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--- |
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language: pl |
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tags: |
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- fastText |
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datasets: |
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- kgr10 |
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--- |
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# KGR10 FastText Polish word embeddings |
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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): |
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- dimension: 100, 300 |
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- method: skipgram, cbow |
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- tool: FastText, Magnitude |
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- source text: plain, plain.lower, plain.lemma, plain.lemma.lower |
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## Models |
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In the repository you can find 4 selected models, that were examined in the paper (see Citation). |
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A model that performed the best is the default model/config (see `default_config.json`). |
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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/fastText-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/fastText-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.fasttext import KGR10FastTextConfig |
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from flair.data import Sentence |
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config = KGR10FastTextConfig(method='cbow', dimension=100) |
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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.fasttext import KGR10FastTextConfig |
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from flair.data import Sentence |
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config = KGR10FastTextConfig(method='cbow', dimension=100) |
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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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The link below leads to the NextCloud directory with all variants of embeddings. If you use it, please cite the following article: |
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``` |
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@article{kocon2018embeddings, |
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author = {Koco\'{n}, Jan and Gawor, Micha{\l}}, |
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title = {Evaluating {KGR10} {P}olish word embeddings in the recognition of temporal |
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expressions using {BiLSTM-CRF}}, |
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journal = {Schedae Informaticae}, |
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volume = {27}, |
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year = {2018}, |
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url = {http://www.ejournals.eu/Schedae-Informaticae/2018/Volume-27/art/13931/}, |
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doi = {10.4467/20838476SI.18.008.10413} |
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} |
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``` |
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