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* bug fixes in keras example * created contributor agreement * baseline for Parikh model * initial version of parikh 2016 implemented * tested asymmetric models * fixed grevious error in normalization * use standard SNLI test file * begin to rework parikh example * initial version of running example * start to document the new version * start to document the new version * Update Decompositional Attention.ipynb * fixed calls to similarity * updated the README * import sys package duh * simplified indexing on mapping word to IDs * stupid python indent error * added code from https://github.com/tensorflow/tensorflow/issues/3388 for tf bug workaround
115 lines
5.1 KiB
Markdown
115 lines
5.1 KiB
Markdown
<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
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# A decomposable attention model for Natural Language Inference
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**by Matthew Honnibal, [@honnibal](https://github.com/honnibal)**
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**Updated for spaCy 2.0+ and Keras 2.2.2+ by John Stewart, [@free-variation](https://github.com/free-variation)**
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This directory contains an implementation of the entailment prediction model described
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by [Parikh et al. (2016)](https://arxiv.org/pdf/1606.01933.pdf). The model is notable
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for its competitive performance with very few parameters.
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The model is implemented using [Keras](https://keras.io/) and [spaCy](https://spacy.io).
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Keras is used to build and train the network. spaCy is used to load
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the [GloVe](http://nlp.stanford.edu/projects/glove/) vectors, perform the
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feature extraction, and help you apply the model at run-time. The following
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demo code shows how the entailment model can be used at runtime, once the
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hook is installed to customise the `.similarity()` method of spaCy's `Doc`
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and `Span` objects:
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```python
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def demo(shape):
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nlp = spacy.load('en_vectors_web_lg')
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nlp.add_pipe(KerasSimilarityShim.load(nlp.path / 'similarity', nlp, shape[0]))
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doc1 = nlp(u'The king of France is bald.')
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doc2 = nlp(u'France has no king.')
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print("Sentence 1:", doc1)
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print("Sentence 2:", doc2)
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entailment_type, confidence = doc1.similarity(doc2)
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print("Entailment type:", entailment_type, "(Confidence:", confidence, ")")
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```
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Which gives the output `Entailment type: contradiction (Confidence: 0.60604566)`, showing that
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the system has definite opinions about Betrand Russell's [famous conundrum](https://users.drew.edu/jlenz/br-on-denoting.html)!
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I'm working on a blog post to explain Parikh et al.'s model in more detail.
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A [notebook](https://github.com/free-variation/spaCy/blob/master/examples/notebooks/Decompositional%20Attention.ipynb) is available that briefly explains this implementation.
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I think it is a very interesting example of the attention mechanism, which
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I didn't understand very well before working through this paper. There are
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lots of ways to extend the model.
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## What's where
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| File | Description |
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| --- | --- |
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| `__main__.py` | The script that will be executed. Defines the CLI, the data reading, etc — all the boring stuff. |
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| `spacy_hook.py` | Provides a class `KerasSimilarityShim` that lets you use an arbitrary function to customize spaCy's `doc.similarity()` method. Instead of the default average-of-vectors algorithm, when you call `doc1.similarity(doc2)`, you'll get the result of `your_model(doc1, doc2)`. |
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| `keras_decomposable_attention.py` | Defines the neural network model. |
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## Setting up
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First, install [Keras](https://keras.io/), [spaCy](https://spacy.io) and the spaCy
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English models (about 1GB of data):
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```bash
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pip install keras
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pip install spacy
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python -m spacy download en_vectors_web_lg
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```
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You'll also want to get Keras working on your GPU, and you will need a backend, such as TensorFlow or Theano.
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This will depend on your set up, so you're mostly on your own for this step. If you're using AWS, try the
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[NVidia AMI](https://aws.amazon.com/marketplace/pp/B00FYCDDTE). It made things pretty easy.
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Once you've installed the dependencies, you can run a small preliminary test of
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the Keras model:
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```bash
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py.test keras_parikh_entailment/keras_decomposable_attention.py
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```
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This compiles the model and fits it with some dummy data. You should see that
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both tests passed.
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Finally, download the [Stanford Natural Language Inference corpus](http://nlp.stanford.edu/projects/snli/).
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## Running the example
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You can run the `keras_parikh_entailment/` directory as a script, which executes the file
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[`keras_parikh_entailment/__main__.py`](__main__.py). If you run the script without arguments
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the usage is shown. Running it with `-h` explains the command line arguments.
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The first thing you'll want to do is train the model:
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```bash
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python keras_parikh_entailment/ train -t <path to SNLI train JSON> -s <path to SNLI dev JSON>
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```
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Training takes about 300 epochs for full accuracy, and I haven't rerun the full
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experiment since refactoring things to publish this example — please let me
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know if I've broken something. You should get to at least 85% on the development data even after 10-15 epochs.
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The other two modes demonstrate run-time usage. I never like relying on the accuracy printed
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by `.fit()` methods. I never really feel confident until I've run a new process that loads
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the model and starts making predictions, without access to the gold labels. I've therefore
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included an `evaluate` mode.
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```bash
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python keras_parikh_entailment/ evaluate -s <path to SNLI train JSON>
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```
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Finally, there's also a little demo, which mostly exists to show
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you how run-time usage will eventually look.
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```bash
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python keras_parikh_entailment/ demo
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```
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## Getting updates
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We should have the blog post explaining the model ready before the end of the week. To get
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notified when it's published, you can either follow me on [Twitter](https://twitter.com/honnibal)
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or subscribe to our [mailing list](http://eepurl.com/ckUpQ5).
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