Modify prompt and add citation (#338)

lm_eval/tasks/nsmc.py

@@ -3,15 +3,15 @@ NSMC:
 """
 import numpy as np
 from lm_eval.base import rf, Task
-from lm_eval.metrics import mean, matthews_corrcoef, f1_score, yesno
+from lm_eval.metrics import mean
 from lm_eval.utils import general_detokenize
 
 _CITATION = """
-@inproceedings{zellers2019hellaswag,
-    title={NSMC: Can a Machine Really Finish Your Sentence?},
-    author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},
-    booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
-    year={2019}
+@InProceedings{Park:2016,
+  title        = "Naver Sentiment Movie Corpus",
+  author       = "Lucy Park",
+  year         = "2016",
+  howpublished = {\\url{https://github.com/e9t/nsmc}}
 }
 """
 
@@ -39,16 +39,14 @@ class NSMC(Task):
         return self.dataset["test"]
 
     def doc_to_text(self, doc):
-        return "다음 문장은 긍정일까요 부정일까요?\n{}\n정답:".format(
-            general_detokenize(doc["document"]),
-        )
+        return "{}".format(general_detokenize(doc["document"]))
 
     def doc_to_target(self, doc):
-        return " {}".format({1: "긍정", 0: "부정"}[doc["label"]])
+        return " ({})".format({1: "긍정", 0: "부정"}[doc["label"]])
 
     def construct_requests(self, doc, ctx):
-        ll_positive, _ = rf.loglikelihood(ctx, " 긍정")
-        ll_negative, _ = rf.loglikelihood(ctx, " 부정")
+        ll_positive, _ = rf.loglikelihood(ctx, " (긍정)")
+        ll_negative, _ = rf.loglikelihood(ctx, " (부정)")
         return ll_positive, ll_negative
 
     def process_results(self, doc, results):