qangaroo

Referencias:

• Código
• cara de abrazo

medhop

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:qangaroo/medhop')

• Descripción :

We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.

Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.

Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.

The two QAngaroo datasets provide a training and evaluation resource for such methods.

• Licencia : Sin licencia conocida
• Versión : 1.0.0
• Divisiones :

• Características :

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}

enmascarado_medhop

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:qangaroo/masked_medhop')

• Descripción :

We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.

Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.

Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.

The two QAngaroo datasets provide a training and evaluation resource for such methods.

• Licencia : Sin licencia conocida
• Versión : 1.0.0
• Divisiones :

• Características :

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        "dtype": "string",
        "id": null,
        "_type": "Value"
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}

wikihop

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:qangaroo/wikihop')

• Descripción :

We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.

Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.

Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.

The two QAngaroo datasets provide a training and evaluation resource for such methods.

• Licencia : Sin licencia conocida
• Versión : 1.0.0
• Divisiones :

• Características :

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        "_type": "Value"
    },
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        },
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    },
    "candidates": {
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    "answer": {
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    },
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

masked_wikihop

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:qangaroo/masked_wikihop')

• Descripción :

We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.

Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.

Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents.

The two QAngaroo datasets provide a training and evaluation resource for such methods.

• Licencia : Sin licencia conocida
• Versión : 1.0.0
• Divisiones :

• Características :

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