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The Stanford NLP Group's official Python NLP library. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python. For detailed information please visit ourofficial website.

🔥  A new collection ofbiomedical andclinical English model packages are now available, offering seamless experience for syntactic analysis and named entity recognition (NER) from biomedical literature text and clinical notes. For more information, check out ourBiomedical models documentation page.

If you use this library in your research, please kindly cite ourACL2020 Stanza system demo paper:

@inproceedings{qi2020stanza,title={Stanza: A {Python} Natural Language Processing Toolkit for Many Human Languages},author={Qi, Peng and Zhang, Yuhao and Zhang, Yuhui and Bolton, Jason and Manning, Christopher D.},booktitle ="Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",year={2020}}

If you use our biomedical and clinical models, please also cite ourStanza Biomedical Models description paper:

@article{zhang2021biomedical,author ={Zhang, Yuhao and Zhang, Yuhui and Qi, Peng and Manning, Christopher D and Langlotz, Curtis P},title ={Biomedical and clinical {E}nglish model packages for the {S}tanza {P}ython {NLP} library},journal ={Journal of the American Medical Informatics Association},year ={2021},month ={06},issn ={1527-974X}}

The PyTorch implementation of the neural pipeline in this repository is due toPeng Qi (@qipeng),Yuhao Zhang (@yuhaozhang), andYuhui Zhang (@yuhui-zh15), with help fromJason Bolton (@j38),Tim Dozat (@tdozat) andJohn Bauer (@AngledLuffa). Maintenance of this repo is currently led byJohn Bauer.

If you use the CoreNLP software through Stanza, please cite the CoreNLP software package and the respective modules as describedhere ("Citing Stanford CoreNLP in papers"). The CoreNLP client is mostly written byArun Chaganty, andJason Bolton spearheaded merging the two projects together.

If you use the Semgrex or Ssurgeon part of CoreNLP, please citeour GURT paper on Semgrex and Ssurgeon:

@inproceedings{bauer-etal-2023-semgrex,title ="Semgrex and Ssurgeon, Searching and Manipulating Dependency Graphs",author ="Bauer, John  and      Kiddon, Chlo{\'e}  and      Yeh, Eric  and      Shan, Alex  and      D. Manning, Christopher",booktitle ="Proceedings of the 21st International Workshop on Treebanks and Linguistic Theories (TLT, GURT/SyntaxFest 2023)",month = mar,year ="2023",address ="Washington, D.C.",publisher ="Association for Computational Linguistics",url ="https://aclanthology.org/2023.tlt-1.7",pages ="67--73",abstract ="Searching dependency graphs and manipulating them can be a time consuming and challenging task to get right. We document Semgrex, a system for searching dependency graphs, and introduce Ssurgeon, a system for manipulating the output of Semgrex. The compact language used by these systems allows for easy command line or API processing of dependencies. Additionally, integration with publicly released toolkits in Java and Python allows for searching text relations and attributes over natural text.",}

To ask questions, report issues or request features 🤔, please use theGitHub Issue Tracker. Before creating a new issue, please make sure to search for existing issues that may solve your problem, or visit theFrequently Asked Questions (FAQ) page on our website.

We welcome community contributions to Stanza in the form of bugfixes 🛠️ and enhancements 💡! If you want to contribute, please first readour contribution guideline.

Stanza supports Python 3.6 or later. We recommend that you install Stanza viapip, the Python package manager. To install, simply run:

pip install stanza

This should also help resolve all of the dependencies of Stanza, for instancePyTorch 1.3.0 or above.

If you currently have a previous version ofstanza installed, use:

pip install stanza -U

To install Stanza via Anaconda, use the following conda command:

conda install -c stanfordnlp stanza

Note that for now installing Stanza via Anaconda does not work for Python 3.10. For Python 3.10 please use pip installation.

Alternatively, you can also install from source of this git repository, which will give you more flexibility in developing on top of Stanza. For this option, run

git clone https://github.com/stanfordnlp/stanza.gitcd stanzapip install -e.

To run your first Stanza pipeline, simply following these steps in your Python interactive interpreter:

>>>importstanza>>>stanza.download('en')# This downloads the English models for the neural pipeline>>>nlp=stanza.Pipeline('en')# This sets up a default neural pipeline in English>>>doc=nlp("Barack Obama was born in Hawaii.  He was elected president in 2008.")>>>doc.sentences[0].print_dependencies()

If you encounterrequests.exceptions.ConnectionError, please try to use a proxy:

>>>importstanza>>>proxies= {'http':'http://ip:port','https':'http://ip:port'}>>>stanza.download('en',proxies=proxies)# This downloads the English models for the neural pipeline>>>nlp=stanza.Pipeline('en')# This sets up a default neural pipeline in English>>>doc=nlp("Barack Obama was born in Hawaii.  He was elected president in 2008.")>>>doc.sentences[0].print_dependencies()

The last command will print out the words in the first sentence in the input string (orDocument, as it is represented in Stanza), as well as the indices for the word that governs it in the Universal Dependencies parse of that sentence (its "head"), along with the dependency relation between the words. The output should look like:

('Barack', '4', 'nsubj:pass')('Obama', '1', 'flat')('was', '4', 'aux:pass')('born', '0', 'root')('in', '6', 'case')('Hawaii', '4', 'obl')('.', '4', 'punct')

Seeour getting started guide for more details.

Aside from the neural pipeline, this package also includes an official wrapper for accessing the Java Stanford CoreNLP software with Python code.

There are a few initial setup steps.

• DownloadStanford CoreNLP and models for the language you wish to use
• Put the model jars in the distribution folder
• Tell the Python code where Stanford CoreNLP is located by setting theCORENLP_HOME environment variable (e.g., in *nix):export CORENLP_HOME=/path/to/stanford-corenlp-4.5.3

We providecomprehensive examples in our documentation that show how one can use CoreNLP through Stanza and extract various annotations from it.

To get your started, we also provide interactive Jupyter notebooks in thedemo folder. You can also open these notebooks and run them interactively onGoogle Colab. To view all available notebooks, follow these steps:

• Go to theGoogle Colab website
• Navigate toFile ->Open notebook, and chooseGitHub in the pop-up menu
• Note that you donot need to give Colab access permission to your GitHub account
• Typestanfordnlp/stanza in the search bar, and click enter

We currently provide models for all of theUniversal Dependencies treebanks v2.8, as well as NER models for a few widely-spoken languages. You can find instructions for downloading and using these modelshere.

To maximize speed performance, it is essential to run the pipeline on batches of documents. Running a for loop on one sentence at a time will be very slow. The best approach at this time is to concatenate documents together, with each document separated by a blank line (i.e., two line breaks\n\n). The tokenizer will recognize blank lines as sentence breaks. We are actively working on improving multi-document processing.

All neural modules in this library can be trained with your own data. The tokenizer, the multi-word token (MWT) expander, the POS/morphological features tagger, the lemmatizer and the dependency parser requireCoNLL-U formatted data, while the NER model requires the BIOES format. Currently, we do not support model training via thePipeline interface. Therefore, to train your own models, you need to clone this git repository and run training from the source.

For detailed step-by-step guidance on how to train and evaluate your own models, please visit ourtraining documentation.

Stanza is released under the Apache License, Version 2.0. See theLICENSE file for more details.