Implement LAMBADA

lm_eval/tasks/__init__.py

@@ -13,6 +13,7 @@ from . import squad
 from . import naturalqs
 from . import sat
 from . import arithmetic
+from . import lambada
 
 TASK_REGISTRY = {
     # GLUE
@@ -37,6 +38,8 @@ TASK_REGISTRY = {
     
     # Order by benchmark/genre?
 
+    "lambada": lambada.LAMBADA,
+
     # "arc_easy": arc.ARCEasy, # not implemented yet
     # "arc_challenge": arc.ARCChallenge, # not implemented yet
     # "quac": quac.QuAC, # not implemented yet

lm_eval/tasks/lambada.py

-from lm_eval.base import Dataset
+from lm_eval.base import Dataset, rf, mean
 from lm_eval.utils import sh
 import json
 import requests
 import ftfy
+import math
+from best_download import download_file
 
 
-class Lambada(Dataset):
-    def __init__(self):
-        self.download()
+class LAMBADA(Dataset):
     def download(self):
         sh("mkdir -p data/lambada")
-        with open("data/lambada/lambada_test.json", 'w') as f:
-            req = requests.get("https://storage.googleapis.com/gpt-2/data/lambada_test.jsonl")
-            req.raise_for_status()
-            jsons = [json.loads(l) for l in req.iter_lines()]
-            texts = [ftfy.fix_text(j['text'], normalization='NFKC') for j in jsons]
-            json.dump(texts, f)
+        download_file(
+            "https://storage.googleapis.com/gpt-2/data/lambada_test.jsonl", 
+            "data/lambada/lambada_test.jsonl", 
+            "4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226"
+        )
 
     def has_training_docs(self):
         return False
@@ -32,61 +31,42 @@ class Lambada(Dataset):
     def validation_docs(self):
         pass
 
-    def load_doc(self, myjson):
-        return [doc for doc in myjson]
-
     def test_docs(self):
-        myjson = json.load(open("data/lambada/lambada_test.json"))
-        return self.load_doc(myjson)
+        with open("data/lambada/lambada_test.jsonl") as fh:
+            for line in fh:
+                yield json.loads(line)
+
+    def doc_to_text(self, doc):
+        return doc['text'].rsplit(' ', 1)[0]
 
-    def doc_to_text(self, doc, include_target=True):
-        # TODO: implement.
+    def doc_to_target(self, doc):
+        return " " + doc['text'].rsplit(' ', 1)[1]
     
     def fewshot_description(self):
         # TODO: figure out description
         return ""
 
     def construct_requests(self, doc, ctx):
-        """ Uses RequestFactory to construct Requests and returns an iterable of 
-        Requests which will be sent to the LM.
+        ll, is_greedy = rf.loglikelihood(doc, self.doc_to_target(doc))
 
-        :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')
+        return ll, is_greedy
     
     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')
+        ll, is_greedy = results
 
+        return {
+            'perplexity': math.exp(-ll),
+            'accuracy': int(is_greedy)
+        }
+        
     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 {
+            'perplexity': mean,
+            'accuracy': 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
-        """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
\ No newline at end of file
+        return {
+            'perplexity': False,
+            'accuracy': True
+        }
\ No newline at end of file