Update README.md

README.md

@@ -32,7 +32,7 @@ The annotation results from both cohorts (Class of 2022 and Class of 2023) will
 
 ## Content 
  
-# Cohort 2022 
+# Cohort  
 
 This dataset contains 5880 tweets that cover a wide range of topics common in conversations about Asians, Blacks, Jews, Latines, and Muslims. 357 tweets (6.1 %) are labeled as biased and 5523 (93.9 %) are labeled as not biased. 1365 tweets (23.2 %) are labeled as calling out or denouncing bias.  
 
@@ -47,7 +47,7 @@ This dataset contains 5880 tweets that cover a wide range of topics common in co
 1182 out of 5880 tweets (20.1 %) contain the keyword "Muslims," 593 were posted in 2020 and 589 in 2021. 105 tweets (8.9 %) are biased against Muslims. 260 tweets (22 %) call out bias against Muslims.  
 
 
-# Cohort 2023 
+# Cohort  
 
 The dataset contains 5363 tweets with the keywords “Asians, Blacks, Jews, Latinos and Muslims” from 2021 and 2022. 261 tweets (4.9 %) are labeled as biased, and 5102 tweets (95.1 %) were labeled as not biased. 975 tweets (18.1 %) were labeled as calling out or denouncing bias.