adversarial_qa

مراجع:

• شفرة
• معانقة

عدائيةQA

استخدم الأمر التالي لتحميل مجموعة البيانات هذه في TFDS:

ds = tfds.load('huggingface:adversarial_qa/adversarialQA')

• وصف :

AdversarialQA is a Reading Comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples.
The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging.

• الترخيص : لا يوجد ترخيص معروف
• الإصدار : 1.0.0
• الإنشقاقات :

ينقسم	أمثلة
'test'	3000
'train'	30000
'validation'	3000

• سمات :

{
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        "_type": "Value"
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    "title": {
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    "context": {
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    "answers": {
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        "model_in_the_loop": {
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        }
    }
}

com.dbidaf

استخدم الأمر التالي لتحميل مجموعة البيانات هذه في TFDS:

ds = tfds.load('huggingface:adversarial_qa/dbidaf')

• وصف :

AdversarialQA is a Reading Comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples.
The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging.

• الترخيص : لا يوجد ترخيص معروف
• الإصدار : 1.0.0
• الإنشقاقات :

ينقسم	أمثلة
'test'	1000
'train'	10000
'validation'	1000

• سمات :

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
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    "context": {
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    "question": {
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    "answers": {
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        "length": -1,
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    "metadata": {
        "split": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "model_in_the_loop": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        }
    }
}

dbert

استخدم الأمر التالي لتحميل مجموعة البيانات هذه في TFDS:

ds = tfds.load('huggingface:adversarial_qa/dbert')

• وصف :

AdversarialQA is a Reading Comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples.
The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging.

• الترخيص : لا يوجد ترخيص معروف
• الإصدار : 1.0.0
• الإنشقاقات :

ينقسم	أمثلة
'test'	1000
'train'	10000
'validation'	1000

• سمات :

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
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    },
    "context": {
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    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answers": {
        "feature": {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "answer_start": {
                "dtype": "int32",
                "id": null,
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            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "metadata": {
        "split": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "model_in_the_loop": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        }
    }
}

دروبرتا

استخدم الأمر التالي لتحميل مجموعة البيانات هذه في TFDS:

ds = tfds.load('huggingface:adversarial_qa/droberta')

• وصف :

AdversarialQA is a Reading Comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples.
The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging.

• الترخيص : لا يوجد ترخيص معروف
• الإصدار : 1.0.0
• الإنشقاقات :

ينقسم	أمثلة
'test'	1000
'train'	10000
'validation'	1000

• سمات :

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
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    "context": {
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    },
    "answers": {
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            },
            "answer_start": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "metadata": {
        "split": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "model_in_the_loop": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        }
    }
}