added bigscience-lama evaluation script.

lm_eval/tasks/bigsciencelama.py

+from lm_eval.base import PromptSourceTask
+_CITATION = """
+@inproceedings{petroni2019language, title={Language Models as Knowledge Bases?},
+               author={F. Petroni, T. Rockt{"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
+               booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} }
+
+@inproceedings{petroni2020how,
+               title={How Context Affects Language Models' Factual Predictions},
+               author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
+               booktitle={Automated Knowledge Base Construction}, year={2020}, url={https://openreview.net/forum?id=025X0zPfn} }
+"""
+
+
+class BigScienceLAMA(PromptSourceTask):
+    VERSION = 0
+    DATASET_PATH = "janck/bigscience-lama"
+    DATASET_NAME = None
+
+
+    def has_training_docs(self):
+        # TODO: Fill in the return with `True` if the Task has training data; else `False`.
+        return False
+    def has_validation_docs(self):
+        # TODO: Fill in the return with `True` if the Task has validation data; else `False`.
+        return False
+    def has_test_docs(self):
+        # TODO: Fill in the return with `True` if the Task has test data; else `False`.
+        return True
+    def training_docs(self):
+        if self.has_training_docs():
+            return self.dataset["train"]
+
+    def validation_docs(self):
+        if self.has_validation_docs():
+            return self.dataset["train"]
+
+    def test_docs(self):
+        if self.has_test_docs():
+            self._test_docs = list(self.dataset["test"])
+            return self._test_docs
+
+
+
+