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Author: dhchenx

LICENSE

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+MIT License
+
+Copyright (c) 2022 The Python Packaging Authority
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

MANIFEST.in

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+# include pyproject.toml
+
+# Include the README
+include *.md
+
+# Include the license file
+include LICENSE
+
+# Include setup.py
+include setup.py
+
+# Include the data files
+# recursive-include data *
+# recursive-include examples *
+recursive-include src *
+# recursive-include images *

README.md

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-# ner-kit
-Rapidly extracting useful entities from text using various Python packages
+## Named Entity Recognition Toolkit
+
+Provide a toolkit for rapidly extracting useful entities from text using various Python packages, including [Stanza](https://stanfordnlp.github.io/stanza/index.html). 
+
+### Features
+We try to bring the complicated use of existing NLP toolkits down to earth by keeping APIs as simple as possible with best practice. 
+
+### Installation
+```pip
+pip install ner-kit
+```
+
+### Examples
+
+Example 1: Word segmention
+```python
+from nerkit.StanzaApi import StanzaWrapper
+if __name__=="__main__":
+    sw=StanzaWrapper()
+    sw.download(lang="en")
+    text='This is a test sentence for stanza. This is another sentence.'
+    result1=sw.tokenize(text)
+    sw.print_result(result1)
+```
+
+Example 2: Chinese word segmentation
+```python
+from nerkit.StanzaApi import StanzaWrapper
+if __name__=="__main__":
+    sw=StanzaWrapper()
+    sw.download(lang="zh")
+    text='我在北京吃苹果！'
+    result1=sw.tokenize(text,lang='zh')
+    sw.print_result(result1)
+```
+
+Example 3: Multi-Word Token (MWT) Expansion
+```python
+from nerkit.StanzaApi import StanzaWrapper
+if __name__=="__main__":
+    sw=StanzaWrapper()
+    sw.download(lang="fr")
+    text='Nous avons atteint la fin du sentier.'
+    result1=sw.mwt_expand(text,lang='fr')
+    sw.print_result(result1)
+```
+
+Example 4: POS tagging
+```python
+from nerkit.StanzaApi import StanzaWrapper
+if __name__=="__main__":
+    sw=StanzaWrapper()
+    sw.download(lang='en')
+    text='I like apple'
+    result1=sw.tag(text)
+    sw.print_result(result1)
+    sw.download_chinese_model()
+    text='我喜欢苹果'
+    result2=sw.tag_chinese(text,lang='zh')
+    sw.print_result(result2)
+```
+
+Example 5: Named Entity Recognition
+```python
+from nerkit.StanzaApi import StanzaWrapper
+
+if __name__=="__main__":
+    sw=StanzaWrapper()
+
+    sw.download(lang='en')
+    sw.download_chinese_model()
+
+    text_en = 'I like Beijing!'
+    result1 = sw.ner(text_en)
+    sw.print_result(result1)
+
+    text='我喜欢北京！'
+    result2=sw.ner_chinese(text)
+    sw.print_result(result2)
+
+```
+
+Example 6: Sentiment Analysis
+```python
+from nerkit.StanzaApi import StanzaWrapper
+
+if __name__=="__main__":
+    sw=StanzaWrapper()
+    text_en = 'I like Beijing!'
+    result1 = sw.sentiment(text_en)
+    sw.print_result(result1)
+
+    text_zh='我讨厌苹果！'
+    result2=sw.sentiment_chinese(text_zh)
+    sw.print_result(result2)
+```
+
+Example 7: Language detection from text
+```python
+from nerkit.StanzaApi import StanzaWrapper
+if __name__=="__main__":
+    sw=StanzaWrapper()
+    list_text = ['I like Beijing!','我喜欢北京！', "Bonjour le monde!"]
+    result1 = sw.lang(list_text)
+    sw.print_result(result1)
+```
+
+Example 8: Language detection from text with a user-defined processing function
+```python
+from nerkit.StanzaApi import StanzaWrapper
+if __name__=="__main__":
+    sw=StanzaWrapper()
+    list_text = ['I like Beijing!','我喜欢北京！', "Bonjour le monde!"]
+    def process(model):# do your own business
+        doc=model["doc"]
+        print(f"{doc.sentences[0].dependencies_string()}")
+    result1 = sw.lang_multi(list_text,func_process=process,download_lang='en,zh,fr')
+    print(result1)
+    sw.print_result(result1)
+```
+
+Example 9: Stanza's NER (Legacy use for Java-based Stanford CoreNLP)
+```python
+from nerkit.StanzaApi import *
+# First, set environment variable CORENLP_HOME to the CoreNLP folder
+corenlp_root_path=r"stanford-corenlp-4.3.2"
+text="我喜欢游览广东孙中山故居景点！"
+list_token=get_entity_list(text,corenlp_root_path=corenlp_root_path,language="chinese")
+for token in list_token:
+    print(f"{token['value']}\t{token['pos']}\t{token['ner']}")
+```
+
+Example 10: Stanford CoreNLP (Not official version)
+```python
+import os
+from nerkit.StanfordCoreNLP import get_entity_list
+text="我喜欢游览广东孙中山故居景点！"
+current_path = os.path.dirname(os.path.realpath(__file__))
+res=get_entity_list(text,resource_path=f"{current_path}/stanfordcorenlp/stanford-corenlp-latest/stanford-corenlp-4.3.2")
+print(res)
+for w,tag in res:
+    if tag in ['PERSON','ORGANIZATION','LOCATION']:
+        print(w,tag)
+```
+
+### Credits & References
+
+- [Stanza](https://stanfordnlp.github.io/stanza/index.html)
+- [Stanford CoreNLP](https://stanfordnlp.github.io/CoreNLP/)
+
+### License
+The `ner-kit` project is provided by [Donghua Chen](https://github.com/dhchenx). 
+

dist/ner-kit-0.0.1.tar.gz

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+[metadata]
+# This includes the license file(s) in the wheel.
+# https://wheel.readthedocs.io/en/stable/user_guide.html#including-license-files-in-the-generated-wheel-file
+license_files = LICENSE