reader running for all langs

offenseval_2020.py

@@ -26,12 +26,6 @@ logger = datasets.logging.get_logger(__name__)
 
 
 _CITATION = """\
-@inproceedings{zampieri-etal-2020-semeval,
-    title = {{SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)}},
-    author = {Zampieri, Marcos and Nakov, Preslav and Rosenthal, Sara and Atanasova, Pepa and Karadzhov, Georgi and Mubarak, Hamdy and Derczynski, Leon and Pitenis, Zeses and \c{C}\"{o}ltekin, \c{C}a\u{g}r{\i}},
-    booktitle = {Proceedings of SemEval},
-    year = {2020}
-}
 """
 
 _DESCRIPTION = """\
@@ -87,8 +81,9 @@ class OffensEval2020(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "id": datasets.Value("string"),
+                    "original_id": datasets.Value("string"),
                     "text": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(
+                    "subtask_a": datasets.features.ClassLabel(
                         names=[
                             "OFF",
                             "NOT",
@@ -104,21 +99,42 @@ class OffensEval2020(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
         train_text   = dl_manager.download_and_extract(f"offenseval-{self.config.name}-training-v1.tsv")
-        train_labels = dl_manager.download_and_extract(f"offenseval-{self.config.name}-labela-v1.tsv")
-        test         = dl_manager.download_and_extract(f"offenseval-{self.config.name}-test-v1.tsv")
+        test_labels  = dl_manager.download_and_extract(f"offenseval-{self.config.name}-labela-v1.csv")
+        test_text    = dl_manager.download_and_extract(f"offenseval-{self.config.name}-test-v1.tsv")
 
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file}),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_text, "split": 'train'}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": {'labels':test_labels, 'text':test_text}, "split": 'test'}),
         ]
 
-    def _generate_examples(self, filepath):
-        logger.info("⏳ Generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            OffensEval2020_reader = csv.DictReader(f, delimiter="\t", quotechar='"')
-            guid = 0
-            for instance in OffensEval2020_reader:
-                instance["id"] = str(guid)
-                for dropfield in ('post_id', 'search_keywords', 'itr', 'disagreement_solving', 'created_at', 'in_reply_to_status_id'):
-                    instance.pop(dropfield)
-                yield guid, instance
-                guid += 1
+    def _generate_examples(self, filepath, split=None):
+        if split == "train":
+            logger.info("⏳ Generating examples from = %s", filepath)
+            with open(filepath, encoding="utf-8") as f:
+                OffensEval2020_reader = csv.DictReader(f, delimiter="\t", quotechar='"')
+                guid = 0
+                for instance in OffensEval2020_reader:
+                    instance["text"] = instance.pop("tweet")
+                    instance["original_id"] = instance.pop("id")
+                    instance["id"] = str(guid)
+                    yield guid, instance
+                    guid += 1
+        elif split == 'test':
+            logger.info("⏳ Generating examples from = %s", filepath['text'])
+            labeldict = {}
+            with open(filepath['labels']) as labels:
+                for line in labels:
+                    line = line.strip().split(',')
+                    if line:
+                        labeldict[line[0]] = line[1]
+            with open(filepath['text']) as f:
+                OffensEval2020_reader = csv.DictReader(f, delimiter="\t", quotechar='"')
+                guid = 0
+                for instance in OffensEval2020_reader:
+                    instance["text"] = instance.pop("tweet")
+                    instance["original_id"] = instance.pop("id")
+                    instance["id"] = str(guid)
+                    instance["subtask_a"] = labeldict[instance["original_id"]]
+                    yield guid, instance
+                    guid += 1
+