added sentiment/emoji labels

data/tweet_hate/map.txt

@@ -0,0 +1,8 @@
+0,hate_gender
+1,hate_race
+2,hate_sexuality
+3,hate_religion
+4,hate_origin
+5,hate_disability
+6,hate_age
+7,not_hate

process/tweet_sentiment.py

@@ -59,6 +59,19 @@ train['text'] = train['text'].apply(clean_text)
 validation['text'] = validation['text'].apply(clean_text)
 test['text'] = test['text'].apply(clean_text)
 
+# map classes to 0-4
+class_map = {
+    -2:0,
+    -1:1,
+     0:2,
+     1:3,
+     2:4
+}
+train['gold_label'] = train['gold_label'].map(class_map)
+validation['gold_label'] = validation['gold_label'].map(class_map)
+test['gold_label'] = test['gold_label'].map(class_map)
+
+
 # save splits
 cols_to_keep = ['gold_label', 'topic', 'text']
 train[cols_to_keep].to_json(

super_tweet_eval.py

@@ -2,7 +2,7 @@
 import json
 import datasets
 
-_VERSION = "0.1.37"
+_VERSION = "0.1.38"
 _SUPER_TWEET_EVAL_CITATION = """TBA"""
 _SUPER_TWEET_EVAL_DESCRIPTION = """TBA"""
 _TWEET_TOPIC_DESCRIPTION = """
@@ -274,8 +274,6 @@ class SuperTweetEval(datasets.GeneratorBasedBuilder):
             features["gold_score"] = datasets.Value("float32")
         if self.config.name == "tempo_wic":
             features["gold_label_binary"] = datasets.Value("int32")
-            # features["token_idx_1"] = datasets.Value("int32")
-            # features["token_idx_2"] = datasets.Value("int32")
             features["text_start_1"] = datasets.Value("int32")
             features["text_start_2"] = datasets.Value("int32")
             features["text_end_1"] = datasets.Value("int32")
@@ -285,9 +283,8 @@ class SuperTweetEval(datasets.GeneratorBasedBuilder):
         if self.config.name == "tweet_hate":
             label_classes = [
                     'hate_gender','hate_race', 'hate_sexuality', 'hate_religion','hate_origin', 'hate_disability',
-                    'target_age', 'not_hate']
+                    'hate_age', 'not_hate']
             features['gold_label'] = datasets.features.ClassLabel(names=label_classes)
-            #features["gold_label"] = datasets.Value("int32")
             features["text"] = datasets.Value("string")
         if self.config.name == "tweet_nerd":
             features['target'] = datasets.Value("string")
@@ -297,10 +294,14 @@ class SuperTweetEval(datasets.GeneratorBasedBuilder):
             features['text_end'] = datasets.Value("int32")
             features['gold_label_binary'] = datasets.Value("int32")
         if self.config.name == "tweet_emoji":
-            features["gold_label"] = datasets.Value("int32")
+            with open('./data/tweet_emoji/map.txt') as f:
+                label_classes = f.readlines()
+            label_classes = [x.strip('\n') for x in label_classes]
+            features['gold_label'] = datasets.features.ClassLabel(names=label_classes)
             features["text"] = datasets.Value("string")
         if self.config.name == "tweet_sentiment":
-            features["gold_label"] = datasets.Value("int32")
+            label_classes = ["strongly negative", "negative", "negative or neutral", "positive", "strongly positive"]
+            features['gold_label'] = datasets.features.ClassLabel(names=label_classes)
             features["text"] = datasets.Value("string")
             features["target"] = datasets.Value("string")