hle2000/RerankKGQA

该数据集包含两个配置：t5largessm_subgraphs和t5xlssm_subgraphs。每个配置都包含多个特征，如问题、答案实体、问题实体、真实答案实体、复杂性类型、图结构、正确性、T5序列、GAP序列、问题答案、节点数、边数、密度、环数、桥数、Katz中心性、PageRank、平均最短路径长度、确定序列、确定序列嵌入、GAP序列嵌入、T5序列嵌入、问题答案嵌入、高亮确定序列、非高亮确定序列、高亮T5序列、非高亮T5序列、高亮GAP序列、非高亮GAP序列和索引级别。数据集被分为训练集、验证集和测试集，每个部分都有相应的字节数和示例数。

The dataset contains two configurations: t5largessm_subgraphs and t5xlssm_subgraphs. Each configuration includes multiple features such as question, answer entity, question entity, ground truth answer entity, complexity type, graph structure, correctness, T5 sequence, GAP sequence, question answer, number of nodes, number of edges, density, cycle count, bridge count, Katz centrality, PageRank, average shortest path length, deterministic sequence, deterministic sequence embedding, GAP sequence embedding, T5 sequence embedding, question answer embedding, highlighted deterministic sequence, non-highlighted deterministic sequence, highlighted T5 sequence, non-highlighted T5 sequence, highlighted GAP sequence, non-highlighted GAP sequence, and index level. The dataset is divided into train, validation, and test sets, each with corresponding byte counts and example counts.

原始信息汇总

数据集概述

数据集配置

t5largessm_subgraphs

• 特征

  • id: string
  • question: string
  • answerEntity: string
  • questionEntity: string
  • groundTruthAnswerEntity: string
  • complexityType: string
  • graph: string
  • correct: bool
  • t5_sequence: string
  • gap_sequence: string
  • question_answer: string
  • num_nodes: int64
  • num_edges: int64
  • density: float64
  • cycle: int64
  • bridge: int64
  • katz_centrality: float64
  • page_rank: float64
  • avg_ssp_length: float64
  • determ_sequence: string
  • determ_sequence_embedding: sequence: float64
  • gap_sequence_embedding: sequence: float64
  • t5_sequence_embedding: sequence: float64
  • question_answer_embedding: sequence: float64
  • highlighted_determ_sequence: string
  • no_highlighted_determ_sequence: string
  • highlighted_t5_sequence: string
  • no_highlighted_t5_sequence: string
  • highlighted_gap_sequence: string
  • no_highlighted_gap_sequence: string
  • index_level_0: int64

• 分割

  • train: 65402 examples, 1779211679 bytes
  • validation: 65402 examples, 1779211679 bytes
  • test: 16567 examples, 450591308 bytes

• 数据文件

  • train: t5largessm_subgraphs/train-*
  • validation: t5largessm_subgraphs/validation-*
  • test: t5largessm_subgraphs/test-*

• 下载大小: 3874291189 bytes

• 数据集大小: 4009014666 bytes

t5xlssm_subgraphs

• 特征

  • id: string
  • question: string
  • answerEntity: string
  • questionEntity: string
  • groundTruthAnswerEntity: string
  • complexityType: string
  • graph: string
  • correct: bool
  • t5_sequence: string
  • gap_sequence: string
  • question_answer: string
  • num_nodes: int64
  • num_edges: int64
  • density: float64
  • cycle: int64
  • bridge: int64
  • katz_centrality: float64
  • page_rank: float64
  • avg_ssp_length: float64
  • determ_sequence: string
  • determ_sequence_embedding: sequence: float64
  • gap_sequence_embedding: sequence: float64
  • t5_sequence_embedding: sequence: float64
  • question_answer_embedding: sequence: float64
  • highlighted_determ_sequence: string
  • no_highlighted_determ_sequence: string
  • highlighted_t5_sequence: string
  • no_highlighted_t5_sequence: string
  • highlighted_gap_sequence: string
  • no_highlighted_gap_sequence: string
  • index_level_0: int64

• 分割

  • train: 61833 examples, 1680423042 bytes
  • validation: 61833 examples, 1680423042 bytes
  • test: 15577 examples, 423257572 bytes

• 数据文件

  • train: t5xlssm_subgraphs/train-*
  • validation: t5xlssm_subgraphs/validation-*
  • test: t5xlssm_subgraphs/test-*

• 下载大小: 3675142833 bytes

• 数据集大小: 3784103656 bytes