| --- |
| 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 |
| |
