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78 lines
2.5 KiB
Markdown
78 lines
2.5 KiB
Markdown
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# A Decomposable Attention Model for Natural Language Inference
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This directory contains an implementation of entailment prediction model described
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by Parikh et al. (2016). The model is notable for its competitive performance
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with very few parameters.
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https://arxiv.org/pdf/1606.01933.pdf
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The model is implemented using Keras and spaCy. Keras is used to build and
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train the network, while spaCy is used to load the 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 hook is
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installed to customise the `.similarity()` method of spaCy's `Doc` and `Span`
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objects:
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def demo(model_dir):
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nlp = spacy.load('en', path=model_dir,
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create_pipeline=create_similarity_pipeline)
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doc1 = nlp(u'Worst fries ever! Greasy and horrible...')
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doc2 = nlp(u'The milkshakes are good. The fries are bad.')
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print(doc1.similarity(doc2))
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sent1a, sent1b = doc1.sents
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print(sent1a.similarity(sent1b))
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print(sent1a.similarity(doc2))
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print(sent1b.similarity(doc2))
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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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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.
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# How to run the example
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1. Install spaCy and its English models (about 1GB of data):
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pip install spacy
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python -m spacy.en.download
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This will give you the spaCy's tokenization, tagging, NER and parsing models,
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as well as the GloVe word vectors.
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2. Install Keras
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pip install keras
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3. Get Keras working with your GPU
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You're mostly on your own here. My only advice is, if you're setting up on AWS,
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try using the AMI published by NVidia. With the image, getting everything set
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up wasn't *too* painful.
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4. Test the Keras model:
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py.test nli/keras_decomposable_attention.py
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This should tell you that two tests passed.
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5. Download the Stanford Natural Language Inference data
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http://nlp.stanford.edu/projects/snli/
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6. Train the model:
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python nli/ train <your_model_dir> <train_directory> <dev_directory>
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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.
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You should get to at least 85% on the development data.
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7. Evaluate the model (optional):
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python nli/ evaluate <your_model_dir> <dev_directory>
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8. Run the demo (optional):
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python nli/ demo <your_model_dir>
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