فريق_القصة

ds = tfds.load('huggingface:squad_adversarial/squad_adversarial')

Here are two different adversaries, each of which uses a different procedure to pick the sentence it adds to the paragraph:
AddSent: Generates up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. Picks the one that most confuses the model.
AddOneSent: Similar to AddSent, but just picks one of the candidate sentences at random. This adversary is does not query the model in any way.

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}

ds = tfds.load('huggingface:squad_adversarial/AddSent')

Here are two different adversaries, each of which uses a different procedure to pick the sentence it adds to the paragraph:
AddSent: Generates up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. Picks the one that most confuses the model.
AddOneSent: Similar to AddSent, but just picks one of the candidate sentences at random. This adversary is does not query the model in any way.

{
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        "dtype": "string",
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        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
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    "context": {
        "dtype": "string",
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    "question": {
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        },
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    }
}

ds = tfds.load('huggingface:squad_adversarial/AddOneSent')

Here are two different adversaries, each of which uses a different procedure to pick the sentence it adds to the paragraph:
AddSent: Generates up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. Picks the one that most confuses the model.
AddOneSent: Similar to AddSent, but just picks one of the candidate sentences at random. This adversary is does not query the model in any way.

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