readme: ---
  language: pl
  tags:
  - word2vec
  datasets:
  - KGR10
  ---
  
  # KGR10 word2vec Polish word embeddings
  
  Distributional language models for Polish trained on the KGR10 corpora.
  
  ## Models
  
  In the repository you can find two selected models, that were selected after evaluation (see table below). 
  A model that performed the best is the default model/config (see `default_config.json`). 
  
  |method|dimension|hs|mwe||
  |---|---|---|---| --- |
  |cbow|300|false|true| <-- default |
  |skipgram|300|true|true|
  
  
  ## Usage
  
  To use these embedding models easily, it is required to install [embeddings](https://github.com/CLARIN-PL/embeddings).
  
  ```bash
  pip install clarinpl-embeddings
  ```
  
  ### Utilising the default model (the easiest way)
  
  Word embedding:
  
  ```python
  from embeddings.embedding.auto_flair import AutoFlairWordEmbedding
  from flair.data import Sentence
  
  sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
  
  embedding = AutoFlairWordEmbedding.from_hub("clarin-pl/word2vec-kgr10")
  embedding.embed([sentence])
  
  for token in sentence:
      print(token)
      print(token.embedding)
  ```
  
  Document embedding (averaged over words):
  
  ```python
  from embeddings.embedding.auto_flair import AutoFlairDocumentEmbedding
  from flair.data import Sentence
  
  sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
  
  embedding = AutoFlairDocumentEmbedding.from_hub("clarin-pl/word2vec-kgr10")
  embedding.embed([sentence])
  
  print(sentence.embedding)
  ```
  
  ### Customisable way
  
  Word embedding:
  
  ```python
  from embeddings.embedding.static.embedding import AutoStaticWordEmbedding
  from embeddings.embedding.static.word2vec import KGR10Word2VecConfig
  from flair.data import Sentence
  
  config = KGR10Word2VecConfig(method='skipgram', hs=False)
  embedding = AutoStaticWordEmbedding.from_config(config)
  
  sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
  embedding.embed([sentence])
  
  for token in sentence:
      print(token)
      print(token.embedding)
  ```
  
  Document embedding (averaged over words):
  
  ```python
  from embeddings.embedding.static.embedding import AutoStaticDocumentEmbedding
  from embeddings.embedding.static.word2vec import KGR10Word2VecConfig
  from flair.data import Sentence
  
  config = KGR10Word2VecConfig(method='skipgram', hs=False)
  embedding = AutoStaticDocumentEmbedding.from_config(config)
  
  sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
  embedding.embed([sentence])
  
  print(sentence.embedding)
  ```
  
  ## Citation
  
  ```
  Piasecki, Maciej; Janz, Arkadiusz; Kaszewski, Dominik; et al., 2017,  Word Embeddings for Polish, CLARIN-PL digital repository, http://hdl.handle.net/11321/442.
  ```
  
  or 
  
  
  ```
  @misc{11321/442,	
   title = {Word Embeddings for Polish},	
   author = {Piasecki, Maciej and Janz, Arkadiusz and Kaszewski, Dominik and Czachor, Gabriela},	
   url = {http://hdl.handle.net/11321/442},	
   note = {{CLARIN}-{PL} digital repository},	
   copyright = {{GNU} {GPL3}},	
   year = {2017}	
  }
  ```
  
  