sentence-transformers/parallel-sentences-opensubtitles
Dataset Card for Parallel Sentences - OpenSubtitles This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of the sentences originate from the OPUS website. In particular, this dataset contains the OpenSubtitles dataset. Warning! The quality of this dataset is not great; many of the english and non-english texts don't match well, or are fully empty. Related Datasets The following… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/parallel-sentences-opensubtitles.
Dataset Card for Parallel Sentences - OpenSubtitles
This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of the sentences originate from the OPUS website. In particular, this dataset contains the OpenSubtitles dataset.
Warning! The quality of this dataset is not great; many of the english and non-english texts don't match well, or are fully empty.
Related Datasets
The following datasets are also a part of the Parallel Sentences collection:
- parallel-sentences-europarl
- parallel-sentences-global-voices
- parallel-sentences-muse
- parallel-sentences-jw300
- parallel-sentences-news-commentary
- parallel-sentences-opensubtitles
- parallel-sentences-talks
- parallel-sentences-tatoeba
- parallel-sentences-wikimatrix
- parallel-sentences-wikititles
- parallel-sentences-ccmatrix
These datasets can be used to train multilingual sentence embedding models. For more information, see sbert.net - Multilingual Models.
Dataset Subsets
all subset
- Columns: "english", "non_english"
- Column types:
str,str - Examples:
{
"english": "We can't predict it and we can't control it.",
"non_english": "نحن لا نَستطيعُ تَوَقُّعه ونحن لا نَستطيعُ السَيْطَرَة عليه."
}- Collection strategy: Combining all other subsets from this dataset.
- Deduplified: No
en-... subsets
- Columns: "english", "non_english"
- Column types:
str,str - Examples:
{
"english": "ever faithful, ever true, nothing stops him, he'll get through.",
"non_english": "우리의 한결같은 심부름꾼 황새 아저씨 가는 길을 그 누가 막으랴!"
}- Collection strategy: Processing the raw data from parallel-sentences and formatting it in Parquet, followed by deduplication.
- Deduplified: Yes
