Merge branch 'master' into wsc273-evaluation

lm_eval/tasks/__init__.py

@@ -17,6 +17,7 @@ from . import lambada
 from . import race 
 from . import piqa
 from . import triviaqa
+from . import webqs
 
 
 TASK_REGISTRY = {
@@ -55,7 +56,7 @@ TASK_REGISTRY = {
     # "squad": squad.SQuAD, # not implemented yet
     "race": race.RACE,
     # "naturalqs": naturalqs.NaturalQs, # not implemented yet
-    # "webqs": webqs.WebQs, # not implemented yet
+    "webqs": webqs.WebQs,
     "wsc273": wsc273.WinogradSchemaChallenge273,
     # "winogrande": winogrande.Winogrande, # not implemented yet
     "anli_r1": anli.ANLIRound1,

lm_eval/tasks/webqs.py

 from . common import HFTask
+from lm_eval.base import mean, rf
 
 class WebQs(HFTask):
     DATASET_PATH = "web_questions"
@@ -18,7 +19,6 @@ class WebQs(HFTask):
         return ""
 
     def doc_to_text(self, doc):
-        print(doc)
         return "Q: " + doc['question'] + '\nA:'
 
     def doc_to_target(self, doc):
@@ -26,48 +26,37 @@ class WebQs(HFTask):
         # multiple correct answers being possible.
         # TODO: make sure we're actually handling multi-answer correctly
         return " " + doc['answers'][0]
+        
+    def _remove_prefixes(self, aliases):
+        # Optimization: Remove any alias that has a strict prefix elsewhere in the list
+        # we can do this because if the prefix is acceptable by isgreedy, we can stop looking
+        aliases.sort()
+        ret = [aliases[0]]
+        for alias in aliases[1:]:
+            if not alias.startswith(ret[-1]):
+                ret.append(alias)
+
+        return ret
+        
 
     def construct_requests(self, doc, ctx):
-        """ Uses RequestFactory to construct Requests and returns an iterable of 
-        Requests which will be sent to the LM.
+        ret = []
+        for alias in self._remove_prefixes(doc['answers']):
+            _, is_prediction = rf.loglikelihood(ctx, " " + alias)
+            ret.append(is_prediction)
+        return ret
 
-        :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`. 
-        """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
-    
     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.
-        """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        return {
+            "acc": float(any(results))
+        }
 
     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
-        """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        return {
+            "acc": mean,
+        }
 
     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 {
+            "acc": True
+        }
\ No newline at end of file
-        """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')

main.py

@@ -32,8 +32,7 @@ def main():
         task_names = args.tasks.split(",")
     task_dict = tasks.get_task_dict(task_names)
 
-    # TODO: fall back to test docs
-    task_dict_items = [(name, task) for name, task in task_dict.items() if task.has_validation_docs()]
+    task_dict_items = [(name, task) for name, task in task_dict.items() if(task.has_validation_docs() or task.has_test_docs())]
 
     results = collections.defaultdict(dict)
 
@@ -50,7 +49,14 @@ def main():
 
     # get lists of each type of requeste
     for task_name, task in task_dict_items:
-        for doc_id, doc in enumerate(itertools.islice(task.validation_docs(), 0, args.limit)):
+        #default to validation doc, fall back to test doc if validation unavailable
+        # TODO: the val-fallback-to-test system isn't final, we should revisit it at some point
+        if task.has_validation_docs():
+            task_doc_func = task.validation_docs
+        elif task.has_test_docs():
+            task_doc_func = task.test_docs
+
+        for doc_id, doc in enumerate(itertools.islice(task_doc_func(), 0, args.limit)):
             docs[(task_name, doc_id)] = doc
 
             ctx = task.fewshot_context(