Attend the Women in ML Symposium on December 7 Register now

taskmaster2

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": [
        {
            "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"
                            }
                        }
                    ]
                }
            ]
        }
    ]
}

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": {
                "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"
                            }
                        }
                    ]
                }
            ]
        }
    ]
}

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": {
                "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"
                            }
                        }
                    ]
                }
            ]
        }
    ]
}

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": {
                "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"
                            }
                        }
                    ]
                }
            ]
        }
    ]
}

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": {
                "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"
                            }
                        }
                    ]
                }
            ]
        }
    ]
}

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": {
                "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"
                            }
                        }
                    ]
                }
            ]
        }
    ]
}

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": {
                "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"
                            }
                        }
                    ]
                }
            ]
        }
    ]
}