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---
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}
}
```
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