Datasets:
Tasks:
Sentence Similarity
Formats:
csv
Languages:
German
Size:
10M - 100M
ArXiv:
Tags:
sentence-transformers
License:
| license: | |
| - cc-by-sa-4.0 | |
| language: | |
| - de | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10M<n<100M | |
| task_categories: | |
| - sentence-similarity | |
| # German Backtranslated Paraphrase Dataset | |
| This is a dataset of more than 21 million German paraphrases. | |
| These are text pairs that have the same meaning but are expressed with different words. | |
| The source of the paraphrases are different parallel German / English text corpora. | |
| The English texts were machine translated back into German to obtain the paraphrases. | |
| This dataset can be used for example to train semantic text embeddings. | |
| To do this, for example, [SentenceTransformers](https://www.sbert.net/) | |
| and the [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) | |
| can be used. | |
| ## Maintainers | |
| [](https://www.welove.ai/) | |
| This dataset is open sourced by [Philip May](https://may.la/) | |
| and maintained by the [One Conversation](https://www.welove.ai/) | |
| team of [Deutsche Telekom AG](https://www.telekom.com/). | |
| ## Our pre-processing | |
| Apart from the back translation, we have added more columns (for details see below). We have carried out the following pre-processing and filtering: | |
| - We dropped text pairs where one text was longer than 499 characters. | |
| - In the [GlobalVoices v2018q4](https://opus.nlpl.eu/GlobalVoices-v2018q4.php) texts we have removed the `" · Global Voices"` suffix. | |
| ## Your post-processing | |
| You probably don't want to use the dataset as it is, but filter it further. | |
| This is what the additional columns of the dataset are for. | |
| For us it has proven useful to delete the following pairs of sentences: | |
| - `min_char_len` less than 15 | |
| - `jaccard_similarity` greater than 0.3 | |
| - `de_token_count` greater than 30 | |
| - `en_de_token_count` greater than 30 | |
| - `cos_sim` less than 0.85 | |
| ## Columns description | |
| - **`uuid`**: a uuid calculated with Python `uuid.uuid4()` | |
| - **`en`**: the original English texts from the corpus | |
| - **`de`**: the original German texts from the corpus | |
| - **`en_de`**: the German texts translated back from English (from `en`) | |
| - **`corpus`**: the name of the corpus | |
| - **`min_char_len`**: the number of characters of the shortest text | |
| - **`jaccard_similarity`**: the [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index) of both sentences - see below for more details | |
| - **`de_token_count`**: number of tokens of the `de` text, tokenized with [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) | |
| - **`en_de_token_count`**: number of tokens of the `de` text, tokenized with [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) | |
| - **`cos_sim`**: the [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) of both sentences measured with [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) | |
| ## Anomalies in the texts | |
| It is noticeable that the [OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles-v2018.php) texts have weird dash prefixes. This looks like this: | |
| ``` | |
| - Hast du was draufgetan? | |
| ``` | |
| To remove them you could apply this function: | |
| ```python | |
| import re | |
| def clean_text(text): | |
| text = re.sub("^[-\s]*", "", text) | |
| text = re.sub("[-\s]*$", "", text) | |
| return text | |
| df["de"] = df["de"].apply(clean_text) | |
| df["en_de"] = df["en_de"].apply(clean_text) | |
| ``` | |
| ## Parallel text corpora used | |
| | Corpus name & link | Number of paraphrases | | |
| |-----------------------------------------------------------------------|----------------------:| | |
| | [OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles-v2018.php) | 18,764,810 | | |
| | [WikiMatrix v1](https://opus.nlpl.eu/WikiMatrix-v1.php) | 1,569,231 | | |
| | [Tatoeba v2022-03-03](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php) | 313,105 | | |
| | [TED2020 v1](https://opus.nlpl.eu/TED2020-v1.php) | 289,374 | | |
| | [News-Commentary v16](https://opus.nlpl.eu/News-Commentary-v16.php) | 285,722 | | |
| | [GlobalVoices v2018q4](https://opus.nlpl.eu/GlobalVoices-v2018q4.php) | 70,547 | | |
| | **sum** |. **21,292,789** | | |
| ## Back translation | |
| We have made the back translation from English to German with the help of [Fairseq](https://github.com/facebookresearch/fairseq). | |
| We used the `transformer.wmt19.en-de` model for this purpose: | |
| ```python | |
| en2de = torch.hub.load( | |
| "pytorch/fairseq", | |
| "transformer.wmt19.en-de", | |
| checkpoint_file="model1.pt:model2.pt:model3.pt:model4.pt", | |
| tokenizer="moses", | |
| bpe="fastbpe", | |
| ) | |
| ``` | |
| ## How the Jaccard similarity was calculated | |
| To calculate the [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index) | |
| we are using the [SoMaJo tokenizer](https://github.com/tsproisl/SoMaJo) | |
| to split the texts into tokens. | |
| We then `lower()` the tokens so that upper and lower case letters no longer make a difference. Below you can find a code snippet with the details: | |
| ```python | |
| from somajo import SoMaJo | |
| LANGUAGE = "de_CMC" | |
| somajo_tokenizer = SoMaJo(LANGUAGE) | |
| def get_token_set(text, somajo_tokenizer): | |
| sentences = somajo_tokenizer.tokenize_text([text]) | |
| tokens = [t.text.lower() for sentence in sentences for t in sentence] | |
| token_set = set(tokens) | |
| return token_set | |
| def jaccard_similarity(text1, text2, somajo_tokenizer): | |
| token_set1 = get_token_set(text1, somajo_tokenizer=somajo_tokenizer) | |
| token_set2 = get_token_set(text2, somajo_tokenizer=somajo_tokenizer) | |
| intersection = token_set1.intersection(token_set2) | |
| union = token_set1.union(token_set2) | |
| jaccard_similarity = float(len(intersection)) / len(union) | |
| return jaccard_similarity | |
| ``` | |
| ## Load this dataset | |
| ### With Hugging Face Datasets | |
| ```python | |
| # pip install datasets | |
| from datasets import load_dataset | |
| dataset = load_dataset("deutsche-telekom/ger-backtrans-paraphrase") | |
| train_dataset = dataset["train"] | |
| ``` | |
| ### With Pandas | |
| If you want to download the csv file and then load it with Pandas you can do it like this: | |
| ```python | |
| df = pd.read_csv("train.csv") | |
| ``` | |
| ## Citations, Acknowledgements and Licenses | |
| **OpenSubtitles** | |
| - citation: P. Lison and J. Tiedemann, 2016, [OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles](http://www.lrec-conf.org/proceedings/lrec2016/pdf/947_Paper.pdf). 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](https://arxiv.org/abs/1907.05791), arXiv, July 11 2019 | |
| - license: [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) | |
| **Tatoeba v2022-03-03** | |
| - citation: J. Tiedemann, 2012, [Parallel Data, Tools and Interfaces in OPUS](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php). In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | |
| - license: [CC BY 2.0 FR](https://creativecommons.org/licenses/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](https://arxiv.org/abs/2004.09813), In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, November 2020 | |
| - acknowledgements to [OPUS](https://opus.nlpl.eu/) for this service | |
| - license: please respect the [TED Talks Usage Policy](https://www.ted.com/about/our-organization/our-policies-terms/ted-talks-usage-policy) | |
| **News-Commentary v16** | |
| - citation: J. Tiedemann, 2012, [Parallel Data, Tools and Interfaces in OPUS](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php). 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](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php). 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 | |
| ## Citation | |
| ```latex | |
| @misc{ger-backtrans-paraphrase, | |
| title={Deutsche-Telekom/ger-backtrans-paraphrase - dataset at Hugging Face}, | |
| url={https://huggingface.co/datasets/deutsche-telekom/ger-backtrans-paraphrase}, | |
| year={2022}, | |
| author={May, Philip} | |
| } | |
| ``` | |
| ## Licensing | |
| Copyright (c) 2022 [Philip May](https://may.la/), | |
| [Deutsche Telekom AG](https://www.telekom.com/) | |
| This work is licensed under [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/). | |