added hate labels

.gitignore

@@ -1 +1,2 @@
 misc
+dimos.ipynb

NOTE.md

@@ -4,4 +4,4 @@
 - TweetNER: [link](https://huggingface.co/datasets/tner/tweetner7)
 - TweetQA: [link](https://huggingface.co/datasets/tweet_qa), [link2](https://huggingface.co/datasets/lmqg/qg_tweetqa)
 - TweetIntimacy: [link]()
-
+- TweetSentiment: [link](https://alt.qcri.org/semeval2017/task4/index.php?id=results)

get_stats.py

@@ -9,6 +9,10 @@ task_description = {
     'tweet_similarity': "regression on two texts",
     'tweet_topic': "multi-label classification",
     "tempo_wic": "binary classification on two texts"
+    "tweet_sentiment": "ABSA on a five-point scale"
+    "tweet_hate": "multi-class classification"
+    "tweet_emoji": "multi-class classification"
+    "tweet_nerd": "binary classification"
 }
 for task in task_description.keys():
     data = load_dataset("cardiffnlp/super_tweet_eval", task)

process/tweet_sentiment.py

@@ -1,4 +1,3 @@
-# Original data: https://alt.qcri.org/semeval2017/task4/index.php?id=results
 import pandas as pd
 from glob import glob
 import urllib

super_tweet_eval.py

@@ -2,7 +2,7 @@
 import json
 import datasets
 
-_VERSION = "0.1.32"
+_VERSION = "0.1.33"
 _SUPER_TWEET_EVAL_CITATION = """TBA"""
 _SUPER_TWEET_EVAL_DESCRIPTION = """TBA"""
 _TWEET_TOPIC_DESCRIPTION = """
@@ -279,11 +279,11 @@ class SuperTweetEval(datasets.GeneratorBasedBuilder):
             features["text_1_tokenized"] = datasets.Sequence(datasets.Value("string"))
             features["text_2_tokenized"] = datasets.Sequence(datasets.Value("string"))
         if self.config.name == "tweet_hate":
-            names = [
-                    'target_gender_aggregated','target_race_aggregated', 'target_sexuality_aggregated',
-                    'target_religion_aggregated','target_origin_aggregated', 'target_disability_aggregated','target_age_aggregated',
-                    'not_hate']
-            features["gold_label"] = datasets.Value("int32")
+            label_classes = [
+                    'hate_gender','hate_race', 'hate_sexuality', 'hate_religion','hate_origin', 'hate_disability',
+                    'target_age', 'not_hate']
+            features['gold_label'] = datasets.features.ClassLabel(names=self.config.label_classes)
+            #features["gold_label"] = datasets.Value("int32")
             features["text"] = datasets.Value("string")
         if self.config.name == "tweet_nerd":
             features['target'] = datasets.Value("string")
@@ -308,11 +308,7 @@ class SuperTweetEval(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # TODO: temporary check until we have all splits online
-        if self.config.name == 'tweet_nerd':
-            splits = ['validation']        
-        else:
-            splits = ['train', 'test', 'validation']        
+        splits = ['train', 'test', 'validation']        
         downloaded_file = dl_manager.download_and_extract({s: f"{self.config.data_url}/{s}.jsonl" for s in splits})
         return [datasets.SplitGenerator(name=s, gen_kwargs={"filepath": downloaded_file[s]}) for s in splits]