Add KLUE-MRC task (EM, F1)

lm_eval/tasks/__init__.py

@@ -312,6 +312,7 @@ TASK_REGISTRY = {
     "klue_sts": klue.STS,
     "klue_ynat": klue.YNAT,
     "klue_nli": klue.NLI,
+    "klue_mrc": klue.MRC,
     "nsmc": nsmc.NSMC,    
     "korquad": korquad.Korquad,
     "kobest_boolq": kobest.BoolQ,

lm_eval/tasks/klue.py

@@ -12,6 +12,8 @@ https://arxiv.org/abs/2105.09680
  Homepage: https://klue-benchmark.com/
 """
 
+import datasets
+from math import exp
 import numpy as np
 from lm_eval.base import Task, MultipleChoiceTask, rf
 from lm_eval.metrics import macro_f1_score, mean, matthews_corrcoef, f1_score, yesno
@@ -29,6 +31,17 @@ _CITATION = """
 """
 
 
+def _squad_metric(predictions, references):
+    squad_metric = datasets.load_metric("squad")
+    return squad_metric.compute(predictions=predictions, references=references)
+
+
+def _squad_agg(key, items):
+    predictions, references = zip(*items)
+
+    return _squad_metric(predictions=predictions, references=references)[key]
+
+
 class STS(Task):
     VERSION = 0
     DATASET_PATH = "klue"
@@ -106,7 +119,7 @@ class YNAT(MultipleChoiceTask):
         return self._training_docs
 
     def validation_docs(self):
-        return map(self._process_doc,self.dataset["validation"])
+        return map(self._process_doc, self.dataset["validation"])
 
     def _process_doc(self, doc):
         out_doc = {
@@ -170,9 +183,11 @@ class NLI(Task):
         )
 
     def doc_to_target(self, doc):
-        # 참 = entailment
-        # 거짓 = contradiction
-        # 무관 = neutral
+        """
+        참 = entailment
+        거짓 = contradiction
+        무관 = neutral
+        """
         return " {}".format({0: "참", 1: "중립", 2: "거짓"}[doc["label"]])
 
     def construct_requests(self, doc, ctx):
@@ -191,3 +206,98 @@ class NLI(Task):
 
     def aggregation(self):
         return {"acc": mean}
+
+
+class MRC(Task):
+    VERSION = 0
+    DATASET_PATH = "klue"
+    DATASET_NAME = "mrc"
+
+    def has_training_docs(self):
+        return True
+
+    def has_validation_docs(self):
+        return True
+
+    def has_test_docs(self):
+        return False
+
+    def training_docs(self):
+        return self.dataset["train"]
+
+    def validation_docs(self):
+        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:'
+
+    def doc_to_target(self, doc):
+        answer = "대답 불가" if doc["is_impossible"] else doc["answers"]["text"][0]
+        return " " + answer
+
+    def construct_requests(self, doc, ctx):
+        """ 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 
+            language description, as well as the few shot examples, and the question
+            part of the document for `doc`. 
+        """
+        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 
+        the metric for that one document
+
+        :param doc:
+            The document as returned from training_docs, validation_docs, or test_docs.
+        :param results:
+            The results of the requests created in construct_requests.
+        """
+        continuation, (logprob_unanswerable, _) = results
+
+        no_answer_probability = exp(logprob_unanswerable)
+        
+        predictions = {
+            "id": doc["guid"],
+            "prediction_text": continuation,
+            "no_answer_probability": no_answer_probability
+        }
+
+        references = {
+            "id": doc["guid"],
+            "answers": doc["answers"],
+        }
+
+        return { 
+            'exact_match': (predictions, references), # Exact match (the normalized answer exactly match the gold answer)
+            'f1': (predictions, references), #  The F-score of predicted tokens versus the gold answer
+        }
+
+    def aggregation(self):
+        """
+        :returns: {str: [float] -> float}
+            A dictionary where keys are the names of submetrics and values are 
+            functions that aggregate a list of metrics
+        """
+        return { 
+            'exact_match': partial(_squad_agg, 'exact_match'), # Exact match (the normalized answer exactly match the gold answer)
+            'f1': partial(_squad_agg, 'f1'), #  The F-score of predicted tokens versus the gold answer
+        }
+
+    def higher_is_better(self):
+        """
+        :returns: {str: bool}
+            A dictionary where keys are the names of submetrics and values are 
+            whether a higher value of the submetric is better
+        """
+        return { 
+            'exact_match': True, # Exact match (the normalized answer exactly match the gold answer)
+            'f1': True, #  The F-score of predicted tokens versus the gold answer
+        }