--- language: - ca - es - fr - gl - it - lt - lv - pt tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-itc-bat results: - task: name: Translation cat-lav type: translation args: cat-lav dataset: name: flores101-devtest type: flores_101 args: cat lav devtest metrics: - name: BLEU type: bleu value: 21.9 - name: chr-F type: chrf value: 0.52215 - task: name: Translation cat-lit type: translation args: cat-lit dataset: name: flores101-devtest type: flores_101 args: cat lit devtest metrics: - name: BLEU type: bleu value: 20.2 - name: chr-F type: chrf value: 0.52380 - task: name: Translation fra-lav type: translation args: fra-lav dataset: name: flores101-devtest type: flores_101 args: fra lav devtest metrics: - name: BLEU type: bleu value: 23.0 - name: chr-F type: chrf value: 0.53390 - task: name: Translation fra-lit type: translation args: fra-lit dataset: name: flores101-devtest type: flores_101 args: fra lit devtest metrics: - name: BLEU type: bleu value: 21.1 - name: chr-F type: chrf value: 0.53595 - task: name: Translation glg-lav type: translation args: glg-lav dataset: name: flores101-devtest type: flores_101 args: glg lav devtest metrics: - name: BLEU type: bleu value: 20.7 - name: chr-F type: chrf value: 0.51043 - task: name: Translation glg-lit type: translation args: glg-lit dataset: name: flores101-devtest type: flores_101 args: glg lit devtest metrics: - name: BLEU type: bleu value: 19.9 - name: chr-F type: chrf value: 0.51854 - task: name: Translation ita-lav type: translation args: ita-lav dataset: name: flores101-devtest type: flores_101 args: ita lav devtest metrics: - name: BLEU type: bleu value: 19.6 - name: chr-F type: chrf value: 0.51065 - task: name: Translation ita-lit type: translation args: ita-lit dataset: name: flores101-devtest type: flores_101 args: ita lit devtest metrics: - name: BLEU type: bleu value: 17.4 - name: chr-F type: chrf value: 0.51309 - task: name: Translation por-lav type: translation args: por-lav dataset: name: flores101-devtest type: flores_101 args: por lav devtest metrics: - name: BLEU type: bleu value: 22.9 - name: chr-F type: chrf value: 0.53493 - task: name: Translation por-lit type: translation args: por-lit dataset: name: flores101-devtest type: flores_101 args: por lit devtest metrics: - name: BLEU type: bleu value: 21.8 - name: chr-F type: chrf value: 0.53821 - task: name: Translation spa-lav type: translation args: spa-lav dataset: name: flores101-devtest type: flores_101 args: spa lav devtest metrics: - name: BLEU type: bleu value: 17.4 - name: chr-F type: chrf value: 0.49290 - task: name: Translation spa-lit type: translation args: spa-lit dataset: name: flores101-devtest type: flores_101 args: spa lit devtest metrics: - name: BLEU type: bleu value: 16.2 - name: chr-F type: chrf value: 0.49836 - task: name: Translation ita-lit type: translation args: ita-lit dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ita-lit metrics: - name: BLEU type: bleu value: 40.9 - name: chr-F type: chrf value: 0.67640 - task: name: Translation spa-lit type: translation args: spa-lit dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: spa-lit metrics: - name: BLEU type: bleu value: 45.9 - name: chr-F type: chrf value: 0.68805 --- # opus-mt-tc-big-itc-bat ## 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 Baltic languages (bat). 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**: 2022-07-27 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): cat fra glg ita por spa - Target Language(s): lav lit prg - Language Pair(s): cat-lav cat-lit fra-lav fra-lit glg-lav glg-lit ita-lav ita-lit por-lav por-lit spa-lit - Valid Target Language Labels: >>lav<< >>lit<< >>ltg<< >>ndf<< >>olt<< >>prg<< >>prg_Latn<< >>sgs<< >>svx<< >>sxl<< >>xcu<< >>xgl<< >>xsv<< >>xzm<< - **Original Model**: [opusTCv20210807_transformer-big_2022-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-bat/opusTCv20210807_transformer-big_2022-07-27.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-bat README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/itc-bat/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. `>>lav<<` ## 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 = [ ">>lit<< Els gats són complexos individus.", ">>sgs<< No." ] model_name = "pytorch-models/opus-mt-tc-big-itc-bat" 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: # Katės yra sudėtingi individai. # no no no no no no no no no no no no no no no no no no no no no ``` 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-bat") print(pipe(">>lit<< Els gats són complexos individus.")) # expected output: Katės yra sudėtingi individai. ``` ## 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_2022-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-bat/opusTCv20210807_transformer-big_2022-07-27.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-bat/opusTCv20210807_transformer-big_2022-07-27.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-bat/opusTCv20210807_transformer-big_2022-07-27.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 | |----------|---------|-------|-------|-------|--------| | ita-lit | tatoeba-test-v2021-08-07 | 0.67640 | 40.9 | 224 | 1321 | | spa-lit | tatoeba-test-v2021-08-07 | 0.68805 | 45.9 | 454 | 2352 | | cat-lav | flores101-devtest | 0.52215 | 21.9 | 1012 | 22092 | | cat-lit | flores101-devtest | 0.52380 | 20.2 | 1012 | 20695 | | fra-lav | flores101-devtest | 0.53390 | 23.0 | 1012 | 22092 | | fra-lit | flores101-devtest | 0.53595 | 21.1 | 1012 | 20695 | | glg-lav | flores101-devtest | 0.51043 | 20.7 | 1012 | 22092 | | glg-lit | flores101-devtest | 0.51854 | 19.9 | 1012 | 20695 | | ita-lav | flores101-devtest | 0.51065 | 19.6 | 1012 | 22092 | | ita-lit | flores101-devtest | 0.51309 | 17.4 | 1012 | 20695 | | por-lav | flores101-devtest | 0.53493 | 22.9 | 1012 | 22092 | | por-lit | flores101-devtest | 0.53821 | 21.8 | 1012 | 20695 | | spa-lav | flores101-devtest | 0.49290 | 17.4 | 1012 | 22092 | | spa-lit | flores101-devtest | 0.49836 | 16.2 | 1012 | 20695 | ## 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 00:04:44 EEST 2022 * port machine: LM0-400-22516.local