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