OpenCompass/configs/datasets/mmlu/mmlu_openai_simple_evals_gen_b618ea.py
Fengzhe Zhou d43392a3bb
[Feature] Add mmlu prompt from simple_evals, openai (#1074)
* add mmlu prompt from simple_evals, openai

* return empty str on failure
2024-05-06 13:26:26 +08:00

60 lines
1.9 KiB
Python

from mmengine.config import read_base
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import MMLUDataset
from opencompass.utils.text_postprocessors import match_answer_pattern
with read_base():
from .mmlu_all_sets import mmlu_all_sets
# None of the mmlu dataset in huggingface is correctly parsed, so we use our own dataset reader
# Please download the dataset from https://people.eecs.berkeley.edu/~hendrycks/data.tar
QUERY_TEMPLATE = """
Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.
{input}
A) {A}
B) {B}
C) {C}
D) {D}
""".strip()
mmlu_reader_cfg = dict(
input_columns=["input", "A", "B", "C", "D"],
output_column="target",
train_split='dev')
mmlu_datasets = []
for name in mmlu_all_sets:
mmlu_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(role="HUMAN", prompt=QUERY_TEMPLATE),
],
),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer),
)
mmlu_eval_cfg = dict(
evaluator=dict(type=AccEvaluator),
pred_postprocessor=dict(type=match_answer_pattern, answer_pattern=r"(?i)ANSWER\s*:\s*([A-D])"))
mmlu_datasets.append(
dict(
abbr=f"lukaemon_mmlu_{name}",
type=MMLUDataset,
path="./data/mmlu/",
name=name,
reader_cfg=mmlu_reader_cfg,
infer_cfg=mmlu_infer_cfg,
eval_cfg=mmlu_eval_cfg,
))