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7505b3cadf
* add TheoremQA with 5-shot * add huggingface_above_v4_33 classes * use num_worker partitioner in cli * update theoremqa * update TheoremQA * add TheoremQA * rename theoremqa -> TheoremQA * update TheoremQA output path * rewrite many model configs * update huggingface * further update * refine configs * update configs * update configs * add configs/eval_llama3_instruct.py * add summarizer multi faceted * update bbh datasets * update configs/models/hf_llama/lmdeploy_llama3_8b_instruct.py * rename class * update readme * update hf above v4.33
42 lines
1.2 KiB
Python
42 lines
1.2 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 GenInferencer
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from opencompass.openicl.icl_evaluator import AccEvaluator
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from opencompass.datasets import winograndeDataset_V2
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from opencompass.utils.text_postprocessors import first_option_postprocess
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winogrande_reader_cfg = dict(
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input_columns=["prompt", "only_option1", "only_option2"],
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output_column="answer",
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)
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winogrande_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="Question: {prompt}\nA. {only_option1}\nB. {only_option2}\nAnswer:"),
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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=GenInferencer),
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)
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winogrande_eval_cfg = dict(
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evaluator=dict(type=AccEvaluator),
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pred_role="BOT",
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pred_postprocessor=dict(type=first_option_postprocess, options='AB'),
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)
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winogrande_datasets = [
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dict(
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abbr="winogrande",
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type=winograndeDataset_V2,
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path='./data/winogrande',
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reader_cfg=winogrande_reader_cfg,
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infer_cfg=winogrande_infer_cfg,
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eval_cfg=winogrande_eval_cfg,
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)
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]
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