A curated list of resources dedicated to Natural Language Processing (NLP) in polish. Models, tools, datasets.
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SlavicBert - multilingual BERT model -BERT, Slavic Cased: 4 languages(Bulgarian,Czech, Polish, Russian), 12-layer, 768-hidden, 12-heads, 110M parameters, 600Mb. There is also another SlavicBert model http://docs.deeppavlov.ai/en/master/features/models/bert.html but I have problems to convert it to pytorch.
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Allegro BERT - It has not been publish yet (12.2019) - but there is a poster - https://conference.mlinpl.org/pdf/CfC_AllPosters.pdf
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ELMO embeddings - A model of ELMo embeddings for Polish language trained on large textual corpora (KGR10).
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Zalando Flair polish models - Contextual string embeddings that capture latent syntactic-semantic information that goes beyond standard word embeddings. There are two models "pl-forward and pl-backward"
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Wrocław University of Science and Technology Word2Vec - Distributional language models for Polish trained on different corpora (KGR10, NKJP, Wikipedia).
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FastText polish model FB - train on: Common Crawl, Wikipedia
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Universal Sentence Encoder Multilingual - sentence embeddings, it covers 16 languages (including Polish)
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BPEmb: Subword Embeddings includes polish - easy to use with Flair
- Morfologik (Java) and pyMorfologik (Python wrapper) - dictionary-based morphological analyzer
- Morfeusz - morphological analyzer. See also Elasticsearch plugin
- Stempel (Python port) - algorithmic stemmer. See also Elasticsearch plugin
- scaCy for Polish - extend spaCy, a popular production-ready NLP library, to fully support Polish language.
- Github Repo with list of polish: word embeddings and language models (Word2vec, fasttext, Glove, Elmo) - https://github.com/sdadas/polish-nlp-resources
- Polish Word Embeddings Review - Evaluation of polish word embeddings prepared by various research groups. Evaluation is done by words analogy task https://github.com/Ermlab/polish-word-embeddings-review
- Polish Sentence Evaluation- contains evaluation of eight sentence representation methods (Word2Vec, GloVe, FastText, ELMo, Flair, BERT, LASER, USE) on five polish linguistic tasks
- The KLEJ (Kompleksowa Lista Ewaluacji Językowych) benchmark is a set of nine evaluation tasks for the Polish language understanding.
- PolEval datasets -
- Hate speech classification -distinguish between normal/non-harmful tweets (class: 0) and tweets that contain any kind of harmful information (class: 1) [PolEval 2019 Task6] [mirror GDrive]
- Polish CDSCorpus - The dataset for compositional distributional semantics. Polish CDSCorpus consists of 10K Polish sentence pairs which are human-annotated for semantic relatedness and entailment.
- Wroclaw Corpus of Consumer Reviews Sentiment (WCCRS) - corpus of Polish reviews annotated with sentiment at the level of the whole text (text) and at the level of sentences (sentence) for the following domains: hotels, medicine, products and university (reviews*)
- Ermlab Opineo dataset- opineo reviews - GDrive
- HateSpeech corpus contains over 2000 posts crawled from public Polish web.http://zil.ipipan.waw.pl/HateSpeech
- OSCAR or Open Super-large Crawled ALMAnaCH coRpus - is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus. Contains 109GB or 49GB of polish text.
- Polish Wikipedia dump - regular monthly copy of Polish wikipedia. More then 4GB of text.
- Polish analogy dataset - example: "Ateny Grecja Bagdad Irak" - useful for word embeddings evaluation
- Polish OpenSubtitles - collection of translated movie subtitles from opensubtitles.
- NKJP - National Corpus of Polish. It contains classic literature, daily newspapers, specialist periodicals and journals, transcripts of conversations, and a variety of short-lived and internet texts. Only a small sub-corpus is available for download (GNU GLP v.3). Direct contact and maybe necessary to get the full corpus.
- "Evaluation of Sentence Representations in Polish" - Sławomir Dadas, Michał Perełkiewicz, Rafał Poswiata 2019 https://arxiv.org/pdf/1910.11834.pdf
- "Multi-level analysis and recognition of the text sentiment on the example of consumer opinions" - Kocoń Jan, Zaśko-Zielińska Monika, Miłkowski Piotr, 2019
People who contribute to this project.
- Krzysztof Sopyła - https://ksopyla.com LinkedIn