OpenCompass/configs/eval_subjective_fofo.py
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[Feature] add dataset Fofo (#1224)
* add fofo dataset

* add dataset fofo
2024-06-06 11:40:48 +08:00

70 lines
2.2 KiB
Python

from mmengine.config import read_base
with read_base():
from .datasets.subjective.fofo.fofo_judge import subjective_datasets
from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3, OpenAI
from opencompass.partitioners import NaivePartitioner, SizePartitioner
from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
from opencompass.partitioners.sub_size import SubjectiveSizePartitioner
from opencompass.runners import LocalRunner
from opencompass.runners import SlurmSequentialRunner
from opencompass.tasks import OpenICLInferTask
from opencompass.models import HuggingFacewithChatTemplate
from opencompass.tasks.subjective_eval import SubjectiveEvalTask
from opencompass.summarizers import FofoSummarizer
api_meta_template = dict(
round=[
dict(role='HUMAN', api_role='HUMAN'),
dict(role='BOT', api_role='BOT', generate=True),
]
)
# -------------Inference Stage ----------------------------------------
# For subjective evaluation, we often set do sample for models
models = [
dict(
type=HuggingFacewithChatTemplate,
abbr='internlm2-chat-1.8b-hf',
path='internlm/internlm2-chat-1_8b',
max_out_len=1024,
batch_size=8,
run_cfg=dict(num_gpus=1),
stop_words=['</s>', '<|im_end|>'],
generation_kwargs=dict(
do_sample=True,
),
)
]
datasets = [*subjective_datasets]
# -------------Evalation Stage ----------------------------------------
## ------------- JudgeLLM Configuration
judge_models = [dict(
abbr='GPT4-Turbo',
type=OpenAI,
path='gpt-4-1106-preview',
key='xxxx', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well
meta_template=api_meta_template,
query_per_second=16,
max_out_len=2048,
max_seq_len=2048,
batch_size=8,
temperature=0,
)]
## ------------- Evaluation Configuration
eval = dict(
partitioner=dict(
type=SubjectiveSizePartitioner, max_task_size=10000, mode='singlescore', models=models, judge_models=judge_models,
),
runner=dict(type=LocalRunner, max_num_workers=2, task=dict(type=SubjectiveEvalTask)),
)
summarizer = dict(type=FofoSummarizer, judge_type='general')
work_dir = 'outputs/fofo/'