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.gitattributes

@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+klue-mrc-v1.1_dev.json filter=lfs diff=lfs merge=lfs -text
+klue-mrc-v1.1_train.json filter=lfs diff=lfs merge=lfs -text

klue-mrc-v1.1_dev.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b31bb073e47cdeb19f2f1bae03d916ecabf945140334c75842d6994f700d4f47
+size 18712914

klue-mrc-v1.1_train.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c52c7b82a6015c09ea8be1fea49c912e73012d8b44d3653256d91c65d519c3df
+size 47952737

klue-mrc.py

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+
+import json
+import pandas as pd
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = """\
+Klue Machine Reading Comprehension Data
+"""
+
+_URL = "https://huggingface.co/datasets/LeverageX/klue-mrc/resolve/main/"
+_URLS = {
+    "train_data": _URL + "klue-mrc-v1.1_train.json",
+    "validation_data": _URL + "klue-mrc-v1.1_dev.json",
+}
+
+class KoreanNewspaper(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="KLUE Machine Reading Comprehension",
+            version=datasets.Version("1.0.0", ""),
+            description="For LeverageX Project",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "title": datasets.Value("string"),
+                    "source": datasets.Value("string"),
+                    "news_category": datasets.Value("string"),
+                    "paragraphs":  datasets.Sequence(dict)
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://klue-benchmark.com/tasks/70/overview/description",
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train_data"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation_data"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        key = 0
+        with open(filepath, encoding="utf-8") as f :
+            data = json.load(f)
+
+        data = data['data']
+
+        for info in data :
+            guid = info['title']  
+            news_category = info['news_category']
+            source = info['source']
+            paragraphs = info['paragraphs']
+
+            context = paragraphs[0]['context']
+
+            qas = paragraphs[0]['qas'][0]
+            question = qas['question']
+            answer = qas['answers'][0]
+
+            answer_text = answer['text']
+            answer_start = answer['answer_start']
+            guid = qas['guid']
+
+            print({
+                "guid" : guid,
+                "news_category" : news_category,
+                "source" : source,
+                "context" : context,
+                "question" : question,
+                "answer_text" : answer_text,
+                "answer_start" : answer_start,
+            })
+
+            yield key, {
+                "guid" : guid,
+                "news_category" : news_category,
+                "source" : source,
+                "context" : context,
+                "question" : question,
+                "answer_text" : answer_text,
+                "answer_start" : answer_start,
+            }
+            key += 1