readme: ---
  language: pl
  tags:
  - fastText
  datasets:
  - kgr10
  ---
  
  # KGR10 FastText Polish word embeddings
  
  Distributional language model (both textual and binary) for Polish (word embeddings) trained on KGR10 corpus (over 4 billion of words) using Fasttext with the following variants (all possible combinations):
  - dimension: 100, 300
  - method: skipgram, cbow
  - tool: FastText, Magnitude
  - source text: plain, plain.lower, plain.lemma, plain.lemma.lower
  
  ## Models
  
  In the repository you can find 4 selected models, that were examined in the paper (see Citation). 
  A model that performed the best is the default model/config (see `default_config.json`).
  
  ## 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/fastText-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/fastText-kgr10")
  embedding.embed([sentence])
  
  print(sentence.embedding)
  ```
  
  ### Customisable way
  
  Word embedding:
  
  ```python
  from embeddings.embedding.static.embedding import AutoStaticWordEmbedding
  from embeddings.embedding.static.fasttext import KGR10FastTextConfig
  from flair.data import Sentence
  
  config = KGR10FastTextConfig(method='cbow', dimension=100)
  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.fasttext import KGR10FastTextConfig
  from flair.data import Sentence
  
  config = KGR10FastTextConfig(method='cbow', dimension=100)
  embedding = AutoStaticDocumentEmbedding.from_config(config)
  
  sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
  embedding.embed([sentence])
  
  print(sentence.embedding)
  ```
  
  
  ## Citation
  
  The link below leads to the NextCloud directory with all variants of embeddings. If you use it, please cite the following article:
  
  ```
  @article{kocon2018embeddings,
  author = {Koco\'{n}, Jan and Gawor, Micha{\l}},
  title = {Evaluating {KGR10} {P}olish word embeddings in the recognition of temporal
  expressions using {BiLSTM-CRF}},
  journal = {Schedae Informaticae},
  volume = {27},
  year = {2018},
  url = {http://www.ejournals.eu/Schedae-Informaticae/2018/Volume-27/art/13931/},
  doi = {10.4467/20838476SI.18.008.10413}
  }
  ```
  