I participated in Naver AI Hackathon 2018 and ranked 5th(over 200 teams, MSE: 2.76661) as an inidivisual participant(Team: tantara).
- Fully dockerized environments
- Tokenization with
Twitter
POS Tagger - Stacked LSTM
- Bidirectional LSTM
- CNN + LSTM(but not used)
$ wget https://dl.dropbox.com/s/l2cct7xxi94og2p/idx2word_final.pkl -O movie-prediction/idx2word_final.pkl
Install docker
first, and run commands as follows:
$ sh build.sh && sh run.sh
$ cd movie-predication
$ python lstm.py --epochs 30 --lr 0.001 --num_layers 4 --embedding 300 --hidden_dim 500 --batch 4000 --dr_embed 0.5
Login with nsml
first, and run commands as follows:
$ cd movie-prediction
$ nsml run -d movie_final -e lstm.py -a "--epochs 30 --lr 0.001 --num_layers 4 --embedding 300 --hidden_dim 500 --batch 4000 --dr_embed 0.5"
Name | Value | |
---|---|---|
Training Epoch | 30 | --epochs |
Learning Rate | 0.001 | --lr |
Batch Size | 4000 | --batch, It depends on GPU |
Drouout Rate | 0.5 | --dropout_rate, --dr_embed |
the size of vocab | 110,000 | Pretrained |
the number of tokens | Up to 20 | --strmaxlen, Zero padding |
Word Embedding | 300 | --embedding |
the number of LSTM layers | 4 | --num_layers |
LSTM's hidden size | 300 | --hidden |
Bidirectional | True/False | --bi, Optional |
CNN | True/False | --cnn, Not Used |
- tantara/movie_final/24/xx: Stacked LSTM
- Top Score: 2.77174
- tantara/movie_final/32/xx: Stacked LSTM + Bidirectional
- Top Score: 2.76661
Copyright 2018 tantara.
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