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53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import ZeroRetriever
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from opencompass.openicl.icl_inferencer import ChatInferencer
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from opencompass.openicl.icl_evaluator import TEvalEvaluator
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from opencompass.datasets import teval_postprocess, TEvalDataset
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teval_subject_mapping = {
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"instruct": ["instruct_v1"],
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"plan": ["plan_json_v1", "plan_str_v1"],
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"review": ["review_str_v1"],
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"reason_retrieve_understand": ["reason_retrieve_understand_json_v1"],
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"reason": ["reason_str_v1"],
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"retrieve": ["retrieve_str_v1"],
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"understand": ["understand_str_v1"],
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}
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teval_reader_cfg = dict(input_columns=["prompt"], output_column="ground_truth")
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teval_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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round=[
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dict(role="HUMAN", prompt="{prompt}"),
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],
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),
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),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=ChatInferencer),
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)
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teval_all_sets = list(teval_subject_mapping.keys())
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teval_datasets = []
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for _name in teval_all_sets:
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teval_eval_cfg = dict(
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evaluator=dict(type=TEvalEvaluator, subset=_name),
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pred_postprocessor=dict(type=teval_postprocess),
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num_gpus=1,
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)
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for subset in teval_subject_mapping[_name]:
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teval_datasets.append(
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dict(
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abbr="teval-" + subset,
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type=TEvalDataset,
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path="./data/teval/EN",
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name=subset,
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reader_cfg=teval_reader_cfg,
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infer_cfg=teval_infer_cfg,
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eval_cfg=teval_eval_cfg,
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)
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)
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