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: hrv eng devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 37.1 |
|
- task: |
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name: Translation mkd-eng |
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type: translation |
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args: mkd-eng |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: mkd eng devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
|
value: 43.2 |
|
- task: |
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name: Translation slv-eng |
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type: translation |
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args: slv-eng |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: slv eng devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 35.2 |
|
- task: |
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name: Translation srp_Cyrl-eng |
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type: translation |
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args: srp_Cyrl-eng |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: srp_Cyrl eng devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
|
value: 36.8 |
|
- task: |
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name: Translation bos_Latn-eng |
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type: translation |
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args: bos_Latn-eng |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: bos_Latn-eng |
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metrics: |
|
- name: BLEU |
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type: bleu |
|
value: 66.5 |
|
- task: |
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name: Translation bul-eng |
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type: translation |
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args: bul-eng |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: bul-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 59.3 |
|
- task: |
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name: Translation hbs-eng |
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type: translation |
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args: hbs-eng |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: hbs-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 57.3 |
|
- task: |
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name: Translation hrv-eng |
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type: translation |
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args: hrv-eng |
|
dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: hrv-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 59.2 |
|
- task: |
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name: Translation mkd-eng |
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type: translation |
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args: mkd-eng |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: mkd-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 57.4 |
|
- task: |
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name: Translation slv-eng |
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type: translation |
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args: slv-eng |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
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args: slv-eng |
|
metrics: |
|
- name: BLEU |
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type: bleu |
|
value: 23.5 |
|
- task: |
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name: Translation srp_Cyrl-eng |
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type: translation |
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args: srp_Cyrl-eng |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: srp_Cyrl-eng |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 47.0 |
|
- task: |
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name: Translation srp_Latn-eng |
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type: translation |
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args: srp_Latn-eng |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: srp_Latn-eng |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 58.5 |
|
--- |
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# opus-mt-tc-big-zls-en |
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|
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Neural machine translation model for translating from South Slavic languages (zls) to English (en). |
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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). |
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|
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* 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.) |
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``` |
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@inproceedings{tiedemann-thottingal-2020-opus, |
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title = "{OPUS}-{MT} {--} Building open translation services for the World", |
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author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, |
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booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Lisboa, Portugal", |
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publisher = "European Association for Machine Translation", |
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url = "https://aclanthology.org/2020.eamt-1.61", |
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pages = "479--480", |
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} |
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|
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@inproceedings{tiedemann-2020-tatoeba, |
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title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", |
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author = {Tiedemann, J{\"o}rg}, |
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booktitle = "Proceedings of the Fifth Conference on Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2020.wmt-1.139", |
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pages = "1174--1182", |
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} |
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``` |
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|
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## Model info |
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|
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* Release: 2022-03-17 |
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* source language(s): bos_Latn bul hbs hrv mkd slv srp_Cyrl srp_Latn |
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* target language(s): eng |
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* model: transformer-big |
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* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
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* tokenization: SentencePiece (spm32k,spm32k) |
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* original model: [opusTCv20210807+bt_transformer-big_2022-03-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-eng/opusTCv20210807+bt_transformer-big_2022-03-17.zip) |
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* more information released models: [OPUS-MT zls-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-eng/README.md) |
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## Usage |
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A short example code: |
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```python |
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from transformers import MarianMTModel, MarianTokenizer |
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src_text = [ |
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"Да не би случайно Том да остави Мери да кара колата?", |
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"Какво е времето днес?" |
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] |
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model_name = "pytorch-models/opus-mt-tc-big-zls-en" |
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tokenizer = MarianTokenizer.from_pretrained(model_name) |
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model = MarianMTModel.from_pretrained(model_name) |
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translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) |
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for t in translated: |
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print( tokenizer.decode(t, skip_special_tokens=True) ) |
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|
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# expected output: |
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# Did Tom just let Mary drive the car? |
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# What's the weather like today? |
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``` |
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You can also use OPUS-MT models with the transformers pipelines, for example: |
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```python |
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from transformers import pipeline |
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pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zls-en") |
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print(pipe("Да не би случайно Том да остави Мери да кара колата?")) |
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|
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# expected output: Did Tom just let Mary drive the car? |
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``` |
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## Benchmarks |
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|
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* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-eng/opusTCv20210807+bt_transformer-big_2022-03-17.test.txt) |
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* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-eng/opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt) |
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* benchmark results: [benchmark_results.txt](benchmark_results.txt) |
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* benchmark output: [benchmark_translations.zip](benchmark_translations.zip) |
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| langpair | testset | chr-F | BLEU | #sent | #words | |
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|----------|---------|-------|-------|-------|--------| |
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| bos_Latn-eng | tatoeba-test-v2021-08-07 | 0.79339 | 66.5 | 301 | 1826 | |
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| bul-eng | tatoeba-test-v2021-08-07 | 0.72656 | 59.3 | 10000 | 71872 | |
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| hbs-eng | tatoeba-test-v2021-08-07 | 0.71783 | 57.3 | 10017 | 68934 | |
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| hrv-eng | tatoeba-test-v2021-08-07 | 0.74066 | 59.2 | 1480 | 10620 | |
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| mkd-eng | tatoeba-test-v2021-08-07 | 0.70043 | 57.4 | 10010 | 65667 | |
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| slv-eng | tatoeba-test-v2021-08-07 | 0.39534 | 23.5 | 2495 | 16940 | |
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| srp_Cyrl-eng | tatoeba-test-v2021-08-07 | 0.67628 | 47.0 | 1580 | 10181 | |
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| srp_Latn-eng | tatoeba-test-v2021-08-07 | 0.71878 | 58.5 | 6656 | 46307 | |
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| bul-eng | flores101-devtest | 0.67375 | 42.0 | 1012 | 24721 | |
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| hrv-eng | flores101-devtest | 0.63914 | 37.1 | 1012 | 24721 | |
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| mkd-eng | flores101-devtest | 0.67444 | 43.2 | 1012 | 24721 | |
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| slv-eng | flores101-devtest | 0.62087 | 35.2 | 1012 | 24721 | |
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| srp_Cyrl-eng | flores101-devtest | 0.67810 | 36.8 | 1012 | 24721 | |
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|
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## Acknowledgements |
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|
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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. |
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## Model conversion info |
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* transformers version: 4.16.2 |
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* OPUS-MT git hash: 3405783 |
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* port time: Wed Apr 13 20:12:26 EEST 2022 |
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* port machine: LM0-400-22516.local |
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|
