https://github.com/kozistr/naver-ai-hackathon-2018

NAVER A.I Hackathon 2018
https://github.com/kozistr/naver-ai-hackathon-2018

hackathon machine-learning naver nsml

Last synced: 2 months ago
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NAVER A.I Hackathon 2018

• Host: GitHub
• URL: https://github.com/kozistr/naver-ai-hackathon-2018
• Owner: kozistr
• License: mit
• Created: 2018-05-04T16:57:58.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-05-09T08:41:05.000Z (over 7 years ago)
• Last Synced: 2025-08-26T12:57:43.460Z (3 months ago)
• Topics: hackathon, machine-learning, naver, nsml
• Language: Python
• Homepage: https://github.com/naver/ai-hackathon-2018
• Size: 252 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# [Naver AI Hackathon 2018](https://github.com/naver/ai-hackathon-2018)

### **Team kozistr - Member : 김형찬(kozistr)**

---

## tl;dr

I participated in [Naver A.I Hackathon 2018] and 

**ranked 4th/13th(over 200 teams total)** as an individual participant (Team : kozistr)

And also i uploaded **summary docs** with **the codes**.

### Final LeaderBoard

> 네이버 지식iN 질문 유사도 예측 (결선)

![kin_leaderboard](_images/kin_final_lb.jpg)

> 네이버 영화 평점 예측 (결선)

![movie_leaderboard](_images/movie_final_lb.jpg)

### Result

*Stage* | *Mission*    | *Metric* | *Score* | *Rank* | *Code*

:-----: | :----------: | :------: | :-----: | :----: | :------:

phase 1 | kin          | acc      |         |        |

phase 1 | movie-review | mse      |         |        |

phase 2 | kin          | acc      |         |        |

phase 2 | movie-review | mse      |         |        |

 final  | kin          | acc      | 0.8115  | 4th    | ![code](kin/main.py)

 final  | movie-review | mse      | 0.0310  | 13th   | ![code](movie-review/main.py)

> 전처리를 하나도 하지 않고 기본 도커만 사용해서 시도 한 모델들의 **main.py**만 업로드 합니다!

> _codes 폴더에 default 로 주어진 전처리 파일들 업로드!

## Models

Soon~

## Summary!

Here's **summary docs**! ![Summary](_refs/kozistr-naver_ai_hackathon_2018_report.pdf)

## Hyper-Parameters

### 네이버 지식iN 질문 유사도 예측

|           Name            |   Value        |          Note                 |

| :-----------------------: | :------------: | :---------------------------: |

|          Epochs           |      100       | 70 ~ 80 에서 converge          |

|       Learning Rate       |      1e-3      | exponential decay (rate 0.95) |

|        Batch Size         |     64/128     | 본선에서는 128                  |

|       DropOut Rate        |      0.7       | 0.7 is the best               |

|        Embeddings         |      384       | 384 ~ 400 good                |

|      CNN kernel size      | 10, 9, 7, 5, 3 | 10 이하에서 찾음                |

|      CNN filter size      |      256       | 256 ~ good                    |

|         FC Unit           |     1024       | 512 ~ 1024 good               |

|        Optimizer          |     Adam       | Adam, Momentum, SGD ~         |

...

### 네이버 영화 평점 예측

|           Name            |   Value        |          Note                 |

| :-----------------------: | :------------: | :---------------------------: |

|          Epochs           |      30        | 20 ~ 30 에서 converge          |

|       Learning Rate       |     2e-4       | lr 에 엄청나게 민감             |

|        Batch Size         |      128       | 128 ~                         |

|       DropOut Rate        |      0.6       | 0.6 is the best               |

|        Embeddings         |      128       | 128 ~ 256 good                |

|      CNN kernel size      |    3, 5, 7     | 10 이하에서 찾음                |

|      CNN filter size      |      256       | 256 ~ good                    |

|         FC Unit           |      512       | ~ 512 good                    |

|        Optimizer          |     Adam       | Adam, SGD ~                   |

...

## Author

HyeongChan Kim ([@kozistr](http://kozistr.tech), kozistr@gmail.com)