Update README.md

README.md

@@ -27,10 +27,12 @@ Here is how to use this model:
 >>> from transformers import pipeline
 >>> model_name = "roberta-large-emopillars-contextless-isear"
 >>> threshold = 0.5
->>> emotions = ["admiration", "amusement", "anger", "annoyance", "approval", "caring", "confusion",
->>>             "curiosity", "desire", "disappointment", "disapproval", "disgust", "embarrassment",
->>>             "excitement", "fear", "gratitude", "grief", "joy", "love", "nervousness", "optimism",
->>>             "pride", "realization", "relief", "remorse", "sadness", "surprise", "neutral"]
+>>> emotions = [
+>>>     "admiration", "amusement", "anger", "annoyance", "approval", "caring", "confusion",
+>>>     "curiosity", "desire", "disappointment", "disapproval", "disgust", "embarrassment",
+>>>     "excitement", "fear", "gratitude", "grief", "joy", "love", "nervousness", "optimism",
+>>>     "pride", "realization", "relief", "remorse", "sadness", "surprise", "neutral"
+>>> ]
 >>> label_to_emotion = dict(zip(list(range(len(emotions))), emotions))
 >>> emotion_to_isear = {
 >>>     "anger": "anger",
@@ -45,9 +47,11 @@ Here is how to use this model:
 >>> pipe = pipeline("text-classification", model=model_name, truncation=True,
 >>>                  return_all_scores=True, device=-1 if device.type=="cpu" else 0)
 >>> # input in a format f"{text}"
->>> utterances = ["Ok is it just me or is anyone else getting goosebumps too???",
->>>               "Don’t know what to do",
->>>               "When a car is overtaking another and I am forced to drive off the road."]
+>>> utterances = [
+>>>     "Ok is it just me or is anyone else getting goosebumps too???",
+>>>     "Don’t know what to do",
+>>>     "When a car is overtaking another and I am forced to drive off the road."
+>>> ]
 >>> outcome = pipe(utterances)
 >>> dominant_classes = [
 >>>     [prediction for prediction in example if prediction['score'] >= threshold and