टीएफडीएस अब क्रोइसैन 🥐 प्रारूप का समर्थन करता है! अधिक जानने के लिए दस्तावेज़ पढ़ें.

इस पेज का अनुवाद Cloud Translation API से किया गया है.

प्रभावित करना

सन्दर्भ:

पूर्वधारणा_सभी_n_पूर्वधारणा

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'all_n_presupposition'`	1900

विशेषताएँ :

{
    "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"
    }
}

पूर्वधारणा_दोनों_पूर्वधारणा

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'both_presupposition'`	1900

विशेषताएँ :

{
    "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"
    }
}

स्थिति_परिवर्तन_की_पूर्वधारणा

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'change_of_state'`	1900

विशेषताएँ :

{
    "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_existence

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'cleft_existence'`	1900

विशेषताएँ :

{
    "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

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'cleft_uniqueness'`	1900

विशेषताएँ :

{
    "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"
    }
}

पूर्वधारणा_केवल_पूर्वधारणा

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'only_presupposition'`	1900

विशेषताएँ :

{
    "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"
    }
}

पूर्वधारणा_कब्जे_में_निश्चित_अस्तित्व

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'possessed_definites_existence'`	1900

विशेषताएँ :

{
    "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"
    }
}

पूर्वधारणा_कब्जे_में_निश्चित_अद्वितीयता

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'possessed_definites_uniqueness'`	1900

विशेषताएँ :

{
    "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"
    }
}

पूर्वधारणा_प्रश्न_पूर्वधारणा

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'question_presupposition'`	1900

विशेषताएँ :

{
    "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"
    }
}

implication_connectives

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'connectives'`	1200

विशेषताएँ :

{
    "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"
    }
}

इम्प्लिकेचर_ग्रेडेबल_विशेषण

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'gradable_adjective'`	1200

विशेषताएँ :

{
    "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"
    }
}

इम्प्लिकेचर_ग्रेडेबल_क्रिया

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'gradable_verb'`	1200

विशेषताएँ :

{
    "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"
    }
}

implication_modals

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'modals'`	1200

विशेषताएँ :

{
    "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"
    }
}

निहितार्थ_अंक_10_100

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'numerals_10_100'`	1200

विशेषताएँ :

{
    "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"
    }
}

निहितार्थ_अंक_2_3

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'numerals_2_3'`	1200

विशेषताएँ :

{
    "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"
    }
}

इम्प्लिकेचर_क्वांटिफ़ायर

इस डेटासेट को TFDS में लोड करने के लिए निम्नलिखित कमांड का उपयोग करें:

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

विवरण :

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.

लाइसेंस : क्रिएटिव कॉमन्स एट्रिब्यूशन-नॉन-कमर्शियल 4.0 इंटरनेशनल पब्लिक लाइसेंस
संस्करण : 1.1.0
विभाजन :

विभाजित करना	उदाहरण
`'quantifiers'`	1200

विशेषताएँ :

{
    "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"
    }
}