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You can run the walkthrough Python notebook in [Google Colab](https://colab.research.google.com/github/fastdatascience/drug_named_entity_recognition/blob/main/drug_named_entity_recognition_example_walkthrough.ipynb) with a single click: <a href="https://colab.research.google.com/github/fastdatascience/drug_named_entity_recognition/blob/main/drug_named_entity_recognition_example_walkthrough.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Drug named entity recognition Python library by Fast Data Science
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# 💊 Drug named entity recognition
Developed by Fast Data Science, https://fastdatascience.com
Source code at https://github.com/fastdatascience/drug_named_entity_recognition
Tutorial at https://fastdatascience.com/drug-named-entity-recognition-python-library/
This is a lightweight Python library for finding drug names in a string, otherwise known as [named entity recognition (NER)](https://fastdatascience.com/named-entity-recognition/) and named entity linking.
Please note this library finds only high confidence drugs and doesn't support misspellings at present.
It also only finds the English names of these drugs. Names in other languages are not supported.
It also doesn't find short code names of drugs, such as abbreviations commonly used in medicine, such as "Ceph" for "Cephradin" - as these are highly ambiguous.
# 💻Installing drug named entity recognition Python package
You can install from [PyPI](https://pypi.org/project/drug-named-entity-recognition).
```
pip install drug-named-entity-recognition
```
If you get an error installing, try making a new Python environment in Conda (`conda create -n test-env; conda activate test-env`) or Venv (`python -m testenv; source testenv/bin/activate` / `testenv\Scripts\activate`) and then installing the library.
The library already contains the drug names so if you don't need to update the dictionary, then you should not have to run any of the download scripts.
If you have problems installing, try our [Google Colab](https://colab.research.google.com/github/fastdatascience/drug_named_entity_recognition/blob/main/drug_named_entity_recognition_example_walkthrough.ipynb) walkthrough.
# 💡Usage examples
You must first tokenise your input text using a tokeniser of your choice (NLTK, spaCy, etc).
You pass a list of strings to the `find_drugs` function.
Example 1
```
from drug_named_entity_recognition import find_drugs
find_drugs("i bought some Prednisone".split(" "))
```
outputs a list of tuples.
```
[({'name': 'Prednisone', 'synonyms': {'Sone', 'Sterapred', 'Deltasone', 'Panafcort', 'Prednidib', 'Cortan', 'Rectodelt', 'Prednisone', 'Cutason', 'Meticorten', 'Panasol', 'Enkortolon', 'Ultracorten', 'Decortin', 'Orasone', 'Winpred', 'Dehydrocortisone', 'Dacortin', 'Cortancyl', 'Encorton', 'Encortone', 'Decortisyl', 'Kortancyl', 'Pronisone', 'Prednisona', 'Predniment', 'Prednisonum', 'Rayos'}, 'medline_plus_id': 'a601102', 'mesh_id': 'D018931', 'drugbank_id': 'DB00635'}, 3, 3)]
```
You can ignore case with:
```
find_drugs("i bought some prednisone".split(" "), is_ignore_case=True)
```
# Interested in other kinds of named entity recognition (NER)? 💸Finances, 🎩company names, 🌎countries, 🗺️locations, proteins, 🧬genes, 🧪molecules?
If your NER problem is common across industries and likely to have been seen before, there may be an off-the-shelf NER tool for your purposes, such as our [Country Named Entity Recognition](http://fastdatascience.com//country-named-entity-recognition/) Python library. Dictionary-based named entity recognition is not always the solution, as sometimes the total set of entities is an open set and can't be listed (e.g. personal names), so sometimes a bespoke trained NER model is the answer. For tasks like finding email addresses or phone numbers, regular expressions (simple rules) are sufficient for the job.
If your named entity recognition or named entity linking problem is very niche and unusual, and a product exists for that problem, that product is likely to only solve your problem 80% of the way, and you will have more work trying to fix the final mile than if you had done the whole thing manually. Please [contact Fast Data Science](http://fastdatascience.com//contact) and we'll be glad to discuss. For example, we've worked on [a consultancy engagement to find molecule names in papers, and match author names to customers](http://fastdatascience.com//boehringer-ingelheim-finding-molecules-and-proteins-in-scientific-literature/) where the goal was to trace molecule samples ordered from a pharma company and identify when the samples resulted in a publication. For this case, there was no off-the-shelf library that we could use.
For a problem like identifying country names in English, which is a closed set with well-known variants and aliases, an off-the-shelf library is usually available. You may wish to try our [Country Named Entity Recognition](https://fastdatascience.com/country-named-entity-recognition/) library, also open-source and under MIT license.
For identifying a set of molecules manufactured by a particular company, this is the kind of task more suited to a [consulting engagement](https://fastdatascience.com/portfolio/nlp-consultant/).
# 😊 Using this tool directly from Google Sheets (no-code!)
<img align="left" alt="Google Sheets logo" title="Google Sheets logo" width=150 height=105 src="google_sheets_logo_small.png" />
We have a no-code solution where you can [use the library directly from Google Sheets](https://fastdatascience.com/drug-name-recogniser) as the library has also been wrapped as a Google Sheets plugin.
[Click here](https://www.youtube.com/watch?v=qab1Bv_YpYU) to watch a video of how the plugin works.
You can install the plugin in Google Sheets [here](https://workspace.google.com/marketplace/app/drug_name_recogniser/463844408236).
# Requirements
Python 3.9 and above
## ✉️Who to contact?
You can contact Thomas Wood or the Fast Data Science team at https://fastdatascience.com/.
# 🤝Compatibility with other natural language processing libraries
The Drug Named Entity Recognition library is independent of other NLP tools and has no dependencies. You don't need any advanced system requirements and the tool is lightweight. However, it combines well with other libraries such as [spaCy](https://spacy.io) or the [Natural Language Toolkit (NLTK)](https://www.nltk.org/api/nltk.tokenize.html).
## Using Drug Named Entity Recognition together with spaCy
Here is an example call to the tool with a [spaCy](https://spacy.io) Doc object:
```
from drug_named_entity_recognition import find_drugs
import spacy
nlp = spacy.blank("en")
doc = nlp("i routinely rx rimonabant and pts prefer it")
find_drugs([t.text for t in doc], is_ignore_case=True)
```
outputs:
```
[({'name': 'Rimonabant', 'synonyms': {'Acomplia', 'Rimonabant', 'Zimulti'}, 'mesh_id': 'D063387', 'drugbank_id': 'DB06155'}, 3, 3)]
```
## Using Drug Named Entity Recognition together with NLTK
You can also use the tool together with the [Natural Language Toolkit (NLTK)](https://www.nltk.org/api/nltk.tokenize.html):
```
from drug_named_entity_recognition import find_drugs
from nltk.tokenize import wordpunct_tokenize
tokens = wordpunct_tokenize("i routinely rx rimonabant and pts prefer it")
find_drugs(tokens, is_ignore_case=True)
```
# 📁Data sources
The main data source is from Drugbank, augmented by datasets from the NHS, MeSH, Medline Plus and Wikipedia.
🌟 There is a handy Jupyter Notebook, `update.ipynb` which will update the Drugbank and MeSH data sources (re-download them from the relevant third parties).
## Update the Drugbank dictionary
If you want to update the dictionary, you can use the data dump from Drugbank and replace the file `drugbank vocabulary.csv`:
* Download the open data dump from https://go.drugbank.com/releases/latest#open-data
## Update the Wikipedia dictionary
If you want to update the Wikipedia dictionary, download the dump from:
* https://meta.wikimedia.org/wiki/Data_dump_torrents#English_Wikipedia
and run `extract_drug_names_and_synonyms_from_wikipedia_dump.py`
## Update the MeSH dictionary
If you want to update the dictionary, run
```
python download_mesh_dump_and_extract_drug_names_and_synonyms.py
```
This will download the latest XML file from NIH.
If the link doesn't work, download the open data dump manually from https://www.nlm.nih.gov/. It should be called something like `desc2023.xml`. And comment out the Wget/Curl commands in the code.
## License information for external data sources
* Data from Drugbank is licensed under [CC0](https://go.drugbank.com/releases/latest#open-data).
```
To the extent possible under law, the person who associated CC0 with the DrugBank Open Data has waived all copyright and related or neighboring rights to the DrugBank Open Data. This work is published from: Canada.
```
* Text from Wikipedia data dump is licensed under [GNU Free Documentation License](https://www.gnu.org/licenses/fdl-1.3.html) and [Creative Commons Attribution-Share-Alike 3.0 License](https://creativecommons.org/licenses/by-sa/3.0/). [More information](https://dumps.wikimedia.org/legal.html).
## Contributing to the Drug Named Entity Recognition library
If you'd like to contribute to this project, you can contact us at https://fastdatascience.com/ or make a pull request on our [Github repository](https://github.com/fastdatascience/drug_named_entity_recognition). You can also [raise an issue](https://github.com/fastdatascience/drug_named_entity_recognition/issues).
## Developing the Drug Named Entity Recognition library
### Automated tests
Test code is in **tests/** folder using [unittest](https://docs.python.org/3/library/unittest.html).
The testing tool `tox` is used in the automation with GitHub Actions CI/CD.
### Use tox locally
Install tox and run it:
```
pip install tox
tox
```
In our configuration, tox runs a check of source distribution using [check-manifest](https://pypi.org/project/check-manifest/) (which requires your repo to be git-initialized (`git init`) and added (`git add .`) at least), setuptools's check, and unit tests using pytest. You don't need to install check-manifest and pytest though, tox will install them in a separate environment.
The automated tests are run against several Python versions, but on your machine, you might be using only one version of Python, if that is Python 3.9, then run:
```
tox -e py39
```
Thanks to GitHub Actions' automated process, you don't need to generate distribution files locally. But if you insist, click to read the "Generate distribution files" section.
### 🤖 Continuous integration/deployment to PyPI
This package is based on the template https://pypi.org/project/example-pypi-package/
This package
- uses GitHub Actions for both testing and publishing
- is tested when pushing `master` or `main` branch, and is published when create a release
- includes test files in the source distribution
- uses **setup.cfg** for [version single-sourcing](https://packaging.python.org/guides/single-sourcing-package-version/) (setuptools 46.4.0+)
## 🧍Re-releasing the package manually
The code to re-release Harmony on PyPI is as follows:
```
source activate py311
pip install twine
rm -rf dist
python setup.py sdist
twine upload dist/*
```
## 😊 Who worked on the Drug Named Entity Recognition library?
The tool was developed:
* Thomas Wood ([Fast Data Science](https://fastdatascience.com))
## 📜License of Drug Named Entity Recognition library
MIT License. Copyright (c) 2023 [Fast Data Science](https://fastdatascience.com)
## ✍️ Citing the Drug Named Entity Recognition library
Wood, T.A., Drug Named Entity Recognition [Computer software], Version 1.0.3, accessed at [https://fastdatascience.com/drug-named-entity-recognition-python-library](https://fastdatascience.com/drug-named-entity-recognition-python-library), Fast Data Science Ltd (2023)
```
@unpublished{drugnamedentityrecognition,
AUTHOR = {Wood, T.A.},
TITLE = {Drug Named Entity Recognition (Computer software), Version 1.0.3},
YEAR = {2024},
Note = {To appear},
url = {https://zenodo.org/doi/10.5281/zenodo.10970631},
doi = {10.5281/zenodo.10970631}
}
```
Raw data
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"description": "\n\n<a href=\"https://fastdatascience.com\"><span align=\"left\">\ud83c\udf10 fastdatascience.com</span></a>\n<a href=\"https://www.linkedin.com/company/fastdatascience/\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/linkedin.svg\" alt=\"Fast Data Science | LinkedIn\" width=\"21px\"/></a>\n<a href=\"https://twitter.com/fastdatascienc1\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/x.svg\" alt=\"Fast Data Science | X\" width=\"21px\"/></a>\n<a href=\"https://www.instagram.com/fastdatascience/\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/instagram.svg\" alt=\"Fast Data Science | Instagram\" width=\"21px\"/></a>\n<a href=\"https://www.facebook.com/fastdatascienceltd\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/fb.svg\" alt=\"Fast Data Science | Facebook\" width=\"21px\"/></a>\n<a href=\"https://www.youtube.com/channel/UCLPrDH7SoRT55F6i50xMg5g\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/yt.svg\" alt=\"Fast Data Science | YouTube\" width=\"21px\"/></a>\n<a href=\"https://g.page/fast-data-science\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/google.svg\" alt=\"Fast Data Science | Google\" width=\"21px\"/></a>\n<a href=\"https://medium.com/fast-data-science\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/medium.svg\" alt=\"Fast Data Science | Medium\" width=\"21px\"/></a>\n<a href=\"https://mastodon.social/@fastdatascience\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/mastodon.svg\" alt=\"Fast Data Science | Mastodon\" width=\"21px\"/></a>\n\nYou can run the walkthrough Python notebook in [Google Colab](https://colab.research.google.com/github/fastdatascience/drug_named_entity_recognition/blob/main/drug_named_entity_recognition_example_walkthrough.ipynb) with a single click: <a href=\"https://colab.research.google.com/github/fastdatascience/drug_named_entity_recognition/blob/main/drug_named_entity_recognition_example_walkthrough.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n\n# Drug named entity recognition Python library by Fast Data Science\n\n<!-- badges: start -->\n\n[](https://pypi.org/project/drug-named-entity-recognition/) [](https://github.com/fastdatascience/drug_named_entity_recognition/releases) [](https://github.com/fastdatascience/drug_named_entity_recognition/blob/main/LICENSE)\n[](https://pypi.org/project/drug_named_entity_recognition/)\n [](https://github.com/fastdatascience/drug_named_entity_recognition/releases) [](https://pypi.org/project/drug_named_entity_recognition/)\n[](https://github.com/fastdatascience/drug_named_entity_recognition/forks)\n\n<!-- badges: end -->\n\n# \ud83d\udc8a Drug named entity recognition\n\nDeveloped by Fast Data Science, https://fastdatascience.com\n\nSource code at https://github.com/fastdatascience/drug_named_entity_recognition\n\nTutorial at https://fastdatascience.com/drug-named-entity-recognition-python-library/\n\nThis is a lightweight Python library for finding drug names in a string, otherwise known as [named entity recognition (NER)](https://fastdatascience.com/named-entity-recognition/) and named entity linking.\n\nPlease note this library finds only high confidence drugs and doesn't support misspellings at present.\n\nIt also only finds the English names of these drugs. Names in other languages are not supported.\n\nIt also doesn't find short code names of drugs, such as abbreviations commonly used in medicine, such as \"Ceph\" for \"Cephradin\" - as these are highly ambiguous.\n\n# \ud83d\udcbbInstalling drug named entity recognition Python package\n\nYou can install from [PyPI](https://pypi.org/project/drug-named-entity-recognition).\n\n```\npip install drug-named-entity-recognition\n```\n\nIf you get an error installing, try making a new Python environment in Conda (`conda create -n test-env; conda activate test-env`) or Venv (`python -m testenv; source testenv/bin/activate` / `testenv\\Scripts\\activate`) and then installing the library.\n\nThe library already contains the drug names so if you don't need to update the dictionary, then you should not have to run any of the download scripts.\n\nIf you have problems installing, try our [Google Colab](https://colab.research.google.com/github/fastdatascience/drug_named_entity_recognition/blob/main/drug_named_entity_recognition_example_walkthrough.ipynb) walkthrough.\n\n# \ud83d\udca1Usage examples\n\nYou must first tokenise your input text using a tokeniser of your choice (NLTK, spaCy, etc).\n\nYou pass a list of strings to the `find_drugs` function.\n\nExample 1\n\n```\nfrom drug_named_entity_recognition import find_drugs\n\nfind_drugs(\"i bought some Prednisone\".split(\" \"))\n```\n\noutputs a list of tuples.\n\n```\n[({'name': 'Prednisone', 'synonyms': {'Sone', 'Sterapred', 'Deltasone', 'Panafcort', 'Prednidib', 'Cortan', 'Rectodelt', 'Prednisone', 'Cutason', 'Meticorten', 'Panasol', 'Enkortolon', 'Ultracorten', 'Decortin', 'Orasone', 'Winpred', 'Dehydrocortisone', 'Dacortin', 'Cortancyl', 'Encorton', 'Encortone', 'Decortisyl', 'Kortancyl', 'Pronisone', 'Prednisona', 'Predniment', 'Prednisonum', 'Rayos'}, 'medline_plus_id': 'a601102', 'mesh_id': 'D018931', 'drugbank_id': 'DB00635'}, 3, 3)]\n```\n\nYou can ignore case with:\n\n```\nfind_drugs(\"i bought some prednisone\".split(\" \"), is_ignore_case=True)\n```\n\n\n# Interested in other kinds of named entity recognition (NER)? \ud83d\udcb8Finances, \ud83c\udfa9company names, \ud83c\udf0ecountries, \ud83d\uddfa\ufe0flocations, proteins, \ud83e\uddecgenes, \ud83e\uddeamolecules?\n\nIf your NER problem is common across industries and likely to have been seen before, there may be an off-the-shelf NER tool for your purposes, such as our [Country Named Entity Recognition](http://fastdatascience.com//country-named-entity-recognition/) Python library. Dictionary-based named entity recognition is not always the solution, as sometimes the total set of entities is an open set and can't be listed (e.g. personal names), so sometimes a bespoke trained NER model is the answer. For tasks like finding email addresses or phone numbers, regular expressions (simple rules) are sufficient for the job.\n\nIf your named entity recognition or named entity linking problem is very niche and unusual, and a product exists for that problem, that product is likely to only solve your problem 80% of the way, and you will have more work trying to fix the final mile than if you had done the whole thing manually. Please [contact Fast Data Science](http://fastdatascience.com//contact) and we'll be glad to discuss. For example, we've worked on [a consultancy engagement to find molecule names in papers, and match author names to customers](http://fastdatascience.com//boehringer-ingelheim-finding-molecules-and-proteins-in-scientific-literature/) where the goal was to trace molecule samples ordered from a pharma company and identify when the samples resulted in a publication. For this case, there was no off-the-shelf library that we could use.\n\nFor a problem like identifying country names in English, which is a closed set with well-known variants and aliases, an off-the-shelf library is usually available. You may wish to try our [Country Named Entity Recognition](https://fastdatascience.com/country-named-entity-recognition/) library, also open-source and under MIT license.\n\nFor identifying a set of molecules manufactured by a particular company, this is the kind of task more suited to a [consulting engagement](https://fastdatascience.com/portfolio/nlp-consultant/).\n\n# \ud83d\ude0a Using this tool directly from Google Sheets (no-code!)\n\n<img align=\"left\" alt=\"Google Sheets logo\" title=\"Google Sheets logo\" width=150 height=105 src=\"google_sheets_logo_small.png\" />\n\nWe have a no-code solution where you can [use the library directly from Google Sheets](https://fastdatascience.com/drug-name-recogniser) as the library has also been wrapped as a Google Sheets plugin.\n\n[Click here](https://www.youtube.com/watch?v=qab1Bv_YpYU) to watch a video of how the plugin works.\n\nYou can install the plugin in Google Sheets [here](https://workspace.google.com/marketplace/app/drug_name_recogniser/463844408236).\n\n\n\n# Requirements\n\nPython 3.9 and above\n\n## \u2709\ufe0fWho to contact?\n\nYou can contact Thomas Wood or the Fast Data Science team at https://fastdatascience.com/.\n\n# \ud83e\udd1dCompatibility with other natural language processing libraries\n\nThe Drug Named Entity Recognition library is independent of other NLP tools and has no dependencies. You don't need any advanced system requirements and the tool is lightweight. However, it combines well with other libraries such as [spaCy](https://spacy.io) or the [Natural Language Toolkit (NLTK)](https://www.nltk.org/api/nltk.tokenize.html).\n\n## Using Drug Named Entity Recognition together with spaCy\n\nHere is an example call to the tool with a [spaCy](https://spacy.io) Doc object:\n\n```\nfrom drug_named_entity_recognition import find_drugs\nimport spacy\nnlp = spacy.blank(\"en\")\ndoc = nlp(\"i routinely rx rimonabant and pts prefer it\")\nfind_drugs([t.text for t in doc], is_ignore_case=True)\n```\n\noutputs:\n\n```\n[({'name': 'Rimonabant', 'synonyms': {'Acomplia', 'Rimonabant', 'Zimulti'}, 'mesh_id': 'D063387', 'drugbank_id': 'DB06155'}, 3, 3)]\n```\n\n## Using Drug Named Entity Recognition together with NLTK\n\nYou can also use the tool together with the [Natural Language Toolkit (NLTK)](https://www.nltk.org/api/nltk.tokenize.html):\n\n```\nfrom drug_named_entity_recognition import find_drugs\nfrom nltk.tokenize import wordpunct_tokenize\ntokens = wordpunct_tokenize(\"i routinely rx rimonabant and pts prefer it\")\nfind_drugs(tokens, is_ignore_case=True)\n```\n\n# \ud83d\udcc1Data sources\n\nThe main data source is from Drugbank, augmented by datasets from the NHS, MeSH, Medline Plus and Wikipedia.\n\n\ud83c\udf1f There is a handy Jupyter Notebook, `update.ipynb` which will update the Drugbank and MeSH data sources (re-download them from the relevant third parties). \n\n## Update the Drugbank dictionary\n\nIf you want to update the dictionary, you can use the data dump from Drugbank and replace the file `drugbank vocabulary.csv`:\n\n* Download the open data dump from https://go.drugbank.com/releases/latest#open-data\n\n## Update the Wikipedia dictionary\n\nIf you want to update the Wikipedia dictionary, download the dump from:\n\n* https://meta.wikimedia.org/wiki/Data_dump_torrents#English_Wikipedia\n\nand run `extract_drug_names_and_synonyms_from_wikipedia_dump.py`\n\n## Update the MeSH dictionary\n\nIf you want to update the dictionary, run\n\n```\npython download_mesh_dump_and_extract_drug_names_and_synonyms.py\n```\n\nThis will download the latest XML file from NIH.\n\nIf the link doesn't work, download the open data dump manually from https://www.nlm.nih.gov/. It should be called something like `desc2023.xml`. And comment out the Wget/Curl commands in the code.\n\n## License information for external data sources\n\n* Data from Drugbank is licensed under [CC0](https://go.drugbank.com/releases/latest#open-data).\n\n```\nTo the extent possible under law, the person who associated CC0 with the DrugBank Open Data has waived all copyright and related or neighboring rights to the DrugBank Open Data. This work is published from: Canada.\n```\n\n* Text from Wikipedia data dump is licensed under [GNU Free Documentation License](https://www.gnu.org/licenses/fdl-1.3.html) and [Creative Commons Attribution-Share-Alike 3.0 License](https://creativecommons.org/licenses/by-sa/3.0/). [More information](https://dumps.wikimedia.org/legal.html).\n\n## Contributing to the Drug Named Entity Recognition library\n\nIf you'd like to contribute to this project, you can contact us at https://fastdatascience.com/ or make a pull request on our [Github repository](https://github.com/fastdatascience/drug_named_entity_recognition). You can also [raise an issue](https://github.com/fastdatascience/drug_named_entity_recognition/issues). \n\n## Developing the Drug Named Entity Recognition library\n\n### Automated tests\n\nTest code is in **tests/** folder using [unittest](https://docs.python.org/3/library/unittest.html).\n\nThe testing tool `tox` is used in the automation with GitHub Actions CI/CD.\n\n### Use tox locally\n\nInstall tox and run it:\n\n```\npip install tox\ntox\n```\n\nIn our configuration, tox runs a check of source distribution using [check-manifest](https://pypi.org/project/check-manifest/) (which requires your repo to be git-initialized (`git init`) and added (`git add .`) at least), setuptools's check, and unit tests using pytest. You don't need to install check-manifest and pytest though, tox will install them in a separate environment.\n\nThe automated tests are run against several Python versions, but on your machine, you might be using only one version of Python, if that is Python 3.9, then run:\n\n```\ntox -e py39\n```\n\nThanks to GitHub Actions' automated process, you don't need to generate distribution files locally. But if you insist, click to read the \"Generate distribution files\" section.\n\n### \ud83e\udd16 Continuous integration/deployment to PyPI\n\nThis package is based on the template https://pypi.org/project/example-pypi-package/\n\nThis package\n\n- uses GitHub Actions for both testing and publishing\n- is tested when pushing `master` or `main` branch, and is published when create a release\n- includes test files in the source distribution\n- uses **setup.cfg** for [version single-sourcing](https://packaging.python.org/guides/single-sourcing-package-version/) (setuptools 46.4.0+)\n\n## \ud83e\uddcdRe-releasing the package manually\n\nThe code to re-release Harmony on PyPI is as follows:\n\n```\nsource activate py311\npip install twine\nrm -rf dist\npython setup.py sdist\ntwine upload dist/*\n```\n\n## \ud83d\ude0a Who worked on the Drug Named Entity Recognition library?\n\nThe tool was developed:\n\n* Thomas Wood ([Fast Data Science](https://fastdatascience.com))\n\n## \ud83d\udcdcLicense of Drug Named Entity Recognition library\n\nMIT License. Copyright (c) 2023 [Fast Data Science](https://fastdatascience.com)\n\n## \u270d\ufe0f Citing the Drug Named Entity Recognition library\n\nWood, T.A., Drug Named Entity Recognition [Computer software], Version 1.0.3, accessed at [https://fastdatascience.com/drug-named-entity-recognition-python-library](https://fastdatascience.com/drug-named-entity-recognition-python-library), Fast Data Science Ltd (2023)\n\n```\n@unpublished{drugnamedentityrecognition,\n AUTHOR = {Wood, T.A.},\n TITLE = {Drug Named Entity Recognition (Computer software), Version 1.0.3},\n YEAR = {2024},\n Note = {To appear},\n url = {https://zenodo.org/doi/10.5281/zenodo.10970631},\n doi = {10.5281/zenodo.10970631}\n}\n```\n",
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