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Ralph Tang
e7ca33de28
* Add SST data preprocessing * Add ConvRNN model * Add LR scheduler * Add grid search on hyperparameters * Add random search * Add CLI options * Add usage to README.md * Refactor code * Fix randomized search parameters * Update README.md with results * Use Dataset and DataLoader
731 B
731 B
Convolutional RNN
Implementation based on [1].
Usage
Run ./getData.sh to fetch the data. The project structure should now look like this:
├── conv_rnn/
│ ├── data/
│ ├── saves/
│ └── *.*
You may then run python train.py and python test.py for training and testing, respectively. For more options, add the -h switch.
Empirical results
| Best dev | Test |
|---|---|
| 51.1 | 50.7 |
References
[1] Chenglong Wang, Feijun Jiang, and Hongxia Yang. 2017. A Hybrid Framework for Text Modeling with Convolutional RNN. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '17).