References:
flights
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:taskmaster2/flights')
- Description:
Taskmaster is dataset for goal oriented conversations. The Taskmaster-2 dataset consists of 17,289 dialogs in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports. Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs. All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. In this way, users were led to believe they were interacting with an automated system that “spoke” using text-to-speech (TTS) even though it was in fact a human behind the scenes. As a result, users could express themselves however they chose in the context of an automated interface.
- License: No known license
- Version: 1.0.0
- Splits:
| Split | Examples |
|---|---|
'train' |
2481 |
- Features:
{
"conversation_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"instruction_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"utterances": [
{
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"id": null,
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"speaker": {
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"id": null,
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"text": {
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},
"annotations": [
{
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
]
}
]
}
]
}
food-ordering
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:taskmaster2/food-ordering')
- Description:
Taskmaster is dataset for goal oriented conversationas. The Taskmaster-2 dataset consists of 17,289 dialogs in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports. Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs. All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. In this way, users were led to believe they were interacting with an automated system that “spoke” using text-to-speech (TTS) even though it was in fact a human behind the scenes. As a result, users could express themselves however they chose in the context of an automated interface.
- License: No known license
- Version: 1.0.0
- Splits:
| Split | Examples |
|---|---|
'train' |
1050 |
- Features:
{
"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": {
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"_type": "Value"
},
"text": {
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},
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"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"annotations": [
{
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
]
}
]
}
]
}
hotels
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:taskmaster2/hotels')
- Description:
Taskmaster is dataset for goal oriented conversationas. The Taskmaster-2 dataset consists of 17,289 dialogs in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports. Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs. All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. In this way, users were led to believe they were interacting with an automated system that “spoke” using text-to-speech (TTS) even though it was in fact a human behind the scenes. As a result, users could express themselves however they chose in the context of an automated interface.
- License: No known license
- Version: 1.0.0
- Splits:
| Split | Examples |
|---|---|
'train' |
2357 |
- Features:
{
"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": {
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"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"annotations": [
{
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
]
}
]
}
]
}
movies
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:taskmaster2/movies')
- Description:
Taskmaster is dataset for goal oriented conversationas. The Taskmaster-2 dataset consists of 17,289 dialogs in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports. Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs. All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. In this way, users were led to believe they were interacting with an automated system that “spoke” using text-to-speech (TTS) even though it was in fact a human behind the scenes. As a result, users could express themselves however they chose in the context of an automated interface.
- License: No known license
- Version: 1.0.0
- Splits:
| Split | Examples |
|---|---|
'train' |
3056 |
- Features:
{
"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": {
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"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"annotations": [
{
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
]
}
]
}
]
}
music
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:taskmaster2/music')
- Description:
Taskmaster is dataset for goal oriented conversationas. The Taskmaster-2 dataset consists of 17,289 dialogs in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports. Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs. All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. In this way, users were led to believe they were interacting with an automated system that “spoke” using text-to-speech (TTS) even though it was in fact a human behind the scenes. As a result, users could express themselves however they chose in the context of an automated interface.
- License: No known license
- Version: 1.0.0
- Splits:
| Split | Examples |
|---|---|
'train' |
1603 |
- Features:
{
"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": {
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"id": null,
"_type": "Value"
},
"text": {
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"text": {
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"_type": "Value"
},
"annotations": [
{
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
]
}
]
}
]
}
restaurant-search
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:taskmaster2/restaurant-search')
- Description:
Taskmaster is dataset for goal oriented conversationas. The Taskmaster-2 dataset consists of 17,289 dialogs in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports. Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs. All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. In this way, users were led to believe they were interacting with an automated system that “spoke” using text-to-speech (TTS) even though it was in fact a human behind the scenes. As a result, users could express themselves however they chose in the context of an automated interface.
- License: No known license
- Version: 1.0.0
- Splits:
| Split | Examples |
|---|---|
'train' |
3276 |
- Features:
{
"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": {
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"id": null,
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},
"text": {
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"text": {
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},
"annotations": [
{
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
]
}
]
}
]
}
sports
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:taskmaster2/sports')
- Description:
Taskmaster is dataset for goal oriented conversationas. The Taskmaster-2 dataset consists of 17,289 dialogs in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports. Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs. All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. In this way, users were led to believe they were interacting with an automated system that “spoke” using text-to-speech (TTS) even though it was in fact a human behind the scenes. As a result, users could express themselves however they chose in the context of an automated interface.
- License: No known license
- Version: 1.0.0
- Splits:
| Split | Examples |
|---|---|
'train' |
3481 |
- Features:
{
"conversation_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"instruction_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"utterances": [
{
"index": {
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"annotations": [
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}
]
}
]
}
]
}