The demos implement FastText[1] for sentence classification.
Code: tutorial_imdb_fasttext.py
FastText is a simple model for text classification with performance often close to state-of-the-art, and is useful as a solid baseline.
There are some important differences between this implementation and what is described in the paper. Instead of Hogwild! SGD[2], we use Adam optimizer with mini-batches. Hierarchical softmax is also not supported; if you have a large label space, consider utilizing candidate sampling methods provided by TensorFlow[3].
After 5 epochs, you should get test accuracy around 90.3%.
[1] Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). Bag of Tricks for Efficient Text Classification. http://arxiv.org/abs/1607.01759
[2] Recht, B., Re, C., Wright, S., & Niu, F. (2011). Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. In Advances in Neural Information Processing Systems 24 (pp. 693–701).
[3] https://www.tensorflow.org/api_guides/python/nn#Candidate_Sampling