References:
air_dialogue_data
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:air_dialogue/air_dialogue_data')
- Description:
AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions.
- License: cc-by-nc-4.0
- Version: 1.1.0
- Splits:
| Split | Examples |
|---|---|
'train' |
321459 |
'validation' |
40363 |
- Features:
{
"action": {
"status": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"flight": {
"feature": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"intent": {
"return_month": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"return_day": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"max_price": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"departure_airport": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"max_connections": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"departure_day": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"goal": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"departure_month": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"return_airport": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"timestamps": {
"feature": {
"dtype": "int64",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"dialogue": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"expected_action": {
"status": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"flight": {
"feature": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"search_info": [
{
"button_name": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field_name": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field_value": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"timestmamp": {
"dtype": "int64",
"id": null,
"_type": "Value"
}
}
],
"correct_sample": {
"dtype": "bool_",
"id": null,
"_type": "Value"
}
}
air_dialogue_kb
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:air_dialogue/air_dialogue_kb')
- Description:
AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions.
- License: cc-by-nc-4.0
- Version: 1.1.0
- Splits:
| Split | Examples |
|---|---|
'train' |
321459 |
'validation' |
40363 |
- Features:
{
"kb": [
{
"airline": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"class": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"departure_airport": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"departure_day": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"departure_month": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"departure_time_num": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"flight_number": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"num_connections": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"price": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"return_airport": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"return_day": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"return_month": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"return_time_num": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
],
"reservation": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}