Update README.md

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

@@ -28,36 +28,25 @@ 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
+Index Ingester: This is an indexing pipeline that handles the tweets as they are generated. It is the main input of Earlybird, producing Tweet Data (the basic information about the tweet, such as the text, URLs, media entities, facets, etc.) and Static Features (features that can be computed directly from a tweet, such as whether it has a URL, cards, quotes, etc.). All information computed here is stored in the index and flushed as each real-time index segment becomes full. They are loaded back later from disk when Earlybird restarts. Note that the features may be computed in a non-trivial way (like deciding the value of hasUrl), as they could be computed and combined from some more "raw" information in the tweet and from other services.
-  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
-  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
-  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
-  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
-  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:
+Signal Ingester: This is the ingester for Realtime Features, which are per-tweet features that can change after the tweet has been indexed. They mostly include social engagements like retweetCount, favCount, replyCount, etc., along with some (future) spam signals that are computed with later activities. These features are collected and computed in a Heron topology by processing multiple event streams and can be extended to support more features.
+
+User Table Features: This is another set of features per user, which are from User Table Updater, a different input that processes a stream written by our user service. It is 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: These are basically the information of the current searcher, such as 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 real-time features. Examples of static features used are:- 
 
 - Whether the tweet is a retweet
 - Whether the tweet contains a link
 - Whether this tweet has any trend words at ingestion time
 - Whether the tweet is a reply
-- A score for the static quality of the text, computed in TweetTextScorer.java in the Ingester. Based on the factors
+- A score for the static quality of the text, computed in TweetTextScorer.java in the Ingester. Based on factors such as offensiveness, content entropy, "shout" score, length, and readability.
-  such as offensiveness, content entropy, "shout" score, length, and readability.
+- Tweepcred (see top-level README.md)
-- tweepcred, see top-level README.md
 
-Examples of realtime features used are:
+Examples of real-time features used are:
 
 - Number of tweet likes/replies/retweets
 - pToxicity and pBlock scores provided by health models
+