--- language: - bs - en - hr - sh - sr language_bcp47: - bs_Latn - sr_Cyrl - sr_Latn tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-base-en-sh results: - task: name: Translation eng-hrv type: translation args: eng-hrv dataset: name: flores200-dev type: flores200-dev args: eng-hrv metrics: - name: BLEU type: bleu value: 28.1 - name: chr-F type: chrf value: 0.57963 - task: name: Translation eng-srp_Cyrl type: translation args: eng-srp_Cyrl dataset: name: flores200-dev type: flores200-dev args: eng-srp_Cyrl metrics: - name: BLEU type: bleu value: 32.2 - name: chr-F type: chrf value: 0.60096 - task: name: Translation eng-hrv type: translation args: eng-hrv dataset: name: flores200-devtest type: flores200-devtest args: eng-hrv metrics: - name: BLEU type: bleu value: 28.9 - name: chr-F type: chrf value: 0.58652 - task: name: Translation eng-srp_Cyrl type: translation args: eng-srp_Cyrl dataset: name: flores200-devtest type: flores200-devtest args: eng-srp_Cyrl metrics: - name: BLEU type: bleu value: 31.7 - name: chr-F type: chrf value: 0.59874 - task: name: Translation eng-hrv type: translation args: eng-hrv dataset: name: flores101-devtest type: flores_101 args: eng hrv devtest metrics: - name: BLEU type: bleu value: 28.7 - name: chr-F type: chrf value: 0.586 - task: name: Translation eng-srp_Cyrl type: translation args: eng-srp_Cyrl dataset: name: flores101-devtest type: flores_101 args: eng srp_Cyrl devtest metrics: - name: BLEU type: bleu value: 31.7 - name: chr-F type: chrf value: 0.59874 - task: name: Translation eng-bos_Latn type: translation args: eng-bos_Latn dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-bos_Latn metrics: - name: BLEU type: bleu value: 46.3 - name: chr-F type: chrf value: 0.666 - task: name: Translation eng-hbs type: translation args: eng-hbs dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-hbs metrics: - name: BLEU type: bleu value: 42.1 - name: chr-F type: chrf value: 0.631 - task: name: Translation eng-hrv type: translation args: eng-hrv dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-hrv metrics: - name: BLEU type: bleu value: 49.7 - name: chr-F type: chrf value: 0.691 - task: name: Translation eng-srp_Cyrl type: translation args: eng-srp_Cyrl dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-srp_Cyrl metrics: - name: BLEU type: bleu value: 45.1 - name: chr-F type: chrf value: 0.645 - task: name: Translation eng-srp_Latn type: translation args: eng-srp_Latn dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: eng-srp_Latn metrics: - name: BLEU type: bleu value: 39.8 - name: chr-F type: chrf value: 0.613 --- # opus-mt-tc-base-en-sh ## 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 English (en) to Serbo-Croatian (sh). 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-align) - **Release**: 2021-04-20 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): eng - Target Language(s): bos_Latn hbs hrv srp_Cyrl srp_Latn - Language Pair(s): eng-bos_Latn eng-hbs eng-hrv eng-srp_Cyrl eng-srp_Latn - Valid Target Language Labels: >>bos_Cyrl<< >>bos_Latn<< >>cnr<< >>cnr_Latn<< >>hbs<< >>hbs_Cyrl<< >>hrv<< >>srp_Cyrl<< >>srp_Latn<< - **Original Model**: [opus+bt-2021-04-20.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hbs/opus+bt-2021-04-20.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 eng-hbs README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hbs/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. `>>bos_Latn<<` ## 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 = [ ">>hrv<< You're about to make a very serious mistake.", ">>hbs<< I've just been too busy." ] model_name = "pytorch-models/opus-mt-tc-base-en-sh" 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: # Ti si o tome napraviti vrlo ozbiljnu pogrešku. # [4] ``` 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-base-en-sh") print(pipe(">>hrv<< You're about to make a very serious mistake.")) # expected output: Ti si o tome napraviti vrlo ozbiljnu pogrešku. ``` ## Training - **Data**: opus+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-align - **Original MarianNMT Model**: [opus+bt-2021-04-20.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hbs/opus+bt-2021-04-20.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * test set translations: [opus+bt-2021-04-20.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hbs/opus+bt-2021-04-20.test.txt) * test set scores: [opus+bt-2021-04-20.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hbs/opus+bt-2021-04-20.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 | |----------|---------|-------|-------|-------|--------| | eng-bos_Latn | tatoeba-test-v2021-08-07 | 0.666 | 46.3 | 301 | 1650 | | eng-hbs | tatoeba-test-v2021-08-07 | 0.631 | 42.1 | 10017 | 63927 | | eng-hrv | tatoeba-test-v2021-08-07 | 0.691 | 49.7 | 1480 | 9396 | | eng-srp_Cyrl | tatoeba-test-v2021-08-07 | 0.645 | 45.1 | 1580 | 9152 | | eng-srp_Latn | tatoeba-test-v2021-08-07 | 0.613 | 39.8 | 6656 | 43729 | | eng-hrv | flores101-devtest | 0.586 | 28.7 | 1012 | 22423 | | eng-hrv | flores200-dev | 0.57963 | 28.1 | 997 | 21567 | | eng-hrv | flores200-devtest | 0.58652 | 28.9 | 1012 | 22423 | | eng-srp_Cyrl | flores101-devtest | 0.59874 | 31.7 | 1012 | 23456 | | eng-srp_Cyrl | flores200-dev | 0.60096 | 32.2 | 997 | 22384 | | eng-srp_Cyrl | flores200-devtest | 0.59874 | 31.7 | 1012 | 23456 | ## 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: e2a6299 * port time: Tue Oct 11 10:14:32 CEST 2022 * port machine: LM0-400-22516.local