README.md in disco-0.2.5 vs README.md in disco-0.2.6

@@ -42,11 +42,11 @@
   {user_id: 1, item_id: 1, value: 1},
   {user_id: 2, item_id: 1, value: 1}
 ])
 ```
 
-> Use `value` instead of rating for implicit feedback
+> Use `value` instead of `rating` for implicit feedback
 
 Get user-based recommendations - “users like you also liked”
 
 ```ruby
 recommender.user_recs(user_id)
@@ -245,11 +245,11 @@
 recommender = Disco::Recommender.new(top_items: true)
 recommender.fit(data)
 recommender.top_items
 ```
 
-This uses [Wilson score](https://www.evanmiller.org/how-not-to-sort-by-average-rating.html) for explicit feedback (add [wilson_score](https://github.com/instacart/wilson_score) your application’s Gemfile) and item frequency for implicit feedback.
+This uses [Wilson score](https://www.evanmiller.org/how-not-to-sort-by-average-rating.html) for explicit feedback (add [wilson_score](https://github.com/instacart/wilson_score) to your application’s Gemfile) and item frequency for implicit feedback.
 
 ## Data
 
 Data can be an array of hashes
 
@@ -267,10 +267,10 @@
 
 ```ruby
 Daru::DataFrame.from_csv("ratings.csv")
 ```
 
-## Performance [master]
+## Performance
 
 If you have a large number of users or items, you can use an approximate nearest neighbors library like [Faiss](https://github.com/ankane/faiss) to improve the performance of certain methods.
 
 Add this line to your application’s Gemfile: