fix grammar

src/python/twitter/deepbird/projects/timelines/scripts/models/earlybird/README.md

@@ -1,7 +1,7 @@
 # Earlybird Light Ranker
 
 *Note: the light ranker is an old part of the stack which we are currently in the process of replacing.
-The current model was last trained several years ago, and uses some very strange features.
+The current model was last trained several years ago and uses some very strange features.
 We are working on training a new model, and eventually rebuilding this part of the stack entirely.*
 
 The Earlybird light ranker is a logistic regression model which predicts the likelihood that the user will engage with a
@@ -10,15 +10,15 @@ It is intended to be a simplified version of the heavy ranker which can run on a
 
 There are currently 2 main light ranker models in use: one for ranking in network tweets (`recap_earlybird`), and
 another for
-out of network (UTEG) tweets (`rectweet_earlybird`). Both models are trained using the `train.py` script which is
+out-of-network (UTEG) tweets (`rectweet_earlybird`). Both models are trained using the `train.py` script which is
 included in this directory. They differ mainly in the set of features
 used by the model.
-The in network model uses
+The in-network model uses
 the `src/python/twitter/deepbird/projects/timelines/configs/recap/feature_config.py` file to define the
 feature configuration, while the
-out of network model uses `src/python/twitter/deepbird/projects/timelines/configs/rectweet_earlybird/feature_config.py`.
+out-of-network model uses `src/python/twitter/deepbird/projects/timelines/configs/rectweet_earlybird/feature_config.py`.
 
-The `train.py` script is essentially a series of hooks provided to for Twitter's `twml` framework to execute,
+The `train.py` script is essentially a series of hooks provided for Twitter's `twml` framework to execute,
 which is included under `twml/`.
 
 ### Features
@@ -29,25 +29,25 @@ The light ranker features pipeline is as follows:
 Some of these components are explained below:
 
 - Index Ingester: an indexing pipeline that handles the tweets as they are generated. This is the main input of
-  Earlybird, it produces Tweet Data (the basic information about the tweet, the text, the urls, media entities, facets,
-  etc) and Static Features (the features you can compute directly from a tweet right now, like whether it has URL, has
-  Cards, has quotes, etc); All information computed here are stored in index and flushed as each realtime index segments
-  become full. They are loaded back later from disk when Earlybird restarts. Note that the features may be computed in a
+  Earlybird produces Tweet Data (the basic information about the tweet, the text, the URLs, media entities, facets,
+  etc) and Static Features (the features you can compute directly from a tweet right now, like whether it has a URL, has
+  Cards, has quotes, etc); All information computed here is stored in an index and flushed as each real-time index segments
+  become full. They are loaded back later from the disk when Earlybird restarts. Note that the features may be computed in a
   non-trivial way (like deciding the value of hasUrl), they could be computed and combined from some more "raw"
   information in the tweet and from other services.
   Signal Ingester: the ingester for Realtime Features, per-tweet features that can change after the tweet has been
   indexed, mostly social engagements like retweetCount, favCount, replyCount, etc, along with some (future) spam signals
-  that's computed with later activities. These were collected and computed in a Heron topology by processing multiple
+  that are computed with later activities. These were collected and computed in a Heron topology by processing multiple
   event streams and can be extended to support more features.
 - User Table Features is another set of features per user. They are from User Table Updater, a different input that
-  processes a stream written by our user service. It's used to store sparse realtime user
+  processes a stream written by our user service. It's used to store sparse real-time user
   information. These per-user features are propagated to the tweet being scored by
   looking up the author of the tweet.
-- Search Context Features are basically the information of current searcher, like their UI language, their own
+- Search Context Features are the information of current searcher, like their UI language, their own
   produced/consumed language, and the current time (implied). They are combined with Tweet Data to compute some of the
   features used in scoring.
 
-The scoring function in Earlybird uses both static and realtime features. Examples of static features used are:
+The scoring function in Earlybird uses both static and real-time features. Examples of static features used are:
 
 - Whether the tweet is a retweet
 - Whether the tweet contains a link