OpenCompass/configs/datasets/lawbench/lawbench_zero_shot_gen_002588.py
2024-05-14 15:35:58 +08:00

63 lines
2.0 KiB
Python

from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import LawBenchDataset
names = [
['1-1', 'article_recitation'],
['1-2', 'knowledge_question_answering'],
['2-1', 'document_proofreading'],
['2-2', 'dispute_focus_identification'],
['2-3', 'marital_disputes_identification'],
['2-4', 'issue_topic_identification'],
['2-5', 'reading_comprehension'],
['2-6', 'named_entity_recognition'],
['2-7', 'opinion_summarization'],
['2-8', 'argument_mining'],
['2-9', 'event_detection'],
['2-10', 'trigger_word_extraction'],
['3-1', 'fact_based_article_prediction'],
['3-2', 'scene_based_article_prediction'],
['3-3', 'charge_prediction'],
['3-4', 'prison_term_prediction_wo_article'],
['3-5', 'prison_term_prediction_w_article'],
['3-6', 'case_analysis'],
['3-7', 'criminal_damages_calculation'],
['3-8', 'consultation'],
]
lawbench_datasets = []
for index, name in names:
lawbench_reader_cfg = dict(
input_columns=['instruction', 'question'],
output_column='answer')
lawbench_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(role='HUMAN', prompt='{instruction}\n{question}'),
]
),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=1024)
)
lawbench_eval_cfg = dict(
evaluator=dict(type='LawBenchEvaluator_' + index.replace('-', '_'))
)
lawbench_datasets.append(
dict(
abbr='lawbench-' + index + '-' + name + '-0-shot',
type=LawBenchDataset,
path='./data/lawbench/zero_shot',
index=index,
reader_cfg=lawbench_reader_cfg,
infer_cfg=lawbench_infer_cfg,
eval_cfg=lawbench_eval_cfg
)
)