Références :
one_person_dialogs
Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :
ds = tfds.load('huggingface:taskmaster1/one_person_dialogs')
- Description :
Taskmaster-1:Toward a Realistic and Diverse Dialog Dataset) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.
- Licence : Aucune licence connue
- Version : 1.0.0
- Divisions :
| Diviser | Exemples |
|---|---|
'test' | 770 |
'train' | 6168 |
'validation' | 770 |
- Caractéristiques :
{
"conversation_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"instruction_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"utterances": [
{
"index": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"speaker": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"segments": [
{
"start_index": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"end_index": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"annotations": [
{
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
]
}
]
}
]
}
woz_dialogs
Utilisez la commande suivante pour charger cet ensemble de données dans TFDS :
ds = tfds.load('huggingface:taskmaster1/woz_dialogs')
- Description :
Taskmaster-1:Toward a Realistic and Diverse Dialog Dataset) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.
- Licence : Aucune licence connue
- Version : 1.0.0
- Divisions :
| Diviser | Exemples |
|---|---|
'train' | 5507 |
- Caractéristiques :
{
"conversation_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"instruction_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"utterances": [
{
"index": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"speaker": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"segments": [
{
"start_index": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"end_index": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"annotations": [
{
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
]
}
]
}
]
}