Merge branch 'master' of github.com:EleutherAI/lm_evaluation_harness

lm_eval/tasks/__init__.py

@@ -35,6 +35,7 @@ from . import unscramble
 from . import logiqa
 from . import hendrycks_test
 from . import hendrycks_math
+from . import cbt
 from . import lambada_cloze
 
 ########################################
@@ -93,6 +94,9 @@ TASK_REGISTRY = {
     "drop": drop.DROP,
     "lambada": lambada.LAMBADA,
     "lambada_cloze": lambada_cloze.LAMBADA_cloze,
+    "cbt-cn": cbt.CBTCN,
+    "cbt-ne": cbt.CBTNE,
+
     "piqa": piqa.PiQA,
 
     # Science related

lm_eval/tasks/cbt.py

+import numpy as np
+from lm_eval.base import rf
+from lm_eval.metrics import mean
+from .common import HFTask
+
+
+class CBTBase(HFTask):
+    """The Children’s Book Test (CBT) from the paper:
+    https://research.fb.com/wp-content/uploads/2016/11/the_goldilocks_principle_reading_children_s_books_with_explicit_memory_representations.pdf
+    NOTE: This evaluation is based on the (context + query) question-answering variant
+    used by the Recurrent Language Models described in the aforementioned paper.
+    See section 4.4.
+    """
+
+    DATASET_PATH = "cbt"
+    DATASET_NAME = None
+
+    def fewshot_description(self):
+        # TODO: Figure out description.
+        return ""
+
+    def detokenize(self, text):
+        text = text.replace(" '", "'")
+        text = text.replace(" \n", "\n")
+        text = text.replace("\n ", "\n")
+        text = text.replace(" n't", "n't")
+        text = text.replace("`` ", '"')
+        text = text.replace("''", '"')
+        # punctuation
+        text = text.replace(" :", ":")
+        text = text.replace(" ;", ";")
+        text = text.replace(" !", "!")
+        text = text.replace(" ?", "?")
+        text = text.replace(" ,", ",")
+        text = text.replace(" .", ".")
+        return text
+
+    def doc_to_text(self, doc):
+        passage = " ".join(doc["sentences"])
+        text = "Passage: " + passage + "\nQuestion: " + doc["question"]
+        return self.detokenize(text)
+
+    def doc_to_target(self, doc):
+        return ""
+
+    def fewshot_examples(self, k, rnd):
+        assert k == 0, f"CBT is only implemented for the zero-shot setting. Given k={k}."
+        return super().fewshot_examples(k, rnd)
+
+    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`.
+        """
+        lls = []
+        for option in doc["options"]:
+            # Following Section 4.4 "Recurrent Language Models" in the CBT paper:
+            # "we rank candidate [option] c based on p(q1 . . . qk−1, c, qk+1 . . . ql)
+            # rather than simply p(q1 . . . qk−1, c)."
+            lls.append(rf.loglikelihood("", ctx.replace("XXXXX", option))[0])
+        return lls
+
+    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.
+        """
+        gold = doc["options"].index(doc["answer"])
+        pred = np.argmax(results)
+        return {
+            "acc": pred == gold
+        }
+
+    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 {
+            "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
+        }
+
+
+class CBTCN(CBTBase):
+    DATASET_NAME = "CN"
+
+
+class CBTNE(CBTBase):
+    DATASET_NAME = "NE"