Implement the HellaSwag evaluation

[StellaAthena]

From the GPT-3 paper

The HellaSwag dataset [ZHB+19] involves picking the best ending to a story or set of instructions. The examples were adversarially mined to be difficult for language models while remaining easy for humans (who achieve 95.6% accuracy). GPT-3 achieves 78.1% accuracy in the one-shot setting and 79.3% accuracy in the few-shot setting, outperforming the 75.4% accuracy of a fine-tuned 1.5B parameter language model [ZHR+19] but still a fair amount lower than the overall SOTA of 85.6% achieved by the fine-tuned multi-task model ALUM.

• Data processing code implemented
• Evaluation implemented

The evaluation code should be modeled after the interface in lm_eval/base.py and the example of the BoolQ task in lm_eval/tasks/suerglue.py

[cfoster0]

Note: HuggingFace includes this in its datasets package.

https://huggingface.co/datasets/hellaswag

[cfoster0]

Working on adding this for dedupe.