--- language: - ca - es - fr - gl - he - it - pt - ro tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-itc-he results: - task: name: Translation cat-heb type: translation args: cat-heb dataset: name: flores101-devtest type: flores_101 args: cat heb devtest metrics: - name: BLEU type: bleu value: 23.0 - name: chr-F type: chrf value: 0.52457 - task: name: Translation fra-heb type: translation args: fra-heb dataset: name: flores101-devtest type: flores_101 args: fra heb devtest metrics: - name: BLEU type: bleu value: 23.2 - name: chr-F type: chrf value: 0.52953 - task: name: Translation glg-heb type: translation args: glg-heb dataset: name: flores101-devtest type: flores_101 args: glg heb devtest metrics: - name: BLEU type: bleu value: 20.8 - name: chr-F type: chrf value: 0.50918 - task: name: Translation ita-heb type: translation args: ita-heb dataset: name: flores101-devtest type: flores_101 args: ita heb devtest metrics: - name: BLEU type: bleu value: 18.3 - name: chr-F type: chrf value: 0.49007 - task: name: Translation por-heb type: translation args: por-heb dataset: name: flores101-devtest type: flores_101 args: por heb devtest metrics: - name: BLEU type: bleu value: 24.4 - name: chr-F type: chrf value: 0.53906 - task: name: Translation ron-heb type: translation args: ron-heb dataset: name: flores101-devtest type: flores_101 args: ron heb devtest metrics: - name: BLEU type: bleu value: 22.1 - name: chr-F type: chrf value: 0.52103 - task: name: Translation spa-heb type: translation args: spa-heb dataset: name: flores101-devtest type: flores_101 args: spa heb devtest metrics: - name: BLEU type: bleu value: 16.5 - name: chr-F type: chrf value: 0.47646 - task: name: Translation fra-heb type: translation args: fra-heb dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: fra-heb metrics: - name: BLEU type: bleu value: 39.6 - name: chr-F type: chrf value: 0.60539 - task: name: Translation ita-heb type: translation args: ita-heb dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ita-heb metrics: - name: BLEU type: bleu value: 40.0 - name: chr-F type: chrf value: 0.60264 - task: name: Translation por-heb type: translation args: por-heb dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: por-heb metrics: - name: BLEU type: bleu value: 44.4 - name: chr-F type: chrf value: 0.63087 - task: name: Translation spa-heb type: translation args: spa-heb dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: spa-heb metrics: - name: BLEU type: bleu value: 44.5 - name: chr-F type: chrf value: 0.63883 --- # opus-mt-tc-big-itc-he ## 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 Hebrew (he). 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**: 2022-08-03 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): cat fra glg ita lad_Latn por ron spa - Target Language(s): heb - Language Pair(s): cat-heb fra-heb glg-heb ita-heb por-heb ron-heb spa-heb - Valid Target Language Labels: - **Original Model**: [opusTCv20210807_transformer-big_2022-08-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-heb/opusTCv20210807_transformer-big_2022-08-03.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-heb README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/itc-heb/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/ ## 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 = [ "La María és feminista.", "Contribuyan en Tatoeba." ] model_name = "pytorch-models/opus-mt-tc-big-itc-he" 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: # מרי היא פמיניסטית. # תרום לטאטואבה. ``` 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-he") print(pipe("La María és feminista.")) # expected output: מרי היא פמיניסטית. ``` ## 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_2022-08-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-heb/opusTCv20210807_transformer-big_2022-08-03.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-08-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-heb/opusTCv20210807_transformer-big_2022-08-03.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-08-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-heb/opusTCv20210807_transformer-big_2022-08-03.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 | |----------|---------|-------|-------|-------|--------| | fra-heb | tatoeba-test-v2021-08-07 | 0.60539 | 39.6 | 3281 | 20655 | | ita-heb | tatoeba-test-v2021-08-07 | 0.60264 | 40.0 | 1706 | 9796 | | por-heb | tatoeba-test-v2021-08-07 | 0.63087 | 44.4 | 719 | 4423 | | spa-heb | tatoeba-test-v2021-08-07 | 0.63883 | 44.5 | 1849 | 12112 | | cat-heb | flores101-devtest | 0.52457 | 23.0 | 1012 | 20749 | | fra-heb | flores101-devtest | 0.52953 | 23.2 | 1012 | 20749 | | glg-heb | flores101-devtest | 0.50918 | 20.8 | 1012 | 20749 | | ita-heb | flores101-devtest | 0.49007 | 18.3 | 1012 | 20749 | | por-heb | flores101-devtest | 0.53906 | 24.4 | 1012 | 20749 | | ron-heb | flores101-devtest | 0.52103 | 22.1 | 1012 | 20749 | | spa-heb | flores101-devtest | 0.47646 | 16.5 | 1012 | 20749 | ## 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 00:02:03 EEST 2022 * port machine: LM0-400-22516.local