
## Description
German word embedding model trained by Müller with the following parameter configuration:
- a corpus as big as possible (and as diverse as possible without being informal)    filtering of punctuation and stopwords
- forming bigramm tokens
- using skip-gram as training algorithm with hierarchical softmax
- window size between 5 and 10
- dimensionality of feature vectors of 300 or more
- using negative sampling with 10 samples
- ignoring all words with total frequency lower than 50

For more information, see [https://devmount.github.io/GermanWordEmbeddings/](https://devmount.github.io/GermanWordEmbeddings/)

## How to use?

```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/german_model", filename="german.model"), binary=True, unicode_errors="ignore")
```

## 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}
}
```
{"language": "de", "license": "mit", "tags": ["word2vec"], "datasets": ["wikipedia"]}
word2vec de dataset:wikipedia license:mit region:us
TAGS
#word2vec #de #dataset-wikipedia #license-mit #region-us 


## Description
German word embedding model trained by Müller with the following parameter configuration:
- a corpus as big as possible (and as diverse as possible without being informal)    filtering of punctuation and stopwords
- forming bigramm tokens
- using skip-gram as training algorithm with hierarchical softmax
- window size between 5 and 10
- dimensionality of feature vectors of 300 or more
- using negative sampling with 10 samples
- ignoring all words with total frequency lower than 50

For more information, see URL

## How to use?




## Description
German word embedding model trained by Müller with the following parameter configuration:
- a corpus as big as possible (and as diverse as possible without being informal)    filtering of punctuation and stopwords
- forming bigramm tokens
- using skip-gram as training algorithm with hierarchical softmax
- window size between 5 and 10
- dimensionality of feature vectors of 300 or more
- using negative sampling with 10 samples
- ignoring all words with total frequency lower than 50

For more information, see URL ## How to use?
TAGS
#word2vec #de #dataset-wikipedia #license-mit #region-us 
 ## Description
German word embedding model trained by Müller with the following parameter configuration:
- a corpus as big as possible (and as diverse as possible without being informal)    filtering of punctuation and stopwords
- forming bigramm tokens
- using skip-gram as training algorithm with hierarchical softmax
- window size between 5 and 10
- dimensionality of feature vectors of 300 or more
- using negative sampling with 10 samples
- ignoring all words with total frequency lower than 50

For more information, see URL ## How to use?