keishinkickback / Pytorch Rnn Text Classification
Word Embedding + LSTM + FC
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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
-hfor optional arguments.
python main.py
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