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--- |
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tags: |
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- word2vec |
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language: de |
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license: mit |
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datasets: |
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- wikipedia |
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--- |
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|
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## Description |
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German word embedding model trained by Müller with the following parameter configuration: |
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- a corpus as big as possible (and as diverse as possible without being informal) filtering of punctuation and stopwords |
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- forming bigramm tokens |
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- using skip-gram as training algorithm with hierarchical softmax |
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- window size between 5 and 10 |
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- dimensionality of feature vectors of 300 or more |
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- using negative sampling with 10 samples |
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- ignoring all words with total frequency lower than 50 |
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For more information, see [https://devmount.github.io/GermanWordEmbeddings/](https://devmount.github.io/GermanWordEmbeddings/) |
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## How to use? |
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``` |
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from gensim.models import KeyedVectors |
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from huggingface_hub import hf_hub_download |
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model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/german_model", filename="german.model"), binary=True, unicode_errors="ignore") |
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``` |
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## Citation |
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|
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``` |
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@thesis{mueller2015, |
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author = {{Müller}, Andreas}, |
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title = "{Analyse von Wort-Vektoren deutscher Textkorpora}", |
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school = {Technische Universität Berlin}, |
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year = 2015, |
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month = jun, |
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type = {Bachelor's Thesis}, |
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url = {https://devmount.github.io/GermanWordEmbeddings} |
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} |
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``` |
