diff --git a/examples/keras_parikh_entailment/README.md b/examples/keras_parikh_entailment/README.md
index 571ba6e73..adc80ce89 100644
--- a/examples/keras_parikh_entailment/README.md
+++ b/examples/keras_parikh_entailment/README.md
@@ -78,7 +78,7 @@ You can run the `keras_parikh_entailment/` directory as a script, which executes
 [`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>
+python keras_parikh_entailment/ train <train_directory> <dev_directory>
 ```
 
 Training takes about 300 epochs for full accuracy, and I haven't rerun the full
diff --git a/examples/keras_parikh_entailment/__main__.py b/examples/keras_parikh_entailment/__main__.py
index 5c3132bab..08bccb648 100644
--- a/examples/keras_parikh_entailment/__main__.py
+++ b/examples/keras_parikh_entailment/__main__.py
@@ -52,7 +52,7 @@ def train(train_loc, dev_loc, shape, settings):
         file_.write(model.to_json())
 
 
-def evaluate(model_dir, dev_loc):
+def evaluate(dev_loc):
     dev_texts1, dev_texts2, dev_labels = read_snli(dev_loc)
     nlp = spacy.load('en',
             create_pipeline=create_similarity_pipeline)
diff --git a/examples/keras_parikh_entailment/spacy_hook.py b/examples/keras_parikh_entailment/spacy_hook.py
index 0177da001..c1e36327c 100644
--- a/examples/keras_parikh_entailment/spacy_hook.py
+++ b/examples/keras_parikh_entailment/spacy_hook.py
@@ -80,10 +80,10 @@ def get_word_ids(docs, rnn_encode=False, tree_truncate=False, max_length=100, nr
     return Xs
 
 
-def create_similarity_pipeline(nlp):
+def create_similarity_pipeline(nlp, max_length=100):
     return [
         nlp.tagger,
         nlp.entity,
         nlp.parser,
-        KerasSimilarityShim.load(nlp.path / 'similarity', nlp, max_length=10)
+        KerasSimilarityShim.load(nlp.path / 'similarity', nlp, max_length)
     ]