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Make the README more concise

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Matthew Honnibal 2016-11-01 13:05:17 +11:00 committed by GitHub
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@ -30,45 +30,53 @@ def demo(model_dir):
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.
I didn't understand very well before working through this paper. There are
lots of ways to extend the model.
## How to run the example
## What's where
### 1. Install spaCy and its English models (about 1GB of data)
* `keras_parikh_entailment/__main__.py`: The script that will be executed.
Defines the CLI, the data reading, etc — all the boring stuff.
* `keras_parikh_entailment/spacy_hook.py`: Provides a class `SimilarityShim`
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)`.
* `keras_parikh_entailment/keras_decomposable_attention.py`: Defines the neural network model.
This part knows nothing of spaCy --- its ordinary Keras usage.
## Setting up
First, install keras, spaCy and the spaCy English models (about 1GB of data):
```bash
pip install spacy
pip install keras 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.
You'll also want to get keras working on your GPU. This will depend on your
set up, so you're mostly on your own for this step. If you're using AWS, try the NVidia
AMI. It made things pretty easy.
### 2. Install Keras
```bash
pip install keras
```
### 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:
Once you've installed the dependencies, you can run a small preliminary test of
the keras model:
```bash
py.test keras_parikh_entailment/keras_decomposable_attention.py
```
This should tell you that two tests passed.
This compiles the model and fits it with some dummy data. You should see that
both tests passed.
### 5. Download the Stanford Natural Language Inference data
Finally, download the Stanford Natural Language Inference corpus.
Source: http://nlp.stanford.edu/projects/snli/
### 6. Train the model:
## Running the example
You can run the `keras_parikh_entailment/` directory as a script, which executes the file
`keras_parikh_entailment/__main__.py`. The first thing you'll want to do is train the model:
```bash
python keras_parikh_entailment/ train <your_model_dir> <train_directory> <dev_directory>
@ -76,18 +84,15 @@ python keras_parikh_entailment/ train <your_model_dir> <train_directory> <dev_di
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
know if I've broken something.
know if I've broken something. You should get to at least 85% on the development data.
You should get to at least 85% on the development data.
The other two modes demonstrate run-time usage. I never like relying on the accuracy printed
by `.fit()` methods. I never really feel confident until I've run a new process that loads
the model and starts making predictions, without access to the gold labels. I've therefore
included an `evaluate` mode. Finally, there's also a little demo, which mostly exists to show
you how run-time usage will eventually look.
### 7. Evaluate the model (optional):
## Getting updates
```bash
python keras_parikh_entailment/ evaluate <your_model_dir> <dev_directory>
```
### 8. Run the demo (optional):
```bash
python keras_parikh_entailment/ demo <your_model_dir>
```
We should have the blog post explaining the model ready before the end of the week. To get
notified when it's published, you can either the follow me on Twitter, or subscribe to our mailing list.