import os import torch from dataclasses import dataclass from enum import Enum from src.envs import CACHE_PATH @dataclass class Task: benchmark: str metric: str col_name: str num_fewshot: int class Tasks(Enum): # task0 = Task("nq_open", "em", "NQ Open", 64) # 64, as in the ATLAS paper # task1 = Task("triviaqa", "em", "TriviaQA", 64) # 64, as in the ATLAS paper # task11 = Task("nq8", "em", "NQ Open 8", 8) # task12 = Task("tqa8", "em", "TriviaQA 8", 8) # TruthfulQA is intended as a zero-shot benchmark [5, 47]. https://owainevans.github.io/pdfs/truthfulQA_lin_evans.pdf # task2 = Task("truthfulqa_gen", "rougeL_acc", "TruthfulQA Gen", 0) # task3 = Task("truthfulqa_mc1", "acc", "TruthfulQA MC1", 0) # task4 = Task("truthfulqa_mc2", "acc", "TruthfulQA MC2", 0) # task5 = Task("halueval_qa", "acc", "HaluEval QA", 0) # task6 = Task("halueval_dialogue", "acc", "HaluEval Dialogue", 0) # task7 = Task("halueval_summarization", "acc", "HaluEval Summarization", 0) # task8 = Task("xsum", "rougeL", "XSum", 2) # task9 = Task("cnndm", "rougeL", "CNN/DM", 2) # task8_1 = Task("xsum_v2", "rougeL", "XSum", 0) # task9_1 = Task("cnndm_v2", "rougeL", "CNN/DM", 0) # task10 = Task("memo-trap", "acc", "memo-trap", 0) # task10_2 = Task("memo-trap_v2", "acc", "memo-trap", 0) # task13 = Task("ifeval", "prompt_level_strict_acc", "IFEval", 0) task14 = Task("selfcheckgpt", "max-selfcheckgpt", "SelfCheckGPT", 0) # task15 = Task("fever10", "acc", "FEVER", 16) # task15_1 = Task("fever11", "acc", "FEVER", 8) # task16 = Task("squadv2", "exact", "SQuADv2", 4) # task17 = Task("truefalse_cieacf", "acc", "TrueFalse", 8) # task18 = Task("faithdial_hallu", "acc", "FaithDial", 8) # task19 = Task("faithdial_hallu_v2", "acc", "FaithDial", 8) # task20 = Task("race", "acc", "RACE", 0) task21 = Task("mmlu", "acc", "MMLU", 5) EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk") EVAL_REQUESTS_PATH_BACKEND_SYNC = os.path.join(CACHE_PATH, "eval-queue-bk-sync") EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk") DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"