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<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
# A Decomposable Attention Model for Natural Language Inference
**by Matthew Honnibal, [@honnibal](https://github.com/honnibal)**
This directory contains an implementation of entailment prediction model described
by Parikh et al. (2016). The model is notable for its competitive performance
with very few parameters.
by [Parikh et al. (2016)](https://arxiv.org/pdf/1606.01933.pdf). The model is notable
for its competitive performance with very few parameters.
https://arxiv.org/pdf/1606.01933.pdf
The model is implemented using [Keras](https://keras.io/) and [spaCy](https://spacy.io).
Keras is used to build and train the network, while spaCy is used to load
the [GloVe](http://nlp.stanford.edu/projects/glove/) vectors, perform the
feature extraction, and help you apply the model at run-time. The following
demo code shows how the entailment model can be used at runtime, once the
hook is installed to customise the `.similarity()` method of spaCy's `Doc`
and `Span` objects:
The model is implemented using Keras and spaCy. Keras is used to build and
train the network, while spaCy is used to load the GloVe vectors, perform the
feature extraction, and help you apply the model at run-time. The following
demo code shows how the entailment model can be used at runtime, once the hook is
installed to customise the `.similarity()` method of spaCy's `Doc` and `Span`
objects:
def demo(model_dir):
nlp = spacy.load('en', path=model_dir,
create_pipeline=create_similarity_pipeline)
doc1 = nlp(u'Worst fries ever! Greasy and horrible...')
doc2 = nlp(u'The milkshakes are good. The fries are bad.')
print(doc1.similarity(doc2))
sent1a, sent1b = doc1.sents
print(sent1a.similarity(sent1b))
print(sent1a.similarity(doc2))
print(sent1b.similarity(doc2))
```python
def demo(model_dir):
nlp = spacy.load('en', path=model_dir,
create_pipeline=create_similarity_pipeline)
doc1 = nlp(u'Worst fries ever! Greasy and horrible...')
doc2 = nlp(u'The milkshakes are good. The fries are bad.')
print(doc1.similarity(doc2))
sent1a, sent1b = doc1.sents
print(sent1a.similarity(sent1b))
print(sent1a.similarity(doc2))
print(sent1b.similarity(doc2))
```
I'm working on a blog post to explain Parikh et al.'s model in more detail.
I think it is a very interesting example of the attention mechanism, which
I didn't understand very well before working through this paper.
# How to run the example
## How to run the example
1. Install spaCy and its English models (about 1GB of data):
### 1. Install spaCy and its English models (about 1GB of data)
pip install spacy
python -m spacy.en.download
```bash
pip install spacy
python -m spacy.en.download
```
This will give you the spaCy's tokenization, tagging, NER and parsing models,
as well as the GloVe word vectors.
2. Install Keras
### 2. Install Keras
pip install keras
```bash
pip install keras
```
3. Get Keras working with your GPU
### 3. Get Keras working with your GPU
You're mostly on your own here. My only advice is, if you're setting up on AWS,
try using the AMI published by NVidia. With the image, getting everything set
up wasn't *too* painful.
4. Test the Keras model:
###4. Test the Keras model:
py.test nli/keras_decomposable_attention.py
```bash
py.test keras_parikh_entailment/keras_decomposable_attention.py
```
This should tell you that two tests passed.
5. Download the Stanford Natural Language Inference data
### 5. Download the Stanford Natural Language Inference data
http://nlp.stanford.edu/projects/snli/
Source: http://nlp.stanford.edu/projects/snli/
6. Train the model:
### 6. Train the model:
python nli/ train <your_model_dir> <train_directory> <dev_directory>
```bash
python keras_parikh_entailment/ train <your_model_dir> <train_directory> <dev_directory>
```
Training takes about 300 epochs for full accuracy, and I haven't rerun the full
experiment since refactoring things to publish this example --- please let me
experiment since refactoring things to publish this example — please let me
know if I've broken something.
You should get to at least 85% on the development data.
7. Evaluate the model (optional):
### 7. Evaluate the model (optional):
python nli/ evaluate <your_model_dir> <dev_directory>
```bash
python keras_parikh_entailment/ evaluate <your_model_dir> <dev_directory>
```
8. Run the demo (optional):
### 8. Run the demo (optional):
python nli/ demo <your_model_dir>
```bash
python keras_parikh_entailment/ demo <your_model_dir>
```