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clarin-pl/
fastText-kgr10

Name: fastText-kgr10
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fastText

kgr10

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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.

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}
}