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MLP-Lemma/Instruct-datasets-preprocessed

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Hugging Face2024-05-13 更新2024-06-12 收录
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--- dataset_info: - config_name: BigPatent features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 2187341940 num_examples: 41383 download_size: 430231363 dataset_size: 2187341940 - config_name: BookSum features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 560667212 num_examples: 9371 download_size: 131314418 dataset_size: 560667212 - config_name: BoolQ features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 26210184 num_examples: 9426 download_size: 3930274 dataset_size: 26210184 - config_name: CNN-DM features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 1001044244 num_examples: 99951 download_size: 224147945 dataset_size: 1001044244 - config_name: CosmosQA features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 86154456 num_examples: 25262 download_size: 11075155 dataset_size: 86154456 - config_name: DROP features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 41230848 num_examples: 9977 download_size: 6663863 dataset_size: 41230848 - config_name: GovReport features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 1484331476 num_examples: 16590 download_size: 314257236 dataset_size: 1484331476 - config_name: HotpotQA features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 1184530448 num_examples: 90208 download_size: 244328551 dataset_size: 1184530448 - config_name: LongAlpaca features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 751565872 num_examples: 7573 download_size: 148032145 dataset_size: 751565872 - config_name: MultiNews features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 1084301372 num_examples: 44398 download_size: 251648587 dataset_size: 1084301372 - config_name: MultiRC features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 52418040 num_examples: 12025 download_size: 2643742 dataset_size: 52418040 - config_name: NarrativeQA features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 8622923508 num_examples: 12857 download_size: 1844849479 dataset_size: 8622923508 - config_name: QMsum features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 149132292 num_examples: 1257 download_size: 22634545 dataset_size: 149132292 - config_name: Qasper features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 119525772 num_examples: 2461 download_size: 23057137 dataset_size: 119525772 - config_name: Quality features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 144079688 num_examples: 2523 download_size: 24262763 dataset_size: 144079688 - config_name: ReCoRD features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 396630060 num_examples: 100682 download_size: 69341165 dataset_size: 396630060 - config_name: SQuAD features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 263068320 num_examples: 87580 download_size: 35289932 dataset_size: 263068320 - config_name: TriviaQA features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 4937139228 num_examples: 52359 download_size: 1021208372 dataset_size: 4937139228 - config_name: XSum features: - name: input_ids sequence: int32 - name: input_sentences_ids sequence: sequence: int64 - name: labels sequence: int64 - name: inst_ids sequence: sequence: int64 splits: - name: train num_bytes: 586055844 num_examples: 99827 download_size: 125544836 dataset_size: 586055844 configs: - config_name: BigPatent data_files: - split: train path: BigPatent/train-* - config_name: BookSum data_files: - split: train path: BookSum/train-* - config_name: BoolQ data_files: - split: train path: BoolQ/train-* - config_name: CNN-DM data_files: - split: train path: CNN-DM/train-* - config_name: CosmosQA data_files: - split: train path: CosmosQA/train-* - config_name: DROP data_files: - split: train path: DROP/train-* - config_name: GovReport data_files: - split: train path: GovReport/train-* - config_name: HotpotQA data_files: - split: train path: HotpotQA/train-* - config_name: LongAlpaca data_files: - split: train path: LongAlpaca/train-* - config_name: MultiNews data_files: - split: train path: MultiNews/train-* - config_name: MultiRC data_files: - split: train path: MultiRC/train-* - config_name: NarrativeQA data_files: - split: train path: NarrativeQA/train-* - config_name: QMsum data_files: - split: train path: QMsum/train-* - config_name: Qasper data_files: - split: train path: Qasper/train-* - config_name: Quality data_files: - split: train path: Quality/train-* - config_name: ReCoRD data_files: - split: train path: ReCoRD/train-* - config_name: SQuAD data_files: - split: train path: SQuAD/train-* - config_name: TriviaQA data_files: - split: train path: TriviaQA/train-* - config_name: XSum data_files: - split: train path: XSum/train-* ---

This dataset consists of multiple sub-datasets, each with a specific configuration name, features, and details about the training split. The features include input_ids, input_sentences_ids, labels, and inst_ids, all of which are sequences of integers. The datasets cover various domains such as patents, books, news, and question-answering. Each sub-dataset provides information about the number of examples, download size, and dataset size for the training split.
提供机构：
MLP-Lemma
原始信息汇总

数据集概述

BigPatent

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 2187341940 字节
    • 示例数量: 41383

BookSum

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 560667212 字节
    • 示例数量: 9371

BoolQ

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 26210184 字节
    • 示例数量: 9426

CNN-DM

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 1001044244 字节
    • 示例数量: 99951

CosmosQA

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 86154456 字节
    • 示例数量: 25262

DROP

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 41230848 字节
    • 示例数量: 9977

GovReport

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 1484331476 字节
    • 示例数量: 16590

HotpotQA

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 1184530448 字节
    • 示例数量: 90208

LongAlpaca

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 751565872 字节
    • 示例数量: 7573

MultiNews

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 1084301372 字节
    • 示例数量: 44398

MultiRC

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 52418040 字节
    • 示例数量: 12025

NarrativeQA

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 8622923508 字节
    • 示例数量: 12857

QMsum

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 149132292 字节
    • 示例数量: 1257

Qasper

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 119525772 字节
    • 示例数量: 2461

Quality

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 144079688 字节
    • 示例数量: 2523

ReCoRD

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 396630060 字节
    • 示例数量: 100682

SQuAD

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 263068320 字节
    • 示例数量: 87580

TriviaQA

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 4937139228 字节
    • 示例数量: 52359

XSum

  • 特征:
    • input_ids: 序列类型为 int32
    • input_sentences_ids: 序列类型为 int64
    • labels: 序列类型为 int64
    • inst_ids: 序列类型为 int64
  • 训练集:
    • 大小: 586055844 字节
    • 示例数量: 99827
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