A curated list of resources dedicated to Natural Language Processing (NLP) in polish. Models, tools, datasets.

• 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.

• Allegro BERT - It has not been publish yet (12.2019) - but there is a poster - https://conference.mlinpl.org/pdf/CfC_AllPosters.pdf

• ELMO embeddings - A model of ELMo embeddings for Polish language trained on large textual corpora (KGR10).

• 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"

• IPIPAN Word2vec polish models

• Wrocław University of Science and Technology Word2Vec - Distributional language models for Polish trained on different corpora (KGR10, NKJP, Wikipedia).

• FastText polish model FB - train on: Common Crawl, Wikipedia

• FastText KGR10 polish model binary

• Universal Sentence Encoder Multilingual - sentence embeddings, it covers 16 languages (including Polish)

• 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