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https://github.com/open-compass/opencompass.git
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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
57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
import os
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from mmengine.config import read_base
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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.datasets import BBHDataset, bbh_mcq_postprocess, BBHEvaluator, BBHEvaluator_mcq
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with read_base():
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from .bbh_subset_settings import settings
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bbh_datasets = []
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for name, test_type in settings:
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with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{name}.txt'), 'r') as f:
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hint = f.read()
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task_prompt, body = hint.split('\n\nQ:', 1)
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sections = ('Q:' + body).split('\n\n')
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prompt_rounds = []
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for index, section in enumerate(sections):
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question, answer = section.split('\nA:')
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answer = 'A:' + answer
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if index == 0:
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desc = task_prompt.strip() + '\n'
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else:
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desc = ''
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prompt_rounds.append(dict(role="HUMAN", prompt=f"{desc}{question.strip()}"))
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prompt_rounds.append(dict(role="BOT", prompt=answer.strip()))
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prompt_rounds.append(dict(role="HUMAN", prompt="Q: {input}"))
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bbh_reader_cfg = dict(input_columns=["input"], output_column="target")
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bbh_infer_cfg = dict(
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prompt_template=dict(type=PromptTemplate, template=dict(round=prompt_rounds)),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_out_len=512))
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if test_type == 'mcq':
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bbh_eval_cfg = dict(
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evaluator=dict(type=BBHEvaluator_mcq),
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pred_role="BOT",
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pred_postprocessor=dict(type=bbh_mcq_postprocess),
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dataset_postprocessor=dict(type=bbh_mcq_postprocess))
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else:
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bbh_eval_cfg = dict(
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evaluator=dict(type=BBHEvaluator),
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pred_role="BOT")
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bbh_datasets.append(
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dict(
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type=BBHDataset,
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path="./data/BBH/data",
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name=name,
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abbr='bbh-' + name,
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reader_cfg=bbh_reader_cfg.copy(),
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infer_cfg=bbh_infer_cfg.copy(),
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eval_cfg=bbh_eval_cfg.copy()))
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