[Feature] Upgrade the needle-in-a-haystack experiment to Needlebench (#913)

* add needlebench

* simplify needlebench 32k, 128k, 200k for eval

* update act prompt

* fix bug in needlebench summarizer

* add needlebench intro, fix summarizer

* lint summarizer

* fix linting error

* move readme.md

* update readme for needlebench

* update docs of needlebench

* simplify needlebench summarizers

configs/datasets/needlebench/needlebench_8k/needlebench_multi_retrieval.py

+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.datasets.needlebench.parallel import NeedleBenchParallelDataset
+from opencompass.datasets.needlebench.parallel import NeedleBenchParallelEvaluator
+from opencompass.datasets.needlebench.origin import needlebench_postprocess
+from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
+import math
+
+
+def logistic(x, L=100, x0=50, k=0.1):
+    return round(L / (1 + math.exp(-k * (x - x0))), 3)
+
+
+def generate_linear_space(start, end, num):
+    if num == 1:
+        return [start]
+    elif num < 1:
+        raise ValueError("num must be at least 1.")
+    step = (end - start) / (num - 1)
+    return [start + step * i for i in range(num)]
+
+
+def generate_depth_percents(intervals, interval_type):
+    if interval_type == 'linear':
+        return generate_linear_space(0, 100, intervals)
+    elif interval_type == 'sigmoid':
+        linear_space = generate_linear_space(0, 100, intervals)
+        return [logistic(x) for x in linear_space]
+    else:
+        raise ValueError('Unsupported interval type')
+
+
+needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
+
+needlebench_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(role='HUMAN', prompt='{prompt}'),
+                dict(role='BOT', prompt='{answer}\n'),
+            ]
+        )
+        ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer))
+
+needlebench_eval_cfg = dict(
+    evaluator=dict(type=NeedleBenchParallelEvaluator),
+    pred_postprocessor=dict(type=needlebench_postprocess),
+    dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
+    pred_role='BOT')
+
+context_lengths = list(range(5000, 9000, 1000))
+document_depth_percent_intervals = 20
+document_depth_percent_interval_type = "linear"
+
+base_path = './data/needlebench'
+file_list = ['PaulGrahamEssays.jsonl']
+needlebench_datasets_en = []
+needle_file_name = 'needles.jsonl'
+depths_float = generate_depth_percents(
+            document_depth_percent_intervals,
+            document_depth_percent_interval_type)
+depths = [int(depth) for depth in depths_float]
+
+for original_context_length in context_lengths:
+    dataset_dict = {
+        'abbr': f'Length{original_context_length}'
+                f'_parallel_en_8k',
+        'type': NeedleBenchParallelDataset,
+        'path': base_path,
+        'needle_file_name': needle_file_name,
+        'length': original_context_length,
+        'depths': depths,
+        'tokenizer_model': 'gpt-4',
+        'file_list': file_list,
+        'num_repeats_per_file': 25,
+        'length_buffer': 1300,
+        'guide': True,
+        'language': 'English',
+        'reader_cfg': needlebench_reader_cfg,
+        'infer_cfg': needlebench_infer_cfg,
+        'eval_cfg': needlebench_eval_cfg
+    }
+    needlebench_datasets_en.append(dataset_dict)
+
+file_list = ['zh_finance.jsonl']
+needlebench_datasets_zh = []
+
+for original_context_length in context_lengths:
+    dataset_dict = {
+        'abbr': f'Length{original_context_length}'
+                f'_parallel_zh_8k',
+        'type': NeedleBenchParallelDataset,
+        'path': base_path,
+        'needle_file_name': needle_file_name,
+        'length': original_context_length,
+        'depths': depths,
+        'tokenizer_model': 'gpt-4',
+        'file_list': file_list,
+        'num_repeats_per_file': 25,
+        'length_buffer': 200,
+        'guide': True,
+        'language': 'Chinese',
+        'reader_cfg': needlebench_reader_cfg,
+        'infer_cfg': needlebench_infer_cfg,
+        'eval_cfg': needlebench_eval_cfg
+    }
+    needlebench_datasets_zh.append(dataset_dict)

configs/datasets/needlebench/needlebench_8k/needlebench_multi_retrieval_compare_batch.py

+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.datasets.needlebench.parallel import NeedleBenchParallelDataset
+from opencompass.datasets.needlebench.parallel import NeedleBenchParallelEvaluator
+from opencompass.datasets.needlebench.origin import needlebench_postprocess
+from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
+import math
+
+
+def logistic(x, L=100, x0=50, k=0.1):
+    return round(L / (1 + math.exp(-k * (x - x0))), 3)
+
+
+def generate_linear_space(start, end, num):
+    if num == 1:
+        return [start]
+    elif num < 1:
+        raise ValueError("num must be at least 1.")
+    step = (end - start) / (num - 1)
+    return [start + step * i for i in range(num)]
+
+
+def generate_depth_percents(intervals, interval_type):
+    if interval_type == 'linear':
+        return generate_linear_space(0, 100, intervals)
+    elif interval_type == 'sigmoid':
+        linear_space = generate_linear_space(0, 100, intervals)
+        return [logistic(x) for x in linear_space]
+    else:
+        raise ValueError('Unsupported interval type')
+
+
+needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
+
+needlebench_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(role='HUMAN', prompt='{prompt}'),
+                dict(role='BOT', prompt='{answer}\n'),
+            ]
+        )
+        ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer))
+
+needlebench_eval_cfg = dict(
+    evaluator=dict(type=NeedleBenchParallelEvaluator),
+    pred_postprocessor=dict(type=needlebench_postprocess),
+    dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
+    pred_role='BOT')
+
+context_lengths = list(range(5000, 9000, 1000))
+document_depth_percent_intervals_list = [1, 5, 10, 15, 20]
+document_depth_percent_interval_type = "linear"
+
+base_path = './data/needlebench'
+file_list = ['PaulGrahamEssays.jsonl']
+needlebench_datasets_en = []
+needle_file_name = 'needles.jsonl'
+
+for document_depth_percent_intervals in document_depth_percent_intervals_list:
+    depths_float = generate_depth_percents(
+                       document_depth_percent_intervals,
+                       document_depth_percent_interval_type)
+    depths = [int(depth) for depth in depths_float]
+
+    for original_context_length in context_lengths:
+        dataset_dict = {
+            'abbr': f'Length{original_context_length}'
+                    f'_parallel_en_8k_batch{document_depth_percent_intervals}',
+            'type': NeedleBenchParallelDataset,
+            'path': base_path,
+            'needle_file_name': needle_file_name,
+            'length': original_context_length,
+            'depths': depths,
+            'tokenizer_model': 'gpt-4',
+            'file_list': file_list,
+            'num_repeats_per_file': 50,
+            'length_buffer': 1300,
+            'guide': True,
+            'language': 'English',
+            'reader_cfg': needlebench_reader_cfg,
+            'infer_cfg': needlebench_infer_cfg,
+            'eval_cfg': needlebench_eval_cfg
+        }
+        needlebench_datasets_en.append(dataset_dict)
+
+file_list = ['zh_finance.jsonl']
+needlebench_datasets_zh = []
+needle_file_name = 'needles.jsonl'
+
+for document_depth_percent_intervals in document_depth_percent_intervals_list:
+    depths_float = generate_depth_percents(
+                       document_depth_percent_intervals,
+                       document_depth_percent_interval_type)
+    depths = [int(depth) for depth in depths_float]
+
+    for original_context_length in context_lengths:
+        dataset_dict = {
+            'abbr': f'Length{original_context_length}'
+                    f'_parallel_zh_8k_batch{document_depth_percent_intervals}',
+            'type': NeedleBenchParallelDataset,
+            'path': base_path,
+            'needle_file_name': needle_file_name,
+            'length': original_context_length,
+            'depths': depths,
+            'tokenizer_model': 'gpt-4',
+            'file_list': file_list,
+            'num_repeats_per_file': 50,
+            'length_buffer': 200,
+            'guide': True,
+            'language': 'Chinese',
+            'reader_cfg': needlebench_reader_cfg,
+            'infer_cfg': needlebench_infer_cfg,
+            'eval_cfg': needlebench_eval_cfg
+        }
+        needlebench_datasets_zh.append(dataset_dict)

configs/datasets/cdme/multi_needle/cdme8k_cot3_italy.py → configs/datasets/needlebench/needlebench_8k/needlebench_single.py

 from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import ZeroRetriever
 from opencompass.openicl.icl_inferencer import GenInferencer
-from opencompass.datasets.cdme.cdme_multi import CDMEDataset
-from opencompass.datasets.cdme.cdme_multi import CDMEEvaluator
-from opencompass.datasets.cdme.cdme_multi import cdme_postprocess
-from opencompass.datasets.cdme.cdme_multi import cdme_dataset_postprocess
+from opencompass.datasets.needlebench.origin import NeedleBenchOriginDataset
+from opencompass.datasets.needlebench.origin import NeedleBenchOriginEvaluator
+from opencompass.datasets.needlebench.origin import needlebench_postprocess
+from opencompass.datasets.needlebench.origin import needlebench_dataset_postprocess
 import math
 
 
@@ -31,47 +31,72 @@ def generate_depth_percents(intervals, interval_type):
         raise ValueError('Unsupported interval type')
 
 
-cdme_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
+needlebench_reader_cfg = dict(input_columns=['prompt'], output_column='answer')
 
-cdme_infer_cfg = dict(
+needlebench_infer_cfg = dict(
     prompt_template=dict(
         type=PromptTemplate,
-        template='''{prompt}'''),
+        template=dict(
+            round=[
+                dict(role='HUMAN', prompt='{prompt}'),
+                dict(role='BOT', prompt='{answer}\n'),
+            ]
+        )
+        ),
     retriever=dict(type=ZeroRetriever),
-    inferencer=dict(type=GenInferencer, max_out_len=512))
+    inferencer=dict(type=GenInferencer))
 
-cdme_eval_cfg = dict(
-    evaluator=dict(type=CDMEEvaluator),
-    pred_postprocessor=dict(type=cdme_postprocess),
-    dataset_postprocessor=dict(type=cdme_dataset_postprocess),
+needlebench_eval_cfg = dict(
+    evaluator=dict(type=NeedleBenchOriginEvaluator),
+    pred_postprocessor=dict(type=needlebench_postprocess),
+    dataset_postprocessor=dict(type=needlebench_dataset_postprocess),
     pred_role='BOT')
 
-context_lengths = list(range(1000, 9000, 1000))
+context_lengths = list(range(5000, 9000, 1000))
 document_depth_percent_intervals = 20
 document_depth_percent_interval_type = "linear"
 
-base_path = './data/CDME'
-file_list = ['zh_finance.jsonl']
-cdme_datasets = []
+base_path = './data/needlebench'
+file_list = ['PaulGrahamEssays.jsonl']
+needlebench_datasets_en = []
+needle_file_name = 'needles.jsonl'
 
-needles = ['\n意大利的佛罗伦萨有一家名为"La Giostra"的餐馆，是整个佛罗伦萨中排行第一的餐馆。\n',
-           '"La Giostra"餐馆的特色菜肴是松露奶酪通心粉。',
-           '松露奶酪通心粉是该家餐馆的有着意大利皇室烹饪血统的大厨Jack制作',]
-retrieval_question = ("制作佛罗伦萨中排行第一的餐馆的特色菜肴的人叫什么？"
-                      "请按照'制作佛罗伦萨中排行第一的餐馆的特色菜肴的人叫______。'的格式回答。")
-answer = "制作佛罗伦萨中排行第一的餐馆的特色菜肴的人叫Jack"
+for original_context_length in context_lengths:
+    for depth_percent in generate_depth_percents(
+            document_depth_percent_intervals,
+            document_depth_percent_interval_type):
+        dataset_dict = {
+            'abbr': f'Length{original_context_length}'
+                    f'Depth{int(depth_percent)}_origin_en_8k',
+            'type': NeedleBenchOriginDataset,
+            'path': base_path,
+            'length': original_context_length,
+            'depth': int(depth_percent),
+            'tokenizer_model': 'gpt-4',
+            'file_list': file_list,
+            'num_repeats_per_file': 10,
+            'length_buffer': 800,
+            'guide': True,
+            'language': 'English',
+            'needle_file_name': needle_file_name,
+            'reader_cfg': needlebench_reader_cfg,
+            'infer_cfg': needlebench_infer_cfg,
+            'eval_cfg': needlebench_eval_cfg
+        }
+        needlebench_datasets_en.append(dataset_dict)
 
-keyword = "Jack"
-diff = 25
+file_list = ['zh_finance.jsonl']
+needlebench_datasets_zh = []
+needle_file_name = 'needles.jsonl'
 
 for original_context_length in context_lengths:
     for depth_percent in generate_depth_percents(
             document_depth_percent_intervals,
             document_depth_percent_interval_type):
         dataset_dict = {
-            'abbr': f'CDME_Length{original_context_length}'
-            f'Depth{int(depth_percent)}',
-            'type': CDMEDataset,
+            'abbr': f'Length{original_context_length}'
+                    f'Depth{int(depth_percent)}_origin_zh_8k',
+            'type': NeedleBenchOriginDataset,
             'path': base_path,
             'length': original_context_length,
             'depth': int(depth_percent),
@@ -81,13 +106,9 @@ for original_context_length in context_lengths:
             'length_buffer': 200,
             'guide': True,
             'language': 'Chinese',
-            'needles': needles,
-            'diff': diff,
-            'retrieval_question': retrieval_question,
-            'answer': answer,
-            'keyword': keyword,
-            'reader_cfg': cdme_reader_cfg,
-            'infer_cfg': cdme_infer_cfg,
-            'eval_cfg': cdme_eval_cfg
+            'needle_file_name': needle_file_name,
+            'reader_cfg': needlebench_reader_cfg,
+            'infer_cfg': needlebench_infer_cfg,
+            'eval_cfg': needlebench_eval_cfg
         }
-        cdme_datasets.append(dataset_dict)
+        needlebench_datasets_zh.append(dataset_dict)

configs/datasets/needlebench/readme.md

+# Needlebench: A Benchmark for Needle-In-A-Haystack Evaluations
+
+English | [简体中文](readme_zh-CN.md)
+
+## Overview
+
+Needlebench is an exhaustive benchmark designed to rigorously assess the information retrieval and reasoning capabilities of large language models (LLMs). Drawing inspiration from the NeedleInAHaystack experiment, Needlebench broadens the scope to include a variety of tasks, each aimed at testing different facets of LLMs' abilities in long-context scenarios.
+
+### Directory Structure
+
+```
+configs/datasets/needlebench/
+├── atc
+├── needlebench_4k
+├── needlebench_8k
+├── needlebench_32k
+├── needlebench_128k
+├── needlebench_200k
+├── needlebench.py
+├── readme.md
+└── readme_zh-CN.md
+```
+
+Within each configuration directory (e.g., `needlebench_4k`), there are scripts tailored for testing within that specific length setting:
+
+```
+needlebench_4k/
+├── needlebench_multi_reasoning.py
+├── needlebench_multi_retrieval.py
+├── needlebench.py
+└── needlebench_single.py
+```
+
+## Task Descriptions and Length Configurations
+
+Needlebench offers tasks in various length configurations, such as 4k, 8k, etc., to accommodate different scales of language model evaluation needs. Each length configuration provides specialized test scripts for the following tasks:
+
+### Single-Needle Retrieval (`needlebench_single.py`)
+
+The Single-Needle Retrieval task evaluates the LLMs' ability to recall a single piece of crucial information from a haystack text of a specific length. This task mirrors the original NeedleInAHaystack test's objective, assessing the model's precision in identifying and recalling specific information from extended texts.
+
+### Multi-Needle Retrieval (`needlebench_multi_retrieval.py`)
+
+The Multi-Needle Retrieval task challenges the LLMs' ability to identify and extract multiple key information points from extensive texts. It simulates real-world scenarios where multiple data points, facts, or figures need to be retrieved from documents or reports, evaluating the model's efficiency in navigating and extracting relevant information from dense texts.
+
+### Multi-Needle Reasoning (`needlebench_multi_reasoning.py`)
+
+Building on the retrieval tasks, the Multi-Needle Reasoning task emphasizes the LLMs' capacity for complex reasoning with the retrieved information. The model must not only recall multiple pieces of information but also engage in logical reasoning, synthesizing answers that reflect an understanding of the intricate relationships between various information points.
+
+### Ancestral Trace Challenge (ATC)
+
+The Ancestral Trace Challenge is Needlebench's most complex task, requiring models to recall and analyze every detail in long texts for problem-solving that demands an understanding of complex relationships, such as genealogical inquiries or detailed case analysis. This task highlights the need for models to process and reason with information at a granular level, mirroring the demands of sophisticated real-world analytical tasks.

configs/datasets/needlebench/readme_zh-CN.md

+# Needlebench：大海捞针测试评估基准
+
+[English](readme.md) | 简体中文
+
+## 概览
+
+Needlebench是一个全面的基准测试，旨在严格评估大型语言模型（LLMs）的信息检索和推理能力。借鉴了NeedleInAHaystack实验的灵感，Needlebench扩展了范围，包括多种任务，每个任务都旨在测试LLMs处理长文本中关键信息的不同方面的能力。
+
+### 目录结构
+
+```
+configs/datasets/needlebench/
+├── atc
+├── needlebench_4k
+├── needlebench_8k
+├── needlebench_32k
+├── needlebench_128k
+├── needlebench_200k
+├── needlebench.py
+├── readme.md
+└── readme_zh-CN.md
+```
+
+在每个长度配置目录下（如 `needlebench_4k`），包含了专门针对该长度设置的测试任务脚本：
+
+```
+needlebench_4k/
+├── needlebench_multi_reasoning.py
+├── needlebench_multi_retrieval.py
+├── needlebench.py
+└── needlebench_single.py
+```
+
+## 任务描述与长度配置
+
+Needlebench提供了不同长度配置的任务，如4k、8k等，以适应不同规模的语言模型评估需求。每种长度配置针对以下任务提供了专门的测试脚本：
+
+### 单针信息检索 (`needlebench_single.py`)
+
+单针信息检索任务评估LLMs从特定长度的无关信息文本中回忆单个重要信息的能力。这个任务反映了原始的NeedleInAHaystack测试的目标，评估模型长文本中识别和回忆特定信息的精确性。
+
+### 多针信息检索 (`needlebench_multi_retrieval.py`)
+
+多针信息检索任务挑战LLMs识别和提取广泛文本中的多个关键信息点的能力。它模拟了现实世界中的场景，其中需要从文档或报告中检索多个数据点、事实或数字，评估模型在浏览和从密集文本中提取相关信息的效率。
+
+### 多针信息推理 (`needlebench_multi_reasoning.py`)
+
+在检索任务的基础上，多针信息推理任务强调LLMs使用检索到的信息进行复杂推理的能力。模型不仅需要回忆多个信息点，还需要进行逻辑推理，综合回答反映对不同信息点之间复杂关系理解的答案。
+
+### 祖源追溯挑战 (ATC)
+
+祖源追溯挑战是Needlebench中最复杂的任务，要求模型回忆和分析长文本中的每个细节，以解决需要理解复杂关系的问题，如家谱查询或详细案例分析。这个任务突出了模型处理和推理详细信息的需要，反映了现实世界中对复杂实际任务的要求。

configs/eval_needlebench.py

+from opencompass.models import HuggingFaceCausalLM
+from opencompass.models.turbomind import TurboMindModel
+from opencompass.runners import SlurmSequentialRunner
+from opencompass.partitioners import SizePartitioner, NaivePartitioner
+from opencompass.tasks import OpenICLInferTask, OpenICLEvalTask
+
+from mmengine.config import read_base
+with read_base():
+    # eval needlebench_4k
+    from .datasets.needlebench.needlebench_4k.needlebench import needlebench_datasets
+    from .summarizers.needlebench import needlebench_4k_summarizer as summarizer
+
+    # only eval original "needle in a haystack test" in needlebench_4k
+    # from .datasets.needlebench.needlebench_4k.needlebench_single import needlebench_datasets_zh, needlebench_datasets_en
+    # from .summarizers.needlebench import needlebench_4k_summarizer as summarizer
+
+    # eval Ancestral Tracing Challenge(ATC)
+    # from .datasets.needlebench.atc.atc import needlebench_atc_datasets_zh, needlebench_atc_datasets_en
+    # from .summarizers.needlebench import needlebench_atc_summarizer as summarizer
+
+datasets = sum([v for k, v in locals().items() if ('datasets' in k)], [])
+
+hf_internlm2_chat_7b_model_meta_template = dict(
+    round=[
+        dict(role='HUMAN',
+             begin='<|im_start|>user\n', end='<|im_end|>\n'),
+        dict(role='BOT', begin='<|im_start|>assistant\n',
+             end='<|im_end|>\n', generate=True),
+    ],
+)
+hf_internlm2_chat_7b = dict(
+        type=HuggingFaceCausalLM,
+        abbr='internlm2-chat-7b-hf',
+        path="internlm/internlm2-chat-7b",
+        tokenizer_path='internlm/internlm2-chat-7b',
+        model_kwargs=dict(
+            trust_remote_code=True,
+            device_map='auto',
+        ),
+        tokenizer_kwargs=dict(
+            padding_side='left',
+            truncation_side='left',
+            use_fast=False,
+            trust_remote_code=True,
+        ),
+        max_out_len=2000,
+        max_seq_len=32768,
+        batch_size=8,
+        meta_template=hf_internlm2_chat_7b_model_meta_template,
+        run_cfg=dict(num_gpus=1, num_procs=1),
+        end_str='<|im_end|>',
+        )
+
+internlm2_chat_7b_200k = dict(
+        type=TurboMindModel,
+        abbr='internlm2-chat-7b-200k',
+        path="internlm/internlm2-chat-7b",
+        meta_template=hf_internlm2_chat_7b_model_meta_template,
+        engine_config=dict(session_len=210000,
+                           max_batch_size=8,
+                           rope_scaling_factor=2.0,
+                           model_name="internlm2-chat-7b"),
+        gen_config=dict(top_k=1, top_p=0.8,
+                        temperature=1.0,
+                        max_new_tokens=2000),
+        max_out_len=2000,
+        max_seq_len=210000,
+        batch_size=8,
+        concurrency=8,
+        run_cfg=dict(num_gpus=1, num_procs=1),
+    )
+
+models = [
+    # hf_internlm2_chat_7b,
+    internlm2_chat_7b_200k,
+]
+
+work_dir = './outputs/needlebench'

configs/eval_needleinahaystack.py

-from opencompass.models import HuggingFaceCausalLM
-
-from mmengine.config import read_base
-with read_base():
-    from .datasets.cdme.cdme8k import cdme_datasets
-
-datasets = [*cdme_datasets]
-
-
-_meta_template = dict(
-    round=[
-        dict(role='HUMAN', begin='<|User|>:', end='\n'),
-        dict(role='BOT', begin='<|Bot|>:', end='<eoa>\n', generate=True),
-    ],
-)
-
-models = [
-    dict(
-        type=HuggingFaceCausalLM,
-        abbr='internlm-chat-20b-hf',
-        path="internlm/internlm-chat-20b",
-        tokenizer_path='internlm/internlm-chat-20b',
-        model_kwargs=dict(
-            trust_remote_code=True,
-            device_map='auto',
-        ),
-        tokenizer_kwargs=dict(
-            padding_side='left',
-            truncation_side='left',
-            use_fast=False,
-            trust_remote_code=True,
-        ),
-        max_out_len=100,
-        max_seq_len=8192,
-        batch_size=8,
-        meta_template=_meta_template,
-        run_cfg=dict(num_gpus=2, num_procs=1),
-        end_str='<eoa>',
-    )
-]

configs/eval_needleinahaystack_turbomind.py

-from opencompass.models.turbomind import TurboMindModel
-
-from mmengine.config import read_base
-with read_base():
-    from .datasets.cdme.cdme200k import cdme_datasets
-
-datasets = [*cdme_datasets]
-
-internlm_meta_template = dict(round=[
-    dict(role='HUMAN', begin='<|User|>:', end='\n'),
-    dict(role='BOT', begin='<|Bot|>:', end='<eoa>\n', generate=True),
-],
-                              eos_token_id=103028)
-
-models = [
-    # config for internlm-chat-20b
-    dict(
-        type=TurboMindModel,
-        abbr='internlm-chat-20b-turbomind',
-        path='./turbomind',
-        max_out_len=100,
-        max_seq_len=201000,
-        batch_size=8,
-        concurrency=8,
-        meta_template=internlm_meta_template,
-        run_cfg=dict(num_gpus=1, num_procs=1),
-    )
-]

configs/summarizers/needlebench.py

+from opencompass.summarizers.needlebench import NeedleBenchSummarizer
+from opencompass.summarizers.needlebench import NeedleBenchATCSummarizer
+
+# ----------NeedleBench-4k-summarizer----------
+context_lengths_4k = list(range(1000, 5000, 1000))
+depths = [0, 5, 10, 15, 21, 26, 31, 36, 42, 47, 52, 57, 63, 68, 73, 78, 84, 89, 94, 100]
+depths_list_sparse = [0, 10, 21, 31, 42, 52, 63, 73, 84, 94, 100]
+
+# Initialize the lists
+_needlebench_4k_2needle_en = []
+_needlebench_4k_3needle_en = []
+_needlebench_4k_4needle_en = []
+_needlebench_4k_5needle_en = []
+_needlebench_4k_2needle_zh = []
+_needlebench_4k_3needle_zh = []
+_needlebench_4k_4needle_zh = []
+_needlebench_4k_5needle_zh = []
+_needlebench_4k_origin_en = []
+_needlebench_4k_origin_zh = []
+
+# Fill the lists using nested loops
+for original_context_length in context_lengths_4k:
+    for depth_percent in depths:
+        _needlebench_4k_2needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_2needle_en_4k')
+        _needlebench_4k_3needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_3needle_en_4k')
+        _needlebench_4k_4needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_4needle_en_4k')
+        _needlebench_4k_5needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_5needle_en_4k')
+        _needlebench_4k_2needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_2needle_zh_4k')
+        _needlebench_4k_3needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_3needle_zh_4k')
+        _needlebench_4k_4needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_4needle_zh_4k')
+        _needlebench_4k_5needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_5needle_zh_4k')
+
+        _needlebench_4k_origin_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_origin_en_4k')
+        _needlebench_4k_origin_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_origin_zh_4k')
+
+# Concatenate the multi-needle and origin lists
+_needlebench_4k_multi_needle_en = _needlebench_4k_2needle_en + _needlebench_4k_3needle_en + _needlebench_4k_4needle_en + _needlebench_4k_5needle_en
+_needlebench_4k_multi_needle_zh = _needlebench_4k_2needle_zh + _needlebench_4k_3needle_zh + _needlebench_4k_4needle_zh + _needlebench_4k_5needle_zh
+_needlebench_4k_origin = _needlebench_4k_origin_en + _needlebench_4k_origin_zh
+_needlebench_4k_multi_needle = _needlebench_4k_multi_needle_en + _needlebench_4k_multi_needle_zh
+
+# Repeating the same process for parallel (assuming it's similar to origin_en)
+_needlebench_4k_parallel_en = []
+_needlebench_4k_parallel_zh = []
+for original_context_length in context_lengths_4k:
+    _needlebench_4k_parallel_en.append(f'Length{original_context_length}_parallel_en_4k')
+for original_context_length in context_lengths_4k:
+    _needlebench_4k_parallel_zh.append(f'Length{original_context_length}_parallel_zh_4k')
+_needlebench_4k_parallel = _needlebench_4k_parallel_en + _needlebench_4k_parallel_zh
+
+needlebench_summary_groups = [
+    {'name': 'original_version', 'subsets': _needlebench_4k_origin},
+    {'name': 'original_version_zh', 'subsets': _needlebench_4k_origin_zh},
+    {'name': 'original_version_en', 'subsets': _needlebench_4k_origin_en},
+
+    {'name': 'multi_needle_en', 'subsets': _needlebench_4k_multi_needle_en},
+    {'name': 'multi_needle2_en', 'subsets': _needlebench_4k_2needle_en},
+    {'name': 'multi_needle3_en', 'subsets': _needlebench_4k_3needle_en},
+    {'name': 'multi_needle4_en', 'subsets': _needlebench_4k_4needle_en},
+    {'name': 'multi_needle5_en', 'subsets': _needlebench_4k_5needle_en},
+
+    {'name': 'multi_needle_zh', 'subsets': _needlebench_4k_multi_needle_zh},
+    {'name': 'multi_needle2_zh', 'subsets': _needlebench_4k_2needle_zh},
+    {'name': 'multi_needle3_zh', 'subsets': _needlebench_4k_3needle_zh},
+    {'name': 'multi_needle4_zh', 'subsets': _needlebench_4k_4needle_zh},
+    {'name': 'multi_needle5_zh', 'subsets': _needlebench_4k_5needle_zh},
+
+    {'name': 'multi_needle', 'subsets': _needlebench_4k_multi_needle},
+
+    {'name': 'parallel_version', 'subsets': _needlebench_4k_parallel},
+    {'name': 'parallel_version_zh', 'subsets': _needlebench_4k_parallel_zh},
+    {'name': 'parallel_version_en', 'subsets': _needlebench_4k_parallel_en},
+
+
+    {'name': 'overall',
+     'subsets': [['original_version', 'naive_average'],
+                 ['multi_needle', 'naive_average'],
+                 ['parallel_version', 'average_score']],
+     'weights': {'original_version': 0.4,
+                 'multi_needle': 0.3,
+                 'parallel_version': 0.3}},
+]
+needlebench_4k_summarizer = dict(
+    type=NeedleBenchSummarizer,
+    dataset_abbrs=[
+        'overall',
+        '--------- NeedleBench-4k Single-Needle ---------',  # category
+        'original_version',
+        'original_version_zh',
+        'original_version_en',
+        '--------- NeedleBench-4k Parallel-Needles ---------',  # category
+        'parallel_version',
+        'parallel_version_zh',
+        'parallel_version_en',
+        '--------- NeedleBench-4k Multi-Needles ---------',  # category
+        'multi_needle',
+        'multi_needle_en',
+        'multi_needle_zh',
+        'multi_needle2_en',
+        'multi_needle3_en',
+        'multi_needle4_en',
+        'multi_needle5_en',
+        'multi_needle2_zh',
+        'multi_needle3_zh',
+        'multi_needle4_zh',
+        'multi_needle5_zh',
+
+        # *_needlebench_4k_origin, *_needlebench_4k_multi_needle, *_needlebench_4k_parallel,
+    ],
+    summary_groups=needlebench_summary_groups,
+)
+
+# ----------NeedleBench-8k-summarizer----------
+
+context_lengths_8k = list(range(5000, 9000, 1000))
+
+# Initialize the lists
+_needlebench_8k_2needle_en = []
+_needlebench_8k_3needle_en = []
+_needlebench_8k_4needle_en = []
+_needlebench_8k_5needle_en = []
+_needlebench_8k_2needle_zh = []
+_needlebench_8k_3needle_zh = []
+_needlebench_8k_4needle_zh = []
+_needlebench_8k_5needle_zh = []
+_needlebench_8k_origin_en = []
+_needlebench_8k_origin_zh = []
+
+# Fill the lists using nested loops
+for original_context_length in context_lengths_8k:
+    for depth_percent in depths:
+        _needlebench_8k_2needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_2needle_en_8k')
+        _needlebench_8k_3needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_3needle_en_8k')
+        _needlebench_8k_4needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_4needle_en_8k')
+        _needlebench_8k_5needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_5needle_en_8k')
+        _needlebench_8k_2needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_2needle_zh_8k')
+        _needlebench_8k_3needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_3needle_zh_8k')
+        _needlebench_8k_4needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_4needle_zh_8k')
+        _needlebench_8k_5needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_5needle_zh_8k')
+
+        _needlebench_8k_origin_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_origin_en_8k')
+        _needlebench_8k_origin_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_origin_zh_8k')
+
+# Concatenate the multi-needle and origin lists
+_needlebench_8k_multi_needle_en = _needlebench_8k_2needle_en + _needlebench_8k_3needle_en + _needlebench_8k_4needle_en + _needlebench_8k_5needle_en
+_needlebench_8k_multi_needle_zh = _needlebench_8k_2needle_zh + _needlebench_8k_3needle_zh + _needlebench_8k_4needle_zh + _needlebench_8k_5needle_zh
+_needlebench_8k_origin = _needlebench_8k_origin_en + _needlebench_8k_origin_zh
+_needlebench_8k_multi_needle = _needlebench_8k_multi_needle_en + _needlebench_8k_multi_needle_zh
+
+# Repeating the same process for parallel (assuming it's similar to origin_en)
+_needlebench_8k_parallel_en = []
+_needlebench_8k_parallel_zh = []
+for original_context_length in context_lengths_8k:
+    _needlebench_8k_parallel_en.append(f'Length{original_context_length}_parallel_en_8k')
+for original_context_length in context_lengths_8k:
+    _needlebench_8k_parallel_zh.append(f'Length{original_context_length}_parallel_zh_8k')
+_needlebench_8k_parallel = _needlebench_8k_parallel_en + _needlebench_8k_parallel_zh
+
+needlebench_summary_groups = [
+    {'name': 'original_version', 'subsets': _needlebench_8k_origin},
+    {'name': 'original_version_zh', 'subsets': _needlebench_8k_origin_zh},
+    {'name': 'original_version_en', 'subsets': _needlebench_8k_origin_en},
+
+    {'name': 'multi_needle_en', 'subsets': _needlebench_8k_multi_needle_en},
+    {'name': 'multi_needle2_en', 'subsets': _needlebench_8k_2needle_en},
+    {'name': 'multi_needle3_en', 'subsets': _needlebench_8k_3needle_en},
+    {'name': 'multi_needle4_en', 'subsets': _needlebench_8k_4needle_en},
+    {'name': 'multi_needle5_en', 'subsets': _needlebench_8k_5needle_en},
+
+    {'name': 'multi_needle_zh', 'subsets': _needlebench_8k_multi_needle_zh},
+    {'name': 'multi_needle2_zh', 'subsets': _needlebench_8k_2needle_zh},
+    {'name': 'multi_needle3_zh', 'subsets': _needlebench_8k_3needle_zh},
+    {'name': 'multi_needle4_zh', 'subsets': _needlebench_8k_4needle_zh},
+    {'name': 'multi_needle5_zh', 'subsets': _needlebench_8k_5needle_zh},
+
+    {'name': 'multi_needle', 'subsets': _needlebench_8k_multi_needle},
+
+    {'name': 'parallel_version', 'subsets': _needlebench_8k_parallel},
+    {'name': 'parallel_version_zh', 'subsets': _needlebench_8k_parallel_zh},
+    {'name': 'parallel_version_en', 'subsets': _needlebench_8k_parallel_en},
+
+
+    {'name': 'overall',
+     'subsets': [['original_version', 'naive_average'],
+                 ['multi_needle', 'naive_average'],
+                 ['parallel_version', 'average_score']],
+     'weights': {'original_version': 0.4,
+                 'multi_needle': 0.3,
+                 'parallel_version': 0.3}},
+]
+needlebench_8k_summarizer = dict(
+    type=NeedleBenchSummarizer,
+    dataset_abbrs=[
+        'overall',
+        '--------- NeedleBench-8k Single-Needle ---------',  # category
+        'original_version',
+        'original_version_zh',
+        'original_version_en',
+        '--------- NeedleBench-8k Parallel-Needles ---------',  # category
+        'parallel_version',
+        'parallel_version_zh',
+        'parallel_version_en',
+        '--------- NeedleBench-8k Multi-Needles ---------',  # category
+        'multi_needle',
+        'multi_needle_en',
+        'multi_needle_zh',
+        'multi_needle2_en',
+        'multi_needle3_en',
+        'multi_needle4_en',
+        'multi_needle5_en',
+        'multi_needle2_zh',
+        'multi_needle3_zh',
+        'multi_needle4_zh',
+        'multi_needle5_zh',
+
+        # *_needlebench_8k_origin, *_needlebench_8k_multi_needle, *_needlebench_8k_parallel,
+    ],
+    summary_groups=needlebench_summary_groups,
+)
+
+# ----------NeedleBench-32k-summarizer----------
+
+context_lengths_32k = [9000, 13000, 17000, 21000, 25000, 29000, 31000, 32000]
+
+# Initialize the lists
+_needlebench_32k_2needle_en = []
+_needlebench_32k_3needle_en = []
+_needlebench_32k_4needle_en = []
+_needlebench_32k_5needle_en = []
+_needlebench_32k_2needle_zh = []
+_needlebench_32k_3needle_zh = []
+_needlebench_32k_4needle_zh = []
+_needlebench_32k_5needle_zh = []
+_needlebench_32k_origin_en = []
+_needlebench_32k_origin_zh = []
+
+# Fill the lists using nested loops
+for original_context_length in context_lengths_32k:
+    for depth_percent in depths_list_sparse:
+        _needlebench_32k_2needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_2needle_en_32k')
+        _needlebench_32k_3needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_3needle_en_32k')
+        _needlebench_32k_4needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_4needle_en_32k')
+        _needlebench_32k_5needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_5needle_en_32k')
+        _needlebench_32k_2needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_2needle_zh_32k')
+        _needlebench_32k_3needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_3needle_zh_32k')
+        _needlebench_32k_4needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_4needle_zh_32k')
+        _needlebench_32k_5needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_5needle_zh_32k')
+
+        _needlebench_32k_origin_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_origin_en_32k')
+        _needlebench_32k_origin_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_origin_zh_32k')
+
+# Concatenate the multi-needle and origin lists
+_needlebench_32k_multi_needle_en = _needlebench_32k_2needle_en + _needlebench_32k_3needle_en + _needlebench_32k_4needle_en + _needlebench_32k_5needle_en
+_needlebench_32k_multi_needle_zh = _needlebench_32k_2needle_zh + _needlebench_32k_3needle_zh + _needlebench_32k_4needle_zh + _needlebench_32k_5needle_zh
+_needlebench_32k_origin = _needlebench_32k_origin_en + _needlebench_32k_origin_zh
+_needlebench_32k_multi_needle = _needlebench_32k_multi_needle_en + _needlebench_32k_multi_needle_zh
+
+# Repeating the same process for parallel (assuming it's similar to origin_en)
+_needlebench_32k_parallel_en = []
+_needlebench_32k_parallel_zh = []
+for original_context_length in context_lengths_32k:
+    _needlebench_32k_parallel_en.append(f'Length{original_context_length}_parallel_en_32k')
+for original_context_length in context_lengths_32k:
+    _needlebench_32k_parallel_zh.append(f'Length{original_context_length}_parallel_zh_32k')
+_needlebench_32k_parallel = _needlebench_32k_parallel_en + _needlebench_32k_parallel_zh
+
+needlebench_summary_groups = [
+    {'name': 'original_version', 'subsets': _needlebench_32k_origin},
+    {'name': 'original_version_zh', 'subsets': _needlebench_32k_origin_zh},
+    {'name': 'original_version_en', 'subsets': _needlebench_32k_origin_en},
+
+    {'name': 'multi_needle_en', 'subsets': _needlebench_32k_multi_needle_en},
+    {'name': 'multi_needle2_en', 'subsets': _needlebench_32k_2needle_en},
+    {'name': 'multi_needle3_en', 'subsets': _needlebench_32k_3needle_en},
+    {'name': 'multi_needle4_en', 'subsets': _needlebench_32k_4needle_en},
+    {'name': 'multi_needle5_en', 'subsets': _needlebench_32k_5needle_en},
+
+    {'name': 'multi_needle_zh', 'subsets': _needlebench_32k_multi_needle_zh},
+    {'name': 'multi_needle2_zh', 'subsets': _needlebench_32k_2needle_zh},
+    {'name': 'multi_needle3_zh', 'subsets': _needlebench_32k_3needle_zh},
+    {'name': 'multi_needle4_zh', 'subsets': _needlebench_32k_4needle_zh},
+    {'name': 'multi_needle5_zh', 'subsets': _needlebench_32k_5needle_zh},
+
+    {'name': 'multi_needle', 'subsets': _needlebench_32k_multi_needle},
+
+    {'name': 'parallel_version', 'subsets': _needlebench_32k_parallel},
+    {'name': 'parallel_version_zh', 'subsets': _needlebench_32k_parallel_zh},
+    {'name': 'parallel_version_en', 'subsets': _needlebench_32k_parallel_en},
+
+
+    {'name': 'overall',
+     'subsets': [['original_version', 'naive_average'],
+                 ['multi_needle', 'naive_average'],
+                 ['parallel_version', 'average_score']],
+     'weights': {'original_version': 0.4,
+                 'multi_needle': 0.3,
+                 'parallel_version': 0.3}},
+]
+needlebench_32k_summarizer = dict(
+    type=NeedleBenchSummarizer,
+    dataset_abbrs=[
+        'overall',
+        '--------- NeedleBench-32k Single-Needle ---------',  # category
+        'original_version',
+        'original_version_zh',
+        'original_version_en',
+        '--------- NeedleBench-32k Parallel-Needles ---------',  # category
+        'parallel_version',
+        'parallel_version_zh',
+        'parallel_version_en',
+        '--------- NeedleBench-32k Multi-Needles ---------',  # category
+        'multi_needle',
+        'multi_needle_en',
+        'multi_needle_zh',
+        'multi_needle2_en',
+        'multi_needle3_en',
+        'multi_needle4_en',
+        'multi_needle5_en',
+        'multi_needle2_zh',
+        'multi_needle3_zh',
+        'multi_needle4_zh',
+        'multi_needle5_zh',
+
+        # *_needlebench_32k_origin, *_needlebench_32k_multi_needle, *_needlebench_32k_parallel,
+    ],
+    summary_groups=needlebench_summary_groups,
+)
+
+# ----------NeedleBench-128k-summarizer----------
+
+context_lengths_128k = list([16000, 32000, 48000, 64000, 80000, 96000, 112000, 128000])
+
+# Initialize the lists
+_needlebench_128k_2needle_en = []
+_needlebench_128k_3needle_en = []
+_needlebench_128k_4needle_en = []
+_needlebench_128k_5needle_en = []
+_needlebench_128k_2needle_zh = []
+_needlebench_128k_3needle_zh = []
+_needlebench_128k_4needle_zh = []
+_needlebench_128k_5needle_zh = []
+_needlebench_128k_origin_en = []
+_needlebench_128k_origin_zh = []
+
+# Fill the lists using nested loops
+for original_context_length in context_lengths_128k:
+    for depth_percent in depths_list_sparse:
+        _needlebench_128k_2needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_2needle_en_128k')
+        _needlebench_128k_3needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_3needle_en_128k')
+        _needlebench_128k_4needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_4needle_en_128k')
+        _needlebench_128k_5needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_5needle_en_128k')
+        _needlebench_128k_2needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_2needle_zh_128k')
+        _needlebench_128k_3needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_3needle_zh_128k')
+        _needlebench_128k_4needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_4needle_zh_128k')
+        _needlebench_128k_5needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_5needle_zh_128k')
+
+        _needlebench_128k_origin_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_origin_en_128k')
+        _needlebench_128k_origin_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_origin_zh_128k')
+
+# Concatenate the multi-needle and origin lists
+_needlebench_128k_multi_needle_en = _needlebench_128k_2needle_en + _needlebench_128k_3needle_en + _needlebench_128k_4needle_en + _needlebench_128k_5needle_en
+_needlebench_128k_multi_needle_zh = _needlebench_128k_2needle_zh + _needlebench_128k_3needle_zh + _needlebench_128k_4needle_zh + _needlebench_128k_5needle_zh
+_needlebench_128k_origin = _needlebench_128k_origin_en + _needlebench_128k_origin_zh
+_needlebench_128k_multi_needle = _needlebench_128k_multi_needle_en + _needlebench_128k_multi_needle_zh
+
+# Repeating the same process for parallel (assuming it's similar to origin_en)
+_needlebench_128k_parallel_en = []
+_needlebench_128k_parallel_zh = []
+for original_context_length in context_lengths_128k:
+    _needlebench_128k_parallel_en.append(f'Length{original_context_length}_parallel_en_128k')
+for original_context_length in context_lengths_128k:
+    _needlebench_128k_parallel_zh.append(f'Length{original_context_length}_parallel_zh_128k')
+_needlebench_128k_parallel = _needlebench_128k_parallel_en + _needlebench_128k_parallel_zh
+
+needlebench_summary_groups = [
+    {'name': 'original_version', 'subsets': _needlebench_128k_origin},
+    {'name': 'original_version_zh', 'subsets': _needlebench_128k_origin_zh},
+    {'name': 'original_version_en', 'subsets': _needlebench_128k_origin_en},
+
+    {'name': 'multi_needle_en', 'subsets': _needlebench_128k_multi_needle_en},
+    {'name': 'multi_needle2_en', 'subsets': _needlebench_128k_2needle_en},
+    {'name': 'multi_needle3_en', 'subsets': _needlebench_128k_3needle_en},
+    {'name': 'multi_needle4_en', 'subsets': _needlebench_128k_4needle_en},
+    {'name': 'multi_needle5_en', 'subsets': _needlebench_128k_5needle_en},
+
+    {'name': 'multi_needle_zh', 'subsets': _needlebench_128k_multi_needle_zh},
+    {'name': 'multi_needle2_zh', 'subsets': _needlebench_128k_2needle_zh},
+    {'name': 'multi_needle3_zh', 'subsets': _needlebench_128k_3needle_zh},
+    {'name': 'multi_needle4_zh', 'subsets': _needlebench_128k_4needle_zh},
+    {'name': 'multi_needle5_zh', 'subsets': _needlebench_128k_5needle_zh},
+
+    {'name': 'multi_needle', 'subsets': _needlebench_128k_multi_needle},
+
+    {'name': 'parallel_version', 'subsets': _needlebench_128k_parallel},
+    {'name': 'parallel_version_zh', 'subsets': _needlebench_128k_parallel_zh},
+    {'name': 'parallel_version_en', 'subsets': _needlebench_128k_parallel_en},
+
+
+    {'name': 'overall',
+     'subsets': [['original_version', 'naive_average'],
+                 ['multi_needle', 'naive_average'],
+                 ['parallel_version', 'average_score']],
+     'weights': {'original_version': 0.4,
+                 'multi_needle': 0.3,
+                 'parallel_version': 0.3}},
+]
+needlebench_128k_summarizer = dict(
+    type=NeedleBenchSummarizer,
+    dataset_abbrs=[
+        'overall',
+        '--------- NeedleBench-128k Single-Needle ---------',  # category
+        'original_version',
+        'original_version_zh',
+        'original_version_en',
+        '--------- NeedleBench-128k Parallel-Needles ---------',  # category
+        'parallel_version',
+        'parallel_version_zh',
+        'parallel_version_en',
+        '--------- NeedleBench-128k Multi-Needles ---------',  # category
+        'multi_needle',
+        'multi_needle_en',
+        'multi_needle_zh',
+        'multi_needle2_en',
+        'multi_needle3_en',
+        'multi_needle4_en',
+        'multi_needle5_en',
+        'multi_needle2_zh',
+        'multi_needle3_zh',
+        'multi_needle4_zh',
+        'multi_needle5_zh',
+
+        # *_needlebench_128k_origin, *_needlebench_128k_multi_needle, *_needlebench_128k_parallel,
+    ],
+    summary_groups=needlebench_summary_groups,
+)
+
+# ----------NeedleBench-200k-summarizer----------
+
+context_lengths_200k = list([16000, 48000, 80000, 112000, 128000, 144000, 176000, 200000])
+# Initialize the lists
+_needlebench_200k_2needle_en = []
+_needlebench_200k_3needle_en = []
+_needlebench_200k_4needle_en = []
+_needlebench_200k_5needle_en = []
+_needlebench_200k_2needle_zh = []
+_needlebench_200k_3needle_zh = []
+_needlebench_200k_4needle_zh = []
+_needlebench_200k_5needle_zh = []
+_needlebench_200k_origin_en = []
+_needlebench_200k_origin_zh = []
+
+# Fill the lists using nested loops
+for original_context_length in context_lengths_200k:
+    for depth_percent in depths_list_sparse:
+        _needlebench_200k_2needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_2needle_en_200k')
+        _needlebench_200k_3needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_3needle_en_200k')
+        _needlebench_200k_4needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_4needle_en_200k')
+        _needlebench_200k_5needle_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_5needle_en_200k')
+        _needlebench_200k_2needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_2needle_zh_200k')
+        _needlebench_200k_3needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_3needle_zh_200k')
+        _needlebench_200k_4needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_4needle_zh_200k')
+        _needlebench_200k_5needle_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_5needle_zh_200k')
+
+        _needlebench_200k_origin_en.append(f'Length{original_context_length}Depth{int(depth_percent)}_origin_en_200k')
+        _needlebench_200k_origin_zh.append(f'Length{original_context_length}Depth{int(depth_percent)}_origin_zh_200k')
+
+# Concatenate the multi-needle and origin lists
+_needlebench_200k_multi_needle_en = _needlebench_200k_2needle_en + _needlebench_200k_3needle_en + _needlebench_200k_4needle_en + _needlebench_200k_5needle_en
+_needlebench_200k_multi_needle_zh = _needlebench_200k_2needle_zh + _needlebench_200k_3needle_zh + _needlebench_200k_4needle_zh + _needlebench_200k_5needle_zh
+_needlebench_200k_origin = _needlebench_200k_origin_en + _needlebench_200k_origin_zh
+_needlebench_200k_multi_needle = _needlebench_200k_multi_needle_en + _needlebench_200k_multi_needle_zh
+
+# Repeating the same process for parallel (assuming it's similar to origin_en)
+_needlebench_200k_parallel_en = []
+_needlebench_200k_parallel_zh = []
+for original_context_length in context_lengths_200k:
+    _needlebench_200k_parallel_en.append(f'Length{original_context_length}_parallel_en_200k')
+for original_context_length in context_lengths_200k:
+    _needlebench_200k_parallel_zh.append(f'Length{original_context_length}_parallel_zh_200k')
+_needlebench_200k_parallel = _needlebench_200k_parallel_en + _needlebench_200k_parallel_zh
+
+needlebench_summary_groups = [
+    {'name': 'original_version', 'subsets': _needlebench_200k_origin},
+    {'name': 'original_version_zh', 'subsets': _needlebench_200k_origin_zh},
+    {'name': 'original_version_en', 'subsets': _needlebench_200k_origin_en},
+
+    {'name': 'multi_needle_en', 'subsets': _needlebench_200k_multi_needle_en},
+    {'name': 'multi_needle2_en', 'subsets': _needlebench_200k_2needle_en},
+    {'name': 'multi_needle3_en', 'subsets': _needlebench_200k_3needle_en},
+    {'name': 'multi_needle4_en', 'subsets': _needlebench_200k_4needle_en},
+    {'name': 'multi_needle5_en', 'subsets': _needlebench_200k_5needle_en},
+
+    {'name': 'multi_needle_zh', 'subsets': _needlebench_200k_multi_needle_zh},
+    {'name': 'multi_needle2_zh', 'subsets': _needlebench_200k_2needle_zh},
+    {'name': 'multi_needle3_zh', 'subsets': _needlebench_200k_3needle_zh},
+    {'name': 'multi_needle4_zh', 'subsets': _needlebench_200k_4needle_zh},
+    {'name': 'multi_needle5_zh', 'subsets': _needlebench_200k_5needle_zh},
+
+    {'name': 'multi_needle', 'subsets': _needlebench_200k_multi_needle},
+
+    {'name': 'parallel_version', 'subsets': _needlebench_200k_parallel},
+    {'name': 'parallel_version_zh', 'subsets': _needlebench_200k_parallel_zh},
+    {'name': 'parallel_version_en', 'subsets': _needlebench_200k_parallel_en},
+
+    {'name': 'overall',
+     'subsets': [['original_version', 'naive_average'],
+                 ['multi_needle', 'naive_average'],
+                 ['parallel_version', 'average_score']],
+     'weights': {'original_version': 0.4,
+                 'multi_needle': 0.3,
+                 'parallel_version': 0.3}},
+]
+needlebench_200k_summarizer = dict(
+    type=NeedleBenchSummarizer,
+    dataset_abbrs=[
+        'overall',
+        '--------- NeedleBench-200k Single-Needle ---------',  # category
+        'original_version',
+        'original_version_zh',
+        'original_version_en',
+        '--------- NeedleBench-200k Parallel-Needles ---------',  # category
+        'parallel_version',
+        'parallel_version_zh',
+        'parallel_version_en',
+        '--------- NeedleBench-200k Multi-Needles ---------',  # category
+        'multi_needle',
+        'multi_needle_en',
+        'multi_needle_zh',
+        'multi_needle2_en',
+        'multi_needle3_en',
+        'multi_needle4_en',
+        'multi_needle5_en',
+        'multi_needle2_zh',
+        'multi_needle3_zh',
+        'multi_needle4_zh',
+        'multi_needle5_zh',
+
+        # *_needlebench_200k_origin, *_needlebench_200k_multi_needle, *_needlebench_200k_parallel,
+    ],
+    summary_groups=needlebench_summary_groups,
+)
+context_lengths_8k = list(range(5000, 9000, 1000))
+
+# Repeating the same process for parallel (assuming it's similar to origin_en)
+_needlebench_8k_parallel_en_batch1 = []
+_needlebench_8k_parallel_en_batch5 = []
+_needlebench_8k_parallel_en_batch10 = []
+_needlebench_8k_parallel_en_batch15 = []
+_needlebench_8k_parallel_en_batch20 = []
+_needlebench_8k_parallel_zh_batch1 = []
+_needlebench_8k_parallel_zh_batch5 = []
+_needlebench_8k_parallel_zh_batch10 = []
+_needlebench_8k_parallel_zh_batch15 = []
+_needlebench_8k_parallel_zh_batch20 = []
+for original_context_length in context_lengths_8k:
+    _needlebench_8k_parallel_en_batch1.append(f'Length{original_context_length}_parallel_en_8k_batch1')
+    _needlebench_8k_parallel_en_batch5.append(f'Length{original_context_length}_parallel_en_8k_batch5')
+    _needlebench_8k_parallel_en_batch10.append(f'Length{original_context_length}_parallel_en_8k_batch10')
+    _needlebench_8k_parallel_en_batch15.append(f'Length{original_context_length}_parallel_en_8k_batch15')
+    _needlebench_8k_parallel_en_batch20.append(f'Length{original_context_length}_parallel_en_8k_batch20')
+    _needlebench_8k_parallel_zh_batch1.append(f'Length{original_context_length}_parallel_zh_8k_batch1')
+    _needlebench_8k_parallel_zh_batch5.append(f'Length{original_context_length}_parallel_zh_8k_batch5')
+    _needlebench_8k_parallel_zh_batch10.append(f'Length{original_context_length}_parallel_zh_8k_batch10')
+    _needlebench_8k_parallel_zh_batch15.append(f'Length{original_context_length}_parallel_zh_8k_batch15')
+    _needlebench_8k_parallel_zh_batch20.append(f'Length{original_context_length}_parallel_zh_8k_batch20')
+
+
+_needlebench_8k_parallel_batch1 = _needlebench_8k_parallel_en_batch1 + _needlebench_8k_parallel_zh_batch1
+_needlebench_8k_parallel_batch5 = _needlebench_8k_parallel_en_batch5 + _needlebench_8k_parallel_zh_batch5
+_needlebench_8k_parallel_batch10 = _needlebench_8k_parallel_en_batch10 + _needlebench_8k_parallel_zh_batch10
+_needlebench_8k_parallel_batch15 = _needlebench_8k_parallel_en_batch15 + _needlebench_8k_parallel_zh_batch15
+_needlebench_8k_parallel_batch20 = _needlebench_8k_parallel_en_batch20 + _needlebench_8k_parallel_zh_batch20
+
+needlebench_summary_groups = [
+    {'name': 'parallel_version_batch1', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_batch1]},
+    {'name': 'parallel_version_zh_batch1', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_zh_batch1]},
+    {'name': 'parallel_version_en_batch1', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_en_batch1]},
+    {'name': 'parallel_version_batch5', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_batch5]},
+    {'name': 'parallel_version_zh_batch5', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_zh_batch5]},
+    {'name': 'parallel_version_en_batch5', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_en_batch5]},
+    {'name': 'parallel_version_batch10', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_batch10]},
+    {'name': 'parallel_version_zh_batch10', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_zh_batch10]},
+    {'name': 'parallel_version_en_batch10', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_en_batch10]},
+    {'name': 'parallel_version_batch15', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_batch15]},
+    {'name': 'parallel_version_zh_batch15', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_zh_batch15]},
+    {'name': 'parallel_version_en_batch15', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_en_batch15]},
+    {'name': 'parallel_version_batch20', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_batch20]},
+    {'name': 'parallel_version_zh_batch20', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_zh_batch20]},
+    {'name': 'parallel_version_en_batch20', 'subsets': [[_dataset, "average_score"] for _dataset in _needlebench_8k_parallel_en_batch20]},
+]
+
+needlebench_8k_batch_overall_summarizer = dict(
+    dataset_abbrs=[
+        '--------- NeedleBench-8k Parallel-Needles ---------',  # category
+        'parallel_version_batch1',
+        'parallel_version_batch5',
+        'parallel_version_batch10',
+        'parallel_version_batch15',
+        'parallel_version_batch20',
+        'parallel_version_zh_batch1',
+        'parallel_version_en_batch1',
+        'parallel_version_zh_batch5',
+        'parallel_version_en_batch5',
+        'parallel_version_zh_batch10',
+        'parallel_version_en_batch10',
+        'parallel_version_zh_batch15',
+        'parallel_version_en_batch15',
+        'parallel_version_zh_batch20',
+        'parallel_version_en_batch20',
+        # *_needlebench_8k_origin, *_needlebench_8k_multi_needle, *_needlebench_8k_parallel,
+    ],
+    summary_groups=needlebench_summary_groups,
+)
+
+needlebench_summary_groups = [
+    {'name': 'parallel_version_batch1', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_batch1]},
+    {'name': 'parallel_version_zh_batch1', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_zh_batch1]},
+    {'name': 'parallel_version_en_batch1', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_en_batch1]},
+    {'name': 'parallel_version_batch5', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_batch5]},
+    {'name': 'parallel_version_zh_batch5', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_zh_batch5]},
+    {'name': 'parallel_version_en_batch5', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_en_batch5]},
+    {'name': 'parallel_version_batch10', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_batch10]},
+    {'name': 'parallel_version_zh_batch10', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_zh_batch10]},
+    {'name': 'parallel_version_en_batch10', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_en_batch10]},
+    {'name': 'parallel_version_batch15', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_batch15]},
+    {'name': 'parallel_version_zh_batch15', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_zh_batch15]},
+    {'name': 'parallel_version_en_batch15', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_en_batch15]},
+    {'name': 'parallel_version_batch20', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_batch20]},
+    {'name': 'parallel_version_zh_batch20', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_zh_batch20]},
+    {'name': 'parallel_version_en_batch20', 'subsets': [[_dataset, "Depth0"] for _dataset in _needlebench_8k_parallel_en_batch20]},
+]
+
+needlebench_8k_batch_depth0_summarizer = dict(
+    dataset_abbrs=[
+        '--------- NeedleBench-8k Parallel-Needles ---------',  # category
+        'parallel_version_batch1',
+        'parallel_version_batch5',
+        'parallel_version_batch10',
+        'parallel_version_batch15',
+        'parallel_version_batch20',
+        'parallel_version_zh_batch1',
+        'parallel_version_en_batch1',
+        'parallel_version_zh_batch5',
+        'parallel_version_en_batch5',
+        'parallel_version_zh_batch10',
+        'parallel_version_en_batch10',
+        'parallel_version_zh_batch15',
+        'parallel_version_en_batch15',
+        'parallel_version_zh_batch20',
+        'parallel_version_en_batch20',
+        # *_needlebench_8k_origin, *_needlebench_8k_multi_needle, *_needlebench_8k_parallel,
+    ],
+    summary_groups=needlebench_summary_groups,
+)
+needlebench_atc_summarizer = dict(
+    type=NeedleBenchATCSummarizer,
+)

opencompass/datasets/needlebench/atc.py

+# flake8: noqa
+import json
+import random
+
+from datasets import Dataset
+
+from opencompass.datasets.base import BaseDataset
+from opencompass.registry import LOAD_DATASET
+
+
+@LOAD_DATASET.register_module()
+class NeedleBenchATCDataset(BaseDataset):
+
+    @staticmethod
+    def load(
+        path,
+        num_needles: int,
+        language: str,
+        repeats: int,
+    ):
+        data = {'prompt': [], 'answer': []}
+
+        with open(path, 'r', encoding='utf-8') as file:
+            names_data = json.load(file)
+
+        all_names = names_data[language].split(',')
+
+        for _ in range(repeats):
+            names = random.sample(all_names, num_needles)
+            if language == 'Chinese':
+
+                relationship_terms = [
+                    '父亲', '母亲', '爸爸', '妈妈', '爷爷', '奶奶', '姥姥', '姥爷', '外公', '外婆'
+                ]
+
+                relationship_templates = [
+                    '{A}是{B}的{relationship}。',
+                    '{B}的{relationship}是{A}。',
+                    '{A}作为{B}的{relationship}，对{B}的成长有重要影响。',
+                    '{A}不仅是{B}的{relationship}，还是{B}的榜样。',
+                    '{B}是{A}所生的孩子。',
+                    '{A}对{B}来说，不只是一个{relationship}，还是一个朋友。',
+                    '{A}在{B}的生命中扮演着{relationship}的角色。',
+                    '{B}把{A}视为其{relationship}。',
+                ]
+            elif language == 'English':
+
+                relationship_terms = [
+                    'father', 'mother', 'dad', 'mom', 'grandfather',
+                    'grandmother', 'maternal grandmother',
+                    'maternal grandfather', 'paternal grandfather',
+                    'paternal grandmother'
+                ]
+
+                relationship_templates = [
+                    "{A} is {B}'s {relationship}.",
+                    "{B}'s {relationship} is {A}.",
+                    ("{A}, as {B}'s {relationship}, "
+                     "has a significant impact on {B}'s upbringing."),
+                    ("{A} is not only {B}'s {relationship} "
+                     "but also {B}'s role model."),
+                    '{B} is the child of {A}.',
+                    ('For {B}, {A} is not just a {relationship}, '
+                     'but also a friend.'),
+                    ("{A} plays the role of {B}'s {relationship} "
+                     "in {B}'s life."),
+                    '{B} considers {A} as their {relationship}.',
+                ]
+
+            def generate_chain_family_story(names, templates,
+                                            relationship_terms):
+                story = ''
+                for i in range(len(names) - 1):
+                    template = random.choice(templates)
+                    relation_term = random.choice(relationship_terms)
+                    relation = template.format(A=names[i],
+                                               B=names[i + 1],
+                                               relationship=relation_term)
+                    story += f'{relation}*'
+                return story
+
+            chain_story = generate_chain_family_story(names,
+                                                      relationship_templates,
+                                                      relationship_terms)
+
+            # Splitting the chain_story into a list of fragments
+            family_story_fragments = chain_story.split('*')
+
+            # Shuffling the list of fragments
+            random.shuffle(family_story_fragments)
+
+            # Joining the shuffled fragments back into a string
+            shuffled_story = ''.join(family_story_fragments)
+
+            last_person = names[-1]
+
+            # Generating the prompt based on the language
+            if language == 'Chinese':
+                prompt = (f"""
+在上面提供的打乱的家族关系文本中，'{last_person}'的能够向上追溯到的最年长的亲人是谁？
+例如：
+例子1.如果张强的父亲是马克，除此以外提供的文本中没有更多关于亲属关系的信息，那么在提供的文本中张强能够向上追溯到的最年长的亲人就是马克。
+例子2.如果李明的姥姥是张红，而张红的父亲是张强，除此以外提供的文本中没有更多关于亲属关系的信息，那么在提供的文本中李明能够向上追溯到的最年长的亲人就是张强。
+例子3.如果小明是张红的曾孙女，张红的祖母是王华，王华的父亲是王刚，除此以外提供的文本中没有更多关于亲属关系的信息，那么小明能够向上追溯到的最年长的亲人就是王刚。
+""")
+            elif language == 'English':
+                prompt = (f"""
+Given the scrambled family relationships described above, who is the eldest relative that '{last_person}' can trace back to in the context?
+For example:
+Example 1: If Zhang Qiang's father is Mark, and no further information about familial relationships is provided in the text, then the oldest relative Zhang Qiang can trace back to in the provided text is Mark.
+Example 2: If Li Ming's grandmother is Zhang Hong, and Zhang Hong's father is Zhang Qiang, and no further information about familial relationships is provided in the text, then the oldest relative Li Ming can trace back to in the provided text is Zhang Qiang.
+Example 3: If Xiao Ming is Zhang Hong's great-granddaughter, Zhang Hong's grandmother is Wang Hua, and Wang Hua's father is Wang Gang, and no further information about familial relationships is provided in the text, then the oldest relative Xiao Ming can trace back to in the provided text is Wang Gang."""
+                          )
+            else:
+                prompt = 'Language not supported.'
+                raise Exception('Unsupported language specified. '
+                                "Please choose either 'Chinese' or 'English'.")
+
+            # Combine story and prompt
+            shuffled_story_with_prompt = shuffled_story + ' ' + prompt
+
+            data['prompt'].append(shuffled_story_with_prompt)
+            data['answer'].append(names[0] + '*' + names[0])
+
+        dataset = Dataset.from_dict({
+            'prompt': data['prompt'],
+            'answer': data['answer'],
+        })
+        return dataset
+
+
+@LOAD_DATASET.register_module()
+class NeedleBenchATCOrderedDataset(BaseDataset):
+
+    @staticmethod
+    def load(
+        path,
+        num_needles: int,
+        language: str,
+        repeats: int,
+    ):
+        data = {'prompt': [], 'answer': []}
+
+        with open(path, 'r', encoding='utf-8') as file:
+            names_data = json.load(file)
+
+        all_names = names_data[language].split(',')
+
+        for _ in range(repeats):
+            names = random.sample(all_names, num_needles)
+            if language == 'Chinese':
+
+                relationship_terms = [
+                    '父亲', '母亲', '爸爸', '妈妈', '爷爷', '奶奶', '姥姥', '姥爷', '外公', '外婆'
+                ]
+
+                relationship_templates = [
+                    '{A}是{B}的{relationship}。',
+                    '{B}的{relationship}是{A}。',
+                    '{A}作为{B}的{relationship}，对{B}的成长有重要影响。',
+                    '{A}不仅是{B}的{relationship}，还是{B}的榜样。',
+                    '{B}是{A}所生的孩子。',
+                    '{A}对{B}来说，不只是一个{relationship}，还是一个朋友。',
+                    '{A}在{B}的生命中扮演着{relationship}的角色。',
+                    '{B}把{A}视为其{relationship}。',
+                ]
+            elif language == 'English':
+
+                relationship_terms = [
+                    'father', 'mother', 'dad', 'mom', 'grandfather',
+                    'grandmother', 'maternal grandmother',
+                    'maternal grandfather', 'paternal grandfather',
+                    'paternal grandmother'
+                ]
+
+                relationship_templates = [
+                    "{A} is {B}'s {relationship}.",
+                    "{B}'s {relationship} is {A}.",
+                    ("{A}, as {B}'s {relationship}, "
+                     "has a significant impact on {B}'s upbringing."),
+                    ("{A} is not only {B}'s {relationship} "
+                     "but also {B}'s role model."),
+                    '{B} is the child of {A}.',
+                    ('For {B}, {A} is not just a {relationship}, '
+                     'but also a friend.'),
+                    ("{A} plays the role of {B}'s {relationship} "
+                     "in {B}'s life."),
+                    '{B} considers {A} as their {relationship}.',
+                ]
+
+            def generate_chain_family_story(names, templates,
+                                            relationship_terms):
+                story = ''
+                for i in range(len(names) - 1):
+                    template = random.choice(templates)
+                    relation_term = random.choice(relationship_terms)
+                    relation = template.format(A=names[i],
+                                               B=names[i + 1],
+                                               relationship=relation_term)
+                    story += f'{relation}*'
+                return story
+
+            chain_story = generate_chain_family_story(names,
+                                                      relationship_templates,
+                                                      relationship_terms)
+
+            # Splitting the chain_story into a list of fragments
+            family_story_fragments = chain_story.split('*')
+
+            # Joining the shuffled fragments back into a string
+            shuffled_story = ''.join(family_story_fragments)
+
+            last_person = names[-1]
+
+            # Generating the prompt based on the language
+            if language == 'Chinese':
+                prompt = (f"""
+在上面提供的打乱的家族关系文本中，'{last_person}'的能够向上追溯到的最年长的亲人是谁？
+例如：
+例子1.如果张强的父亲是马克，除此以外提供的文本中没有更多关于亲属关系的信息，那么在提供的文本中张强能够向上追溯到的最年长的亲人就是马克。
+例子2.如果李明的姥姥是张红，而张红的父亲是张强，除此以外提供的文本中没有更多关于亲属关系的信息，那么在提供的文本中李明能够向上追溯到的最年长的亲人就是张强。
+例子3.如果小明是张红的曾孙女，张红的祖母是王华，王华的父亲是王刚，除此以外提供的文本中没有更多关于亲属关系的信息，那么小明能够向上追溯到的最年长的亲人就是王刚。
+""")
+            elif language == 'English':
+                prompt = (f"""
+Given the scrambled family relationships described above, who is the eldest relative that '{last_person}' can trace back to in the context?
+For example:
+Example 1: If Zhang Qiang's father is Mark, and no further information about familial relationships is provided in the text, then the oldest relative Zhang Qiang can trace back to in the provided text is Mark.
+Example 2: If Li Ming's grandmother is Zhang Hong, and Zhang Hong's father is Zhang Qiang, and no further information about familial relationships is provided in the text, then the oldest relative Li Ming can trace back to in the provided text is Zhang Qiang.
+Example 3: If Xiao Ming is Zhang Hong's great-granddaughter, Zhang Hong's grandmother is Wang Hua, and Wang Hua's father is Wang Gang, and no further information about familial relationships is provided in the text, then the oldest relative Xiao Ming can trace back to in the provided text is Wang Gang."""
+                          )
+            else:
+                prompt = 'Language not supported.'
+                raise Exception('Unsupported language specified. '
+                                "Please choose either 'Chinese' or 'English'.")
+
+            # Combine story and prompt
+            shuffled_story_with_prompt = shuffled_story + ' ' + prompt
+
+            data['prompt'].append(shuffled_story_with_prompt)
+            data['answer'].append(names[0] + '*' + names[0])
+
+        dataset = Dataset.from_dict({
+            'prompt': data['prompt'],
+            'answer': data['answer'],
+        })
+        return dataset

opencompass/datasets/cdme/cdme_multi.py → opencompass/datasets/needlebench/multi.py

 import json
+import os
 import random
-import re
 from pathlib import Path
 
 import tiktoken
@@ -8,11 +8,31 @@ from datasets import Dataset
 
 from opencompass.datasets.base import BaseDataset
 from opencompass.openicl import BaseEvaluator
-from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS
+from opencompass.registry import LOAD_DATASET
+
+
+def get_random_needles(file_path, needle_count):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        data = json.load(file)
+
+    matching_records = [
+        record for record in data
+        if record.get('derivation_count') == needle_count
+    ]
+
+    if matching_records:
+        random_record = random.choice(matching_records)
+        return {
+            'needles': random_record['derivations'],
+            'answer': random_record['answer'],
+            'retrieval_question': random_record['question']
+        }
+    else:
+        return None
 
 
 @LOAD_DATASET.register_module()
-class CDMEDataset(BaseDataset):
+class NeedleBenchMultiDataset(BaseDataset):
 
     @staticmethod
     def load(
@@ -25,11 +45,9 @@ class CDMEDataset(BaseDataset):
         length_buffer: int,
         guide: bool,
         language: str,
-        needles: 'list[str]',
+        needle_file_name: str,
+        num_needles: int,
         diff: int,
-        retrieval_question: str,
-        answer: str,
-        keyword: str,
     ):
         data = {'prompt': [], 'answer': []}
         tokenizer = tiktoken.encoding_for_model(tokenizer_model)
@@ -73,16 +91,14 @@ class CDMEDataset(BaseDataset):
 
         def _modify_retrieval_question(retrieval_question):
             if language == 'Chinese':
-                parts = retrieval_question.split('请按照')
-                guide_retrieval_question = (parts[0] + '在回答之前，请思考文档中与此问题'
-                                            '最相关的内容是什么。请按照' + parts[1])
+                guide_retrieval_question = (retrieval_question +
+                                            '在回答之前，请思考文档中与此问题'
+                                            '最相关的内容是什么。')
                 return guide_retrieval_question
             elif language == 'English':
-                parts = retrieval_question.split('Please answer in the format')
                 guide_retrieval_question = (
-                    parts[0] + 'Before answering, please consider'
-                    ' what in the document is most relevant to this question.'
-                    ' Please answer in the format' + parts[1])
+                    retrieval_question + 'Before answering, please consider'
+                    ' what in the document is most relevant to this question.')
                 return guide_retrieval_question
             else:
                 raise ValueError(f"Language '{language}' is not supported.")
@@ -112,6 +128,7 @@ class CDMEDataset(BaseDataset):
             return prompt
 
         files = Path(path).glob('*.jsonl')
+        needle_file_path = os.path.join(path, needle_file_name)
         for file in files:
             if file.name not in file_list:
                 continue
@@ -122,12 +139,20 @@ class CDMEDataset(BaseDataset):
             for counter in range(num_repeats_per_file):
                 random.seed(counter)
                 random.shuffle(lines)
-
+                random_needle_data = get_random_needles(
+                    needle_file_path, num_needles)
+                needles = [
+                    '\n' + needle + '\n'
+                    for needle in random_needle_data['needles']
+                ]
+                answer = random_needle_data['answer']
+                keyword = answer
+                retrieval_question = random_needle_data['retrieval_question']
                 context_length = length - length_buffer
                 target_length_per_record = context_length - \
                     sum(len(tokens) for tokens
                         in _get_tokens_from_context(needles))
-
+                target_length_per_record = max(target_length_per_record, 0)
                 accumulated_tokens = []
                 for line in lines:
                     tokens_current_line = _get_tokens_from_context(
@@ -154,7 +179,7 @@ class CDMEDataset(BaseDataset):
         return dataset
 
 
-class CDMEEvaluator(BaseEvaluator):
+class NeedleBenchMultiEvaluator(BaseEvaluator):
 
     def levenshtein_distance(self, s1, s2):
         if len(s1) < len(s2):
@@ -175,50 +200,33 @@ class CDMEEvaluator(BaseEvaluator):
 
         return previous_row[-1]
 
-    def score(self, predictions, references):
-        if len(predictions) != len(references):
-            return {
-                'error': 'predictions and references have different lengths'
-            }
+    def score(self, predictions, gold):
+        if len(predictions) != len(gold):
+            return {'error': 'predictions and gold have different lengths'}
 
         total_score = 0
         details = []
-        for prediction, reference in zip(predictions, references):
-            keyword = reference.split('*')[1]
-            reference = reference.split('*')[0]
-            prediction = re.sub(r'\s+', '', prediction)
-            reference = re.sub(r'\s+', '', reference)
-            edit_distance = self.levenshtein_distance(prediction, reference)
-            max_len = max(len(prediction), len(reference))
-            score = 100 * (1 -
-                           edit_distance / max_len) if max_len != 0 else 100
-
-            if keyword in prediction:
-                print(f'{keyword} is in {prediction}')
-                score = 100
-            else:
-                print(f'{keyword} is not in {prediction}')
-                score = 0.2 * score
+
+        for prediction, reference in zip(predictions, gold):
+            answer, keyword = reference.split('*')
+            keywords = keyword.lower().split()
+            prediction = prediction.lower()
+
+            keyword_score = 100 / len(keywords) if keywords else 0
+
+            matched_keywords = sum(1 for kword in keywords
+                                   if kword in prediction)
+            score = matched_keywords * keyword_score
 
             detail = {
                 'pred': prediction,
                 'answer': reference,
-                'edit_distance': edit_distance,
+                'matched_keywords': matched_keywords,
                 'score': score
             }
+
             total_score += score
             details.append(detail)
 
         average_score = total_score / len(predictions) if predictions else 0
-        result = {'score': average_score, 'details': details}
-        return result
-
-
-@TEXT_POSTPROCESSORS.register_module('cdme')
-def cdme_postprocess(text: str) -> str:
-    return text
-
-
-@TEXT_POSTPROCESSORS.register_module('cdme_dataset')
-def cdme_dataset_postprocess(text: str) -> str:
-    return text
+        return {'score': average_score, 'details': details}

opencompass/datasets/cdme/cdme.py → opencompass/datasets/needlebench/origin.py

 import json
+import os
 import random
 import re
 from pathlib import Path
@@ -11,8 +12,26 @@ from opencompass.openicl import BaseEvaluator
 from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS
 
 
+def get_random_line_by_language(file_path, language):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        lines = [
+            json.loads(line.strip()) for line in file
+            if json.loads(line.strip())['language'] == language
+        ]
+
+    if lines:
+        random_line = random.choice(lines)
+        return {
+            'needle': random_line['needle'],
+            'retrieval_question': random_line['retrieval_question'],
+            'keyword': random_line['arg2']
+        }
+    else:
+        return None
+
+
 @LOAD_DATASET.register_module()
-class CDMEDataset(BaseDataset):
+class NeedleBenchOriginDataset(BaseDataset):
 
     @staticmethod
     def load(
@@ -25,8 +44,7 @@ class CDMEDataset(BaseDataset):
         length_buffer: int,
         guide: bool,
         language: str,
-        needle: str,
-        retrieval_question: str,
+        needle_file_name: str,
     ):
         data = {'prompt': [], 'answer': []}
         tokenizer = tiktoken.encoding_for_model(tokenizer_model)
@@ -96,11 +114,17 @@ class CDMEDataset(BaseDataset):
             for counter in range(num_repeats_per_file):
                 random.seed(counter)
                 random.shuffle(lines)
+                needle_file_path = os.path.join(path, needle_file_name)
+                random_needle = get_random_line_by_language(
+                    needle_file_path, language)
+                needle = '\n' + random_needle['needle'] + '\n'
+                retrieval_question = random_needle['retrieval_question']
+                keyword = random_needle['keyword']
 
                 context_length = length - length_buffer
                 target_length_per_record = context_length - len(
                     _get_tokens_from_context(needle))
-
+                target_length_per_record = max(target_length_per_record, 0)
                 accumulated_tokens = []
                 for line in lines:
                     tokens_current_line = _get_tokens_from_context(
@@ -118,7 +142,7 @@ class CDMEDataset(BaseDataset):
                                                     retrieval_question)
 
                 data['prompt'].append(processed_prompt)
-                data['answer'].append(needle)
+                data['answer'].append(needle + '*' + keyword)
 
         dataset = Dataset.from_dict({
             'prompt': data['prompt'],
@@ -127,7 +151,7 @@ class CDMEDataset(BaseDataset):
         return dataset
 
 
-class CDMEEvaluator(BaseEvaluator):
+class NeedleBenchOriginEvaluator(BaseEvaluator):
 
     def __init__(self, use_trim=False):
         self.use_trim = use_trim
@@ -174,20 +198,22 @@ class CDMEEvaluator(BaseEvaluator):
 
         return previous_row[-1]
 
-    def score(self, predictions, references):
-        if len(predictions) != len(references):
-            return {
-                'error': 'predictions and references have different lengths'
-            }
+    def score(self, predictions, gold):
+
+        if len(predictions) != len(gold):
+            return {'error': 'predictions and gold have different lengths'}
 
         total_score = 0
         details = []
-        for prediction, reference in zip(predictions, references):
+        for prediction, reference in zip(predictions, gold):
+            keyword = reference.split('*')[1]
+            reference = reference.split('*')[0]
+            raw_prediction = prediction
             prediction = re.sub(r'\s+', '', prediction)
             reference = re.sub(r'\s+', '', reference)
 
             if self.use_trim:
-                prediction = CDMEEvaluator._trim_prediction(
+                prediction = NeedleBenchOriginEvaluator._trim_prediction(
                     prediction, reference)
 
             edit_distance = self.levenshtein_distance(prediction, reference)
@@ -195,6 +221,13 @@ class CDMEEvaluator(BaseEvaluator):
             score = 100 * (1 -
                            edit_distance / max_len) if max_len != 0 else 100
 
+            if keyword in raw_prediction:
+                print(f'{keyword} is in {prediction}')
+                score = 100
+            else:
+                print(f'{keyword} is not in {prediction}')
+                score = 0.2 * score
+
             detail = {
                 'pred': prediction,
                 'answer': reference,
@@ -209,11 +242,11 @@ class CDMEEvaluator(BaseEvaluator):
         return result
 
 
-@TEXT_POSTPROCESSORS.register_module('cdme')
-def cdme_postprocess(text: str) -> str:
+@TEXT_POSTPROCESSORS.register_module('needlebench')
+def needlebench_postprocess(text: str) -> str:
     return text
 
 
-@TEXT_POSTPROCESSORS.register_module('cdme_dataset')
-def cdme_dataset_postprocess(text: str) -> str:
+@TEXT_POSTPROCESSORS.register_module('needlebench_dataset')
+def needlebench_dataset_postprocess(text: str) -> str:
     return text

opencompass/datasets/needlebench/parallel.py

+import json
+import random
+from pathlib import Path
+
+import tiktoken
+from datasets import Dataset
+
+from opencompass.datasets.base import BaseDataset
+from opencompass.openicl import BaseEvaluator
+from opencompass.registry import LOAD_DATASET
+
+
+def get_unique_entries(file_path,
+                       n,
+                       language,
+                       unique_arg1=False,
+                       unique_arg2=False,
+                       unique_combination=False):
+    seen_arg1 = set()
+    seen_arg2 = set()
+    seen_combinations = set()
+    results = []
+
+    with open(file_path, 'r', encoding='utf-8') as file:
+        lines = file.readlines()
+    random.shuffle(lines)
+
+    for line in lines:
+        try:
+            entry = json.loads(line.strip())
+        except json.JSONDecodeError:
+            continue
+
+        if entry.get('language') != language:
+            continue
+
+        key1 = entry.get('arg1', '') if unique_arg1 else ''
+        key2 = entry.get('arg2', '') if unique_arg2 else ''
+        combination = (key1, key2) if unique_combination else ''
+
+        if (key1 not in seen_arg1 or not unique_arg1) and \
+           (key2 not in seen_arg2 or not unique_arg2) and \
+           (combination not in seen_combinations or not unique_combination):
+            seen_arg1.add(key1)
+            seen_arg2.add(key2)
+            seen_combinations.add(combination)
+            results.append(entry)
+
+        if len(results) == n:
+            break
+
+    return results
+
+
+@LOAD_DATASET.register_module()
+class NeedleBenchParallelDataset(BaseDataset):
+
+    @staticmethod
+    def load(
+        path: str,
+        needle_file_name: str,
+        length: int,
+        depths: list[int],
+        tokenizer_model: str,
+        file_list: list[str],
+        num_repeats_per_file: int,
+        length_buffer: int,
+        guide: bool,
+        language: str,
+    ):
+        data = {'prompt': [], 'answer': []}
+        tokenizer = tiktoken.encoding_for_model(tokenizer_model)
+
+        files = Path(path).glob('*.jsonl')
+        for file in files:
+            if file.name == needle_file_name:
+                needle_file_path = file
+
+        predefined_needles_bak = get_unique_entries(needle_file_path,
+                                                    len(depths),
+                                                    language,
+                                                    unique_arg1=True,
+                                                    unique_arg2=True,
+                                                    unique_combination=True)
+
+        def _generate_context(tokens_context, depths, needles):
+            insertion_points = [
+                int(len(tokens_context) * (depth / 100)) for depth in depths
+            ]
+
+            cumulative_inserted_length = 0
+
+            for i, needle in enumerate(needles):
+                needle_tokens = _get_tokens_from_context(needle)
+                current_insertion_point = min(
+                    insertion_points[i] + cumulative_inserted_length,
+                    len(tokens_context))
+
+                tokens_context = tokens_context[:current_insertion_point] + \
+                    needle_tokens + tokens_context[current_insertion_point:]
+                cumulative_inserted_length += len(needle_tokens)
+
+            new_context = _decode_tokens(tokens_context)
+            return new_context
+
+        def _get_tokens_from_context(context):
+            if isinstance(context, list):
+                return [tokenizer.encode(item) for item in context]
+            else:
+                return tokenizer.encode(context)
+
+        def _decode_tokens(tokens):
+            return tokenizer.decode(tokens)
+
+        def _modify_retrieval_question(retrieval_question):
+            if language == 'Chinese':
+                parts = retrieval_question.split('请按照')
+                guide_retrieval_question = (parts[0] + '在回答之前，请思考文档中与此问题'
+                                            '最相关的内容是什么。请按照' + parts[1])
+                return guide_retrieval_question
+            elif language == 'English':
+                parts = retrieval_question.split('Please answer in the format')
+                guide_retrieval_question = (
+                    parts[0] + 'Before answering, please consider'
+                    ' what in the document is most relevant to this question.'
+                    ' Please answer in the format' + parts[1])
+                return guide_retrieval_question
+            else:
+                raise ValueError(f"Language '{language}' is not supported.")
+
+        def _generate_prompt(context, retrieval_question):
+            if guide:
+                retrieval_question = _modify_retrieval_question(
+                    retrieval_question)
+
+            if language == 'Chinese':
+                prompt = ('你是一个善于回答用户问题的智能AI助手\n'
+                          '请保持你的回答简洁清楚。不要说和下面文档中的无关的话'
+                          '，或重复你的回答\n请先仔细阅读下面的文档再依次回答'
+                          f'最后提出的问题\n用户现在给你的文档是{context}\n\n'
+                          f'现在请问：{retrieval_question}\n')
+            elif language == 'English':
+                prompt = (
+                    'You are an intelligent AI assistant skilled in '
+                    'answering user questions.\n'
+                    'Please keep your answers concise and clear. Do not'
+                    ' talk about irrelevant topics or repeat your '
+                    'answers.\n'
+                    f'The document given to you by the user is {context}'
+                    f'\n\nNow, the questions are: {retrieval_question}\n')
+            else:
+                raise ValueError(f"Language '{language}' is not supported.")
+
+            return prompt
+
+        files = Path(path).glob('*.jsonl')
+        for file in files:
+            if file.name not in file_list:
+                continue
+
+            with open(file, 'r', encoding='utf-8') as f:
+                lines_bak = [json.loads(line.strip()) for line in f]
+            lines = lines_bak.copy()
+            for counter in range(num_repeats_per_file):
+                random.seed(counter)
+                random.shuffle(lines)
+                predefined_needles = predefined_needles_bak.copy()
+                random.shuffle(predefined_needles)
+
+                needles = [
+                    '\n' + item['needle'] + '\n' for item in predefined_needles
+                ]
+                keywords = [item['arg2'] for item in predefined_needles]
+                if language == 'Chinese':
+                    questions = '、'.join([
+                        item['retrieval_question'].split('？')[0] + '？'
+                        for item in predefined_needles
+                    ])
+
+                    answers_format = '、'.join([
+                        item['retrieval_question'].split("'")[1].split('。')[0]
+                        for item in predefined_needles
+                    ])
+                    retrieval_question = questions + "请按照'" + \
+                        answers_format + "'的格式回答。"
+                elif language == 'English':
+                    questions = '、'.join([
+                        item['retrieval_question'].split('?')[0] + '?'
+                        for item in predefined_needles
+                    ])
+
+                    answers_format = '、'.join([
+                        item['retrieval_question'].split("'")[1].split('.')[0]
+                        for item in predefined_needles
+                    ])
+                    retrieval_question = questions + \
+                        "Please answer in the format of '" + \
+                        answers_format + "'"
+
+                context_length = length - length_buffer
+                target_length_per_record = context_length - \
+                    sum(len(tokens) for tokens
+                        in _get_tokens_from_context(needles))
+                target_length_per_record = max(target_length_per_record, 0)
+                accumulated_tokens = []
+                for line in lines:
+                    tokens_current_line = _get_tokens_from_context(
+                        line['text'])
+                    accumulated_tokens.extend(tokens_current_line)
+
+                    if len(accumulated_tokens) >= target_length_per_record:
+                        break
+
+                processed_text = _generate_context(
+                    accumulated_tokens[:target_length_per_record], depths,
+                    needles)
+
+                processed_prompt = _generate_prompt(processed_text,
+                                                    retrieval_question)
+
+                data['prompt'].append(processed_prompt)
+
+                data['answer'].append('*'.join(keywords) + '#' +
+                                      '*'.join(map(str, depths)))
+
+        dataset = Dataset.from_dict({
+            'prompt': data['prompt'],
+            'answer': data['answer'],
+        })
+        return dataset
+
+
+class NeedleBenchParallelEvaluator(BaseEvaluator):
+
+    def levenshtein_distance(self, s1, s2):
+        if len(s1) < len(s2):
+            return self.levenshtein_distance(s2, s1)
+
+        if len(s2) == 0:
+            return len(s1)
+
+        previous_row = range(len(s2) + 1)
+        for i, c1 in enumerate(s1):
+            current_row = [i + 1]
+            for j, c2 in enumerate(s2):
+                insertions = previous_row[j + 1] + 1
+                deletions = current_row[j] + 1
+                substitutions = previous_row[j] + (c1 != c2)
+                current_row.append(min(insertions, deletions, substitutions))
+            previous_row = current_row
+
+        return previous_row[-1]
+
+    def score(self, predictions, gold):
+        if len(predictions) != len(gold):
+            return {'error': 'predictions and gold have different lengths'}
+        print('predictions:', predictions)
+        print('gold:', gold)
+
+        details = []
+        depths = [int(i) for i in gold[0].split('#')[1].split('*')]
+        scores_by_depth = {depth: 0 for depth in depths}
+
+        for prediction, reference in zip(predictions, gold):
+            print(reference)
+            keywords = reference.split('#')[0].split('*')
+            print(keywords)
+            for keyword, depth in zip(keywords, depths):
+                print('iterating:', keyword, depth)
+                if keyword in prediction:
+                    print(f'{keyword} at depth {depth} is in {prediction}')
+                    scores_by_depth[depth] += 100 / (len(predictions))
+
+        average_score = sum(scores_by_depth.values()) / len(scores_by_depth)
+
+        flattened_scores = {
+            'Depth' + str(depth): score
+            for depth, score in scores_by_depth.items()
+        }
+
+        result = {
+            **flattened_scores, 'details': details,
+            'average_score': average_score
+        }
+        return result

opencompass/summarizers/needlebench.py

+# flake8: noqa
+# yapf: disable
+import functools
+import getpass
+import math
+import os
+import os.path as osp
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+
+import matplotlib.pyplot as plt
+import mmengine
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import tabulate
+from matplotlib.colors import LinearSegmentedColormap
+from mmengine import ConfigDict
+from tqdm import tqdm
+
+from opencompass.summarizers.default import (
+    METRIC_BLACKLIST, METRIC_WHITELIST, DefaultSummarizer,
+    model_abbr_from_cfg_used_in_summarizer)
+from opencompass.utils import (LarkReporter, dataset_abbr_from_cfg,
+                               get_infer_output_path, get_logger,
+                               model_abbr_from_cfg)
+from opencompass.utils.prompt import get_prompt_hash
+
+
+def read_after_specific_line_except_last(file_name, keyword, offset):
+    with open(file_name, 'r', encoding='utf-8') as file:
+        lines = file.readlines()
+
+    for index, line in enumerate(lines):
+        if keyword in line:
+            start_index = index + offset + 1
+            break
+    else:
+        return ''
+
+    return ''.join(lines[start_index:-1])
+
+def create_model_dataframe(nested_dict, model_name, dataset_abbr, parallel=False):
+    if model_name not in nested_dict:
+        print(f'Model {model_name} not found in the provided data.')
+        return pd.DataFrame()
+
+    model_data = nested_dict[model_name]
+    data = []
+
+    for key, value in model_data.items():
+        if parallel:
+            if dataset_abbr in key:
+                new_key_base = key.replace(dataset_abbr, '').strip('_')
+                for depth_key, score in value.items():
+                    new_key = f'{new_key_base}{depth_key}'
+                    if 'average_score' not in new_key:
+                        data.append([new_key, score])
+        else:
+            if dataset_abbr in key:
+                score = value.get('score', None)
+                new_key = key.replace(dataset_abbr, '').strip('_')
+                data.append([new_key, score])
+
+    df = pd.DataFrame(data, columns=['dataset', model_name])
+    return df
+
+def parse_model_scores(text):
+    lines = text.split('\n')
+
+    result_dict = {}
+    current_model = None
+
+    for line in lines:
+        if line.startswith('Model:'):
+            current_model = line.split('Model:')[1].strip()
+            result_dict[current_model] = {}
+        elif current_model and ':' in line:
+            dataset, score_str = line.split(':', 1)
+            score_dict = eval(score_str.strip())
+            result_dict[current_model][dataset] = score_dict
+
+    return result_dict
+
+def visualize(df_raw, save_path: str,model_name: str ,dataset_type:str):
+    df = df_raw.copy()
+    df['Context Length'] = df['dataset'].apply(
+        lambda x: int(x.split('Length')[1].split('Depth')[0]))
+    df['Document Depth'] = df['dataset'].apply(
+        lambda x: float(x.split('Depth')[1].split('_')[0]))
+
+    model_columns = [
+        col for col in df.columns
+        if col not in ['Context Length', 'Document Depth']
+    ]
+
+    for model_name in model_columns[1:]:
+        model_df = df[['Document Depth', 'Context Length',
+                        model_name]].copy()
+        model_df.rename(columns={model_name: 'Score'}, inplace=True)
+
+        # Create pivot table
+        pivot_table = pd.pivot_table(model_df,
+                                        values='Score',
+                                        index=['Document Depth'],
+                                        columns=['Context Length'],
+                                        aggfunc='mean')
+
+        # Calculate mean scores
+        mean_scores = pivot_table.mean().values
+
+        # Calculate overall score
+        overall_score = mean_scores.mean()
+
+        # Create heatmap and line plot
+        plt.figure(figsize=(15.5, 8))
+        ax = plt.gca()
+        cmap = LinearSegmentedColormap.from_list(
+            'custom_cmap', ['#F0496E', '#EBB839', '#0CD79F'])
+
+        # Draw heatmap
+        sns.heatmap(pivot_table,
+                    cmap=cmap,
+                    ax=ax,
+                    cbar_kws={'label': 'Score'},
+                    vmin=0,
+                    vmax=100)
+
+        # Set line plot data
+        x_data = [i + 0.5 for i in range(len(mean_scores))]
+        y_data = mean_scores
+
+        # Create twin axis for line plot
+        ax2 = ax.twinx()
+        # Draw line plot
+        ax2.plot(x_data,
+                    y_data,
+                    color='white',
+                    marker='o',
+                    linestyle='-',
+                    linewidth=2,
+                    markersize=8,
+                    label='Average Depth Score')
+        # Set y-axis range
+        ax2.set_ylim(0, 100)
+
+        # Hide original y-axis ticks and labels
+        ax2.set_yticklabels([])
+        ax2.set_yticks([])
+
+        # Add legend
+        ax2.legend(loc='upper left')
+
+        # Set chart title and labels
+        ax.set_title(f'{model_name} {dataset_type} Context '
+                        'Performance\nFact Retrieval Across '
+                        'Context Lengths ("Needle In A Haystack")')
+        ax.set_xlabel('Token Limit')
+        ax.set_ylabel('Depth Percent')
+        ax.set_xticklabels(pivot_table.columns.values, rotation=45)
+        ax.set_yticklabels(pivot_table.index.values, rotation=0)
+        # Add overall score as a subtitle
+        plt.text(0.5,
+                 -0.13, f'Overall Score for {model_name}: '
+                 f'{overall_score:.2f}',
+                 ha='center',
+                 va='center',
+                 transform=ax.transAxes,
+                 fontsize=13)
+
+        plt.tight_layout()
+        plt.subplots_adjust(right=1)
+        plt.draw()
+        plt.savefig(save_path)
+        print(f'Saved :{save_path}')
+        plt.close()  # Close figure to prevent memory leaks
+    return overall_score
+
+def save_results_to_plots(txt_results_save_path):
+
+    content = read_after_specific_line_except_last(txt_results_save_path, 'raw format', 2)
+
+    parsed_data = parse_model_scores(content)
+    model_names = get_dict_model_names(parsed_data)
+    numbers = [2, 3, 4, 5]
+    languages = ['en', 'zh']
+    size_exists = []
+    sizes_origin = ['_4k', '_8k', '_32k', '_128k', '_200k']
+
+    for size in sizes_origin:
+        if size in content:
+            size_exists.append(size)
+
+    multi_dataset_abbrs = [f'{num}needle_{lang}{size}' for num in numbers for lang in languages for size in size_exists]
+    origin_dataset_abbrs = [f'origin_{lang}{size}' for lang in languages for size in size_exists]
+    parallel_dataset_abbrs = [f'parallel_{lang}{size}' for lang in languages for size in size_exists]
+
+    dataset_abbrs = multi_dataset_abbrs + origin_dataset_abbrs + \
+                        parallel_dataset_abbrs
+    base_path = os.path.dirname(txt_results_save_path)
+    plot_path = os.path.join(base_path, 'plots')
+    model_scores = {}
+    for model_name in tqdm(model_names):
+        model_datasets_scores = {}  # Dictionary to store scores for each dataset for the current model
+        for dataset_abbr in dataset_abbrs:
+            parallel_flag = 'parallel' in dataset_abbr
+
+            # Create a directory for each dataset_abbr
+            folder_path = os.path.join(plot_path, dataset_abbr)
+            ensure_directory(folder_path)
+
+            # Construct the full path to save the image
+            save_path = os.path.join(folder_path, f'{model_name}.png')
+
+            # Create DataFrame for the model and dataset
+            df = create_model_dataframe(parsed_data, model_name, dataset_abbr, parallel=parallel_flag)
+
+            # Generate visualization and get the score
+            score = visualize(df, save_path, model_name, dataset_abbr)
+
+            # Store the score in the dictionary
+            model_datasets_scores[dataset_abbr] = '{:.02f}'.format(score)
+
+        # Process and visualize the overall score
+        overall_score_pic_path = os.path.join(plot_path, f'{model_name}_overall.png')
+        merged_df = merge_dataframes(model_name, dataset_abbrs, parsed_data)
+
+        print(merge_dataframes)
+        averaged_df = calculate_elementwise_average(merged_df)
+
+        # Assume visualize returns the average score for the overall visualization
+        overall_score = visualize(averaged_df, overall_score_pic_path, 'weighted_average_score', 'Overall Score')
+
+        # Add the overall score to the dictionary
+        model_datasets_scores['Overall'] = '{:.02f}'.format(overall_score)
+
+        # Add the model's scores to the main dictionary
+        model_scores[model_name] = model_datasets_scores
+
+def ensure_directory(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+def get_dict_model_names(nested_dict):
+    model_names = []
+    for first_level_key in nested_dict:
+        model_names.append(first_level_key)
+    return model_names
+
+def merge_dataframes(model_name, dataset_abbrs, parsed_data):
+    dfs = []
+    for dataset_abbr in dataset_abbrs:
+        parallel_flag = 'parallel' in dataset_abbr
+        df = create_model_dataframe(parsed_data, model_name, dataset_abbr, parallel=parallel_flag)
+
+        if not df.empty and len(df.columns) > 1:
+            score_column = df.columns[-1]
+            df.rename(columns={score_column: dataset_abbr}, inplace=True)
+
+        dfs.append(df)
+
+    from functools import reduce
+    merged_df = reduce(lambda left, right: pd.merge(left, right, on='dataset', how='outer'), dfs)
+
+    if merged_df.isnull().any().any():
+        print('Warning: Some rows were filtered out due to NaN values. This is often due to mismatched row counts among DataFrames.')
+        merged_df = merged_df.dropna()
+    return merged_df
+
+def calculate_elementwise_average(merged_df):
+    score_columns = [col for col in merged_df.columns if col != 'dataset']
+
+    origin_columns = [col for col in score_columns if 'origin' in col]
+    parallel_columns = [col for col in score_columns if 'parallel' in col]
+    multi_columns = [col for col in score_columns if 'needle'  in col]
+
+    if origin_columns and parallel_columns and multi_columns:
+        origin_avg = merged_df[origin_columns].mean(axis=1) * 0.4
+        parallel_avg = merged_df[parallel_columns].mean(axis=1) * 0.3
+        multi_avg = merged_df[multi_columns].mean(axis=1) * 0.3
+
+        merged_df['weighted_average_score'] = origin_avg + parallel_avg + multi_avg
+    else:
+        merged_df['weighted_average_score'] = pd.Series([0] * len(merged_df))
+
+    return merged_df.iloc[:, [0, -1]]
+
+
+class NeedleBenchSummarizer(DefaultSummarizer):
+    """NeedleBench summarizer in OpenCompass.
+
+    Args:
+        config (ConfigDict): The configuration object of the evaluation task. It's expected to be filled out at runtime.
+        dataset_abbrs (list[str], optional): Dataset abbreviations to be listed in the summary.
+        summary_groups (list): The dataset groups whose results need to be averaged out. For example, mmlu. Each item it a dict with
+            'name' (str) and 'subsets' (list of dataset abbrs), and optionally
+            'weights' if weighted average is needed.
+        prompt_db: A deprecated field.
+    """
+    def _format_table(self, parsed_results, dataset_metrics, dataset_eval_mode):
+        dataset_abbrs = [dataset_abbr_from_cfg(dataset) for dataset in self.dataset_cfgs]
+        prompt_version = {dataset_abbr_from_cfg(d): get_prompt_hash(d)[:6] for d in self.dataset_cfgs}
+
+        summarizer_dataset_abbrs = []
+        if self.dataset_abbrs is None:
+            # display all dataset metrics included in the config
+            for dataset_abbr in dataset_abbrs:
+                if dataset_abbr in dataset_metrics:
+                    for metric in dataset_metrics[dataset_abbr]:
+                        summarizer_dataset_abbrs.append((dataset_abbr, metric))
+                else:
+                    summarizer_dataset_abbrs.append((dataset_abbr, None))
+            # along with all possible group metrics
+            for dataset_abbr in dataset_metrics:
+                for metric in dataset_metrics[dataset_abbr]:
+                    if (dataset_abbr, metric) not in summarizer_dataset_abbrs:
+                        summarizer_dataset_abbrs.append((dataset_abbr, metric))
+        else:
+            # follow the required order
+            for item in self.dataset_abbrs:
+                if isinstance(item, str):
+                    summarizer_dataset_abbrs.append((item, None))
+                elif isinstance(item, (list, tuple)):
+                    summarizer_dataset_abbrs.append((item[0], item[1]))
+
+        table = []
+        header = ['dataset', 'version', 'metric', 'mode'] + self.model_abbrs
+        table.append(header)
+
+        for key in dataset_metrics:
+            dataset_metrics[key] = list(set(dataset_metrics[key]))
+
+        for dataset_abbr, metric in summarizer_dataset_abbrs:
+            if dataset_abbr not in dataset_metrics:
+                table.append([dataset_abbr, '-', '-', '-'] + ['-'] * len(self.model_abbrs))
+                table.append(header)
+                continue
+            if len(dataset_metrics[dataset_abbr]) >= 10:
+                metric = 'average_score'
+
+            if metric is None:
+                metric = dataset_metrics[dataset_abbr][0]
+            elif metric in dataset_metrics[dataset_abbr]:
+                pass
+            else:
+                table.append([dataset_abbr, '-', '-', '-'] + ['-'] * len(self.model_abbrs))
+                continue
+
+            row = [dataset_abbr, prompt_version.get(dataset_abbr, '-'), metric, dataset_eval_mode.get(dataset_abbr, '-')]
+            for model_abbr in self.model_abbrs:
+                if dataset_abbr in parsed_results[model_abbr]:
+                    row.append('{:.02f}'.format(parsed_results[model_abbr][dataset_abbr][metric]))
+                else:
+                    row.append('-')
+
+            table.append(row)
+        for i in range(len(table)):
+            if i == 0 or table[i][0].startswith('---------'):
+                table[i] = [table[i][0]] + table[i][4:]
+            else:
+                table[i] = [table[i][0]] + table[i][4:]
+
+        return table
+
+    def _format_raw_txt(self, raw_results):
+        raw_dataset_abbrs = []
+        for model_abbr in self.model_abbrs:
+            for dataset_abbr in raw_results[model_abbr]:
+                if dataset_abbr not in raw_dataset_abbrs:
+                    raw_dataset_abbrs.append(dataset_abbr)
+        raw_txts = []
+        for model_abbr in self.model_abbrs:
+            raw_txts.append('-------------------------------')
+            raw_txts.append(f'Model: {model_abbr}')
+            for dataset_abbr in raw_dataset_abbrs:
+                result = raw_results[model_abbr].get(dataset_abbr, '{}')
+                raw_txts.append(f'{dataset_abbr}: {result}')
+        raw_txts = '\n'.join(raw_txts)
+        return raw_txts
+
+    def _read_and_sort_dataframe(self, file_path):
+        # Read the file without treating the first row as a header
+        df = pd.read_csv(file_path, header=None)
+
+        # Function to sort columns based on the value of a specific row, excluding the first column
+        def sort_columns_based_on_row_corrected(df, base_row_idx, start_row_idx, end_row_idx):
+            # Extract the rows for sorting
+            sort_values_row = df.iloc[base_row_idx, 1:].replace('-', np.nan).apply(pd.to_numeric, errors='coerce')
+            # Handle NaNs by setting them to a value less than the minimum or using a method to keep them at the end
+            min_possible_value = sort_values_row.min(skipna=True) - 1  # Use min value in the row minus 1 or another method
+            sort_values_row_filled = sort_values_row.fillna(min_possible_value)
+            # Get the sorted order of indices, excluding the first column
+            sorted_col_indices = sort_values_row_filled.sort_values(ascending=False).index
+            # Apply the sorted column indices to the whole DataFrame, adjusting for Python's 0-based index
+            df.iloc[start_row_idx:end_row_idx+1] = df.iloc[start_row_idx:end_row_idx+1, [0] + sorted_col_indices.tolist()]
+
+        # Apply the corrected sorting function based on the description
+        sort_columns_based_on_row_corrected(df, 1, 0, 2)  # For rows 1-2 based on row 2's values
+        sort_columns_based_on_row_corrected(df, 4, 3, 7)  # For rows 4-7 based on row 5's values
+        sort_columns_based_on_row_corrected(df, 9, 8, 12)  # For rows 9-12 based on row 10's values
+        sort_columns_based_on_row_corrected(df, 14, 13, 25)  # For rows 14-25 based on row 15's values
+
+        # Return the sorted DataFrame
+        return df
+
+    def _output_to_file(self, output_path, time_str, table, raw_txts):
+        # output to file
+        if output_path is None:
+            output_path = osp.join(self.work_dir, 'summary', f'summary_{time_str}.txt')
+            output_csv_path = osp.join(self.work_dir, 'summary', f'summary_{time_str}.csv')
+        else:
+            output_csv_path = output_path.replace('.txt', '.csv')
+
+        output_dir = osp.split(output_path)[0]
+        mmengine.mkdir_or_exist(output_dir)
+        with open(output_path, 'w', encoding='utf-8') as f:
+            text = f'{time_str}\n' + \
+                    'tabulate format\n' + \
+                    '^' * 128 + '\n' + \
+                    tabulate.tabulate(table, headers='firstrow') + '\n' + \
+                    '$' * 128 + '\n\n' + \
+                    '-' * 128 + ' THIS IS A DIVIDER ' + '-' * 128 + '\n\n' + \
+                    'csv format\n' + \
+                    '^' * 128 + '\n' + \
+                    '\n'.join([','.join(row) for row in table]) + '\n' + \
+                    '$' * 128 + '\n\n' + \
+                    '-' * 128 + ' THIS IS A DIVIDER ' + '-' * 128 + '\n\n' + \
+                    'raw format\n' + \
+                    '^' * 128 + '\n' + \
+                    raw_txts + '\n' + \
+                    '$' * 128 + '\n'
+            f.write(text)
+        self.logger.info(f'write summary to {osp.abspath(output_path)}')
+
+        with open(output_csv_path, 'w', encoding='utf-8') as f:
+            f.write('\n'.join([','.join(row) for row in table]) + '\n')
+        self.logger.info(f'write csv to {osp.abspath(output_csv_path)}')
+
+        df_sorted = self._read_and_sort_dataframe(output_csv_path)
+
+        sorted_file_path = osp.abspath(output_csv_path).split('.')[0] + '_sorted.csv'
+        df_sorted.to_csv(sorted_file_path, index=False, header=False)
+
+        self.logger.info(f'write sorted csv to {sorted_file_path}')
+
+
+    def summarize(
+        self,
+        output_path: str = None,
+        time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S')):  # noqa
+
+        # pick up results
+        raw_results, parsed_results, dataset_metrics, dataset_eval_mode = self._pick_up_results()
+
+        # calculate group metrics
+        raw_results, parsed_results, dataset_metrics, dataset_eval_mode = \
+            self._calculate_group_metrics(raw_results, parsed_results, dataset_metrics, dataset_eval_mode)
+
+        # format table
+        table = self._format_table(parsed_results, dataset_metrics, dataset_eval_mode)
+
+        # format raw txt
+        raw_txts = self._format_raw_txt(raw_results)
+
+        # output to screen
+        print(tabulate.tabulate(table, headers='firstrow'))
+
+        # output to .text / .csv files
+        self._output_to_file(output_path, time_str, table, raw_txts)
+
+        if self.lark_reporter:
+            content = f'{getpass.getuser()} 的'
+            content += f'详细评测汇总已输出至 {osp.abspath(output_path)}'
+            self.lark_reporter.post(content)
+
+        if output_path is None:
+            output_path = osp.join(self.work_dir, 'summary', f'summary_{time_str}.txt')
+        # plot to show visualize results
+        save_results_to_plots(output_path)
+
+class NeedleBenchATCSummarizer(DefaultSummarizer):
+    """NeedleBench-ATC summarizer in OpenCompass.
+
+    Args:
+        config (ConfigDict): The configuration object of the evaluation task. It's expected to be filled out at runtime.
+        dataset_abbrs (list[str], optional): Dataset abbreviations to be listed in the summary.
+        summary_groups (list): The dataset groups whose results need to be averaged out. For example, mmlu. Each item it a dict with
+            'name' (str) and 'subsets' (list of dataset abbrs), and optionally
+            'weights' if weighted average is needed.
+        prompt_db: A deprecated field.
+    """
+
+    def _format_table(self, parsed_results, dataset_metrics, dataset_eval_mode):
+        dataset_abbrs = [dataset_abbr_from_cfg(dataset) for dataset in self.dataset_cfgs]
+        prompt_version = {dataset_abbr_from_cfg(d): get_prompt_hash(d)[:6] for d in self.dataset_cfgs}
+
+        summarizer_dataset_abbrs = []
+        if self.dataset_abbrs is None:
+            # display all dataset metrics included in the config
+            for dataset_abbr in dataset_abbrs:
+                if dataset_abbr in dataset_metrics:
+                    for metric in dataset_metrics[dataset_abbr]:
+                        summarizer_dataset_abbrs.append((dataset_abbr, metric))
+                else:
+                    summarizer_dataset_abbrs.append((dataset_abbr, None))
+            # along with all possible group metrics
+            for dataset_abbr in dataset_metrics:
+                for metric in dataset_metrics[dataset_abbr]:
+                    if (dataset_abbr, metric) not in summarizer_dataset_abbrs:
+                        summarizer_dataset_abbrs.append((dataset_abbr, metric))
+        else:
+            # follow the required order
+            for item in self.dataset_abbrs:
+                if isinstance(item, str):
+                    summarizer_dataset_abbrs.append((item, None))
+                elif isinstance(item, (list, tuple)):
+                    summarizer_dataset_abbrs.append((item[0], item[1]))
+
+        table = []
+        header = ['dataset', 'version', 'metric', 'mode'] + self.model_abbrs
+        table.append(header)
+
+        for key in dataset_metrics:
+            dataset_metrics[key] = list(set(dataset_metrics[key]))
+
+        for dataset_abbr, metric in summarizer_dataset_abbrs:
+            if dataset_abbr not in dataset_metrics:
+                table.append([dataset_abbr, '-', '-', '-'] + ['-'] * len(self.model_abbrs))
+                table.append(header)
+                continue
+            if len(dataset_metrics[dataset_abbr]) >= 10:
+                metric = 'average_score'
+
+            if metric is None:
+                metric = dataset_metrics[dataset_abbr][0]
+            elif metric in dataset_metrics[dataset_abbr]:
+                pass
+            else:
+                table.append([dataset_abbr, '-', '-', '-'] + ['-'] * len(self.model_abbrs))
+                continue
+
+            row = [dataset_abbr, prompt_version.get(dataset_abbr, '-'), metric, dataset_eval_mode.get(dataset_abbr, '-')]
+            for model_abbr in self.model_abbrs:
+                if dataset_abbr in parsed_results[model_abbr]:
+                    row.append('{:.02f}'.format(parsed_results[model_abbr][dataset_abbr][metric]))
+                else:
+                    row.append('-')
+
+            table.append(row)
+        for i in range(len(table)):
+            if i == 0 or table[i][0].startswith('---------'):
+                table[i] = [table[i][0]] + table[i][4:]
+            else:
+                table[i] = [table[i][0]] + table[i][4:]
+
+        return table
+
+    def _read_and_sort_dataframe(self, file_path):
+        # Read the file without treating the first row as a header
+        data = pd.read_csv(file_path)
+        # print(data)
+        # Correct the extraction of needle counts for all settings
+        data['needle_count'] = data['dataset'].str.extract(r'needle_(\d+)_').astype(float)
+        data['needle_count'] = data['needle_count'].astype(int)
+
+        # Define experimental settings groups
+        experimental_settings = {
+            'en': '_en$',
+            'zh': '_zh$',
+            'en_ordered': '_en_ordered',
+            'zh_ordered': '_zh_ordered',
+        }
+
+        # Function to calculate maximum needles
+        def calculate_max_needles(dataset):
+            max_needles = {model: None for model in dataset.columns if 'b' in model}
+            for model in max_needles.keys():
+                consecutive_low_scores = 0
+                previous_needle_count = 0
+                for index, row in dataset.sort_values(by='needle_count').iterrows():
+                    try:
+                        score = float(row[model])
+                    except ValueError as e:
+                        score = -1
+                    if score < 60:
+                        consecutive_low_scores += 1
+                        if consecutive_low_scores == 1:
+                            max_needles[model] = previous_needle_count
+                    else:
+                        consecutive_low_scores = 0
+                    previous_needle_count = row['needle_count']
+                max_needle_count_seen = dataset['needle_count'].max()
+                max_needles[model] = max_needle_count_seen if max_needles[model] is None else max_needles[model]
+            return max_needles
+
+        # Calculate max needles for each group and organize results in a DataFrame
+        results = {}
+        for setting, regex in experimental_settings.items():
+            filtered_data = data[data['dataset'].str.contains(regex)]
+            results[setting] = calculate_max_needles(filtered_data)
+
+        # Convert results to DataFrame and transpose it
+        results_df = pd.DataFrame(results).transpose()
+
+        # Return the sorted DataFrame
+        results_df.index.name = 'ATC Experiment Type'
+        return results_df
+
+    def _output_to_file(self, output_path, time_str, table, raw_txts):
+        # output to file
+        if output_path is None:
+            output_path = osp.join(self.work_dir, 'summary', f'summary_{time_str}.txt')
+            output_csv_path = osp.join(self.work_dir, 'summary', f'summary_{time_str}.csv')
+        else:
+            output_csv_path = output_path.replace('.txt', '.csv')
+
+        output_dir = osp.split(output_path)[0]
+        mmengine.mkdir_or_exist(output_dir)
+        with open(output_path, 'w', encoding='utf-8') as f:
+            text = f'{time_str}\n' + \
+                    'tabulate format\n' + \
+                    '^' * 128 + '\n' + \
+                    tabulate.tabulate(table, headers='firstrow') + '\n' + \
+                    '$' * 128 + '\n\n' + \
+                    '-' * 128 + ' THIS IS A DIVIDER ' + '-' * 128 + '\n\n' + \
+                    'csv format\n' + \
+                    '^' * 128 + '\n' + \
+                    '\n'.join([','.join(row) for row in table]) + '\n' + \
+                    '$' * 128 + '\n\n' + \
+                    '-' * 128 + ' THIS IS A DIVIDER ' + '-' * 128 + '\n\n' + \
+                    'raw format\n' + \
+                    '^' * 128 + '\n' + \
+                    raw_txts + '\n' + \
+                    '$' * 128 + '\n'
+            f.write(text)
+        self.logger.info(f'write summary to {osp.abspath(output_path)}')
+
+        with open(output_csv_path, 'w', encoding='utf-8') as f:
+            f.write('\n'.join([','.join(row) for row in table]) + '\n')
+        # self.logger.info(f'write csv to {osp.abspath(output_csv_path)}')
+
+        df_sorted = self._read_and_sort_dataframe(output_csv_path)
+
+        df_sorted.to_csv(output_csv_path)
+
+        self.logger.info(f'write sorted csv to {output_csv_path}')
+
+
+    def summarize(
+        self,
+        output_path: str = None,
+        time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S')):  # noqa
+
+        # pick up results
+        raw_results, parsed_results, dataset_metrics, dataset_eval_mode = self._pick_up_results()
+
+        # calculate group metrics
+        raw_results, parsed_results, dataset_metrics, dataset_eval_mode = \
+            self._calculate_group_metrics(raw_results, parsed_results, dataset_metrics, dataset_eval_mode)
+
+        # format table
+        table = self._format_table(parsed_results, dataset_metrics, dataset_eval_mode)
+
+        # format raw txt
+        raw_txts = self._format_raw_txt(raw_results)
+
+        # output to .text / .csv files
+        self._output_to_file(output_path, time_str, table, raw_txts)
+
+        if self.lark_reporter:
+            content = f'{getpass.getuser()} 的'
+            content += f'详细评测汇总已输出至 {osp.abspath(output_path)}'
+            self.lark_reporter.post(content)
+
+        if output_path is None:
+            output_path = osp.join(self.work_dir, 'summary', f'summary_{time_str}.txt')

tools/tools_needleinahaystack.py

-import argparse
-
-import matplotlib.pyplot as plt
-import pandas as pd
-import seaborn as sns
-from matplotlib.colors import LinearSegmentedColormap
-
-
-class CDMEDataset():
-
-    @staticmethod
-    def visualize(path: str, dataset_length: str):
-        for file_path in path:
-            df = pd.read_csv(file_path)
-
-            df['Context Length'] = df['dataset'].apply(
-                lambda x: int(x.split('Length')[1].split('Depth')[0]))
-            df['Document Depth'] = df['dataset'].apply(
-                lambda x: float(x.split('Depth')[1].split('_')[0]))
-
-            # Exclude 'Context Length' and 'Document Depth' columns
-            model_columns = [
-                col for col in df.columns
-                if col not in ['Context Length', 'Document Depth']
-            ]
-
-            for model_name in model_columns[4:]:
-                model_df = df[['Document Depth', 'Context Length',
-                               model_name]].copy()
-                model_df.rename(columns={model_name: 'Score'}, inplace=True)
-
-                # Create pivot table
-                pivot_table = pd.pivot_table(model_df,
-                                             values='Score',
-                                             index=['Document Depth'],
-                                             columns=['Context Length'],
-                                             aggfunc='mean')
-
-                # Calculate mean scores
-                mean_scores = pivot_table.mean().values
-
-                # Calculate overall score
-                overall_score = mean_scores.mean()
-
-                # Create heatmap and line plot
-                plt.figure(figsize=(15.5, 8))
-                ax = plt.gca()
-                cmap = LinearSegmentedColormap.from_list(
-                    'custom_cmap', ['#F0496E', '#EBB839', '#0CD79F'])
-
-                # Draw heatmap
-                sns.heatmap(pivot_table,
-                            cmap=cmap,
-                            ax=ax,
-                            cbar_kws={'label': 'Score'},
-                            vmin=0,
-                            vmax=100)
-
-                # Set line plot data
-                x_data = [i + 0.5 for i in range(len(mean_scores))]
-                y_data = mean_scores
-
-                # Create twin axis for line plot
-                ax2 = ax.twinx()
-                # Draw line plot
-                ax2.plot(x_data,
-                         y_data,
-                         color='white',
-                         marker='o',
-                         linestyle='-',
-                         linewidth=2,
-                         markersize=8,
-                         label='Average Depth Score')
-                # Set y-axis range
-                ax2.set_ylim(0, 100)
-
-                # Hide original y-axis ticks and labels
-                ax2.set_yticklabels([])
-                ax2.set_yticks([])
-
-                # Add legend
-                ax2.legend(loc='upper left')
-
-                # Set chart title and labels
-                ax.set_title(f'{model_name} {dataset_length} Context '
-                             'Performance\nFact Retrieval Across '
-                             'Context Lengths ("Needle In A Haystack")')
-                ax.set_xlabel('Token Limit')
-                ax.set_ylabel('Depth Percent')
-                ax.set_xticklabels(pivot_table.columns.values, rotation=45)
-                ax.set_yticklabels(pivot_table.index.values, rotation=0)
-                # Add overall score as a subtitle
-                plt.text(0.5,
-                         -0.13, f'Overall Score for {model_name}: '
-                         f'{overall_score:.2f}',
-                         ha='center',
-                         va='center',
-                         transform=ax.transAxes,
-                         fontsize=13)
-
-                # Save heatmap as PNG
-                png_file_path = file_path.replace('.csv', f'_{model_name}.png')
-                plt.tight_layout()
-                plt.subplots_adjust(right=1)
-                plt.draw()
-                plt.savefig(png_file_path)
-                plt.show()
-
-                plt.close()  # Close figure to prevent memory leaks
-
-                # Print saved PNG file path
-                print(f'Heatmap for {model_name} saved as: {png_file_path}')
-
-
-def main():
-    parser = argparse.ArgumentParser(description='Generate NeedleInAHaystack'
-                                     'Test Plots')
-
-    parser.add_argument('--path',
-                        nargs='*',
-                        default=['path/to/your/result.csv'],
-                        help='Paths to CSV files for visualization')
-    parser.add_argument('--dataset_length',
-                        default='8K',
-                        type=str,
-                        help='Dataset_length for visualization')
-    args = parser.parse_args()
-
-    if not args.path:
-        print("Error: '--path' is required for visualization.")
-        exit(1)
-    CDMEDataset.visualize(args.path, args.dataset_length)
-
-
-if __name__ == '__main__':
-    main()