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memn2n-for-knowledge-embedding's Introduction

MemN2N in Knowledge Embedding

Use End-To-End Memory Network to perform topic transfer in the first step.

Training

LicDataset

DATA_PATH = data/lic/toyDataset.py # for toyLic
DATA_PATH = data/lic/licDataset.py # for Lic
python src/run.py -c config/cfg.py -d $DATA_PATH -m models/trainer.py -o result -mode train -v --net.net_path models/memn2n.py --device cpu --init.loss_func bce  --train.batch_size 32 --valid.batch_size 32

bAbIDataset

DATA_PATH = data/bAbI/bAbIDataset.py # for bAbI
python src/run.py -c config/cfg.py -d data/bAbI/bAbIDataset.py -m models/trainer.py -o result -mode train -v --net.net_path models/memn2n.py --init.loss_func ce --train.batch_size 32 --valid.batch_size 32

Test

pureRule

DATA_PATH = data/lic/toyDataset.py # for toyLic
DATA_PATH = data/lic/licDataset.py # for Lic
python models/run.py -c config/cfg.py -d $DATA_PATH -m models/trainer.py -o result -mode eval -v --net.net_path models/memn2n.py --init.loss_func bce --net.saved_params_path result/final_params.ptp

MemN2N + Rule

DATA_PATH = data/lic/toyDataset.py # for toyLic
DATA_PATH = data/lic/licDataset.py # for Lic
python src/run.py -c config/cfg.py -d $DATA_PATH -m models/trainer.py -o result -mode eval -v --net.net_path models/memn2n.py --init.loss_func bce --net.saved_params_path result/final_params.ptp --test.batch_size 32

Results

F1_result To be continued

memn2n-for-knowledge-embedding's People

Contributors

nmhkahn avatar

Watchers

James Cloos avatar Yanbin Wang avatar Zihan Zhang avatar

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