init

data/tweet_similarity/test.jsonl

@@ -1,3 +1,3 @@
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+oid sha256:340f81539bdd3f8206935ef19c81e617fee8def21eecf805f95d7a7a9e15f36a
+size 119831

data/tweet_similarity/train.jsonl

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+oid sha256:ce1ce50aee71149badb4201d21e253234d9d392ca2c01852881006bff8ad2434
+size 123024

data/tweet_similarity/validation.jsonl

@@ -1,3 +1,3 @@
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super_tweet_eval.py

@@ -2,7 +2,7 @@
 import json
 import datasets
 
-_VERSION = "0.0.8"
+_VERSION = "0.0.9"
 _SUPER_TWEET_EVAL_CITATION = """TBA"""
 _SUPER_TWEET_EVAL_DESCRIPTION = """TBA"""
 _TWEET_TOPIC_DESCRIPTION = """
@@ -136,42 +136,42 @@ class SuperTweetEval(datasets.GeneratorBasedBuilder):
             name="tweet_topic",
             description=_TWEET_TOPIC_DESCRIPTION,
             citation=_TWEET_TOPIC_CITATION,
-            features=["text", "label_list", "id", "date"],
+            features=["text", "gold_label_list", "date"],
             data_url="https://huggingface.co/datasets/cardiffnlp/super_tweet_eval/resolve/main/data/tweet_topic",
         ),
         SuperTweetEvalConfig(
             name="tweet_ner7",
             description=_TWEET_NER7_DESCRIPTION,
             citation=_TWEET_NER7_CITATION,
-            features=["text_tokenized", "label_sequence", "id", "date"],
+            features=["text", "text_tokenized", "gold_label_sequence", "date"],
             data_url="https://huggingface.co/datasets/cardiffnlp/super_tweet_eval/resolve/main/data/tweet_ner7",
         ),
         SuperTweetEvalConfig(
             name="tweet_qa",
             description=_TWEET_QA_DESCRIPTION,
             citation=_TWEET_QA_CITATION,
-            features=["text", "label_str", "paragraph", "question"],
+            features=["text", "gold_label_str", "paragraph", "question"],
             data_url="https://huggingface.co/datasets/cardiffnlp/super_tweet_eval/resolve/main/data/tweet_qa",
         ),
         SuperTweetEvalConfig(
             name="tweet_intimacy",
             description=_TWEET_INTIMACY_DESCRIPTION,
             citation=_TWEET_INTIMACY_CITATION,
-            features=["text", "label_float"],
+            features=["text", "gold_score"],
             data_url="https://huggingface.co/datasets/cardiffnlp/super_tweet_eval/resolve/main/data/tweet_intimacy",
         ),
         SuperTweetEvalConfig(
             name="tweet_similarity",
             description=_TWEET_SIMILARITY_DESCRIPTION,
             citation=_TWEET_SIMILARITY_CITATION,
-            features=["text_1", "text_2", "label_float"],
+            features=["text_1", "text_2", "gold_score"],
             data_url="https://huggingface.co/datasets/cardiffnlp/super_tweet_eval/resolve/main/data/tweet_similarity",
         ),
         SuperTweetEvalConfig(
             name="tempo_wic",
             description=_TEMPO_WIC_DESCRIPTION,
             citation=_TEMPO_WIC_CITATION,
-            features=['label_binary', 'id', 'word',
+            features=['label_binary', 'word',
                       'text_1', 'text_1_tokenized', 'token_idx_1', 'text_start_1', 'text_end_1', 'date_1',
                       'text_2', 'text_2_tokenized', 'token_idx_2', 'text_start_2', 'text_end_2', 'date_2'],
             data_url="https://huggingface.co/datasets/cardiffnlp/super_tweet_eval/resolve/main/data/tempo_wic",
@@ -186,17 +186,17 @@ class SuperTweetEval(datasets.GeneratorBasedBuilder):
                 "family", "fashion_&_style", "film_tv_&_video", "fitness_&_health", "food_&_dining", "gaming",
                 "learning_&_educational", "music", "news_&_social_concern", "other_hobbies", "relationships",
                 "science_&_technology", "sports", "travel_&_adventure", "youth_&_student_life"]
-            features["label_list"] = datasets.Sequence(datasets.features.ClassLabel(names=names))
+            features["gold_label_list"] = datasets.Sequence(datasets.features.ClassLabel(names=names))
         if self.config.name == "tweet_ner7":
             names = [
                 'B-corporation', 'B-creative_work', 'B-event', 'B-group', 'B-location', 'B-person', 'B-product',
                 'I-corporation', 'I-creative_work', 'I-event', 'I-group', 'I-location', 'I-person', 'I-product', 'O']
-            features["label_sequence"] = datasets.Sequence(datasets.features.ClassLabel(names=names))
+            features["gold_label_sequence"] = datasets.Sequence(datasets.features.ClassLabel(names=names))
             features["text_tokenized"] = datasets.Sequence(datasets.Value("string"))
         if self.config.name in ["tweet_intimacy", "tweet_similarity"]:
-            features["label_float"] = datasets.Value("float32")
+            features["gold_score"] = datasets.Value("float32")
         if self.config.name == "tempo_wic":
-            features["label_binary"] = datasets.Value("int32")
+            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")