This repository contains source files we used in our paper
Sho Takase, Jun Suzuki, Masaaki Nagata
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Python 3.5, PyTorch 0.2.0
About 9.5 GB of VRAM (tested on K80).
./get_data.sh
First, train the model
python main.py --data data/penn --dropouti 0.4 --dropoutl 0.6 --dropouth 0.225 --seed 28 --batch_size 12 --lr 20.0 --epoch 500 --nhid 960 --nhidlast 620 --emsize 280 --n_experts 15 --num4second 5 --var 0.001 --nonmono 60 --save PTB --single_gpu
Second, finetune the model
python finetune.py --data data/penn --dropouti 0.4 --dropoutl 0.6 --dropouth 0.225 --seed 28 --batch_size 12 --lr 20.0 --epoch 500 --var 0.001 --nonmono 60 --save PATH_TO_FOLDER --single_gpu
where PATH_TO_FOLDER is the folder created by the first step (concatenation of PTB with a timestamp).
Third, run evaluation
python cal_ppl.py --data data/penn --save PATH_TO_FOLDER/finetune_model.pt --bptt 1000
First, train the model
python main.py --epochs 500 --data data/wikitext-2 --save WT2 --dropouth 0.2 --seed 1882 --n_experts 15 --num4second 5 --var 0.001 --nhid 1150 --nhidlast 650 --emsize 300 --batch_size 15 --lr 15.0 --dropoutl 0.6 --small_batch_size 5 --max_seq_len_delta 20 --dropouti 0.55 --nonmono 60 --single_gpu
Second, finetune the model
python finetune.py --epochs 500 --data data/wikitext-2 --save PATH_TO_FOLDER --dropouth 0.2 --seed 1882 --var 0.001 --batch_size 15 --lr 15.0 --dropoutl 0.6 --small_batch_size 5 --max_seq_len_delta 20 --dropouti 0.55 --nonmono 60 --single_gpu
Third, run evaluation
python cal_ppl.py --data data/wikitext-2 --save PATH_TO_FOLDER/finetune_model.pt --bptt 1000
https://drive.google.com/open?id=1ug-6ISrXHEGcWTk5KIw8Ojdjuww-i-Ci
ptb, wikitext2: models to obtain the single model results
ptb_ensemble, wikitext2_ensemble: other trained models to obtain ensemble results
Files listed in NTT_LICENSE's EXHIBIT A are applied the NTT_LICENSE.
Other files are applied the LICENSE in this repository.