RNN-based short text classification This is for multi-class short text classification. Model is built with Word Embedding, LSTM ( or GRU), and Fully-connected layer by Pytorch. A mini-batch is created by 0 padding and processed by using torch.nn.utils.rnn.PackedSequence. Cross-entropy Loss + Adam optimizer. Support pretrained word embedding (GloVe). Model Embedding --> Dropout --> LSTM(GRU) --> Dropout --> FC. Preprocessing The following command will download the dataset used in Learning to Classify Short and Sparse Text & Web with Hidden Topics from Large-scale Data Collections from here and process it for training. Also it download GloVe embeddings. python preprocess.py Training The following command starts training. Run it with -h for optional arguments. python main.py