new tasks, preprocessing+postprocessing

src/tasksource/preprocess.py

@@ -34,6 +34,7 @@ def __map_to_target(x,fn=lambda x:None, target=None):
         return x
 
     def __call__(self,dataset, max_rows=None, max_rows_eval=None):
+        dataset = self.pre_process(dataset)
         for k,v in zip(self.default_splits, self.splits):
             if v and k!=v:
                 dataset[k]=dataset[v]
@@ -52,7 +53,7 @@ def __call__(self,dataset, max_rows=None, max_rows_eval=None):
                                         and type(v)==str and k!=v)})
         for k in self.to_dict().keys():
             v=getattr(self, k)
-            if callable(v) and k!="post_process":
+            if callable(v) and k not in {"post_process","pre_process"}:
                 dataset=dataset.map(self.__map_to_target,
                                     fn_kwargs={'fn':v,'target':k})
 
@@ -164,6 +165,7 @@ class SharedFields:
     splits:list=Preprocessing.default_splits
     dataset_name:str = None
     config_name:str = None
+    pre_process: callable = lambda x:x
     post_process: callable = lambda x:x
 
 @dataclass

src/tasksource/tasks.py

@@ -7,14 +7,16 @@
 ###################### NLI/paraphrase ###############################
 
 
+anli__a1 = Classification('premise','hypothesis','label', splits=['train_r1','dev_r1','test_r1'])
+anli__a2 = Classification('premise','hypothesis','label', splits=['train_r2','dev_r2','test_r2'])
+anli__a3 = Classification('premise','hypothesis','label', splits=['train_r3','dev_r3','test_r3'])
+
+
 babi_nli = Classification("premise", "hypothesis", "label",
     dataset_name="metaeval/babi_nli",
     config_name=set(get_dataset_config_names("metaeval/babi_nli"))-{"agents-motivations"}
 ) # agents-motivations task is not as clear-cut as the others
 
-anli__a1 = Classification('premise','hypothesis','label', splits=['train_r1','dev_r1','test_r1'])
-anli__a2 = Classification('premise','hypothesis','label', splits=['train_r2','dev_r2','test_r2'])
-anli__a3 = Classification('premise','hypothesis','label', splits=['train_r3','dev_r3','test_r3'])
 
 sick__label         = Classification('sentence_A','sentence_B','label')
 sick__relatedness   = Classification('sentence_A','sentence_B','relatedness_score')
@@ -308,7 +310,20 @@ def split_choices(s):
 ######################## Classification (other) ########################
 
 utilitarianism = Classification("comparison",labels="label",
-dataset_name="")
+dataset_name="metaeval/utilitarianism")
+
+amazon_counterfactual = Classification(
+    "text", labels="label",
+    dataset_name="mteb/amazon_counterfactual",
+    config_name="en")
+
+insincere_questions = Classification(
+    "text", labels="label",
+    dataset_name="SetFit/insincere-questions")
+
+toxic_conversations = Classification(
+    "text", labels="label",
+    dataset_name="SetFit/toxic_conversations")
 
 turingbench = Classification("Generation",labels="label",
     dataset_name="turingbench/TuringBench",
@@ -378,6 +393,10 @@ def split_choices(s):
     "persuasiveness-eloquence", "persuasiveness-premisetype", "persuasiveness-relevance", "persuasiveness-specificity", 
     "persuasiveness-strength", "sarcasm","stac"])
 
+silicone = Classification("Uterance",labels="Label",
+    config_name=['dyda_da', 'dyda_e', 'iemocap', 'maptask', 'meld_e', 'meld_s', 'oasis', 'sem'] # +['swda', 'mrda'] # in pragmeval
+)
+
 #lex_glue___ecthr_a = Classification(sentence1="text", labels="labels") # too long
 #lex_glue___ecthr_b = Classification(sentence1="text", labels="labels") # too long
 lex_glue___eurlex = Classification(sentence1="text", labels="labels") 
@@ -591,6 +610,8 @@ def split_choices(s):
 sarcasm_news = Classification("headline", labels="is_sarcastic",
     dataset_name="raquiba/Sarcasm_News_Headline")
 
+sem_eval_2010_task_8 = Classification("sentence",labels="relation")
+
 ###END
 ################### END OF SUPPORT ######################