Update README.md

README.md

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-# Dataset Card for Agentic Search Dataset
 
-<!-- Provide a quick summary of the dataset. -->
+# Dataset Card for Agentic Retrieval Benchmark
 
-近年来，在文本检索领域已有大量相关工作，但现有工作大多分散在不同的数据集上，且结果对比片面，缺少一个较为全面的文本检索性能指标测试基准。  
-本数据集为Agentic Search Benchmark的配套数据。    
-项目旨在为大模型增强的文本检索建立可复现的基准：在统一的数据与流水线下比较传统 BM25 与向量检索的性能指标，并检验“重写 + 向量化”过程中各环节的贡献。
+In recent years, there has been a large body of work in the field of text retrieval. However, existing studies are often scattered across different datasets, and their comparisons are partial and fragmented, lacking a comprehensive benchmark for evaluating retrieval performance.
+
+This dataset is provided as part of the Agentic Retrieval Benchmark.
+
+The project aims to establish a reproducible benchmark for LLM-augmented text retrieval. Under a unified dataset and pipeline, it compares the performance of traditional BM25 and vector-based retrieval methods, and evaluates the contribution of each component in the “query rewriting + vectorization” process.
 
 ## Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
+The dataset is constructed from two open-source datasets:
+
+* **Multi-CPR**: Covers three application scenarios (medical, e-commerce, and video), formatted as single-turn question-answer pairs.
+* **LexRAG**: Focuses on Chinese legal consultation scenarios, formatted as multi-turn dialogues.
+
+From the Multi-CPR dataset, we sample 1,000 queries and approximately 10,000 corpus passages for each of the three scenarios, using their corresponding indices as ground truth.
+
+For the LexRAG dataset, we adopt a dialogue-history-plus-current-question setup. That is, each query consists of the full conversation history up to the current turn, combined with the latest user question.
+
+The data has been cleaned and preprocessed, and can be directly used as input for query rewriting and evaluation scripts.
+
+* Query data is stored in `.\data\rawData\xxx_query.txt`
+* Passage data is stored in `.\data\rawData\xxx_subset.tsv`
+* Ground truth labels/indices are stored in `.\data\qrelData\xxx_dev.tsv`
+
+# Dataset Card for Agentic Retrieval Benchmark
+
+近年来，在文本检索领域已有大量相关工作，但现有工作大多分散在不同的数据集上，且结果对比片面，缺少一个较为全面的文本检索性能指标测试基准。
+
+本数据集为 Agentic Retrieval Benchmark 的配套数据。
+
+项目旨在为大模型增强的文本检索建立可复现的基准：在统一的数据与流水线下比较传统 BM25 与向量检索的性能指标，并检验“重写 + 向量化”过程中各环节的贡献。
+
+## Dataset Description
 
 项目数据集来自 2 个开放数据集：
 - Multi-CPR：包含三个应用场景（医疗、电商、视频），数据格式为单轮问答
 - LexRAG：包含中文法律咨询场景，数据格式为多轮对话
 
-我们从multi-CPR数据集的3个场景中分别提取了1000条query和约10000条corpus，并以对应的索引作为groundtruth。
+我们从 Multi-CPR 数据集的 3 个场景中分别提取了 1000 条 query 和约 10000 条 corpus，并以对应的索引作为 groundtruth。
 
-对于LexRAG数据集，我们选取了其中的对话历史+最新问题场景，即，每次用于query的文本等于该次会话的前面所有轮次问答历史+当前最新一轮的提问。
+对于 LexRAG 数据集，我们选取了其中的对话历史+最新问题场景，即每次用于 query 的文本等于该次会话的前面所有轮次问答历史+当前最新一轮的提问。
 
 数据已经完成清洗和预处理，可直接用作重写和评估脚本的输入。
 
-- query数据存放在`.\data\rawData\xxx_query.txt`
+- query 数据存放在`.\data\rawData\xxx_query.txt`
+
+- passage 数据`.\data\rawData\xxx_subset.tsv`
 
-- passage数据`.\data\rawData\xxx_subset.tsv`
+- groundtruth 标签/索引存放在`.\data\qrelData\xxx_dev.tsv`
 
-- groundtruth标签/索引存放在`.\data\qrelData\xxx_dev.tsv`