Added openbookqa

lm_eval/tasks/openbookqa.py

+import numpy as np
+from scipy.stats import pearsonr, spearmanr
+from sklearn.metrics import f1_score, matthews_corrcoef
+from tqdm import auto as tqdm_lib
+from . common import HFTask, simple_accuracy_metric, yesno
+
+class OpenBookQA(HFTask):
+    DATASET_PATH = "openbookqa"
+    DATASET_NAME = "main"
+
+    def has_training_docs(self):
+        return True
+
+    def has_validation_docs(self):
+        return True
+
+    def has_test_docs(self):
+        return True
+
+    def training_docs(self):
+        if self.has_training_docs():
+            if self._training_docs is None:
+                self._training_docs = list(self.data["train"])
+            return self._training_docs
+
+    def validation_docs(self):
+        if self.has_validation_docs():
+            return self.data["validation"]
+
+    def test_docs(self):
+        if self.has_test_docs():
+            return self.data["test"]
+
+    def fewshot_description(self):
+        return "Text of the question prompt\nText of the answer completion"
+
+    def doc_to_text(self, doc, include_target=True):
+        text = doc['question_stem'] + '\n'
+        if include_target:
+            letter_answer = doc['answerKey']
+            if letter_answer == 'A':
+                index = 0
+            elif letter_answer == 'B':
+                index = 1
+            elif letter_answer == 'C':
+                index = 2
+            elif letter_answer == 'D':
+                index = 3
+            else:
+                raise ValueError("OpenBookQA from HF datasets contained an invalid answer key")
+            text += doc['choices']['text'][index] + '.'
+        return text
+
+    def evaluate(self, docs, lm, provide_description, num_fewshot):
+        # TODO: Write evaluation function
+        raise NotImplementedError()