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README.md

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----
-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
-[![One Conversation](https://huggingface.co/datasets/deutsche-telekom/ger-backtrans-paraphrase/resolve/main/1c-logo.png)](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
-
-**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
-
-## 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/).

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