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Last Commit
Sep. 22, 2017
Sep. 19, 2017


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.

Key Concept

Beside choosing 1000 random negative tag (for performance reason I guess), I choosed worst positive tag, best negative tag. It's not good for performance but since we don't have much tags(labels) as Facebook, it seems okay.


Download ag news dataset as below

$ tree ./data
└── ag_news_csv
    ├── classes.txt
    ├── readme.txt
    ├── test.csv
    ├── train.csv
    └── train_mini.csv

and then

$ python


Accuracy 0.89 (ag test data, compare 0.91 from StarSpace with same condition [5 epoch, 10 dim])

To-do list

  • Clean up messy code
  • Better class structure
  • improve Tokenizer
  • support multiple dataset
  • improve performance
  • adopt WARP sampling
  • add Tensorboard metrics
  • add Korean