| 김현수 | 이성구 | 이현준 | 조문기 | 조익노 |
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| GitHub | GitHub | GitHub | GitHub | GitHub |
김현수Front/Back-end 구현 • ElasticSearch 구현이성구Product Manager(PM) • Dialogue summarization 고도화이현준Dense retriever baseline 학습 및 평가 • ICT 응용기법 학습 및 평가 데이터 제작조문기Front/Back-end 구현 • Dialogue summarization 및 metric 구현조익노Data crawling scheduler 구현 • MongoDB 적용 • Retriever model 구현
|-- app
| |-- assets
| |-- src
| | |-- elastic
| | └-- models
| |-- templates
| |-- app.py
| |-- config.py
| └-- mongodb.py
|-- train
| |-- summary
| └-- retriever
└-- monstache
└-- mongo-elastic.toml
pip install -r requirements.txt- Summary model train
python ./train/summary/train.py- Retriever model train
python ./train/retriever/train.py- Run chat app with model server
python ./app.py
python ./src/models/summary_model.py
python ./src/models/retriever_model.py
- Video Link: YouTube
- Dense Passage Retrieval for Open-Domain Question Answering
- Latent Retrieval for Weakly Supervised Open Domain Question Answering
- ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
- 네이버 ColBERT 사용방법
- Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization
- Better Fine-Tuning by Reducing Representational Collapse
- Momentum Calibration for Text Generation
- 요약 관련 Survey 자료 : https://github.com/uoneway/Text-Summarization-Repo
- 요약 모델 (KoBART) baseline : https://github.com/seujung/KoBART-summarization
- 요약 모델 성능 개선 방법 (Scatterlab) : https://tech.scatterlab.co.kr/alaggung-dlaggung-dialog-summary/