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# SPINN with TensorFlow eager execution
SPINN, or Stack-Augmented Parser-Interpreter Neural Network, is a recursive
neural network that utilizes syntactic parse information for natural language
understanding.
SPINN was originally described by:
Bowman, S.R., Gauthier, J., Rastogi A., Gupta, R., Manning, C.D., & Potts, C.
(2016). A Fast Unified Model for Parsing and Sentence Understanding.
https://arxiv.org/abs/1603.06021
Our implementation is based on @jekbradbury's PyTorch implementation at:
https://github.com/jekbradbury/examples/blob/spinn/snli/spinn.py,
which was released under the BSD 3-Clause License at:
https://github.com/jekbradbury/examples/blob/spinn/LICENSE
## Content
Python source file(s):
- `spinn.py`: Model definition and training routines written with TensorFlow
eager execution idioms.
## To run
- Make sure you have installed the latest `tf-nightly` or `tf-nightly-gpu` pip
package of TensorFlow in order to access the eager execution feature.
- Download and extract the raw SNLI data and GloVe embedding vectors.
For example:
```bash
curl -fSsL https://nlp.stanford.edu/projects/snli/snli_1.0.zip --create-dirs -o /tmp/spinn-data/snli/snli_1.0.zip
unzip -d /tmp/spinn-data/snli /tmp/spinn-data/snli/snli_1.0.zip
curl -fSsL http://nlp.stanford.edu/data/glove.42B.300d.zip --create-dirs -o /tmp/spinn-data/glove/glove.42B.300d.zip
unzip -d /tmp/spinn-data/glove /tmp/spinn-data/glove/glove.42B.300d.zip
```
- Train model. E.g.,
```bash
python spinn.py --data_root /tmp/spinn-data --logdir /tmp/spinn-logs
```
During training, model checkpoints and TensorBoard summaries will be written
periodically to the directory specified with the `--logdir` flag.
The training script will reload a saved checkpoint from the directory if it
can find one there.
To view the summaries with TensorBoard:
```bash
tensorboard --logdir /tmp/spinn-logs
```
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