--- 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-zls-zle results: - task: name: Translation bul-rus type: translation args: bul-rus dataset: name: flores101-devtest type: flores_101 args: bul rus devtest metrics: - name: BLEU type: bleu value: 24.6 - task: name: Translation bul-ukr type: translation args: bul-ukr dataset: name: flores101-devtest type: flores_101 args: bul ukr devtest metrics: - name: BLEU type: bleu value: 22.9 - task: name: Translation hrv-rus type: translation args: hrv-rus dataset: name: flores101-devtest type: flores_101 args: hrv rus devtest metrics: - name: BLEU type: bleu value: 23.5 - task: name: Translation hrv-ukr type: translation args: hrv-ukr dataset: name: flores101-devtest type: flores_101 args: hrv ukr devtest metrics: - name: BLEU type: bleu value: 21.9 - task: name: Translation mkd-rus type: translation args: mkd-rus dataset: name: flores101-devtest type: flores_101 args: mkd rus devtest metrics: - name: BLEU type: bleu value: 24.3 - task: name: Translation mkd-ukr type: translation args: mkd-ukr dataset: name: flores101-devtest type: flores_101 args: mkd ukr devtest metrics: - name: BLEU type: bleu value: 22.5 - task: name: Translation slv-rus type: translation args: slv-rus dataset: name: flores101-devtest type: flores_101 args: slv rus devtest metrics: - name: BLEU type: bleu value: 22.0 - task: name: Translation slv-ukr type: translation args: slv-ukr dataset: name: flores101-devtest type: flores_101 args: slv ukr devtest metrics: - name: BLEU type: bleu value: 20.2 - task: name: Translation srp_Cyrl-rus type: translation args: srp_Cyrl-rus dataset: name: flores101-devtest type: flores_101 args: srp_Cyrl rus devtest metrics: - name: BLEU type: bleu value: 25.7 - task: name: Translation srp_Cyrl-ukr type: translation args: srp_Cyrl-ukr dataset: name: flores101-devtest type: flores_101 args: srp_Cyrl ukr devtest metrics: - name: BLEU type: bleu value: 24.4 - task: name: Translation bul-rus type: translation args: bul-rus dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: bul-rus metrics: - name: BLEU type: bleu value: 52.6 - task: name: Translation bul-ukr type: translation args: bul-ukr dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: bul-ukr metrics: - name: BLEU type: bleu value: 53.3 - task: name: Translation hbs-rus type: translation args: hbs-rus dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hbs-rus metrics: - name: BLEU type: bleu value: 58.5 - task: name: Translation hbs-ukr type: translation args: hbs-ukr dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hbs-ukr metrics: - name: BLEU type: bleu value: 52.3 - task: name: Translation hrv-ukr type: translation args: hrv-ukr dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hrv-ukr metrics: - name: BLEU type: bleu value: 50.0 - task: name: Translation slv-rus type: translation args: slv-rus dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: slv-rus metrics: - name: BLEU type: bleu value: 27.3 - task: name: Translation srp_Cyrl-rus type: translation args: srp_Cyrl-rus dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: srp_Cyrl-rus metrics: - name: BLEU type: bleu value: 56.2 - task: name: Translation srp_Cyrl-ukr type: translation args: srp_Cyrl-ukr dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: srp_Cyrl-ukr metrics: - name: BLEU type: bleu value: 51.8 - task: name: Translation srp_Latn-rus type: translation args: srp_Latn-rus dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: srp_Latn-rus metrics: - name: BLEU type: bleu value: 60.1 - task: name: Translation srp_Latn-ukr type: translation args: srp_Latn-ukr dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: srp_Latn-ukr metrics: - name: BLEU type: bleu value: 55.8 --- # opus-mt-tc-big-zls-zle Neural machine translation model for translating from South Slavic languages (zls) to East Slavic languages (zle). 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): bul hbs hrv slv srp_Cyrl srp_Latn * target language(s): bel rus ukr * valid target language labels: >>bel<< >>rus<< >>ukr<< * 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/zls-zle/opusTCv20210807+bt_transformer-big_2022-03-23.zip) * more information released models: [OPUS-MT zls-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-zle/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. `>>bel<<` ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>rus<< Gdje je brigadir?", ">>ukr<< Zovem se Seli." ] model_name = "pytorch-models/opus-mt-tc-big-zls-zle" 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: # Где бригадир? # Мене звати Саллі. ``` 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-zls-zle") print(pipe(">>rus<< Gdje je brigadir?")) # expected output: Где бригадир? ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-zle/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/zls-zle/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 | |----------|---------|-------|-------|-------|--------| | bul-rus | tatoeba-test-v2021-08-07 | 0.71467 | 52.6 | 1247 | 7870 | | bul-ukr | tatoeba-test-v2021-08-07 | 0.71757 | 53.3 | 1020 | 4932 | | hbs-rus | tatoeba-test-v2021-08-07 | 0.74593 | 58.5 | 2500 | 14213 | | hbs-ukr | tatoeba-test-v2021-08-07 | 0.70244 | 52.3 | 942 | 4961 | | hrv-ukr | tatoeba-test-v2021-08-07 | 0.68931 | 50.0 | 389 | 2232 | | slv-rus | tatoeba-test-v2021-08-07 | 0.42255 | 27.3 | 657 | 4056 | | srp_Cyrl-rus | tatoeba-test-v2021-08-07 | 0.74112 | 56.2 | 881 | 5117 | | srp_Cyrl-ukr | tatoeba-test-v2021-08-07 | 0.68915 | 51.8 | 205 | 1061 | | srp_Latn-rus | tatoeba-test-v2021-08-07 | 0.75340 | 60.1 | 1483 | 8311 | | srp_Latn-ukr | tatoeba-test-v2021-08-07 | 0.73106 | 55.8 | 348 | 1668 | | bul-rus | flores101-devtest | 0.54226 | 24.6 | 1012 | 23295 | | bul-ukr | flores101-devtest | 0.53382 | 22.9 | 1012 | 22810 | | hrv-rus | flores101-devtest | 0.51726 | 23.5 | 1012 | 23295 | | hrv-ukr | flores101-devtest | 0.51011 | 21.9 | 1012 | 22810 | | mkd-bel | flores101-devtest | 0.40885 | 10.7 | 1012 | 24829 | | mkd-rus | flores101-devtest | 0.52509 | 24.3 | 1012 | 23295 | | mkd-ukr | flores101-devtest | 0.52021 | 22.5 | 1012 | 22810 | | slv-rus | flores101-devtest | 0.50349 | 22.0 | 1012 | 23295 | | slv-ukr | flores101-devtest | 0.49156 | 20.2 | 1012 | 22810 | | srp_Cyrl-rus | flores101-devtest | 0.53656 | 25.7 | 1012 | 23295 | | srp_Cyrl-ukr | flores101-devtest | 0.53623 | 24.4 | 1012 | 22810 | ## 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 04:08:51 EET 2022 * port machine: LM0-400-22516.local