diff --git a/data/sentence_function/greetings.txt b/data/sentence_function/greetings.txt
deleted file mode 100644
index 83fa01b..0000000
--- a/data/sentence_function/greetings.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Hello
-Hi
-Howdy
\ No newline at end of file
diff --git a/data/sentence_function/labels.txt b/data/sentence_function/labels.txt
deleted file mode 100644
index e69de29..0000000
diff --git a/data/sentence_function/sentences.txt b/data/sentence_function/sentences.txt
deleted file mode 100644
index e69de29..0000000
diff --git a/data/tokens.txt b/data/tokens.txt
deleted file mode 100644
index db2b8f0..0000000
--- a/data/tokens.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-self
-i
-I
\ No newline at end of file
diff --git a/src/main/java/org/studiorailgun/sim/space/Location.java b/src/main/java/org/studiorailgun/sim/space/Location.java
index d28f51f..e3a11ba 100644
--- a/src/main/java/org/studiorailgun/sim/space/Location.java
+++ b/src/main/java/org/studiorailgun/sim/space/Location.java
@@ -37,7 +37,9 @@ public class Location {
      * Describes the location
      */
     public void describe(){
-        System.out.println("A " + type);
+        System.out.println("You are in a " + this.type);
+        System.out.println("There are " + chars.size() + " people in the " + this.type);
+        System.out.println("There are " + items.size() + " items in the " + this.type);
     }
 
     /**
diff --git a/src/main/python/sim/command_cat.py b/src/main/python/sim/command_cat.py
index edde3f8..0e32149 100644
--- a/src/main/python/sim/command_cat.py
+++ b/src/main/python/sim/command_cat.py
@@ -41,7 +41,7 @@ embedding_dim: int = 128
 epochs: int = 50
 
 # Maximum size of the vocab for this layer
-max_tokens: int = 5000
+max_tokens: int = 2000
 
 # (Only valid in INT mode) If set, the output will have its time dimension padded or truncated to exactly output_sequence_length values
 output_sequence_length: int = 4
@@ -128,7 +128,7 @@ model: Sequential = Sequential([
     keras.Input(shape=(1,), dtype=tf.string),
     textVec,
     Embedding(max_features + 1, embedding_dim),
-    Bidirectional(LSTM(256)),
+    Bidirectional(LSTM(128)),
     Dense(64, activation='relu'),
     Dense(num_classes, activation='sigmoid')
 ])