Tensorflow implementation of Facebook #TagSpace
You can read more about #TagSpace from here
Special thanks to Facebook research team's Starspace project, it was really good reference.
Beside choosing 1000 random negative tag (for performance reason I guess), I choosed worst positive tag, best negative tag.
Download ag news dataset as below
$ tree ./data
./data
└── ag_news_csv
├── classes.txt
├── readme.txt
├── test.csv
├── train.csv
└── train_mini.csv
and then
$ python model.py
Accuracy 0.89 (ag test data, compare 0.91 from StarSpace with same condition [5 epoch, 10 dim])
- Clean up messy code
- Better class structure
- improve Tokenizer
- support Stackoverflow dataset
- improve performance
- add Tensorboard metrics
- add Korean