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- # Parallel Sentences for 50+ languages
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-
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- This repository contains parallel sentences (i.e. English + same sentences in other language) for 50+ different languages in a simple tsv.gz format:
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- ```
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- english_sentences\tsentence_in_other_language
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- ```
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-
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- Sentences stem from the [OPUS website](https://opus.nlpl.eu/).
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-
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- The following datasets are included:
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- - [Europarl](https://opus.nlpl.eu/Europarl.php)
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- - [GlobalVoices](https://opus.nlpl.eu/GlobalVoices.php)
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- - [JW300](https://opus.nlpl.eu/JW300.php)
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- - [MUSE](https://github.com/facebookresearch/MUSE)
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- - [News-Commentary](https://opus.nlpl.eu/News-Commentary.php)
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- - [OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles.php)
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- - [Tatoeba](https://tatoeba.org/)
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- - [TED2020](https://opus.nlpl.eu/TED2020.php)
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- - [WikiMatrix](https://opus.nlpl.eu/WikiMatrix.php)
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- - WikiTitles - Custom dataset with parallel Wikipedia titles
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-
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- ## Usage
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-
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- These sentences can be used to train multi-lingual sentence embedding models. For more details, see [SBERT.net - Multilingual-Model](https://www.sbert.net/examples/training/multilingual/README.html)
 
 
 
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+ # Parallel Sentences for 50+ languages
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+
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+ This repository contains parallel sentences (i.e. English + same sentences in other language) for 50+ different languages in a simple tsv.gz format:
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+ ```
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+ english_sentences\tsentence_in_other_language
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+ ```
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+
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+ Sentences stem from the [OPUS website](https://opus.nlpl.eu/).
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+
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+ The following datasets are included:
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+ - [Europarl](https://opus.nlpl.eu/Europarl.php)
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+ - [GlobalVoices](https://opus.nlpl.eu/GlobalVoices.php)
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+ - [JW300](https://opus.nlpl.eu/JW300.php)
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+ - [MUSE](https://github.com/facebookresearch/MUSE)
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+ - [News-Commentary](https://opus.nlpl.eu/News-Commentary.php)
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+ - [OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles.php)
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+ - [Tatoeba](https://tatoeba.org/)
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+ - [TED2020](https://opus.nlpl.eu/TED2020.php)
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+ - [WikiMatrix](https://opus.nlpl.eu/WikiMatrix.php)
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+ - WikiTitles - Custom dataset with parallel Wikipedia titles
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+
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+ ## Usage
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+
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+ These sentences can be used to train multi-lingual sentence embedding models. For more details, see [SBERT.net - Multilingual-Model](https://www.sbert.net/examples/training/multilingual/README.html)
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+
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+ **This dataset can not yet be used with Hugging Face dataset library**