diff --git a/examples/keras_parikh_entailment/README.md b/examples/keras_parikh_entailment/README.md
index 71d78f50e..abe9cf241 100644
--- a/examples/keras_parikh_entailment/README.md
+++ b/examples/keras_parikh_entailment/README.md
@@ -1,14 +1,14 @@
 <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
+# 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
+This directory contains an implementation of the entailment prediction model described
 by [Parikh et al. (2016)](https://arxiv.org/pdf/1606.01933.pdf). The model is notable 
 for its competitive performance with very few parameters.
 
 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 
+Keras is used to build and train the network. 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 
@@ -35,20 +35,16 @@ lots of ways to extend the model.
 
 ## What's where
 
-* `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.
+| File | Description |
+| --- | --- |
+| `__main__.py` | The script that will be executed. Defines the CLI, the data reading, etc — all the boring stuff. |                          
+| `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_decomposable_attention.py` | Defines the neural network model. |
 
 ## Setting up
 
-First, install keras, spaCy and the spaCy English models (about 1GB of data):
+First, install [Keras](https://keras.io/), [spaCy](https://spacy.io) and the spaCy 
+English models (about 1GB of data):
 
 ```bash
 pip install keras spacy
@@ -56,11 +52,11 @@ python -m spacy.en.download
 ```
 
 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.
+set up, so you're mostly on your own for this step. If you're using AWS, try the 
+[NVidia AMI](https://aws.amazon.com/marketplace/pp/B00FYCDDTE). It made things pretty easy.
 
 Once you've installed the dependencies, you can run a small preliminary test of
-the keras model:
+the Keras model:
 
 ```bash
 py.test keras_parikh_entailment/keras_decomposable_attention.py
@@ -69,14 +65,12 @@ py.test keras_parikh_entailment/keras_decomposable_attention.py
 This compiles the model and fits it with some dummy data. You should see that
 both tests passed.
 
-Finally, download the Stanford Natural Language Inference corpus.
-
-Source: http://nlp.stanford.edu/projects/snli/
+Finally, download the [Stanford Natural Language Inference corpus](http://nlp.stanford.edu/projects/snli/).
 
 ## 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:
+[`keras_parikh_entailment/__main__.py`](__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>
@@ -95,4 +89,5 @@ you how run-time usage will eventually look.
 ## Getting updates
 
 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.
+notified when it's published, you can either the follow me on [Twitter](https://twitter.com/honnibal), 
+or subscribe to our [mailing list](http://eepurl.com/ckUpQ5).
diff --git a/website/docs/usage/_data.json b/website/docs/usage/_data.json
index 8785f6715..660ccff58 100644
--- a/website/docs/usage/_data.json
+++ b/website/docs/usage/_data.json
@@ -214,6 +214,12 @@
                 "author": "Matthew Honnibal",
                 "tags": [ "keras", "sentiment" ]
             },
+            "A decomposable attention model for Natural Language Inference": {
+                "url": "https://github.com/explosion/spaCy/tree/master/examples/keras_parikh_entailment",
+                "author": "Matthew Honnibal",
+                "tags": [ "keras", "similarity" ]
+            },
+
             "Using the German model": {
                 "url": "https://explosion.ai/blog/german-model",
                 "author": "Wolfgang Seeker",
@@ -263,7 +269,7 @@
                 "tags": [ "big data" ]
             },
             "Inventory count": {
-                "url": "https://github.com/explosion/spaCy/tree/master/examples/InventoryCount",
+                "url": "https://github.com/explosion/spaCy/tree/master/examples/inventory_count",
                 "author": "Oleg Zd"
             },
             "Multi-word matches": {