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mcding-org/Easy2Hard-IRT-tune

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Hugging Face2024-06-04 更新2024-06-12 收录
下载链接：
https://hf-mirror.com/datasets/mcding-org/Easy2Hard-IRT-tune
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资源简介：
--- dataset_info: - config_name: human_v4 features: - name: irt_model dtype: string - name: irt_lr dtype: string - name: irt_epochs dtype: string - name: irt_std_filter_ratio dtype: float64 - name: GPT_2.0_acc dtype: float64 - name: GPT_2.0_ratio dtype: float64 splits: - name: GSM8K num_bytes: 562.0 num_examples: 12 - name: ARC num_bytes: 562.0 num_examples: 12 - name: Winogrande num_bytes: 562.0 num_examples: 12 download_size: 10550 dataset_size: 1686.0 - config_name: human_v4_1 features: - name: irt_model dtype: string - name: irt_lr dtype: string - name: irt_epochs dtype: string - name: irt_std_filter_ratio dtype: float64 - name: GPT_2.0_acc dtype: float64 - name: GPT_2.0_ratio dtype: float64 splits: - name: GSM8K num_bytes: 562.0 num_examples: 12 - name: ARC num_bytes: 562.0 num_examples: 12 - name: Winogrande num_bytes: 562.0 num_examples: 12 download_size: 10534 dataset_size: 1686.0 - config_name: human_v4_2 features: - name: irt_model dtype: string - name: irt_lr dtype: string - name: irt_epochs dtype: string - name: irt_std_filter_ratio dtype: float64 - name: GPT_2.0_acc dtype: float64 - name: GPT_2.0_ratio dtype: float64 splits: - name: GSM8K num_bytes: 562.0 num_examples: 12 - name: ARC num_bytes: 562.0 num_examples: 12 - name: Winogrande num_bytes: 562.0 num_examples: 12 download_size: 10534 dataset_size: 1686.0 - config_name: human_v5 features: - name: sha_index dtype: string - name: irt_model dtype: string - name: irt_lr dtype: string - name: irt_epochs dtype: string - name: irt_std_filter_ratio dtype: float64 - name: GPT_2.0_acc dtype: float64 - name: GPT_2.0_ratio dtype: float64 splits: - name: GSM8K num_bytes: 6220.0 num_examples: 120 - name: ARC num_bytes: 6220.0 num_examples: 120 - name: Winogrande num_bytes: 6220.0 num_examples: 120 download_size: 15853 dataset_size: 18660.0 - config_name: new_v4 features: - name: acc_uncertainty dtype: float64 - name: irt_minlikes dtype: string - name: irt_model dtype: 'null' - name: irt_lr dtype: 'null' - name: irt_epochs dtype: 'null' - name: irt_std_filter_ratio dtype: float64 - name: GPT_0.0_acc dtype: float64 - name: GPT_0.0_ratio dtype: float64 - name: GPT_0.5_acc dtype: float64 - name: GPT_0.5_ratio dtype: float64 - name: GPT_1.0_acc dtype: float64 - name: GPT_1.0_ratio dtype: float64 - name: GPT_1.5_acc dtype: float64 - name: GPT_1.5_ratio dtype: float64 - name: GPT_2.0_acc dtype: float64 - name: GPT_2.0_ratio dtype: float64 - name: GPT_2.5_acc dtype: float64 - name: GPT_2.5_ratio dtype: float64 - name: GPT_3.0_acc dtype: float64 - name: GPT_3.0_ratio dtype: float64 - name: GPT_3.5_acc dtype: float64 - name: GPT_3.5_ratio dtype: float64 - name: GPT_4.0_acc dtype: float64 - name: GPT_4.0_ratio dtype: float64 - name: GPT_4.5_acc dtype: float64 - name: GPT_4.5_ratio dtype: float64 - name: GPT_5.0_acc dtype: float64 - name: GPT_5.0_ratio dtype: float64 splits: - name: GSM8K num_bytes: 15257.0 num_examples: 77 - name: ARC num_bytes: 15257.0 num_examples: 77 - name: HellaSwag num_bytes: 15257.0 num_examples: 77 - name: Winogrande num_bytes: 15257.0 num_examples: 77 download_size: 104580 dataset_size: 61028.0 - config_name: v1 features: - name: 'Unnamed: 0' dtype: int64 - name: irt_params dtype: string - name: IRT_0_GPT_0 dtype: float64 - name: IRT_0_GPT_0.5 dtype: float64 - name: IRT_0_GPT_1.0 dtype: float64 - name: IRT_0_GPT_1.5 dtype: float64 - name: IRT_0.02_GPT_0 dtype: float64 - name: IRT_0.02_GPT_0.5 dtype: float64 - name: IRT_0.02_GPT_1.0 dtype: float64 - name: IRT_0.02_GPT_1.5 dtype: float64 - name: IRT_0.05_GPT_0 dtype: float64 - name: IRT_0.05_GPT_0.5 dtype: float64 - name: IRT_0.05_GPT_1.0 dtype: float64 - name: IRT_0.05_GPT_1.5 dtype: float64 - name: IRT_0.1_GPT_0 dtype: float64 - name: IRT_0.1_GPT_0.5 dtype: float64 - name: IRT_0.1_GPT_1.0 dtype: float64 - name: IRT_0.1_GPT_1.5 dtype: float64 - name: IRT_0.15_GPT_0 dtype: float64 - name: IRT_0.15_GPT_0.5 dtype: float64 - name: IRT_0.15_GPT_1.0 dtype: float64 - name: IRT_0.15_GPT_1.5 dtype: float64 splits: - name: GSM8K num_bytes: 59731 num_examples: 276 - name: ARC num_bytes: 59733 num_examples: 276 - name: HellaSwag num_bytes: 59614 num_examples: 268 - name: Winogrande num_bytes: 59169 num_examples: 266 download_size: 242426 dataset_size: 238247 - config_name: v2 features: - name: 'Unnamed: 0' dtype: int64 - name: irt_params dtype: string - name: irt_std_filter_ratio dtype: float64 - name: GPT_0 dtype: float64 - name: GPT_0.5 dtype: float64 - name: GPT_1.0 dtype: float64 - name: GPT_1.5 dtype: float64 splits: - name: GSM8K num_bytes: 27984 num_examples: 288 - name: ARC num_bytes: 27984 num_examples: 288 - name: HellaSwag num_bytes: 29607 num_examples: 287 - name: Winogrande num_bytes: 29607 num_examples: 287 download_size: 77892 dataset_size: 115182 - config_name: v3 features: - name: acc_uncertainty dtype: float64 - name: irt_minlikes dtype: string - name: irt_model dtype: string - name: irt_lr dtype: float64 - name: irt_epochs dtype: int64 - name: irt_std_filter_ratio dtype: float64 - name: GPT_0.0_acc dtype: float64 - name: GPT_0.0_ratio dtype: float64 - name: GPT_0.5_acc dtype: float64 - name: GPT_0.5_ratio dtype: float64 - name: GPT_1.0_acc dtype: float64 - name: GPT_1.0_ratio dtype: float64 - name: GPT_1.5_acc dtype: float64 - name: GPT_1.5_ratio dtype: float64 - name: GPT_2.0_acc dtype: float64 - name: GPT_2.0_ratio dtype: float64 - name: GPT_2.5_acc dtype: float64 - name: GPT_2.5_ratio dtype: float64 - name: GPT_3.0_acc dtype: float64 - name: GPT_3.0_ratio dtype: float64 - name: GPT_3.5_acc dtype: float64 - name: GPT_3.5_ratio dtype: float64 - name: GPT_4.0_acc dtype: float64 - name: GPT_4.0_ratio dtype: float64 - name: GPT_4.5_acc dtype: float64 - name: GPT_4.5_ratio dtype: float64 - name: GPT_5.0_acc dtype: float64 - name: GPT_5.0_ratio dtype: float64 splits: - name: GSM8K num_bytes: 30372.0 num_examples: 137 - name: ARC num_bytes: 30372.0 num_examples: 137 - name: HellaSwag num_bytes: 30372.0 num_examples: 137 - name: Winogrande num_bytes: 30372.0 num_examples: 137 download_size: 134172 dataset_size: 121488.0 - config_name: v4 features: - name: acc_uncertainty dtype: float64 - name: irt_minlikes dtype: string - name: irt_model dtype: string - name: irt_lr dtype: float64 - name: irt_epochs dtype: int64 - name: irt_std_filter_ratio dtype: float64 - name: GPT_0.0_acc dtype: float64 - name: GPT_0.0_ratio dtype: float64 - name: GPT_0.5_acc dtype: float64 - name: GPT_0.5_ratio dtype: float64 - name: GPT_1.0_acc dtype: float64 - name: GPT_1.0_ratio dtype: float64 - name: GPT_1.5_acc dtype: float64 - name: GPT_1.5_ratio dtype: float64 - name: GPT_2.0_acc dtype: float64 - name: GPT_2.0_ratio dtype: float64 - name: GPT_2.5_acc dtype: float64 - name: GPT_2.5_ratio dtype: float64 - name: GPT_3.0_acc dtype: float64 - name: GPT_3.0_ratio dtype: float64 - name: GPT_3.5_acc dtype: float64 - name: GPT_3.5_ratio dtype: float64 - name: GPT_4.0_acc dtype: float64 - name: GPT_4.0_ratio dtype: float64 - name: GPT_4.5_acc dtype: float64 - name: GPT_4.5_ratio dtype: float64 - name: GPT_5.0_acc dtype: float64 - name: GPT_5.0_ratio dtype: float64 splits: - name: GSM8K num_bytes: 30372.0 num_examples: 137 - name: ARC num_bytes: 30372.0 num_examples: 137 - name: HellaSwag num_bytes: 30372.0 num_examples: 137 - name: Winogrande num_bytes: 30372.0 num_examples: 137 download_size: 138063 dataset_size: 121488.0 configs: - config_name: human_v4 data_files: - split: GSM8K path: human_v4/GSM8K-* - split: ARC path: human_v4/ARC-* - split: Winogrande path: human_v4/Winogrande-* - config_name: human_v4_1 data_files: - split: GSM8K path: human_v4_1/GSM8K-* - split: ARC path: human_v4_1/ARC-* - split: Winogrande path: human_v4_1/Winogrande-* - config_name: human_v4_2 data_files: - split: GSM8K path: human_v4_2/GSM8K-* - split: ARC path: human_v4_2/ARC-* - split: Winogrande path: human_v4_2/Winogrande-* - config_name: human_v5 data_files: - split: GSM8K path: human_v5/GSM8K-* - split: ARC path: human_v5/ARC-* - split: Winogrande path: human_v5/Winogrande-* - config_name: new_v4 data_files: - split: GSM8K path: new_v4/GSM8K-* - split: ARC path: new_v4/ARC-* - split: HellaSwag path: new_v4/HellaSwag-* - split: Winogrande path: new_v4/Winogrande-* - config_name: v1 data_files: - split: GSM8K path: v1/GSM8K-* - split: ARC path: v1/ARC-* - split: HellaSwag path: v1/HellaSwag-* - split: Winogrande path: v1/Winogrande-* - config_name: v2 data_files: - split: GSM8K path: v2/GSM8K-* - split: ARC path: v2/ARC-* - split: HellaSwag path: v2/HellaSwag-* - split: Winogrande path: v2/Winogrande-* - config_name: v3 data_files: - split: GSM8K path: v3/GSM8K-* - split: ARC path: v3/ARC-* - split: HellaSwag path: v3/HellaSwag-* - split: Winogrande path: v3/Winogrande-* - config_name: v4 data_files: - split: GSM8K path: v4/GSM8K-* - split: ARC path: v4/ARC-* - split: HellaSwag path: v4/HellaSwag-* - split: Winogrande path: v4/Winogrande-* ---

The dataset includes multiple configuration versions, each with specific features and data splits. Features primarily involve IRT model parameters, learning rates, training epochs, standard filter ratios, and accuracy and ratios of GPT models. The dataset is divided into several subsets, including GSM8K, ARC, Winogrande, etc., each with different byte counts and example numbers. Additionally, the dataset and download sizes vary across different versions.
提供机构：
mcding-org
原始信息汇总

数据集概述

配置名称：human_v4

  • 特征:
    • irt_model: 字符串
    • irt_lr: 字符串
    • irt_epochs: 字符串
    • irt_std_filter_ratio: 浮点数
    • GPT_2.0_acc: 浮点数
    • GPT_2.0_ratio: 浮点数
  • 分割:
    • GSM8K: 562.0 字节, 12 示例
    • ARC: 562.0 字节, 12 示例
    • Winogrande: 562.0 字节, 12 示例
  • 下载大小: 10550
  • 数据集大小: 1686.0

配置名称：human_v4_1

  • 特征:
    • irt_model: 字符串
    • irt_lr: 字符串
    • irt_epochs: 字符串
    • irt_std_filter_ratio: 浮点数
    • GPT_2.0_acc: 浮点数
    • GPT_2.0_ratio: 浮点数
  • 分割:
    • GSM8K: 562.0 字节, 12 示例
    • ARC: 562.0 字节, 12 示例
    • Winogrande: 562.0 字节, 12 示例
  • 下载大小: 10534
  • 数据集大小: 1686.0

配置名称：human_v4_2

  • 特征:
    • irt_model: 字符串
    • irt_lr: 字符串
    • irt_epochs: 字符串
    • irt_std_filter_ratio: 浮点数
    • GPT_2.0_acc: 浮点数
    • GPT_2.0_ratio: 浮点数
  • 分割:
    • GSM8K: 562.0 字节, 12 示例
    • ARC: 562.0 字节, 12 示例
    • Winogrande: 562.0 字节, 12 示例
  • 下载大小: 10534
  • 数据集大小: 1686.0

配置名称：human_v5

  • 特征:
    • sha_index: 字符串
    • irt_model: 字符串
    • irt_lr: 字符串
    • irt_epochs: 字符串
    • irt_std_filter_ratio: 浮点数
    • GPT_2.0_acc: 浮点数
    • GPT_2.0_ratio: 浮点数
  • 分割:
    • GSM8K: 6220.0 字节, 120 示例
    • ARC: 6220.0 字节, 120 示例
    • Winogrande: 6220.0 字节, 120 示例
  • 下载大小: 15853
  • 数据集大小: 18660.0

配置名称：new_v4

  • 特征:
    • acc_uncertainty: 浮点数
    • irt_minlikes: 字符串
    • irt_model: 空值
    • irt_lr: 空值
    • irt_epochs: 空值
    • irt_std_filter_ratio: 浮点数
    • GPT_0.0_acc: 浮点数
    • GPT_0.0_ratio: 浮点数
    • GPT_0.5_acc: 浮点数
    • GPT_0.5_ratio: 浮点数
    • GPT_1.0_acc: 浮点数
    • GPT_1.0_ratio: 浮点数
    • GPT_1.5_acc: 浮点数
    • GPT_1.5_ratio: 浮点数
    • GPT_2.0_acc: 浮点数
    • GPT_2.0_ratio: 浮点数
    • GPT_2.5_acc: 浮点数
    • GPT_2.5_ratio: 浮点数
    • GPT_3.0_acc: 浮点数
    • GPT_3.0_ratio: 浮点数
    • GPT_3.5_acc: 浮点数
    • GPT_3.5_ratio: 浮点数
    • GPT_4.0_acc: 浮点数
    • GPT_4.0_ratio: 浮点数
    • GPT_4.5_acc: 浮点数
    • GPT_4.5_ratio: 浮点数
    • GPT_5.0_acc: 浮点数
    • GPT_5.0_ratio: 浮点数
  • 分割:
    • GSM8K: 15257.0 字节, 77 示例
    • ARC: 15257.0 字节, 77 示例
    • HellaSwag: 15257.0 字节, 77 示例
    • Winogrande: 15257.0 字节, 77 示例
  • 下载大小: 104580
  • 数据集大小: 61028.0

配置名称：v1

  • 特征:
    • Unnamed: 0: 整数
    • irt_params: 字符串
    • IRT_0_GPT_0: 浮点数
    • IRT_0_GPT_0.5: 浮点数
    • IRT_0_GPT_1.0: 浮点数
    • IRT_0_GPT_1.5: 浮点数
    • IRT_0.02_GPT_0: 浮点数
    • IRT_0.02_GPT_0.5: 浮点数
    • IRT_0.02_GPT_1.0: 浮点数
    • IRT_0.02_GPT_1.5: 浮点数
    • IRT_0.05_GPT_0: 浮点数
    • IRT_0.05_GPT_0.5: 浮点数
    • IRT_0.05_GPT_1.0: 浮点数
    • IRT_0.05_GPT_1.5: 浮点数
    • IRT_0.1_GPT_0: 浮点数
    • IRT_0.1_GPT_0.5: 浮点数
    • IRT_0.1_GPT_1.0: 浮点数
    • IRT_0.1_GPT_1.5: 浮点数
    • IRT_0.15_GPT_0: 浮点数
    • IRT_0.15_GPT_0.5: 浮点数
    • IRT_0.15_GPT_1.0: 浮点数
    • IRT_0.15_GPT_1.5: 浮点数
  • 分割:
    • GSM8K: 59731 字节, 276 示例
    • ARC: 59733 字节, 276 示例
    • HellaSwag: 59614 字节, 268 示例
    • Winogrande: 59169 字节, 266 示例
  • 下载大小: 242426
  • 数据集大小: 238247

配置名称：v2

  • 特征:
    • Unnamed: 0: 整数
    • irt_params: 字符串
    • irt_std_filter_ratio: 浮点数
    • GPT_0: 浮点数
    • GPT_0.5: 浮点数
    • GPT_1.0: 浮点数
    • GPT_1.5: 浮点数
  • 分割:
    • GSM8K: 27984 字节, 288 示例
    • ARC: 27984 字节, 288 示例
    • HellaSwag: 29607 字节, 287 示例
    • Winogrande: 29607 字节, 287 示例
  • 下载大小: 77892
  • 数据集大小: 115182

配置名称：v3

  • 特征:
    • acc_uncertainty: 浮点数
    • irt_minlikes: 字符串
    • irt_model: 字符串
    • irt_lr: 浮点数
    • irt_epochs: 整数
    • irt_std_filter_ratio: 浮点数
    • GPT_0.0_acc: 浮点数
    • GPT_0.0_ratio: 浮点数
    • GPT_0.5_acc: 浮点数
    • GPT_0.5_ratio: 浮点数
    • GPT_1.0_acc: 浮点数
    • GPT_1.0_ratio: 浮点数
    • GPT_1.5_acc: 浮点数
    • GPT_1.5_ratio: 浮点数
    • GPT_2.0_acc: 浮点数
    • GPT_2.0_ratio: 浮点数
    • GPT_2.5_acc: 浮点数
    • GPT_2.5_ratio: 浮点数
    • GPT_3.0_acc: 浮点数
    • GPT_3.0_ratio: 浮点数
    • GPT_3.5_acc: 浮点数
    • GPT_3.5_ratio: 浮点数
    • GPT_4.0_acc: 浮点数
    • GPT_4.0_ratio: 浮点数
    • GPT_4.5_acc: 浮点数
    • GPT_4.5_ratio: 浮点数
    • GPT_5.0_acc: 浮点数
    • GPT_5.0_ratio: 浮点数
  • 分割:
    • GSM8K: 30372.0 字节, 137 示例
    • ARC: 30372.0 字节, 137 示例
    • HellaSwag: 30372.0 字节, 137 示例
    • Winogrande: 30372.0 字节, 137 示例
  • 下载大小: 134172
  • 数据集大小: 121488.0

配置名称：v4

  • 特征:
    • acc_uncertainty: 浮点数
    • irt_minlikes: 字符串
    • irt_model: 字符串
    • irt_lr: 浮点数
    • irt_epochs: 整数
    • irt_std_filter_ratio: 浮点数
    • GPT_0.0_acc: 浮点数
    • GPT_0.0_ratio: 浮点数
    • GPT_0.5_acc: 浮点数
    • GPT_0.5_ratio: 浮点数
    • GPT_1.0_acc: 浮点数
    • GPT_1.0_ratio: 浮点数
    • GPT_1.5_acc: 浮点数
    • GPT_1.5_ratio: 浮点数
    • GPT_2.0_acc: 浮点数
    • GPT_2.0_ratio: 浮点数
    • GPT_2.5_acc: 浮点数
    • GPT_2.5_ratio: 浮点数
    • GPT_3.0_acc: 浮点数
    • GPT_3.0_ratio: 浮点数
    • GPT_3.5_acc: 浮点数
    • GPT_3.5_ratio: 浮点数
    • GPT_4.0_acc: 浮点数
    • GPT_4.0_ratio: 浮点数
    • GPT_4.5_acc: 浮点数
    • GPT_4.5_ratio: 浮点数
    • GPT_5.0_acc: 浮点数
    • GPT_5.0_ratio: 浮点数
  • 分割:
    • GSM8K: 30372.0 字节, 137 示例
    • ARC: 30372.0 字节, 137 示例
    • HellaSwag: 30372.0 字节, 137 示例
    • Winogrande: 30372.0 字节, 137 示例
  • 下载大小: 138063
  • 数据集大小: 121488.0
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