add tweeteval

dataset_stats.py

@@ -8,7 +8,7 @@ def count_word(text):
 
 
 if __name__ == '__main__':
-    data = ["chemprot", "citation_intent", "hyperpartisan_news", "rct-sample", "sciie", "amcd"]
+    data = ["chemprot", "citation_intent", "hyperpartisan_news", "rct-sample", "sciie", "amcd", 'yelp_review', 'tweet_eval_irony', 'tweet_eval_hate', 'tweet_eval_emotion']
     stats = {}
     for d in data:
         _data = load_dataset('asahi417/multi_domain_document_classification', d)

multi_domain_document_classification.py

@@ -6,7 +6,7 @@ import datasets
 logger = datasets.logging.get_logger(__name__)
 _DESCRIPTION = """Multi domain document classification dataset used in [https://arxiv.org/pdf/2004.10964.pdf](https://arxiv.org/pdf/2004.10964.pdf)"""
 _NAME = "multi_domain_document_classification"
-_VERSION = "0.1.1"
+_VERSION = "0.2.0"
 _CITATION = """
 @inproceedings{dontstoppretraining2020,
  author = {Suchin Gururangan and Ana Marasović and Swabha Swayamdipta and Kyle Lo and Iz Beltagy and Doug Downey and Noah A. Smith},