Datasets:
license:
- cc-by-sa-4.0
language:
- de
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
task_categories:
- sentence-similarity
This dataset card is still a draft version. The dataset has not been uploaded yet.
This is a record of German language paraphrases. These are text pairs that have the same meaning but are expressed in different words. The source of the paraphrases are different parallel German / English text corpora. The English texts were machine translated back into German. This is how the paraphrases were obtained.
Columns description
uuid
: a uuid calculated with Pythonuuid.uuid4()
de
: the original German texts from the corpusen_de
: the German texts translated back from Englishcorpus
: the name of the corpusmin_char_len
: the number of characters of the shortest textjaccard_similarity
: the Jaccard similarity coefficient of both sentencesde_token_count
: number of tokens of thede
text, tokenized with deepset/gbert-largeen_de_token_count
: number of tokens of thede
text, tokenized with deepset/gbert-largecos_sim
: the cosine similarity of both sentences measured with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
Load this dataset with Pandas
If you want to download the csv file and then load it with Pandas you can do it like this:
df = pd.read_csv("train.csv")
Parallel text corpora used
Corpus name & link | Number of paraphrases |
---|---|
OpenSubtitles | 18,764,810 |
WikiMatrix v1 | 1,569,231 |
Tatoeba v2022-03-03 | 313,105 |
TED2020 v1 | 289,374 |
News-Commentary v16 | 285,722 |
GlobalVoices v2018q4 | 70,547 |
sum | . 21,292,789 |
To-do
- add column description
- upload dataset
- add jaccard calculation
Back translation
We have made the back translation from English to German with the help of Fairseq.
We used the transformer.wmt19.en-de
model for this purpose:
en2de = torch.hub.load(
"pytorch/fairseq",
"transformer.wmt19.en-de",
checkpoint_file="model1.pt:model2.pt:model3.pt:model4.pt",
tokenizer="moses",
bpe="fastbpe",
)
Citations & Acknowledgements
OpenSubtitles
- citation: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)
- also see http://www.opensubtitles.org/
- license: no special license has been provided at OPUS for this dataset
WikiMatrix v1
- citation: Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia, arXiv, July 11 2019
- license: CC-BY-SA 4.0
Tatoeba v2022-03-03
- citation: J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
- license: CC BY 2.0 FR
- copyright: https://tatoeba.org/eng/terms_of_use
TED2020 v1
- citation: Reimers, Nils and Gurevych, Iryna, Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation, In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, November 2020
- acknowledgements to OPUS for this service
- license: please respect the TED Talks Usage Policy
News-Commentary v16
- citation: J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
- license: no special license has been provided at OPUS for this dataset
GlobalVoices v2018q4
- citation: J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
- license: no special license has been provided at OPUS for this dataset
Licensing
Copyright (c) 2022 Philip May, Deutsche Telekom AG
This work is licensed under CC-BY-SA 4.0.