kochat

Related tags

Text Data & NLPkochat
Overview

Kochat

PyPI version GitHub

introduction_kochat



  • 챗봇 빌더는 성에 안차고, 자신만의 딥러닝 챗봇 애플리케이션을 만드시고 싶으신가요?
  • Kochat을 이용하면 손쉽게 자신만의 딥러닝 챗봇 애플리케이션을 빌드할 수 있습니다.
# 1. 데이터셋 객체 생성
dataset = Dataset(ood=True)

# 2. 임베딩 프로세서 생성
emb = GensimEmbedder(model=embed.FastText())

# 3. 의도(Intent) 분류기 생성
clf = DistanceClassifier(
    model=intent.CNN(dataset.intent_dict),                  
    loss=CenterLoss(dataset.intent_dict)                    
)

# 4. 개체명(Named Entity) 인식기 생성                                                     
rcn = EntityRecognizer(
    model=entity.LSTM(dataset.entity_dict),
    loss=CRFLoss(dataset.entity_dict)
)

# 5. 딥러닝 챗봇 RESTful API 학습 & 빌드
kochat = KochatApi(
    dataset=dataset, 
    embed_processor=(emb, True), 
    intent_classifier=(clf, True),
    entity_recognizer=(rcn, True), 
    scenarios=[
        weather, dust, travel, restaurant
    ]
)

# 6. View 소스파일과 연결                                                                                                        
@kochat.app.route('/')
def index():
    return render_template("index.html")

# 7. 챗봇 애플리케이션 서버 가동                                                          
if __name__ == '__main__':
    kochat.app.template_folder = kochat.root_dir + 'templates'
    kochat.app.static_folder = kochat.root_dir + 'static'
    kochat.app.run(port=8080, host='0.0.0.0')



Why Kochat?

  • 한국어를 지원하는 최초의 오픈소스 딥러닝 챗봇 프레임워크입니다. (빌더와는 다릅니다.)
  • 다양한 Pre built-in 모델과 Loss함수를 지원합니다. NLP를 잘 몰라도 챗봇을 만들 수 있습니다.
  • 자신만의 커스텀 모델, Loss함수를 적용할 수 있습니다. NLP 전문가에겐 더욱 유용합니다.
  • 챗봇에 필요한 데이터 전처리, 모델, 학습 파이프라인, RESTful API까지 모든 부분을 제공합니다.
  • 가격 등을 신경쓸 필요 없으며, 앞으로도 쭉 오픈소스 프로젝트로 제공할 예정입니다.
  • 아래와 같은 다양한 성능 평가 메트릭과 강력한 시각화 기능을 제공합니다.




Documentation

  1. Kochat이란?
  2. About Chatbot
  3. Getting Started
  4. Usage
  5. Visualization Support
  6. Performance Issue
  7. Demo

Reference

License

Copyright 2020 Hyunwoong Ko.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
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