opus-mt-tc-big-gmq-en
Helsinki-NLP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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opus-mt-tc-big-he-en
Helsinki-NLPThis model is part of the OPUS-MT project, 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, 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 and training pipelines use the procedures of OPUS-MT-train. The work is supported by the European Language Grid as pilot project 2866, by the FoTran project, 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, 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, Finland.
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View details ↗opus-mt-tc-big-it-en
Helsinki-NLP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗opus-mt-tc-big-itc-itc
Helsinki-NLP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗opus-mt-tc-big-ko-en
Helsinki-NLP- Model Details - Uses - Risks, Limitations and Biases - How to Get Started With the Model - Training - Evaluation - Citation Information - Acknowledgements Neural machine translation model for translating from Korean (ko) to English (en). This model is part of the OPUS-MT project, 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, 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 and training pipelines use the procedures of OPUS-MT-train. **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation (transformer-big) - **Release**: 2022-07-28 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): kor - Target Language(s): eng - **Original Model**: opusTCv20210807-sepvoc_transformer-big_2022-07-28.zip - **Resources for more information:** - OPUS-MT-train GitHub Repo - More information about released models for this language pair: OPUS-MT kor-eng README - More information about MarianNMT models in the transformers library - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
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View details ↗opus-mt-tc-big-lt-en
Helsinki-NLP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗opus-mt-tc-big-sh-en
Helsinki-NLPThis model is part of the OPUS-MT project, 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, 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 and training pipelines use the procedures of OPUS-MT-train. The work is supported by the European Language Grid as pilot project 2866, by the FoTran project, 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, 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, Finland.
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View details ↗opus-mt-tc-big-tr-en
Helsinki-NLPThis model is part of the OPUS-MT project, 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, 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 and training pipelines use the procedures of OPUS-MT-train. The work is supported by the European Language Grid as pilot project 2866, by the FoTran project, 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, 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, Finland.
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View details ↗opus-mt-tc-big-zh-ja
Helsinki-NLP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗opus-mt-tc-big-zle-de
Helsinki-NLP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗opus-mt-tc-big-zle-en
Helsinki-NLP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗opus-mt-tc-big-zle-zle
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View details ↗opus-mt-tc-big-zle-zlw
Helsinki-NLP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗opus-mt-tc-big-zls-en
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View details ↗opus-mt-tc-big-zlw-zle
Helsinki-NLP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗opus-mt-th-en
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View details ↗opus-mt-th-fr
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View details ↗opus-mt_tiny_deu-eng
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View details ↗opus-mt_tiny_spa-eng
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View details ↗opus-mt-tl-en
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View details ↗opus-mt-tn-en
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View details ↗opus-mt-tr-en
Helsinki-NLP* source languages: tr * target languages: en * OPUS readme: tr-en * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: opus-2020-01-16.zip * test set translations: opus-2020-01-16.test.txt * test set scores: opus-2020-01-16.eval.txt
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View details ↗opus-mt-uk-en
Helsinki-NLP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗opus-mt-uk-pl
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