Update README.md

README.md

@@ -70,7 +70,7 @@ use the GPU if it's available.
 ### Sentiment classification
 
     Task: Categorize the text into an ordinal class that best characterizes the writer's mental state, considering various degrees of positive and negative sentiment intensity. 3: very positive mental state can be inferred. 2: moderately positive mental state can be inferred. 1: slightly positive mental state can be inferred. 0: neutral or mixed mental state can be inferred. -1: slightly negative mental state can be inferred. -2: moderately negative mental state can be inferred. -3: very negative mental state can be inferred
-    Tweet: Beyoncé resentment gets me in my feelings every time. 😩
+    Text: Beyoncé resentment gets me in my feelings every time. 😩
     Intensity Class:
 
     >>-3: very negative emotional state can be inferred
@@ -79,7 +79,7 @@ use the GPU if it's available.
 
     Task: Categorize the text's emotional tone as either 'neutral or no emotion' or identify the presence of one or more of the given emotions (anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, trust).
     Text: Whatever you decide to do make sure it makes you #happy.
-    This tweet contains emotions:
+    This text contains emotions:
 
     >>joy, love, optimism