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* Add NeedleInAHaystack Test * Apply pre-commit formatting * Update configs/eval_hf_internlm_chat_20b_cdme.py Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com> * add needle in haystack test * update needle in haystack test * update plot function in tools_needleinahaystack.py * optimizing needleinahaystack dataset generation strategy * modify minor formatting issues * add English version support * change NeedleInAHaystackDataset to dynamic loading * change NeedleInAHaystackDataset to dynamic loading * fix needleinahaystack test eval bug * fix needleinahaystack config bug --------- Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com>
82 lines
2.9 KiB
Python
82 lines
2.9 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.datasets.cdme.cdme import CDMEDataset
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from opencompass.datasets.cdme.cdme import CDMEEvaluator
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from opencompass.datasets.cdme.cdme import cdme_postprocess
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from opencompass.datasets.cdme.cdme import cdme_dataset_postprocess
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import math
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def logistic(x, L=100, x0=50, k=0.1):
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return round(L / (1 + math.exp(-k * (x - x0))), 3)
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def generate_linear_space(start, end, num):
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if num == 1:
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return [start]
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elif num < 1:
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raise ValueError("num must be at least 1.")
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step = (end - start) / (num - 1)
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return [start + step * i for i in range(num)]
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def generate_depth_percents(intervals, interval_type):
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if interval_type == 'linear':
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return generate_linear_space(0, 100, intervals)
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elif interval_type == 'sigmoid':
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linear_space = generate_linear_space(0, 100, intervals)
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return [logistic(x) for x in linear_space]
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else:
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raise ValueError('Unsupported interval type')
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cdme_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
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cdme_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template='''{prompt}'''),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_out_len=512))
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cdme_eval_cfg = dict(
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evaluator=dict(type=CDMEEvaluator),
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pred_postprocessor=dict(type=cdme_postprocess),
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dataset_postprocessor=dict(type=cdme_dataset_postprocess),
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pred_role='BOT')
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context_lengths = list(range(1000, 33000, 1000))
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document_depth_percent_intervals = 20
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document_depth_percent_interval_type = "linear"
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base_path = './data/CDME'
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file_list = ['zh_finance.jsonl']
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cdme_datasets = []
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for original_context_length in context_lengths:
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for depth_percent in generate_depth_percents(
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document_depth_percent_intervals,
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document_depth_percent_interval_type):
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dataset_dict = {
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'abbr': f'CDME_Length{original_context_length}'
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f'Depth{int(depth_percent)}',
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'type': CDMEDataset,
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'path': base_path,
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'length': original_context_length,
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'depth': int(depth_percent),
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'tokenizer_model': 'gpt-4',
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'file_list': file_list,
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'num_repeats_per_file': 10,
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'length_buffer': 200,
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'guide': True,
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'language': 'Chinese',
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'needle': '\n小明最喜欢的实习的地点就是上海人工智能实验室。\n',
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'retrieval_question': '小明最喜欢的实习地点是哪里？请按照'
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'“小明最喜欢的实习地点就是________。”的格式回答。',
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'reader_cfg': cdme_reader_cfg,
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'infer_cfg': cdme_infer_cfg,
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'eval_cfg': cdme_eval_cfg
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}
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cdme_datasets.append(dataset_dict)
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