印象づける

参考文献:

presupposition_all_n_presupposition

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:imppres/presupposition_all_n_presupposition')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'all_n_presupposition' 1900年
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger1": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger2": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "presupposition": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "UID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "pairID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "paradigmID": {
        "dtype": "int16",
        "id": null,
        "_type": "Value"
    }
}

前提_両方_前提

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:imppres/presupposition_both_presupposition')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'both_presupposition' 1900年
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger1": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger2": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "presupposition": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "UID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "pairID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "paradigmID": {
        "dtype": "int16",
        "id": null,
        "_type": "Value"
    }
}

状態の変化の前提条件

次のコマンドを使用して、このデータセットを TFDS にロードします。

ds = tfds.load('huggingface:imppres/presupposition_change_of_state')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'change_of_state' 1900年
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger1": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger2": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "presupposition": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "UID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "pairID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "paradigmID": {
        "dtype": "int16",
        "id": null,
        "_type": "Value"
    }
}

前提裂け目の存在

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/presupposition_cleft_existence')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'cleft_existence' 1900年
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger1": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger2": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "presupposition": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "UID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "pairID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "paradigmID": {
        "dtype": "int16",
        "id": null,
        "_type": "Value"
    }
}

presupposition_cleft_uniqueness

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/presupposition_cleft_uniqueness')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'cleft_uniqueness' 1900年
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger1": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger2": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "presupposition": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "UID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "pairID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "paradigmID": {
        "dtype": "int16",
        "id": null,
        "_type": "Value"
    }
}

presupposition_only_presupposition

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/presupposition_only_presupposition')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'only_presupposition' 1900年
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger1": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger2": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "presupposition": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "UID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "pairID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "paradigmID": {
        "dtype": "int16",
        "id": null,
        "_type": "Value"
    }
}

前提所有者確定存在

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/presupposition_possessed_definites_existence')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'possessed_definites_existence' 1900年
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger1": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger2": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "presupposition": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "UID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "pairID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "paradigmID": {
        "dtype": "int16",
        "id": null,
        "_type": "Value"
    }
}

前提所有の明確な独自性

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/presupposition_possessed_definites_uniqueness')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'possessed_definites_uniqueness' 1900年
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger1": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger2": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "presupposition": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "UID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "pairID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "paradigmID": {
        "dtype": "int16",
        "id": null,
        "_type": "Value"
    }
}

presupposition_question_presupposition

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/presupposition_question_presupposition')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'question_presupposition' 1900年
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger1": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger2": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "presupposition": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "UID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "pairID": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "paradigmID": {
        "dtype": "int16",
        "id": null,
        "_type": "Value"
    }
}

implicture_connectives

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/implicature_connectives')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'connectives' 1200
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label_log": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "gold_label_prag": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "spec_relation": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "item_type": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lexemes": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

implicture_gradable_adjective

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/implicature_gradable_adjective')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'gradable_adjective' 1200
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label_log": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "gold_label_prag": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "spec_relation": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "item_type": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lexemes": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

implicture_gradable_verb

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/implicature_gradable_verb')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'gradable_verb' 1200
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label_log": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "gold_label_prag": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "spec_relation": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "item_type": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lexemes": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

implicture_modals

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/implicature_modals')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'modals' 1200
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label_log": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "gold_label_prag": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "spec_relation": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "item_type": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lexemes": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

implicture_numerals_10_100

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/implicature_numerals_10_100')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'numerals_10_100' 1200
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label_log": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "gold_label_prag": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "spec_relation": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "item_type": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lexemes": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

implicture_numerals_2_3

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/implicature_numerals_2_3')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'numerals_2_3' 1200
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label_log": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "gold_label_prag": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "spec_relation": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "item_type": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lexemes": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

implicture_quantifiers

次のコマンドを使用して、このデータセットを TFDS にロードします。 

ds = tfds.load('huggingface:imppres/implicature_quantifiers')
  • 説明： 
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
  • ライセンス: クリエイティブ コモンズ 表示 - 非営利 4.0 国際公衆利用許諾書
  • バージョン: 1.1.0
  • 分割:
スプリット例
'quantifiers' 1200
  • 特徴： 
{
    "premise": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "hypothesis": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "gold_label_log": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "gold_label_prag": {
        "num_classes": 3,
        "names": [
            "entailment",
            "neutral",
            "contradiction"
        ],
        "names_file": null,
        "id": null,
        "_type": "ClassLabel"
    },
    "spec_relation": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "item_type": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "trigger": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "lexemes": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}