Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

@@ -0,0 +1,27 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,252 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+task_ids:
+- dialogue-modeling
+---
+
+# Dataset Card Creation Guide
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [Taskmaster-1](https://research.google/tools/datasets/taskmaster-1/)
+- **Repository:** [GitHub](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020)
+- **Paper:** [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://arxiv.org/abs/1909.05358)
+- **Leaderboard:** N/A
+- **Point of Contact:** [Taskmaster Googlegroup](taskmaster-datasets@googlegroups.com)
+
+### Dataset Summary
+
+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.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The dataset is in English language.
+
+## Dataset Structure
+
+### Data Instances
+
+A typical example looks like this
+
+```
+{
+    "conversation_id": "dlg-0047a087-6a3c-4f27-b0e6-268f53a2e013",
+    "instruction_id": "flight-6",
+    "utterances": [
+        {
+            "index": 0,
+            "segments": [],
+            "speaker": "USER",
+            "text": "Hi, I'm looking for a flight. I need to visit a friend."
+        },
+        {
+            "index": 1,
+            "segments": [],
+            "speaker": "ASSISTANT",
+            "text": "Hello, how can I help you?"
+        },
+        {
+            "index": 2,
+            "segments": [],
+            "speaker": "ASSISTANT",
+            "text": "Sure, I can help you with that."
+        },
+        {
+            "index": 3,
+            "segments": [],
+            "speaker": "ASSISTANT",
+            "text": "On what dates?"
+        },
+        {
+            "index": 4,
+            "segments": [
+                {
+                    "annotations": [
+                        {
+                            "name": "flight_search.date.depart_origin"
+                        }
+                    ],
+                    "end_index": 37,
+                    "start_index": 27,
+                    "text": "March 20th"
+                },
+                {
+                    "annotations": [
+                        {
+                            "name": "flight_search.date.return"
+                        }
+                    ],
+                    "end_index": 45,
+                    "start_index": 41,
+                    "text": "22nd"
+                }
+            ],
+            "speaker": "USER",
+            "text": "I'm looking to travel from March 20th to 22nd."
+        }
+    ]
+}
+```
+
+### Data Fields
+
+Each conversation in the data file has the following structure:
+
+- `conversation_id`: A universally unique identifier with the prefix 'dlg-'. The ID has no meaning.
+- `utterances`: A list of utterances that make up the conversation.
+- `instruction_id`: A reference to the file(s) containing the user (and, if applicable, agent) instructions for this conversation.
+
+Each utterance has the following fields:
+
+- `index`: A 0-based index indicating the order of the utterances in the conversation.
+- `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance.
+- `text`: The raw text of the utterance. In case of self dialogs (one_person_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers.
+- `segments`: A list of various text spans with semantic annotations.
+
+Each segment has the following fields:
+
+- `start_index`: The position of the start of the annotation in the utterance text.
+- `end_index`: The position of the end of the annotation in the utterance text.
+- `text`: The raw text that has been annotated.
+- `annotations`: A list of annotation details for this segment.
+
+Each annotation has a single field:
+
+- `name`: The annotation name.
+
+
+
+### Data Splits
+
+There are no deafults splits for all the config. The below table lists the number of examples in each config.
+
+| Config            | Train  |
+|-------------------|--------|
+| flights           | 2481   |
+| food-orderings    | 1050   |
+| hotels            | 2355   |
+| movies            | 3047   |
+| music             | 1602   |
+| restaurant-search | 3276   |
+| sports            | 3478   |
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+The dataset is licensed under `Creative Commons Attribution 4.0 License`
+
+### Citation Information
+
+[More Information Needed]
+```
+@inproceedings{48484,
+title	= {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
+author	= {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
+year	= {2019}
+}
+```

dataset_infos.json

@@ -0,0 +1 @@
+{"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 \u201cspoke\u201d 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.\n", "citation": "@inproceedings{48484,\ntitle\t= {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},\nauthor\t= {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},\nyear\t= {2019}\n}\n", "homepage": "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020", "license": "", "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"}}]}]}]}, "post_processed": null, "supervised_keys": null, "builder_name": "taskmaster2", "config_name": "flights", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7073487, "num_examples": 2481, "dataset_name": "taskmaster2"}}, "download_checksums": {"https://raw.githubusercontent.com/google-research-datasets/Taskmaster/master/TM-2-2020/data/flights.json": {"num_bytes": 23029880, "checksum": "86b37b5ae25f530fd18ced78800d30c3b54f7b34bb208ecb51842718f04e760b"}}, "download_size": 23029880, "post_processing_size": null, "dataset_size": 7073487, "size_in_bytes": 30103367}, "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 \u201cspoke\u201d 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.\n", "citation": "@inproceedings{48484,\ntitle\t= {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},\nauthor\t= {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},\nyear\t= {2019}\n}\n", "homepage": "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020", "license": "", "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"}}]}]}]}, "post_processed": null, "supervised_keys": null, "builder_name": "taskmaster2", "config_name": "food-ordering", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1734825, "num_examples": 1050, "dataset_name": "taskmaster2"}}, "download_checksums": {"https://raw.githubusercontent.com/google-research-datasets/Taskmaster/master/TM-2-2020/data/food-ordering.json": {"num_bytes": 5376675, "checksum": "0a042e566a816a5d0abebe6f7e8cfd6abaa89729ffc42f433d327df7342b12f8"}}, "download_size": 5376675, "post_processing_size": null, "dataset_size": 1734825, "size_in_bytes": 7111500}, "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 \u201cspoke\u201d 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.\n", "citation": "@inproceedings{48484,\ntitle\t= {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},\nauthor\t= {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},\nyear\t= {2019}\n}\n", "homepage": "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020", "license": "", "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"}}]}]}]}, "post_processed": null, "supervised_keys": null, "builder_name": "taskmaster2", "config_name": "hotels", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7436667, "num_examples": 2357, "dataset_name": "taskmaster2"}}, "download_checksums": {"https://raw.githubusercontent.com/google-research-datasets/Taskmaster/master/TM-2-2020/data/hotels.json": {"num_bytes": 22507266, "checksum": "975b0242f1e37ea1ab94ccedd7e0d6ee5831599d5df1f16143e71110d6c6006a"}}, "download_size": 22507266, "post_processing_size": null, "dataset_size": 7436667, "size_in_bytes": 29943933}, "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 \u201cspoke\u201d 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.\n", "citation": "@inproceedings{48484,\ntitle\t= {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},\nauthor\t= {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},\nyear\t= {2019}\n}\n", "homepage": "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020", "license": "", "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"}}]}]}]}, "post_processed": null, "supervised_keys": null, "builder_name": "taskmaster2", "config_name": "movies", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7112301, "num_examples": 3056, "dataset_name": "taskmaster2"}}, "download_checksums": {"https://raw.githubusercontent.com/google-research-datasets/Taskmaster/master/TM-2-2020/data/movies.json": {"num_bytes": 21189893, "checksum": "6f67c9a1f04abc111186e5bcfbe3050be01d0737fd6422901402715bc1f3dd0d"}}, "download_size": 21189893, "post_processing_size": null, "dataset_size": 7112301, "size_in_bytes": 28302194}, "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 \u201cspoke\u201d 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.\n", "citation": "@inproceedings{48484,\ntitle\t= {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},\nauthor\t= {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},\nyear\t= {2019}\n}\n", "homepage": "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020", "license": "", "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"}}]}]}]}, "post_processed": null, "supervised_keys": null, "builder_name": "taskmaster2", "config_name": "music", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2814030, "num_examples": 1603, "dataset_name": "taskmaster2"}}, "download_checksums": {"https://raw.githubusercontent.com/google-research-datasets/Taskmaster/master/TM-2-2020/data/music.json": {"num_bytes": 8981720, "checksum": "e5db60d6576fa010bef87a70a8b371d293d48cde8524c1d3ed7c3022f079d95d"}}, "download_size": 8981720, "post_processing_size": null, "dataset_size": 2814030, "size_in_bytes": 11795750}, "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 \u201cspoke\u201d 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.\n", "citation": "@inproceedings{48484,\ntitle\t= {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},\nauthor\t= {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},\nyear\t= {2019}\n}\n", "homepage": "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020", "license": "", "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"}}]}]}]}, "post_processed": null, "supervised_keys": null, "builder_name": "taskmaster2", "config_name": "restaurant-search", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7341998, "num_examples": 3276, "dataset_name": "taskmaster2"}}, "download_checksums": {"https://raw.githubusercontent.com/google-research-datasets/Taskmaster/master/TM-2-2020/data/restaurant-search.json": {"num_bytes": 21472680, "checksum": "fb9735f89e7ebc7c877f976da4c30391af6a6277991b597c0755564657ff8f47"}}, "download_size": 21472680, "post_processing_size": null, "dataset_size": 7341998, "size_in_bytes": 28814678}, "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 \u201cspoke\u201d 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.\n", "citation": "@inproceedings{48484,\ntitle\t= {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},\nauthor\t= {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},\nyear\t= {2019}\n}\n", "homepage": "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020", "license": "", "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"}}]}]}]}, "post_processed": null, "supervised_keys": null, "builder_name": "taskmaster2", "config_name": "sports", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5738818, "num_examples": 3481, "dataset_name": "taskmaster2"}}, "download_checksums": {"https://raw.githubusercontent.com/google-research-datasets/Taskmaster/master/TM-2-2020/data/sports.json": {"num_bytes": 19549440, "checksum": "8191531bfa5a8426b1508c396ab9886a19c7c620b443c436ec10d8d4708d0eac"}}, "download_size": 19549440, "post_processing_size": null, "dataset_size": 5738818, "size_in_bytes": 25288258}}

dummy/flights/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6601271877fecec2624389bd5b84cf68ac10cd90e504aeb1280c9ef99dbd4d02
+size 4615

dummy/food-ordering/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:90c8e5623884617f1f85c87100333a97094c59dcb1e25fbcdf02e51e4237af24
+size 3261

dummy/hotels/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f0e99c216f227d59d198487f079eafd7d0409b0991da7c20ce63e996196aa3fb
+size 5368

dummy/movies/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:edd0aee5bcbdc81165eaa8237b382e0bed14483b9866ff071dbc2b2a7855df28
+size 2739

dummy/music/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:28c1d6ea95bf798628661ca2e38ca9bde95bd4db06210b55dfd051d603efeff6
+size 2542

dummy/restaurant-search/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f85af3d8124dcead610a4288b1ef76426838627448cd6c9bc3bb35ff29d5280c
+size 3138

dummy/sports/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:109134033b5dd0ad454e04bbf8065c4d66a1d3918809b428d34d8d977955f27a
+size 2710

taskmaster2.py

@@ -0,0 +1,126 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Taskmaster: A dataset for goal oriented conversations."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{48484,
+title	= {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
+author	= {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
+year	= {2019}
+}
+"""
+
+_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.
+"""
+
+_HOMEPAGE = "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020"
+
+_BASE_URL = "https://raw.githubusercontent.com/google-research-datasets/Taskmaster/master/TM-2-2020/data"
+
+
+class Taskmaster2(datasets.GeneratorBasedBuilder):
+    """Taskmaster: A dataset for goal oriented conversations."""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="flights", version=datasets.Version("1.0.0"), description="Taskmaster-2 flights domain."
+        ),
+        datasets.BuilderConfig(
+            name="food-ordering", version=datasets.Version("1.0.0"), description="Taskmaster-2 food-ordering domain"
+        ),
+        datasets.BuilderConfig(
+            name="hotels", version=datasets.Version("1.0.0"), description="Taskmaster-2 hotel domain"
+        ),
+        datasets.BuilderConfig(
+            name="movies", version=datasets.Version("1.0.0"), description="Taskmaster-2 movies domain"
+        ),
+        datasets.BuilderConfig(
+            name="music", version=datasets.Version("1.0.0"), description="Taskmaster-2 music domain"
+        ),
+        datasets.BuilderConfig(
+            name="restaurant-search",
+            version=datasets.Version("1.0.0"),
+            description="Taskmaster-2 restaurant-search domain",
+        ),
+        datasets.BuilderConfig(
+            name="sports", version=datasets.Version("1.0.0"), description="Taskmaster-2 sports domain"
+        ),
+    ]
+
+    def _info(self):
+        features = {
+            "conversation_id": datasets.Value("string"),
+            "instruction_id": datasets.Value("string"),
+            "utterances": [
+                {
+                    "index": datasets.Value("int32"),
+                    "speaker": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "segments": [
+                        {
+                            "start_index": datasets.Value("int32"),
+                            "end_index": datasets.Value("int32"),
+                            "text": datasets.Value("string"),
+                            "annotations": [{"name": datasets.Value("string")}],
+                        }
+                    ],
+                }
+            ],
+        }
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        url = f"{_BASE_URL}/{self.config.name}.json"
+        dialogs_file = dl_manager.download(url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": dialogs_file},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            dialogs = json.load(f)
+            for dialog in dialogs:
+                utterances = dialog["utterances"]
+                for utterance in utterances:
+                    if "segments" not in utterance:
+                        utterance["segments"] = []
+                yield dialog["conversation_id"], dialog