Metadata-Version: 2.1
Name: deeqnlpy
Version: 0.9.2
Summary: The deeq nlp python client library
Home-page: https://baikal.ai/app2/#/morpheme
License: BSD-3-Clause
Keywords: NLP,Korean,Deep Learning,POS tagger
Author: Gihyun YUN
Author-email: gih2yun@baikal.ai
Requires-Python: >=3.6,<4.0
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: Korean
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: OS Independent
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Typing :: Typed
Requires-Dist: grpcio (==1.35.0)
Requires-Dist: protobuf (==3.14.0)
Project-URL: Repository, https://github.com/baikalai/deeqnlpy
Description-Content-Type: text/markdown

# What is this?

`deeqnlpy` is the python 3 library for deeq NLP.

Deeq(pronounce as deeque) NLP is a Korean NLP,
which provides tokenizing, POS tagging for Korean.

## How to install

```shell
pip3 install deeqnlpy
```

## How to get deeq NLP
- Click [this form](https://docs.google.com/forms/d/e/1FAIpQLSfSJQCMwm0pS1nJiirwUNjfj-7jT-T_CLUfgMc-vTpRbHZZnw/viewform)
- Fill it.
- Get emailed download link, a license file.
- Or use docker image.
```shell
docker pull baikalai/deeq-nlp:v1.4.2
```
- Caution: You should use deeq NLP v1.4.2 or later.

## How to use

```python
from deeqnlpy import Tagger

my_tagger = Tagger('localhost') # If you have your own local deeq NLP. 
# or
tagger = Tagger() # With smaller public cloud instance, it may be slow.

# print results. 
res = tagger.tags(["안녕하세요.", "반가워요!"])

# get protobuf message.
res.msg()

# get json object
jo = res.as_json()
print(jo)

# get tuple of pos tagging.
res.pos()

# another methods
res.morphs()
res.nouns()
res.verbs()

# custom dictionary
cust_dic = tagger.custom_dict("my")
cust_dic.copy_np_set({'내고유명사', '우리집고유명사'})
cust_dic.copy_cp_set({'코로나19'})
cust_dic.copy_cp_set({'코로나^백신', '"독감^백신'})
cust_dic.update()

tagger.set_domain('my')
tagger.pos('코로나19는 언제 끝날까요?')
```

