--- language: - bg - es - fr - hr - it - mk - pt - ro - sh - sl - sr language_bcp47: - sr_Cyrl - sr_Latn tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-zls-itc results: - task: name: Translation bul-fra type: translation args: bul-fra dataset: name: flores101-devtest type: flores_101 args: bul fra devtest metrics: - name: BLEU type: bleu value: 34.4 - name: chr-F type: chrf value: 0.60640 - task: name: Translation bul-ita type: translation args: bul-ita dataset: name: flores101-devtest type: flores_101 args: bul ita devtest metrics: - name: BLEU type: bleu value: 24.0 - name: chr-F type: chrf value: 0.54135 - task: name: Translation bul-por type: translation args: bul-por dataset: name: flores101-devtest type: flores_101 args: bul por devtest metrics: - name: BLEU type: bleu value: 32.4 - name: chr-F type: chrf value: 0.59322 - task: name: Translation bul-ron type: translation args: bul-ron dataset: name: flores101-devtest type: flores_101 args: bul ron devtest metrics: - name: BLEU type: bleu value: 27.1 - name: chr-F type: chrf value: 0.55558 - task: name: Translation bul-spa type: translation args: bul-spa dataset: name: flores101-devtest type: flores_101 args: bul spa devtest metrics: - name: BLEU type: bleu value: 22.4 - name: chr-F type: chrf value: 0.50962 - task: name: Translation hrv-fra type: translation args: hrv-fra dataset: name: flores101-devtest type: flores_101 args: hrv fra devtest metrics: - name: BLEU type: bleu value: 33.1 - name: chr-F type: chrf value: 0.59349 - task: name: Translation hrv-ita type: translation args: hrv-ita dataset: name: flores101-devtest type: flores_101 args: hrv ita devtest metrics: - name: BLEU type: bleu value: 23.5 - name: chr-F type: chrf value: 0.52980 - task: name: Translation hrv-por type: translation args: hrv-por dataset: name: flores101-devtest type: flores_101 args: hrv por devtest metrics: - name: BLEU type: bleu value: 30.2 - name: chr-F type: chrf value: 0.57402 - task: name: Translation hrv-ron type: translation args: hrv-ron dataset: name: flores101-devtest type: flores_101 args: hrv ron devtest metrics: - name: BLEU type: bleu value: 25.9 - name: chr-F type: chrf value: 0.53650 - task: name: Translation hrv-spa type: translation args: hrv-spa dataset: name: flores101-devtest type: flores_101 args: hrv spa devtest metrics: - name: BLEU type: bleu value: 21.5 - name: chr-F type: chrf value: 0.50161 - task: name: Translation mkd-fra type: translation args: mkd-fra dataset: name: flores101-devtest type: flores_101 args: mkd fra devtest metrics: - name: BLEU type: bleu value: 35.2 - name: chr-F type: chrf value: 0.60801 - task: name: Translation mkd-ita type: translation args: mkd-ita dataset: name: flores101-devtest type: flores_101 args: mkd ita devtest metrics: - name: BLEU type: bleu value: 23.9 - name: chr-F type: chrf value: 0.53543 - task: name: Translation mkd-por type: translation args: mkd-por dataset: name: flores101-devtest type: flores_101 args: mkd por devtest metrics: - name: BLEU type: bleu value: 33.9 - name: chr-F type: chrf value: 0.59648 - task: name: Translation mkd-ron type: translation args: mkd-ron dataset: name: flores101-devtest type: flores_101 args: mkd ron devtest metrics: - name: BLEU type: bleu value: 28.0 - name: chr-F type: chrf value: 0.54998 - task: name: Translation mkd-spa type: translation args: mkd-spa dataset: name: flores101-devtest type: flores_101 args: mkd spa devtest metrics: - name: BLEU type: bleu value: 22.8 - name: chr-F type: chrf value: 0.51079 - task: name: Translation slv-fra type: translation args: slv-fra dataset: name: flores101-devtest type: flores_101 args: slv fra devtest metrics: - name: BLEU type: bleu value: 31.5 - name: chr-F type: chrf value: 0.58233 - task: name: Translation slv-ita type: translation args: slv-ita dataset: name: flores101-devtest type: flores_101 args: slv ita devtest metrics: - name: BLEU type: bleu value: 22.4 - name: chr-F type: chrf value: 0.52390 - task: name: Translation slv-por type: translation args: slv-por dataset: name: flores101-devtest type: flores_101 args: slv por devtest metrics: - name: BLEU type: bleu value: 29.0 - name: chr-F type: chrf value: 0.56436 - task: name: Translation slv-ron type: translation args: slv-ron dataset: name: flores101-devtest type: flores_101 args: slv ron devtest metrics: - name: BLEU type: bleu value: 25.0 - name: chr-F type: chrf value: 0.53116 - task: name: Translation slv-spa type: translation args: slv-spa dataset: name: flores101-devtest type: flores_101 args: slv spa devtest metrics: - name: BLEU type: bleu value: 21.1 - name: chr-F type: chrf value: 0.49621 - task: name: Translation srp_Cyrl-fra type: translation args: srp_Cyrl-fra dataset: name: flores101-devtest type: flores_101 args: srp_Cyrl fra devtest metrics: - name: BLEU type: bleu value: 36.0 - name: chr-F type: chrf value: 0.62110 - task: name: Translation srp_Cyrl-ita type: translation args: srp_Cyrl-ita dataset: name: flores101-devtest type: flores_101 args: srp_Cyrl ita devtest metrics: - name: BLEU type: bleu value: 23.9 - name: chr-F type: chrf value: 0.54083 - task: name: Translation srp_Cyrl-por type: translation args: srp_Cyrl-por dataset: name: flores101-devtest type: flores_101 args: srp_Cyrl por devtest metrics: - name: BLEU type: bleu value: 34.9 - name: chr-F type: chrf value: 0.61248 - task: name: Translation srp_Cyrl-ron type: translation args: srp_Cyrl-ron dataset: name: flores101-devtest type: flores_101 args: srp_Cyrl ron devtest metrics: - name: BLEU type: bleu value: 28.8 - name: chr-F type: chrf value: 0.56235 - task: name: Translation srp_Cyrl-spa type: translation args: srp_Cyrl-spa dataset: name: flores101-devtest type: flores_101 args: srp_Cyrl spa devtest metrics: - name: BLEU type: bleu value: 22.8 - name: chr-F type: chrf value: 0.51698 - task: name: Translation bul-fra type: translation args: bul-fra dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: bul-fra metrics: - name: BLEU type: bleu value: 52.9 - name: chr-F type: chrf value: 0.68971 - task: name: Translation bul-ita type: translation args: bul-ita dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: bul-ita metrics: - name: BLEU type: bleu value: 45.1 - name: chr-F type: chrf value: 0.66412 - task: name: Translation bul-spa type: translation args: bul-spa dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: bul-spa metrics: - name: BLEU type: bleu value: 49.7 - name: chr-F type: chrf value: 0.66672 - task: name: Translation hbs-fra type: translation args: hbs-fra dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hbs-fra metrics: - name: BLEU type: bleu value: 48.1 - name: chr-F type: chrf value: 0.66434 - task: name: Translation hbs-ita type: translation args: hbs-ita dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hbs-ita metrics: - name: BLEU type: bleu value: 53.5 - name: chr-F type: chrf value: 0.72381 - task: name: Translation hbs-spa type: translation args: hbs-spa dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hbs-spa metrics: - name: BLEU type: bleu value: 58.0 - name: chr-F type: chrf value: 0.73105 - task: name: Translation hrv-fra type: translation args: hrv-fra dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hrv-fra metrics: - name: BLEU type: bleu value: 44.3 - name: chr-F type: chrf value: 0.62800 - task: name: Translation hrv-spa type: translation args: hrv-spa dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: hrv-spa metrics: - name: BLEU type: bleu value: 57.5 - name: chr-F type: chrf value: 0.71370 - task: name: Translation mkd-spa type: translation args: mkd-spa dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: mkd-spa metrics: - name: BLEU type: bleu value: 62.1 - name: chr-F type: chrf value: 0.75366 - task: name: Translation srp_Latn-ita type: translation args: srp_Latn-ita dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: srp_Latn-ita metrics: - name: BLEU type: bleu value: 59.6 - name: chr-F type: chrf value: 0.76045 --- # opus-mt-tc-big-zls-itc ## 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 South Slavic languages (zls) to Italic languages (itc). 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**: 2022-08-10 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): bos_Latn bul hbs hrv mkd slv srp_Cyrl srp_Latn - Target Language(s): fra ita por ron spa - Language Pair(s): bul-fra bul-ita bul-por bul-ron bul-spa hbs-fra hbs-ita hbs-spa hrv-fra hrv-ita hrv-por hrv-ron hrv-spa mkd-fra mkd-ita mkd-por mkd-ron mkd-spa slv-fra slv-ita slv-por slv-ron slv-spa srp_Cyrl-fra srp_Cyrl-ita srp_Cyrl-por srp_Cyrl-ron srp_Cyrl-spa srp_Latn-ita - Valid Target Language Labels: >>acf<< >>aoa<< >>arg<< >>ast<< >>cat<< >>cbk<< >>ccd<< >>cks<< >>cos<< >>cri<< >>crs<< >>dlm<< >>drc<< >>egl<< >>ext<< >>fab<< >>fax<< >>fra<< >>frc<< >>frm<< >>fro<< >>frp<< >>fur<< >>gcf<< >>gcr<< >>glg<< >>hat<< >>idb<< >>ist<< >>ita<< >>itk<< >>kea<< >>kmv<< >>lad<< >>lad_Latn<< >>lat<< >>lat_Latn<< >>lij<< >>lld<< >>lmo<< >>lou<< >>mcm<< >>mfe<< >>mol<< >>mwl<< >>mxi<< >>mzs<< >>nap<< >>nrf<< >>oci<< >>osc<< >>osp<< >>pap<< >>pcd<< >>pln<< >>pms<< >>pob<< >>por<< >>pov<< >>pre<< >>pro<< >>qbb<< >>qhr<< >>rcf<< >>rgn<< >>roh<< >>ron<< >>ruo<< >>rup<< >>ruq<< >>scf<< >>scn<< >>sdc<< >>sdn<< >>spa<< >>spq<< >>spx<< >>src<< >>srd<< >>sro<< >>tmg<< >>tvy<< >>vec<< >>vkp<< >>wln<< >>xfa<< >>xum<< - **Original Model**: [opusTCv20210807_transformer-big_2022-08-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-itc/opusTCv20210807_transformer-big_2022-08-10.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 zls-itc README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-itc/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/ 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. `>>fra<<` ## 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 = [ ">>fra<< Dobar dan, kako si?", ">>spa<< Znam da je ovo čudno." ] model_name = "pytorch-models/opus-mt-tc-big-zls-itc" 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: # Bonjour, comment allez-vous ? # Sé que esto es raro. ``` 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-itc") print(pipe(">>fra<< Dobar dan, kako si?")) # expected output: Bonjour, comment allez-vous ? ``` ## 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_2022-08-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-itc/opusTCv20210807_transformer-big_2022-08-10.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-08-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-itc/opusTCv20210807_transformer-big_2022-08-10.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-08-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-itc/opusTCv20210807_transformer-big_2022-08-10.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-fra | tatoeba-test-v2021-08-07 | 0.68971 | 52.9 | 446 | 3669 | | bul-ita | tatoeba-test-v2021-08-07 | 0.66412 | 45.1 | 2500 | 16951 | | bul-spa | tatoeba-test-v2021-08-07 | 0.66672 | 49.7 | 286 | 1783 | | hbs-fra | tatoeba-test-v2021-08-07 | 0.66434 | 48.1 | 474 | 3370 | | hbs-ita | tatoeba-test-v2021-08-07 | 0.72381 | 53.5 | 534 | 3208 | | hbs-spa | tatoeba-test-v2021-08-07 | 0.73105 | 58.0 | 607 | 3766 | | hrv-fra | tatoeba-test-v2021-08-07 | 0.62800 | 44.3 | 258 | 1943 | | hrv-spa | tatoeba-test-v2021-08-07 | 0.71370 | 57.5 | 254 | 1702 | | mkd-spa | tatoeba-test-v2021-08-07 | 0.75366 | 62.1 | 217 | 1121 | | srp_Latn-ita | tatoeba-test-v2021-08-07 | 0.76045 | 59.6 | 212 | 1292 | | bul-fra | flores101-devtest | 0.60640 | 34.4 | 1012 | 28343 | | bul-ita | flores101-devtest | 0.54135 | 24.0 | 1012 | 27306 | | bul-por | flores101-devtest | 0.59322 | 32.4 | 1012 | 26519 | | bul-ron | flores101-devtest | 0.55558 | 27.1 | 1012 | 26799 | | bul-spa | flores101-devtest | 0.50962 | 22.4 | 1012 | 29199 | | hrv-fra | flores101-devtest | 0.59349 | 33.1 | 1012 | 28343 | | hrv-ita | flores101-devtest | 0.52980 | 23.5 | 1012 | 27306 | | hrv-por | flores101-devtest | 0.57402 | 30.2 | 1012 | 26519 | | hrv-ron | flores101-devtest | 0.53650 | 25.9 | 1012 | 26799 | | hrv-spa | flores101-devtest | 0.50161 | 21.5 | 1012 | 29199 | | mkd-fra | flores101-devtest | 0.60801 | 35.2 | 1012 | 28343 | | mkd-ita | flores101-devtest | 0.53543 | 23.9 | 1012 | 27306 | | mkd-por | flores101-devtest | 0.59648 | 33.9 | 1012 | 26519 | | mkd-ron | flores101-devtest | 0.54998 | 28.0 | 1012 | 26799 | | mkd-spa | flores101-devtest | 0.51079 | 22.8 | 1012 | 29199 | | slv-fra | flores101-devtest | 0.58233 | 31.5 | 1012 | 28343 | | slv-ita | flores101-devtest | 0.52390 | 22.4 | 1012 | 27306 | | slv-por | flores101-devtest | 0.56436 | 29.0 | 1012 | 26519 | | slv-ron | flores101-devtest | 0.53116 | 25.0 | 1012 | 26799 | | slv-spa | flores101-devtest | 0.49621 | 21.1 | 1012 | 29199 | | srp_Cyrl-fra | flores101-devtest | 0.62110 | 36.0 | 1012 | 28343 | | srp_Cyrl-ita | flores101-devtest | 0.54083 | 23.9 | 1012 | 27306 | | srp_Cyrl-por | flores101-devtest | 0.61248 | 34.9 | 1012 | 26519 | | srp_Cyrl-ron | flores101-devtest | 0.56235 | 28.8 | 1012 | 26799 | | srp_Cyrl-spa | flores101-devtest | 0.51698 | 22.8 | 1012 | 29199 | ## 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: Fri Aug 12 23:59:29 EEST 2022 * port machine: LM0-400-22516.local