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