hreensajin/autotrain-data-text_summ

该数据集是AutoTrain为项目text_summ自动处理的数据集，任务类别为条件文本生成。数据集的BCP-47语言代码为unk，表示语言未知。数据集包含feat_guid、feat_source、text、target和feat_label等字段，其中feat_label是一个包含三个类别（contradiction、entailment、neutral）的ClassLabel。数据集划分为训练集和验证集，分别包含22398和5600个样本。

This dataset was automatically processed by AutoTrain for the project text_summ, with its task category being conditional text generation. The BCP-47 language code of this dataset is unk, indicating that the language is unknown. This dataset includes fields such as feat_guid, feat_source, text, target, and feat_label, where feat_label is a ClassLabel with three categories: contradiction, entailment, and neutral. The dataset is split into training and validation sets, which contain 22398 and 5600 samples respectively.

原始信息汇总

AutoTrain Dataset for project: text_summ

Dataset Description

• Task Category: Conditional Text Generation
• Language: The datasets language is identified by the BCP-47 code unk.

Dataset Structure

Data Instances

• Sample Instance: json [ { "feat_guid": "klue-nli-v1_train_06870", "feat_source": "policy", "text": "ub610ud55c ub3c5ub9bducd9cud310ubb3c uc81cuc791 uc6ccud06cuc20duc774ub791 uae00uc4f0uae30 uc18cuc124 uc4f0uae30 uc6ccud06cuc20dub3c4 uc9c4ud589ud558uba70 uc6d0ub370uc774 uc218uc5c5uc744 ud1b5ud574 ub2e4uc591ud55c ud65cub3d9uc744 ud558ub294 uc54cucc2c ubcf5ud569uc801uc778 uacf5uac04uc774ub2e4.", "target": "ub3c5ub9bducd9cud310ubb3c uc81cuc791 uc6ccud06cuc20duc740 uc5b8ub860uc0acuc758 ud6c4uc6d0ud558uc5d0 uc9c4ud589ub41cub2e4.", "feat_label": 2 }, { "feat_guid": "klue-nli-v1_train_02196", "feat_source": "wikitree", "text": "uacf5uc720ub41c uc0acuc9c4uc5d0ub294 uc790uc6b1ud558uac8c uc548uac1cuc640 ubbf8uc138uba3cuc9c0uac00 ub0b4ub824uc549uc740 uc0acuc9c4uc774 uacf5uc720ub410ub2e4.", "target": "uc790uc6b1ud55c uc548uac1cuc640 ubbf8uc138uba3cuc9c0ub9cc ub0b4ub824uc549uc558ub2e4.", "feat_label": 2 } ]

Dataset Fields

• Fields: json { "feat_guid": "Value(dtype=string, id=None)", "feat_source": "Value(dtype=string, id=None)", "text": "Value(dtype=string, id=None)", "target": "Value(dtype=string, id=None)", "feat_label": "ClassLabel(num_classes=3, names=[contradiction, entailment, neutral], id=None)" }

Dataset Splits

• Splits:

  Split name	Num samples
  train	22398
  valid	5600