Referencias:
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 :
| Separar | Ejemplos |
|---|---|
'train' | 1620 |
'validation' | 342 |
- Características :
{
"query": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"supports": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"candidates": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
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 :
| Separar | Ejemplos |
|---|---|
'train' | 1620 |
'validation' | 342 |
- Características :
{
"query": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"supports": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"candidates": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
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 :
| Separar | Ejemplos |
|---|---|
'train' | 43738 |
'validation' | 5129 |
- Características :
{
"query": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"supports": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"candidates": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"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 :
| Separar | Ejemplos |
|---|---|
'train' | 43738 |
'validation' | 5129 |
- Características :
{
"query": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"supports": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"candidates": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"answer": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}