Papers › Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

16 Mar 2020ACL 2020 6arXiv:2003.07082archive 2025-07-28

Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, Christopher D. Manning

We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages. Compared to existing widely used toolkits, Stanza features a language-agnostic fully neural pipeline for text analysis, including tokenization, multi-word token expansion, lemmatization, part-of-speech and morphological feature tagging, dependency parsing, and named entity recognition. We have trained Stanza on a total of 112 datasets, including the Universal Dependencies treebanks and other multilingual corpora, and show that the same neural architecture generalizes well and achieves competitive performance on all languages tested. Additionally, Stanza includes a native Python interface to the widely used Java Stanford CoreNLP software, which further extends its functionality to cover other tasks such as coreference resolution and relation extraction. Source code, documentation, and pretrained models for 66 languages are available at https://stanfordnlp.github.io/stanza.

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stanfordnlp/stanza officialmentioned on GitHubpytorch report
astrazeneca/kazu mentioned on GitHubjaxApache-2.0 report
rasoolims/stanza mentioned on GitHubpytorch report
sktwtrg/stanza_ja_test mentioned on GitHubpytorchNOASSERTION report
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2ran · honoured contract
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Tasks

Coreference ResolutionDependency ParsingLemmatizationNamed Entity RecognitionNamed Entity Recognition (NER)Relation Extraction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Dependency Parsing Universal Dependency Treebank Stanza Macro F1 81.13 #1 of 1 Archive leaderboard report

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