Michael Tu
857cd43934
* MP-CNN: optionally support TensorBoard for learning curve visualization * MP-CNN: bug with SummaryWriter comment * MP-CNN: add instructions on how to setup tensorboard
2.4 KiB
MP-CNN PyTorch Implementation
This is a PyTorch implementation of the following paper
- Hua He, Kevin Gimpel, and Jimmy Lin. Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), pages 1576-1586.
The SICK and MSRVID datasets are available in https://github.com/castorini/data, as well as the GloVe word embeddings.
Directory layout should be like this:
├── Castor
│ ├── README.md
│ ├── ...
│ └── mp_cnn/
├── data
│ ├── README.md
│ ├── ...
│ ├── msrvid/
│ ├── sick/
│ └── GloVe/
To run MP-CNN on the SICK dataset, use the following command. --dropout 0 is for mimicking the original paper, although adding dropout can improve performance.
python main.py mpcnn.sick.model.castor --dataset sick --epochs 19 --epsilon 1e-7 --dropout 0
| Implementation and config | Pearson's r | Spearman's p |
|---|---|---|
| Paper | 0.8686 | 0.8047 |
| PyTorch using above config | 0.8763 | 0.8215 |
To run MP-CNN on the MSRVID dataset, use the following command:
python main.py mpcnn.msrvid.model.castor --dataset msrvid --batch-size 16 --epsilon 1e-7 --epochs 32 --dropout 0 --regularization 0.0025
| Implementation and config | Pearson's r |
|---|---|
| Paper | 0.9090 |
| PyTorch using above config | 0.9050 |
These are not the optimal hyperparameters but they are decent. This README will be updated with more optimal hyperparameters and results in the future.
To see all options available, use
python main.py --help
Optional Dependencies
To optionally visualize the learning curve during training, we make use of https://github.com/lanpa/tensorboard-pytorch to connect to TensorBoard. These projects require TensorFlow as a dependency, so you need to install TensorFlow before running the commands below. After these are installed, just add --tensorboard when running main.py and open TensorBoard in the browser.
pip install tensorboardX
pip install tensorflow-tensorboard