GermanWordEmbeddings

There has been a lot of research about the training of word embeddings on English corpora. This toolkit applies gensim's word2vec on German corpora to train and evaluate German word embeddings. An overview about the project, evaluation results and download links can be found on the project's website or directly in this repository.

Note

This project is old (2015) and trained word embeddings on German text long before LLMs were called AI. I still actively maintain this repo, the models from the past still load and can be used.

1. Get started
2. Obtaining corpora
3. Preprocessing
4. Training models
5. Vocabulary
6. Evaluation
7. Visualization
8. Download

Get started

Make sure you have Python 3.12 or 3.13 installed, as well as the required libraries and NLTK data, preferably in a virtual environment:

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python -m nltk.downloader punkt_tab stopwords

Now you can download word2vec_german.sh and execute it in your shell to automatically download this toolkit and the corresponding corpus files and do the model training and evaluation. The script expects the libraries and NLTK data from above to be installed and trains a model with 300 dimensions, a window size of 5, 10 negative samples and a minimum word count of 50. Be aware that this could take a huge amount of time!

You can also clone this repository and use my already trained model to play around with the evaluation and visualization.

If you just want to see how the different Python scripts work, have a look into the code directory to see Jupyter Notebook script output examples. Mind that these outputs are from the original runs in 2015.

Obtaining corpora

There are multiple possibilities for obtaining huge German corpora that are publicly available and free to use:

German Wikipedia

wget https://dumps.wikimedia.org/dewiki/latest/dewiki-latest-pages-articles.xml.bz2

Statistical Machine Translation

Shuffled German news of the years 2007 to 2013:

for i in 2007 2008 2009 2010 2011 2012 2013; do
  wget https://www.statmt.org/wmt14/training-monolingual-news-crawl/news.$i.de.shuffled.gz
done

The models published with this toolkit are based on the German Wikipedia and only the German news of 2013.

Preprocessing

This tool preprocesses the raw wikipedia XML corpus with the WikiExtractor (a Python Script from Giuseppe Attardi to filter a Wikipedia XML Dump, installed via requirements.txt) and some shell instructions to filter all XML tags and quotations:

python -m wikiextractor.WikiExtractor -c -b 25M -o extracted dewiki-latest-pages-articles.xml.bz2
find extracted -name '*bz2' \! -exec bzip2 -k -c -d {} \; > dewiki.xml
sed -i 's/<[^>]*>//g' dewiki.xml
sed -i 's|["'\''„“‚‘]||g' dewiki.xml
rm -rf extracted

The German news already contain one sentence per line and don't have any XML syntax overhead. Only quotation marks need to be removed:

for i in 2007 2008 2009 2010 2011 2012 2013; do
  gzip -d news.$i.de.shuffled.gz
  sed -i 's|["'\''„“‚‘]||g' news.$i.de.shuffled
done

Afterwards, the preprocessing.py script can be called on these corpus files with the following options:

flag	default	description
-h, --help	-	show a help message and exit
-p, --punctuation	False	filter punctuation tokens
-s, --stopwords	False	filter stop word tokens
-u, --umlauts	False	replace German umlauts with their respective digraphs
-b, --bigram	False	detect and process common bigram phrases
-t [ ], --threads [ ]	NUMBER_OF_PROCESSORS	number of worker threads
--batch_size [ ]	32	batch size for sentence processing

Example usage:

python preprocessing.py dewiki.xml corpus/dewiki.corpus -psub
for file in *.shuffled; do python preprocessing.py $file corpus/$file.corpus -psub; done

Mind that the -b flag creates an additional .bigram file next to each corpus file. As the training uses every file in the corpus directory, only keep one of both versions, e.g. with rm corpus/*.corpus.

Training models

Models are trained with the help of the training.py script with the following options:

flag	default	description
-h, --help	-	show a help message and exit
-s [ ], --size [ ]	100	dimension of word vectors
-w [ ], --window [ ]	5	size of the sliding window
-m [ ], --mincount [ ]	5	minimum number of occurrences of a word to be considered
-t [ ], --threads [ ]	NUMBER_OF_PROCESSORS	number of worker threads to train the model
-g [ ], --sg [ ]	1	training algorithm: Skip-Gram (1), otherwise CBOW (0)
-i [ ], --hs [ ]	1	use of hierarchical softmax for training
-n [ ], --negative [ ]	0	use of negative sampling for training (usually between 5-20)
-o [ ], --cbowmean [ ]	0	for CBOW training algorithm: use sum (0) or mean (1) to merge context vectors
-f, --full	False	additionally store the full model in <target>.full to resume training later
-r [ ], --resume [ ]	-	full model file to resume training from with new corpora

Example usage:

python training.py corpus/ my.model -s 200 -w 5

Mind that the first parameter is a directory and that every contained file will be taken as a corpus file for training.

If the time needed to train the model should be measured and stored into the results file, this would be a possible command:

{ time python training.py corpus/ my.model -s 200 -w 5; } 2>> my.model.result

By default only the word vectors are stored, which is sufficient for evaluation and visualization. To be able to continue the training later, e.g. with new words, the full model has to be stored with the -f flag. It can then be passed to the -r option together with a directory that only contains the additional corpus files:

python training.py corpus/ my.model -s 200 -w 5 -f
python training.py corpus_new/ my.model -r my.model.full -f

Mind that the model parameters (-s, -w, -m, -g, -i, -n, -o) are taken from the resumed model and are ignored when given, and that a full model needs considerably more disk space than the word vectors alone.

Vocabulary

To compute the vocabulary of a trained model, the vocabulary.py script can be used:

python vocabulary.py my.model my.model.vocab

Mind that the binary model format doesn't contain word frequencies, so the stored counts only reflect the frequency rank of each word.

Evaluation

To create test sets and evaluate trained models, the evaluation.py script can be used. It's possible to evaluate both syntactic and semantic features of a trained model. For a successful creation of testsets, the following source files should be created before starting the script (see the configuration part in the script for more information).

Syntactic test set

With the syntactic test, features like singular, plural, 3rd person, past tense, comparative or superlative can be evaluated. Therefore there are 3 source files: adjectives, nouns and verbs. Every file contains a unique word with its conjugations per line, divided by a dash. These combination patterns can be entered in the PATTERN_SYN constant in the script configuration. The script now combines each word with 5 random other words according to the given pattern, to create appropriate analogy questions. Once the data file with the questions is created, it can be evaluated. Normally the evaluation can be done by gensim's word analogy evaluation function, but to get a more specific evaluation result (correct matches, top n matches and coverage), this project uses its own accuracy functions (test_most_similar_groups() and test_most_similar() in evaluation.py).

The given source files of this project contain 100 unique nouns with 2 patterns, 100 unique adjectives with 6 patterns and 100 unique verbs with 12 patterns, resulting in 10k analogy questions. Here are some examples for possible source files:

adjectives.txt

Possible pattern: basic-comparative-superlative

Example content:

gut-besser-beste
laut-lauter-lauteste

See src/adjectives.txt

nouns.txt

Possible pattern: singular-plural

Example content:

Bild-Bilder
Name-Namen

See src/nouns.txt

verbs.txt

Possible pattern: basic-1stPersonSingularPresent-2ndPersonPluralPresent-3rdPersonSingularPast-3rdPersonPluralPast

Example content:

finden-finde-findet-fand-fanden
suchen-suche-sucht-suchte-suchten

See src/verbs.txt

Semantic test set

With the semantic test, features concerning word meanings can be evaluated. Therefore there are 3 source files: opposite, best match and doesn't fit. The given source files result in a total of 950 semantic questions.

opposite.txt

This file contains opposite words, following the pattern of oneword-oppositeword per line, to evaluate the models' ability to find opposites. The script combines each pair with 10 random other pairs, to build analogy questions. The given opposite source file of this project includes 30 unique pairs, resulting in 300 analogy questions.

Example content:

Sommer-Winter
Tag-Nacht

See src/opposite.txt

bestmatch.txt

This file contains groups of content similar word pairs, to evaluate the model's ability to find thematic relevant analogies. The script combines each pair with all other pairs of the same group to build analogy questions. The given bestmatch source file of this project includes 7 groups with a total of 77 unique pairs, resulting in 540 analogy questions.

Example content:

: Politik
Elisabeth-Königin
Charles-Prinz
: Technik
Android-Google
iOS-Apple
Windows-Microsoft

See src/bestmatch.txt

doesntfit.txt

This file contains 3 words (per line) with similar content divided by space and a set of words that do not fit, divided by dash, like fittingword1 fittingword2 fittingword3 notfittingword1-notfittingword2-...-notfittingwordn. This tests the models' ability to find the least fitting word in a set of 4 words. The script combines each matching triple with every not matching word of the list divided by dash, to build doesntfit questions. The available doesntfit source file of this project includes 11 triples, each with 10 words that do not fit, resulting in 110 questions.

Example content:

Hase Hund Katze Baum-Besitzer-Elefant-Essen-Haus-Mensch-Tier-Tierheim-Wiese-Zoo
August April September Jahr-Monat-Tag-Stunde-Minute-Zeit-Kalender-Woche-Quartal-Uhr

See src/doesntfit.txt

Those options for the script execution are possible:

flag	default	description
-h, --help	-	show a help message and exit
-c, --create	False	if set, create testsets before evaluating
-u, --umlauts	False	if set, create additional testsets with transformed umlauts and/or use them instead
-t [ ], --topn [ ]	10	check the top n results (correct answer under top n answers)

Example usage:

python evaluation.py my.model -u

The script has to be executed from the repository root, as it uses relative paths to the test sets in the data directory. The results are printed and also stored in my.model.result.

Mind that the -c flag randomly recreates and overwrites the test sets in the data directory, so results are not comparable to the published ones anymore.

Note: evaluation.py only treats files with the filetypes .bin, .model or without any suffix as binary files. All other scripts always expect the binary format.

Visualization

To visualize word vectors of a trained model, the visualize.py script can be used. It reduces the vectors of the word lists configured in the script to two dimensions with PCA and plots them:

python visualize.py my.model

Mind that the configured words contain transformed umlauts and bigram phrases, so the model has to be trained on a corpus that was preprocessed with the -u and -b flags.

To explore a model interactively with the embedding projector of TensorBoard, the tfvisualize.py script exports the vectors of the most frequent words with the following options:

flag	default	description
-h, --help	-	show a help message and exit
-s [ ], --samples [ ]	10000	number of samples to project
-p [ ], --projector [ ]	projector	target projector path
--prefix [ ]	default	model prefix for projector files

Example usage:

python tfvisualize.py my.model -s 10000
tensorboard --logdir projector

Afterwards the projector is available at http://localhost:6006/#projector.

Download

The optimized German language model, that was trained with this toolkit based on the German Wikipedia and German news articles from 2013 (both retrieved on 15th May 2015) can be downloaded here:

german.model [704 MB]

Mind that this model only contains the word vectors, so it can't be used to resume training.

If you want to use this project for your own work, you can use the following BibTeX entry for citation:

@thesis{mueller2015,
  author = {{Müller}, Andreas},
  title  = "{Analyse von Wort-Vektoren deutscher Textkorpora}",
  school = {Technische Universität Berlin},
  year   = 2015,
  month  = jun,
  type   = {Bachelor's Thesis},
  url    = {https://devmount.github.io/GermanWordEmbeddings}
}

The GermanWordEmbeddings tool and the pretrained language model are completely free to use. If you enjoy it, please consider donating via PayPal for further development. 💚