Update scripts/sentiment.py

scripts/sentiment.py

@@ -6,6 +6,11 @@ from tqdm import tqdm
 import numpy as np
 import numpy as np
 import scipy
+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer
+from scipy.special import softmax
+import os
 
 def tweet_cleaner(tweet: str) -> str:
     # words = set(nltk.corpus.words.words())
@@ -93,13 +98,6 @@ def twitter_sentiment_api_score(
             }
         )
     else:
-
-        from transformers import AutoModelForSequenceClassification
-        from transformers import TFAutoModelForSequenceClassification
-        from transformers import AutoTokenizer
-        from scipy.special import softmax
-        import os
-
         task = "sentiment"
         MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
         tokenizer = AutoTokenizer.from_pretrained(MODEL)
@@ -124,7 +122,7 @@ def twitter_sentiment_api_score(
             results["argmax"] = max_key
             return results
 
-        return [get_sentimet(t) for t in tweet_list]
+        return [get_sentimet(t) for t in tqdm(tweet_list)]
 
     # Loop through the list of sentiment scores and replace the sentiment labels with more intuitive labels
     result = []