--- language: - be - bg - hr - ru - sh - sl - sr_Cyrl - sr_Latn - uk - zle - zls tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-zle-zls results: - task: name: Translation rus-bul type: translation args: rus-bul dataset: name: flores101-devtest type: flores_101 args: rus bul devtest metrics: - name: BLEU type: bleu value: 28.9 - task: name: Translation rus-hrv type: translation args: rus-hrv dataset: name: flores101-devtest type: flores_101 args: rus hrv devtest metrics: - name: BLEU type: bleu value: 23.2 - task: name: Translation rus-mkd type: translation args: rus-mkd dataset: name: flores101-devtest type: flores_101 args: rus mkd devtest metrics: - name: BLEU type: bleu value: 24.3 - task: name: Translation rus-slv type: translation args: rus-slv dataset: name: flores101-devtest type: flores_101 args: rus slv devtest metrics: - name: BLEU type: bleu value: 23.1 - task: name: Translation rus-srp_Cyrl type: translation args: rus-srp_Cyrl dataset: name: flores101-devtest type: flores_101 args: rus srp_Cyrl devtest metrics: - name: BLEU type: bleu value: 24.1 - task: name: Translation ukr-bul type: translation args: ukr-bul dataset: name: flores101-devtest type: flores_101 args: ukr bul devtest metrics: - name: BLEU type: bleu value: 30.8 - task: name: Translation ukr-hrv type: translation args: ukr-hrv dataset: name: flores101-devtest type: flores_101 args: ukr hrv devtest metrics: - name: BLEU type: bleu value: 24.6 - task: name: Translation ukr-mkd type: translation args: ukr-mkd dataset: name: flores101-devtest type: flores_101 args: ukr mkd devtest metrics: - name: BLEU type: bleu value: 26.2 - task: name: Translation ukr-slv type: translation args: ukr-slv dataset: name: flores101-devtest type: flores_101 args: ukr slv devtest metrics: - name: BLEU type: bleu value: 24.2 - task: name: Translation ukr-srp_Cyrl type: translation args: ukr-srp_Cyrl dataset: name: flores101-devtest type: flores_101 args: ukr srp_Cyrl devtest metrics: - name: BLEU type: bleu value: 26.2 - task: name: Translation rus-bul type: translation args: rus-bul dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: rus-bul metrics: - name: BLEU type: bleu value: 53.7 - task: name: Translation rus-hbs type: translation args: rus-hbs dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: rus-hbs metrics: - name: BLEU type: bleu value: 49.4 - task: name: Translation rus-slv type: translation args: rus-slv dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: rus-slv metrics: - name: BLEU type: bleu value: 21.5 - task: name: Translation rus-srp_Cyrl type: translation args: rus-srp_Cyrl dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: rus-srp_Cyrl metrics: - name: BLEU type: bleu value: 46.1 - task: name: Translation rus-srp_Latn type: translation args: rus-srp_Latn dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: rus-srp_Latn metrics: - name: BLEU type: bleu value: 51.7 - task: name: Translation ukr-bul type: translation args: ukr-bul dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ukr-bul metrics: - name: BLEU type: bleu value: 61.3 - task: name: Translation ukr-hbs type: translation args: ukr-hbs dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ukr-hbs metrics: - name: BLEU type: bleu value: 52.1 - task: name: Translation ukr-hrv type: translation args: ukr-hrv dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ukr-hrv metrics: - name: BLEU type: bleu value: 50.1 - task: name: Translation ukr-srp_Cyrl type: translation args: ukr-srp_Cyrl dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ukr-srp_Cyrl metrics: - name: BLEU type: bleu value: 54.7 - task: name: Translation ukr-srp_Latn type: translation args: ukr-srp_Latn dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ukr-srp_Latn metrics: - name: BLEU type: bleu value: 53.4 --- # opus-mt-tc-big-zle-zls Neural machine translation model for translating from East Slavic languages (zle) to South Slavic languages (zls). 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). * 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", } ``` ## Model info * Release: 2022-03-23 * source language(s): bel rus ukr * target language(s): bul hbs hrv slv srp_Cyrl srp_Latn * valid target language labels: >>bul<< >>hbs<< >>hrv<< >>slv<< >>srp_Cyrl<< >>srp_Latn<< * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zls/opusTCv20210807+bt_transformer-big_2022-03-23.zip) * more information released models: [OPUS-MT zle-zls README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-zls/README.md) * more information about the model: [MarianMT](https://huggingface.co./docs/transformers/model_doc/marian) This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>bul<<` ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>bul<< Новы каранавірус вельмі заразны.", ">>srp_Latn<< Моє ім'я — Саллі." ] model_name = "pytorch-models/opus-mt-tc-big-zle-zls" 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: # Короната е силно заразна. # Zovem se Sali. ``` 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-zle-zls") print(pipe(">>bul<< Новы каранавірус вельмі заразны.")) # expected output: Короната е силно заразна. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zls/opusTCv20210807+bt_transformer-big_2022-03-23.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zls/opusTCv20210807+bt_transformer-big_2022-03-23.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 | |----------|---------|-------|-------|-------|--------| | rus-bul | tatoeba-test-v2021-08-07 | 0.71515 | 53.7 | 1247 | 8272 | | rus-hbs | tatoeba-test-v2021-08-07 | 0.69192 | 49.4 | 2500 | 14736 | | rus-slv | tatoeba-test-v2021-08-07 | 0.38051 | 21.5 | 657 | 3969 | | rus-srp_Cyrl | tatoeba-test-v2021-08-07 | 0.66622 | 46.1 | 881 | 5407 | | rus-srp_Latn | tatoeba-test-v2021-08-07 | 0.70990 | 51.7 | 1483 | 8552 | | ukr-bul | tatoeba-test-v2021-08-07 | 0.77283 | 61.3 | 1020 | 5181 | | ukr-hbs | tatoeba-test-v2021-08-07 | 0.69401 | 52.1 | 942 | 5130 | | ukr-hrv | tatoeba-test-v2021-08-07 | 0.67202 | 50.1 | 389 | 2302 | | ukr-srp_Cyrl | tatoeba-test-v2021-08-07 | 0.70064 | 54.7 | 205 | 1112 | | ukr-srp_Latn | tatoeba-test-v2021-08-07 | 0.72405 | 53.4 | 348 | 1716 | | bel-bul | flores101-devtest | 0.49528 | 16.1 | 1012 | 24700 | | bel-hrv | flores101-devtest | 0.46308 | 12.4 | 1012 | 22423 | | bel-mkd | flores101-devtest | 0.48608 | 13.5 | 1012 | 24314 | | bel-slv | flores101-devtest | 0.44452 | 12.2 | 1012 | 23425 | | bel-srp_Cyrl | flores101-devtest | 0.44424 | 12.6 | 1012 | 23456 | | rus-bul | flores101-devtest | 0.58653 | 28.9 | 1012 | 24700 | | rus-hrv | flores101-devtest | 0.53494 | 23.2 | 1012 | 22423 | | rus-mkd | flores101-devtest | 0.55184 | 24.3 | 1012 | 24314 | | rus-slv | flores101-devtest | 0.52201 | 23.1 | 1012 | 23425 | | rus-srp_Cyrl | flores101-devtest | 0.53038 | 24.1 | 1012 | 23456 | | ukr-bul | flores101-devtest | 0.59625 | 30.8 | 1012 | 24700 | | ukr-hrv | flores101-devtest | 0.54530 | 24.6 | 1012 | 22423 | | ukr-mkd | flores101-devtest | 0.56822 | 26.2 | 1012 | 24314 | | ukr-slv | flores101-devtest | 0.53092 | 24.2 | 1012 | 23425 | | ukr-srp_Cyrl | flores101-devtest | 0.54618 | 26.2 | 1012 | 23456 | ## 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: 1bdabf7 * port time: Thu Mar 24 00:46:26 EET 2022 * port machine: LM0-400-22516.local