Update `KLUE-YNAT` prompt

- add a prompt for the klue-ynat task
- remove unnecessary parenthesis from example answers
- reformat the code

lm_eval/tasks/klue.py

@@ -8,7 +8,7 @@ https://arxiv.org/abs/2105.09680
  With ethical considerations in mind, we deliberately design annotation guidelines
  to obtain unambiguous annotations for all datasets. Furthermore, we build an evaluation system
  and carefully choose evaluations metrics for every task, thus establishing fair comparison across Korean language models.
- 
+
  Homepage: https://klue-benchmark.com/
 """
 
@@ -48,7 +48,7 @@ class STS(Task):
     VERSION = 0
     DATASET_PATH = "klue"
     DATASET_NAME = "sts"
-    
+
     def has_training_docs(self):
         return True
 
@@ -68,8 +68,7 @@ class STS(Task):
 
     def doc_to_text(self, doc):
         return "질문: 문장 1과 문장 2는 서로 유사한 의미를 가지나요?\n문장 1: {}\n문장 2: {}\n정답:".format(
-            general_detokenize(doc["sentence1"]),
-            general_detokenize(doc["sentence2"]) 
+            general_detokenize(doc["sentence1"]), general_detokenize(doc["sentence2"])
         )
 
     def doc_to_target(self, doc):
@@ -83,22 +82,13 @@ class STS(Task):
     def process_results(self, doc, results):
         pred = np.argmax(results)
         gold = doc["labels"]["binary-label"]
-        return {
-            "acc": pred == gold,
-            "f1": (gold, pred)
-        }
-    
+        return {"acc": pred == gold, "f1": (gold, pred)}
+
     def higher_is_better(self):
-        return {
-            "acc": True,
-            "f1": True
-        }
+        return {"acc": True, "f1": True}
 
     def aggregation(self):
-        return {
-            "acc": mean,
-            "f1": f1_score
-        }
+        return {"acc": mean, "f1": f1_score}
 
 
 class YNAT(MultipleChoiceTask):
@@ -117,7 +107,7 @@ class YNAT(MultipleChoiceTask):
 
     def training_docs(self):
         if self._training_docs is None:
-            self._training_docs = list(map(self._process_doc,self.dataset["train"]))
+            self._training_docs = list(map(self._process_doc, self.dataset["train"]))
         return self._training_docs
 
     def validation_docs(self):
@@ -127,32 +117,30 @@ class YNAT(MultipleChoiceTask):
         out_doc = {
             "title": doc["title"],
             "choices": ["과학", "경제", "사회", "생활", "세계", "스포츠", "정치"],
-            "gold": doc["label"]
+            "gold": doc["label"],
         }
         return out_doc
 
     def doc_to_text(self, doc):
-        return "{}".format(doc["title"])
+        return "질문: 다음의 제목을 가지는 뉴스는 어느 분야의 뉴스인가요?\n제목: {}\n분야:".format(doc["title"])
 
     def doc_to_target(self, doc):
-        return " ({})".format({0: "과학", 1: "경제", 2: "사회", 3: "생활", 4: "세계", 5: "스포츠", 6: "정치"}[doc["gold"]])
+        return " {}".format(
+            {0: "과학", 1: "경제", 2: "사회", 3: "생활", 4: "세계", 5: "스포츠", 6: "정치"}[
+                doc["gold"]
+            ]
+        )
 
     def process_results(self, doc, results):
         pred = np.argmax(results)
         gold = doc["gold"]
-        return {
-            "f1": (gold, pred)
-        }
+        return {"f1": (gold, pred)}
 
     def higher_is_better(self):
-        return {
-            "f1": True
-        }
+        return {"f1": True}
 
     def aggregation(self):
-        return {
-            "f1": macro_f1_score
-        }
+        return {"f1": macro_f1_score}
 
 
 class NLI(Task):
@@ -231,7 +219,18 @@ class MRC(Task):
         return self.dataset["validation"]
 
     def doc_to_text(self, doc):
-        return '제목: ' + doc['title'] + '\n\n' + '본문: ' + doc['context'] + '\n\n' + '질문: ' + doc['question'] + '\n\n' + '답:'
+        return (
+            "제목: "
+            + doc["title"]
+            + "\n\n"
+            + "본문: "
+            + doc["context"]
+            + "\n\n"
+            + "질문: "
+            + doc["question"]
+            + "\n\n"
+            + "답:"
+        )
 
     def doc_to_target(self, doc):
         answer = doc["answers"]["text"][0]
@@ -240,23 +239,23 @@ class MRC(Task):
         return " " + answer
 
     def construct_requests(self, doc, ctx):
-        """ Uses RequestFactory to construct Requests and returns an iterable of 
+        """Uses RequestFactory to construct Requests and returns an iterable of
         Requests which will be sent to the LM.
 
         :param doc:
             The document as returned from training_docs, validation_docs, or test_docs.
         :param ctx: str
-            The context string, generated by fewshot_context. This includes the natural 
+            The context string, generated by fewshot_context. This includes the natural
             language description, as well as the few shot examples, and the question
-            part of the document for `doc`. 
+            part of the document for `doc`.
         """
-        continuation = rf.greedy_until(ctx, ['\n'])
+        continuation = rf.greedy_until(ctx, ["\n"])
         is_unanswerable = rf.loglikelihood(ctx, " " + "대답 불가")
         return continuation, is_unanswerable
-    
+
     def process_results(self, doc, results):
-        """Take a single document and the LM results and evaluates, returning a 
-        dict where keys are the names of submetrics and values are the values of 
+        """Take a single document and the LM results and evaluates, returning a
+        dict where keys are the names of submetrics and values are the values of
         the metric for that one document
 
         :param doc:
@@ -267,17 +266,17 @@ class MRC(Task):
         continuation, (logprob_unanswerable, _) = results
 
         no_answer_probability = exp(logprob_unanswerable)
-        
+
         predictions = {
-            'id': doc['guid'],
-            'prediction_text': continuation,
-            'no_answer_probability': no_answer_probability,
+            "id": doc["guid"],
+            "prediction_text": continuation,
+            "no_answer_probability": no_answer_probability,
         }
 
         references = {
-            'id': doc['guid'],
-            'answers': doc['answers'],
-            'unanswerable': doc['is_impossible'],
+            "id": doc["guid"],
+            "answers": doc["answers"],
+            "unanswerable": doc["is_impossible"],
         }
 
         return {
@@ -315,7 +314,7 @@ class MRC(Task):
     def aggregation(self):
         """
         :returns: {str: [float] -> float}
-            A dictionary where keys are the names of submetrics and values are 
+            A dictionary where keys are the names of submetrics and values are
             functions that aggregate a list of metrics
         """
         return {
@@ -348,7 +347,7 @@ class MRC(Task):
     def higher_is_better(self):
         """
         :returns: {str: bool}
-            A dictionary where keys are the names of submetrics and values are 
+            A dictionary where keys are the names of submetrics and values are
             whether a higher value of the submetric is better
         """
         return {