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[Update] MUSR dataset config prefix update (#1692)

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Linchen Xiao 2024-11-15 11:06:30 +08:00 committed by GitHub
parent e9e4b69ddb
commit 40a9f0be0d
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3 changed files with 139 additions and 135 deletions
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2
configs/eval_musr.py
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@ -2,7 +2,7 @@ from mmengine.config import read_base
import os.path as osp
with read_base():
from opencompass.configs.datasets.musr.musr_gen import musr_datasets
from opencompass.configs.datasets.musr.musr_gen_3c6e15 import musr_datasets
# from opencompass.configs.models.hf_internlm.hf_internlm2_5_1_8b_chat import models
from opencompass.configs.models.hf_internlm.lmdeploy_internlm2_5_7b_chat import (
models as lmdeploy_internlm2_5_7b_chat_model,

137
opencompass/configs/datasets/musr/musr_gen.py
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@ -1,135 +1,4 @@
from opencompass.datasets import MusrDataset, MusrEvaluator
from opencompass.openicl import PromptTemplate, ZeroRetriever, GenInferencer
from mmengine.config import read_base
DATASET_CONFIGS = {
'murder_mysteries': {
'abbr': 'musr_murder_mysteries',
'name': 'murder_mysteries',
'path': 'opencompass/musr',
'reader_cfg': dict(
input_columns=['context', 'question_text', 'question', 'answer', 'choices', 'choices_str', 'intermediate_trees', 'intermediate_data', 'prompt', 'system_prompt', 'gold_answer', 'scidx', 'self_consistency_n', 'ablation_name'],
output_column='gold_answer',
),
'infer_cfg': dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[
dict(
role='SYSTEM',
fallback_role='HUMAN',
prompt='{system_prompt}'
)
],
round=[
dict(
role='HUMAN',
prompt='{prompt}'
),
]
),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512),
),
'eval_cfg': dict(
evaluator=dict(
type=MusrEvaluator,
answer_index_modifier=1,
self_consistency_n=1
),
),
},
'object_placements': {
'abbr': 'musr_object_placements',
'name': 'object_placements',
'path': 'opencompass/musr',
'reader_cfg': dict(
input_columns=['context', 'question_text', 'question', 'answer', 'choices', 'choices_str', 'intermediate_trees', 'intermediate_data', 'prompt', 'system_prompt', 'gold_answer', 'scidx', 'self_consistency_n', 'ablation_name'],
output_column='gold_answer',
),
'infer_cfg': dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[
dict(
role='SYSTEM',
fallback_role='HUMAN',
prompt='{system_prompt}'
)
],
round=[
dict(
role='HUMAN',
prompt='{prompt}'
),
]
),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512),
),
'eval_cfg': dict(
evaluator=dict(
type=MusrEvaluator,
answer_index_modifier=1,
self_consistency_n=1
),
),
},
'team_allocation': {
'abbr': 'musr_team_allocation',
'name': 'team_allocation',
'path': 'opencompass/musr',
'reader_cfg': dict(
input_columns=['context', 'question_text', 'question', 'answer', 'choices', 'choices_str', 'intermediate_trees', 'intermediate_data', 'prompt', 'system_prompt', 'gold_answer', 'scidx', 'self_consistency_n', 'ablation_name'],
output_column='gold_answer',
),
'infer_cfg': dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[
dict(
role='SYSTEM',
fallback_role='HUMAN',
prompt='{system_prompt}'
)
],
round=[
dict(
role='HUMAN',
prompt='{prompt}'
),
]
),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512),
),
'eval_cfg': dict(
evaluator=dict(
type=MusrEvaluator,
answer_index_modifier=1,
self_consistency_n=1
),
),
},
}
musr_datasets = []
for config in DATASET_CONFIGS.values():
dataset = dict(
abbr=config['abbr'],
type=MusrDataset,
path=config['path'],
name=config['name'],
reader_cfg=config['reader_cfg'],
infer_cfg=config['infer_cfg'],
eval_cfg=config['eval_cfg'],
)
musr_datasets.append(dataset)
with read_base():
from .musr_gen_3c6e15 import musr_datasets # noqa: F401, F403

135
opencompass/configs/datasets/musr/musr_gen_3c6e15.py Normal file
View File

@ -0,0 +1,135 @@
from opencompass.datasets import MusrDataset, MusrEvaluator
from opencompass.openicl import PromptTemplate, ZeroRetriever, GenInferencer
DATASET_CONFIGS = {
'murder_mysteries': {
'abbr': 'musr_murder_mysteries',
'name': 'murder_mysteries',
'path': 'opencompass/musr',
'reader_cfg': dict(
input_columns=['context', 'question_text', 'question', 'answer', 'choices', 'choices_str', 'intermediate_trees', 'intermediate_data', 'prompt', 'system_prompt', 'gold_answer', 'scidx', 'self_consistency_n', 'ablation_name'],
output_column='gold_answer',
),
'infer_cfg': dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[
dict(
role='SYSTEM',
fallback_role='HUMAN',
prompt='{system_prompt}'
)
],
round=[
dict(
role='HUMAN',
prompt='{prompt}'
),
]
),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512),
),
'eval_cfg': dict(
evaluator=dict(
type=MusrEvaluator,
answer_index_modifier=1,
self_consistency_n=1
),
),
},
'object_placements': {
'abbr': 'musr_object_placements',
'name': 'object_placements',
'path': 'opencompass/musr',
'reader_cfg': dict(
input_columns=['context', 'question_text', 'question', 'answer', 'choices', 'choices_str', 'intermediate_trees', 'intermediate_data', 'prompt', 'system_prompt', 'gold_answer', 'scidx', 'self_consistency_n', 'ablation_name'],
output_column='gold_answer',
),
'infer_cfg': dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[
dict(
role='SYSTEM',
fallback_role='HUMAN',
prompt='{system_prompt}'
)
],
round=[
dict(
role='HUMAN',
prompt='{prompt}'
),
]
),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512),
),
'eval_cfg': dict(
evaluator=dict(
type=MusrEvaluator,
answer_index_modifier=1,
self_consistency_n=1
),
),
},
'team_allocation': {
'abbr': 'musr_team_allocation',
'name': 'team_allocation',
'path': 'opencompass/musr',
'reader_cfg': dict(
input_columns=['context', 'question_text', 'question', 'answer', 'choices', 'choices_str', 'intermediate_trees', 'intermediate_data', 'prompt', 'system_prompt', 'gold_answer', 'scidx', 'self_consistency_n', 'ablation_name'],
output_column='gold_answer',
),
'infer_cfg': dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[
dict(
role='SYSTEM',
fallback_role='HUMAN',
prompt='{system_prompt}'
)
],
round=[
dict(
role='HUMAN',
prompt='{prompt}'
),
]
),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512),
),
'eval_cfg': dict(
evaluator=dict(
type=MusrEvaluator,
answer_index_modifier=1,
self_consistency_n=1
),
),
},
}
musr_datasets = []
for config in DATASET_CONFIGS.values():
dataset = dict(
abbr=config['abbr'],
type=MusrDataset,
path=config['path'],
name=config['name'],
reader_cfg=config['reader_cfg'],
infer_cfg=config['infer_cfg'],
eval_cfg=config['eval_cfg'],
)
musr_datasets.append(dataset)