src/backend/envs.py

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):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard
    # task0 = Task("anli_r1", "acc", "ANLI")
    # task1 = Task("logiqa", "acc_norm", "LogiQA")
    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
    # 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)

    task10 = Task("memo-trap", "acc", "memo-trap", 0)

    task11 = Task("nq8", "em", "NQ Open 8", 8)
    task12 = Task("tqa8", "em", "TriviaQA 8", 8)

    task13 = Task("ifeval", "prompt_level_strict_acc", "IFEval", 0)

# NUM_FEWSHOT = 64  # Change with your few shot


EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")
EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk")

DEVICE = "cuda" if torch.cuda.is_available() else 'cpu'

LIMIT = None  # Testing; needs to be None