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STSb Multi MT

Machine translated multilingual STS benchmark dataset.

These are different multilingual translations and the English original of the STSbenchmark dataset. Translation has been done with deepl.com.

It can be used to train sentence embeddings like T-Systems-onsite/cross-en-de-roberta-sentence-transformer.

Please open an issue if you have questions or want to report problems.

This dataset provides pairs of sentences and a score of their similarity.

score2 example sentencesexplanation
5The bird is bathing in the sink.<br/>Birdie is washing itself in the water basin.The two sentences are completely equivalent, as they mean the same thing.
4Two boys on a couch are playing video games.<br/>Two boys are playing a video game.The two sentences are mostly equivalent, but some unimportant details differ.
3John said he is considered a witness but not a suspect.<br/>“He is not a suspect anymore.” John said.The two sentences are roughly equivalent, but some important information differs/missing.
2They flew out of the nest in groups.<br/>They flew into the nest together.The two sentences are not equivalent, but share some details.
1The woman is playing the violin.<br/>The young lady enjoys listening to the guitar.The two sentences are not equivalent, but are on the same topic.
0The black dog is running through the snow.<br/>A race car driver is driving his car through the mud.The two sentences are completely dissimilar.

Content

Examples of Use

import csv

with open(filepath, newline="", encoding="utf-8") as csvfile:
    csv_dict_reader = csv.DictReader(
        csvfile,
        dialect='excel',
        fieldnames=["sentence1", "sentence2", "similarity_score"],
    )
    for row in csv_dict_reader:
        print(row)

Known Issues

none

Manual Testing of Datasets

Language1st train1000st trainlast train1st dev1000st devlast dev1st test1000st testlast test
deokokokokokokokokok
enokokokokokokokokok
es
fr
it
ja
nlokokpartially Englishokokokokokpoor grammar
pl
pt
ru
zh