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imponuje

Bibliografia:

presupposition_all_n_presupposition

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/presupposition_all_n_presupposition')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'all_n_presupposition'`	1900

Cechy :

{
    "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_both_presupposition

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/presupposition_both_presupposition')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'both_presupposition'`	1900

Cechy :

{
    "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"
    }
}

założenie_zmiana_stanu

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/presupposition_change_of_state')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'change_of_state'`	1900

Cechy :

{
    "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"
    }
}

założenie_rozszczep_istnienia

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/presupposition_cleft_existence')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'cleft_existence'`	1900

Cechy :

{
    "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"
    }
}

założenie_rozszczep_wyjątkowość

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/presupposition_cleft_uniqueness')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'cleft_uniqueness'`	1900

Cechy :

{
    "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

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/presupposition_only_presupposition')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'only_presupposition'`	1900

Cechy :

{
    "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"
    }
}

założenie_posiadania_określa_istnienie

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/presupposition_possessed_definites_existence')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'possessed_definites_existence'`	1900

Cechy :

{
    "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"
    }
}

założenie_posiadania_określa_wyjątkowość

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/presupposition_possessed_definites_uniqueness')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'possessed_definites_uniqueness'`	1900

Cechy :

{
    "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

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/presupposition_question_presupposition')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'question_presupposition'`	1900

Cechy :

{
    "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"
    }
}

implicature_connectives

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/implicature_connectives')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'connectives'`	1200

Cechy :

{
    "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"
    }
}

implicature_gradable_adjective

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/implicature_gradable_adjective')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'gradable_adjective'`	1200

Cechy :

{
    "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"
    }
}

czasownik_implicature_gradable_verb

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/implicature_gradable_verb')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'gradable_verb'`	1200

Cechy :

{
    "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"
    }
}

implicature_modals

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/implicature_modals')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'modals'`	1200

Cechy :

{
    "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"
    }
}

implikatury_liczby_10_100

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/implicature_numerals_10_100')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'numerals_10_100'`	1200

Cechy :

{
    "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"
    }
}

implikatury_liczby_2_3

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/implicature_numerals_2_3')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'numerals_2_3'`	1200

Cechy :

{
    "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"
    }
}

kwantyfikatory_implikacji

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:imppres/implicature_quantifiers')

Opis :

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.

Licencja : Creative Commons Uznanie autorstwa-Użycie niekomercyjne 4.0 Międzynarodowa licencja publiczna
Wersja : 1.1.0
Podziały :

Podział	Przykłady
`'quantifiers'`	1200

Cechy :

{
    "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"
    }
}