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https://github.com/astrazeneca/kazu

Fast, world class biomedical NER
https://github.com/astrazeneca/kazu

biomedical-text-mining natural-language-processing nlp

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Fast, world class biomedical NER

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README 

        

![Maturity level-1](https://img.shields.io/badge/Maturity%20Level-ML--2-green)





  Kazu - Biomedical NLP Framework

  






[Find our docs here](https://astrazeneca.github.io/KAZU/index.html)



# Kazu - Biomedical NLP Framework



**Note: the recent 2.0 release has large elements of backwards incompatibility if you are using a custom model pack and curations.**



Welcome to Kazu (Korea AstraZeneca University), a python biomedical NLP framework built in collaboration with Korea University,

designed to handle production workloads.



This library aims to simplify the process of using state of the art NLP research in production systems. Some of the

research contained within are our own, but most of it comes from the community, for which we are immensely grateful.



If you want to use Kazu, please cite our [EMNLP 2022 publication](https://aclanthology.org/2022.emnlp-industry.63)!

([**citation link**](https://aclanthology.org/2022.emnlp-industry.63.bib))



[Please click here for the TinyBERN2 training and evaluation code](https://github.com/dmis-lab/KAZU-NER-module)



# Quickstart



## Install



Python version 3.9 or higher is required (tested with Python 3.11).



Either:



`pip install kazu`



or download the wheel from the release page and install locally.



If you intend to use [Mypy](https://mypy.readthedocs.io/en/stable/#) on your own codebase, consider installing Kazu using:



`pip install kazu[typed]`



This will pull in typing stubs for kazu's dependencies (such as [types-requests](https://pypi.org/project/types-requests/) for [Requests](https://requests.readthedocs.io/en/latest/))

so that mypy has access to as much relevant typing information as possible when type checking your codebase. Otherwise (depending on mypy config), you may see errors when running mypy like:



```

.venv/lib/python3.10/site-packages/kazu/steps/linking/post_processing/xref_manager.py:10: error: Library stubs not installed for "requests" [import]

```



## Getting the model pack



For most functionality, you will also need the Kazu model pack. This is tied to each release, and can be found on the [release page](https://github.com/astrazeneca/kazu/releases). Once downloaded,

extract the archive and:



`export KAZU_MODEL_PACK=`



Kazu is highly configurable (using [Hydra](https://hydra.cc/docs/intro/)), although it comes preconfigured with defaults appropriate for most literature processing use cases.

To make use of these, and process a simple document:



```python

import hydra

from hydra.utils import instantiate



from kazu.data import Document

from kazu.pipeline import Pipeline

from kazu.utils.constants import HYDRA_VERSION_BASE

from pathlib import Path

import os



# the hydra config is kept in the model pack

cdir = Path(os.environ["KAZU_MODEL_PACK"]).joinpath("conf")



@hydra.main(

    version_base=HYDRA_VERSION_BASE, config_path=str(cdir), config_name="config"

)

def kazu_test(cfg):

    pipeline: Pipeline = instantiate(cfg.Pipeline)

    text = "EGFR mutations are often implicated in lung cancer"

    doc = Document.create_simple_document(text)

    pipeline([doc])

    print(f"{doc.get_entities()}")



if __name__ == "__main__":

    kazu_test()

```



## License



Licensed under [Apache 2.0](https://github.com/AstraZeneca/KAZU/blob/main/LICENSE).



Kazu includes elements under compatible licenses (full licenses are in relevant files or as indicated):

- Some elements are a modification of code licensed under MIT by Explosion.AI - see the README [here](https://github.com/AstraZeneca/KAZU/blob/main/kazu/ontology_matching/README.md).

- The doc build process (conf.py's linkcode_resolve function) uses code modified from pandas, in turn modified from numpy. See [PANDAS_LICENSE.txt](https://github.com/AstraZeneca/KAZU/blob/main/docs/PANDAS_LICENSE.txt) and [NUMPY_LICENSE.txt](https://github.com/AstraZeneca/KAZU/blob/main/docs/NUMPY_LICENSE.txt)

- Elements of the model distillation code are inspired by or modified from Huawei Noah's Ark Lab [TinyBERT](https://github.com/huawei-noah/Pretrained-Language-Model/blob/master/TinyBERT) and DMIS-Lab's [BioBERT](https://github.com/dmis-lab/biobert/tree/master).

  See the details in dataprocessor.py, models.py and tiny_transformer.py.

- PLSapbertModel is inspired by the code from [sapbert](https://github.com/cambridgeltl/sapbert), licensed under MIT. See the file for details, and see the [SapBert](#sapbert) section below regarding use of the model.

- GildaUtils in the string_normalizer.py file is modified from [Gilda](https://github.com/indralab/gilda). See the file for full details

  including the full BSD 2-Clause license.

- The AbbreviationFinderStep uses KazuAbbreviationDetector, which is a modified version of

  [SciSpacy](https://allenai.github.io/scispacy/)'s abbreviation finding algorithm, licensed under Apache 2.0 - see the files for full details.

- The JWTAuthenticationBackend Starlette Middleware in jwtauth.py is originally from [starlette-jwt](https://raw.githubusercontent.com/amitripshtos/starlette-jwt/master/starlette_jwt/middleware.py), licensed under BSD 3-Clause.

- The AddRequestIdMiddleware Starlette Middleware in req_id_header.py is modified from 'CustomHeaderMiddleware' in the [Starlette Middleware docs](https://www.starlette.io/middleware/#basehttpmiddleware).

  This is licensed under BSD 3-Clause along with the rest of Starlette.

- The kazu-jvm folder includes files like gradelw and gradelw.bat distributed by gradle under Apache 2.0 - see the files for details.

- [kazu/data.py](https://github.com/AstraZeneca/KAZU/blob/main/kazu/data.py) contains `AutoNameEnum`, which is `AutoName` from

  the [Python Enum Docs](https://docs.python.org/3/howto/enum.html#using-automatic-values) licensed under [Zero-Clause BSD](https://docs.python.org/3/license.html#zero-clause-bsd-license-for-code-in-the-python-release-documentation).



## Dataset licences



For the version of each ontology currently in use, please see the 'data_origin' field in kazu/conf/ontologies



### Under [Creative Commons Attribution-Share Alike 3.0 Unported Licence](https://creativecommons.org/licenses/by/3.0/legalcode)



#### Chembl

ChEMBL data is from http://www.ebi.ac.uk/chembl



#### CLO

CLO data is from http://www.ebi.ac.uk/ols/ontologies/clo



#### UBERON

UBERON data is from http://www.ebi.ac.uk/ols/ontologies/uberon



### Under [Creative Commons Attribution 4.0 Unported License](https://creativecommons.org/licenses/by/4.0/legalcode>)



#### MONDO

MONDO data is from http://www.ebi.ac.uk/ols/ontologies/mondo



#### CELLOSAURUS

CELLOSAURUS data is from https://www.cellosaurus.org/



#### Gene Ontology

Gene Ontology data is from http://purl.obolibrary.org/obo/go.owl



### Other licenced datasets and models



#### HPO



This service/product uses the Human Phenotype Ontology (version information). Find out more at http://www.human-phenotype-ontology.org



Freely licenced under https://hpo.jax.org/app/license



Sebastian Köhler, Michael Gargano, Nicolas Matentzoglu, Leigh C Carmody, David Lewis-Smith,

Nicole A Vasilevsky, Daniel Danis, Ganna Balagura, Gareth Baynam, Amy M Brower,

Tiffany J Callahan, Christopher G Chute, Johanna L Est, Peter D Galer, Shiva Ganesan,

Matthias Griese, Matthias Haimel, Julia Pazmandi, Marc Hanauer, Nomi L Harris,

Michael J Hartnett, Maximilian Hastreiter, Fabian Hauck, Yongqun He, Tim Jeske, Hugh Kearney,

Gerhard Kindle, Christoph Klein, Katrin Knoflach, Roland Krause, David Lagorce, Julie A McMurry,

Jillian A Miller, Monica C Munoz-Torres, Rebecca L Peters, Christina K Rapp, Ana M Rath,

Shahmir A Rind, Avi Z Rosenberg, Michael M Segal, Markus G Seidel, Damian Smedley,

Tomer Talmy, Yarlalu Thomas, Samuel A Wiafe, Julie Xian, Zafer Yüksel, Ingo Helbig,

Christopher J Mungall, Melissa A Haendel, Peter N Robinson,



The Human Phenotype Ontology in 2021,



Nucleic Acids Research, Volume 49, Issue D1, 8 January 2021, Pages D1207–D1217,


https://doi.org/10.1093/nar/gkaa1043



#### OPEN TARGETS

Open Targets datasets are kindly provided by www.opentargets.org, which are free for commercial use cases 



Ochoa, D. et al. (2021). Open Targets Platform: supporting systematic drug–target identification and prioritisation. Nucleic Acids Research.


https://doi.org/10.1093/nar/gkaa1027



#### STANZA



The Stanza framework:



Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton and Christopher D. Manning. 2020. Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. In Association for Computational Linguistics (ACL) System Demonstrations. 2020.


https://arxiv.org/abs/2003.07082



Biomedical NLP models are derived from:



Yuhao Zhang, Yuhui Zhang, Peng Qi, Christopher D. Manning, Curtis P. Langlotz.


Biomedical and Clinical English Model Packages in the Stanza Python NLP Library,


Journal of the American Medical Informatics Association. 2021.


https://doi.org/10.1093/jamia/ocab090



#### SCISPACY



Biomedical scispacy models are derived from



Mark Neumann, Daniel King, Iz Beltagy, Waleed Ammar


ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing


Proceedings of the 18th BioNLP Workshop and Shared Task


ACL 2019


https://www.aclweb.org/anthology/W19-5034



#### SAPBERT



Kazu uses a distilled form of SAPBERT, from



Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng, Marco Basaldella, Nigel Collier


Self-Alignment Pretraining for Biomedical Entity Representations


ACL 2021


https://aclanthology.org/2021.naacl-main.334/



#### GLINER



GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer.


Urchade Zaratiana, Nadi Tomeh, Pierre Holat, Thierry Charnois


https://arxiv.org/abs/2311.08526



#### SETH



Kazu's SethStep uses Py4j to call the SETH mutation finder.



Thomas, P., Rocktäschel, T., Hakenberg, J., Mayer, L., and Leser, U. (2016).


[SETH detects and normalizes genetic variants in text](https://pubmed.ncbi.nlm.nih.gov/27256315/)


Bioinformatics (2016)


http://dx.doi.org/10.1093/bioinformatics/btw234



#### Opsin



Kazu's OpsinStep uses Py4j to call OPSIN: Open Parser for Systematic IUPAC nomenclature.



Daniel M. Lowe, Peter T. Corbett, Peter Murray-Rust, and Robert C. Glen


Chemical Name to Structure: OPSIN, an Open Source Solution


Journal of Chemical Information and Modeling 2011 51 (3), 739-753


DOI: [10.1021/ci100384d](https://doi.org/10.1021/ci100384d)