eval_judgerbench.py

from mmengine.config import read_base

with read_base():
    from opencompass.configs.datasets.subjective.judgerbench.judgerbench import judgerbench_datasets
from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3, OpenAI, TurboMindModelwithChatTemplate
from opencompass.partitioners import NaivePartitioner, SizePartitioner
from opencompass.runners import LocalRunner
from opencompass.runners import SlurmSequentialRunner
from opencompass.tasks import OpenICLInferTask, OpenICLEvalTask


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=TurboMindModelwithChatTemplate,
        abbr='CompassJudger-1-7B-Instruct',
        path='opencompass/CompassJudger-1-7B-Instruct',
        engine_config=dict(session_len=16384, max_batch_size=16, tp=1),
        gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048),
        max_seq_len=16384,
        max_out_len=2048,
        batch_size=16,
        run_cfg=dict(num_gpus=1),
    )
]


datasets = judgerbench_datasets


infer = dict(
    partitioner=dict(type=NaivePartitioner),
    runner=dict(type=LocalRunner, max_num_workers=16, task=dict(type=OpenICLInferTask)),
)
# -------------Evalation Stage ----------------------------------------


## ------------- Evaluation Configuration
eval = dict(
    partitioner=dict(type=NaivePartitioner, n=10,),
    runner=dict(type=LocalRunner, max_num_workers=16, task=dict(type=OpenICLEvalTask)),
)

work_dir = 'outputs/judgerbench/'