Update files from the datasets library (from 1.2.0)

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

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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- Microsoft Research Data License Agreement
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+task_ids:
+- dialogue-modeling
+---
+
+# Dataset Card for MetaLWOz
+
+## 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
+
+- **Repository:** [MetaLWOz Project Website](https://www.microsoft.com/en-us/research/project/metalwoz/)
+- **Paper:** [Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer](https://ieeexplore.ieee.org/abstract/document/9053599), and [Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation](https://arxiv.org/pdf/2003.01680.pdf)
+- **Point of Contact:** [Hannes Schulz](https://www.microsoft.com/en-us/research/people/haschulz/)
+
+### Dataset Summary
+
+MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. 
+We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for 
+conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to 
+quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas 
+of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two 
+human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human 
+user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a 
+particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. 
+Dialogues are a minimum of 10 turns long.
+
+### Supported Tasks and Leaderboards
+
+This dataset supports a range of task.
+- **Generative dialogue modeling** or `dialogue-modeling`: This data can be used to train task-oriented dialogue
+ models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast
+-adaptation models fall into the research areas of transfer learning and meta learning. The text of the dialogues
+  can be used to train a sequence model on the utterances. 
+  Example of sample input/output is given in section [Data Instances](#data-instances)
+ 
+  
+
+### Languages
+
+The text in the dataset is in English (`en`).
+
+## Dataset Structure
+
+### Data Instances
+
+A data instance is a full multi-turn dialogue between two crowd-workers, one had the role of being a `bot`, and the other one was the `user`. Both were
+given a `domain` and a `task`. Each turn has a single utterance, e.g.:
+```
+Domain: Ski
+User Task: You want to know if there are good ski hills an
+hour’s drive from your current location.
+Bot Task: Tell the user that there are no ski hills in their
+immediate location.
+Bot: Hello how may I help you?
+User: Is there any good ski hills an hour’s drive from my
+current location?
+Bot: I’m sorry to inform you that there are no ski hills in your
+immediate location
+User: Can you help me find the nearest?
+Bot: Absolutely! It looks like you’re about 3 hours away from
+Bear Mountain. That seems to be the closest.
+User: Hmm.. sounds good
+Bot: Alright! I can help you get your lift tickets now!When
+will you be going?
+User: Awesome! please get me a ticket for 10pax
+Bot: You’ve got it. Anything else I can help you with?
+User: None. Thanks again!
+Bot: No problem!
+```
+Example of input/output for this dialog:
+```
+Input: dialog history = Hello how may I help you?; Is there
+any good ski hills an hour’s drive from my current location?;
+I’m sorry to inform you that there are no ski hills in your
+immediate location
+Output: user response = Can you help me find the nearest?
+```
+
+### Data Fields
+
+Each dialogue instance has the following fields:
+- `id`: a unique ID identifying the dialog. 
+- `user_id`: a unique ID identifying the user. 
+- `bot_id`: a unique ID identifying the bot.
+- `domain`: a unique ID identifying the domain. Provides a mapping to tasks dataset.
+- `task_id`: a unique ID identifying the task. Provides a mapping to tasks dataset.
+- `turns`: the sequence of utterances alternating between `bot` and `user`, starting with a prompt from `bot`.
+  
+Each task instance has following fields:
+- `task_id`: a unique ID identifying the task. 
+- `domain`: a unique ID identifying the domain. 
+- `bot_prompt`: The task specification for bot. 
+- `bot_role`: The domain oriented role of bot. 
+- `user_prompt`: The task specification for user. 
+- `user_role`: The domain oriented role of user. 
+
+
+
+### Data Splits
+
+The dataset is split into a `train` and `test` split with the following sizes:
+
+|                           | Training MetaLWOz     |  Evaluation MetaLWOz  | Combined |
+| -----                     | ------                | -----                 | ----     |
+| Total Domains             | 47                    | 4                     | 51       |
+| Total Tasks               | 226                   | 14                    | 240      |
+| Total Dialogs             | 37884                 | 2319                  | 40203    |
+
+Below are the various statistics of the dataset:
+ 
+| Statistic                     | Mean     |  Minimum   | Maximum   |
+| -----                         | ------   | -----      | ----      |
+| Number of tasks per domain    | 4.8      | 3          | 11        |
+| Number of dialogs per domain  | 806.0    | 288        | 1990      |
+| Number of dialogs per task    | 167.6    | 32         | 285       |
+| Number of turns per dialog    | 11.4     | 10         | 46        |
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### 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
+The dataset v1 version is created by team of researchers from Microsoft Research (Montreal, Canada)
+
+### Licensing Information
+
+The dataset is released under [Microsoft Research Data License Agreement](https://msropendata-web-api.azurewebsites.net/licenses/2f933be3-284d-500b-7ea3-2aa2fd0f1bb2/view) 
+
+### Citation Information
+
+You can cite the following for the various versions of MetaLWOz:
+
+Version 1.0
+```
+@InProceedings{shalyminov2020fast,
+author = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},
+title = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},
+booktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+year = {2020},
+month = {April},
+url = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a
+-hybrid-generative-retrieval-transformer/},
+}
+```

dataset_infos.json

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+{"dialogues": {"description": "MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. Dialogues are a minimum of 10 turns long.\n", "citation": "@InProceedings{shalyminov2020fast,\nauthor = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},\ntitle = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},\nbooktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},\nyear = {2020},\nmonth = {April},\nurl = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a-hybrid-generative-retrieval-transformer/},\n}\n", "homepage": "https://www.microsoft.com/en-us/research/project/metalwoz/", "license": "Microsoft Research Data License Agreement", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "user_id": {"dtype": "string", "id": null, "_type": "Value"}, "bot_id": {"dtype": "string", "id": null, "_type": "Value"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}, "task_id": {"dtype": "string", "id": null, "_type": "Value"}, "turns": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "meta_woz", "config_name": "dialogues", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 19999218, "num_examples": 37884, "dataset_name": "meta_woz"}, "test": {"name": "test", "num_bytes": 1284287, "num_examples": 2319, "dataset_name": "meta_woz"}}, "download_checksums": {"https://download.microsoft.com/download/E/B/8/EB84CB1A-D57D-455F-B905-3ABDE80404E5/metalwoz-v1.zip": {"num_bytes": 5639228, "checksum": "2a2ae3b25760aa2725e70bc6480562fa5d720c9689a508d28417631496d6764f"}, "https://download.microsoft.com/download/0/c/4/0c4a8893-cbf9-4a43-a44a-09bab9539234/metalwoz-test-v1.zip": {"num_bytes": 2990635, "checksum": "6722d1d9ec05334dd801972767ae3bdefcd15f71bf73fea1d098f214a96a7c6c"}}, "download_size": 8629863, "post_processing_size": null, "dataset_size": 21283505, "size_in_bytes": 29913368}, "tasks": {"description": "MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. Dialogues are a minimum of 10 turns long.\n", "citation": "@InProceedings{shalyminov2020fast,\nauthor = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},\ntitle = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},\nbooktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},\nyear = {2020},\nmonth = {April},\nurl = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a-hybrid-generative-retrieval-transformer/},\n}\n", "homepage": "https://www.microsoft.com/en-us/research/project/metalwoz/", "license": "Microsoft Research Data License Agreement", "features": {"task_id": {"dtype": "string", "id": null, "_type": "Value"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}, "bot_prompt": {"dtype": "string", "id": null, "_type": "Value"}, "bot_role": {"dtype": "string", "id": null, "_type": "Value"}, "user_prompt": {"dtype": "string", "id": null, "_type": "Value"}, "user_role": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "meta_woz", "config_name": "tasks", "version": "0.0.0", "splits": {"train": {"name": "train", "num_bytes": 73768, "num_examples": 227, "dataset_name": "meta_woz"}, "test": {"name": "test", "num_bytes": 4351, "num_examples": 14, "dataset_name": "meta_woz"}}, "download_checksums": {"https://download.microsoft.com/download/E/B/8/EB84CB1A-D57D-455F-B905-3ABDE80404E5/metalwoz-v1.zip": {"num_bytes": 5639228, "checksum": "2a2ae3b25760aa2725e70bc6480562fa5d720c9689a508d28417631496d6764f"}, "https://download.microsoft.com/download/0/c/4/0c4a8893-cbf9-4a43-a44a-09bab9539234/metalwoz-test-v1.zip": {"num_bytes": 2990635, "checksum": "6722d1d9ec05334dd801972767ae3bdefcd15f71bf73fea1d098f214a96a7c6c"}}, "download_size": 8629863, "post_processing_size": null, "dataset_size": 78119, "size_in_bytes": 8707982}}

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meta_woz.py

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+# 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.
+"""MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{shalyminov2020fast,
+author = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},
+title = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},
+booktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+year = {2020},
+month = {April},
+url = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a
+-hybrid-generative-retrieval-transformer/},
+}
+"""
+
+_DESCRIPTION = """\
+MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. \
+We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for \
+conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to \
+quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas \
+of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two \
+human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human \
+user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a \
+particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. \
+Dialogues are a minimum of 10 turns long.
+"""
+
+_HOMEPAGE = "https://www.microsoft.com/en-us/research/project/metalwoz/"
+
+_LICENSE = "Microsoft Research Data License Agreement"
+
+_URLs = {
+    "train": "https://download.microsoft.com/download/E/B/8/EB84CB1A-D57D-455F-B905-3ABDE80404E5/metalwoz-v1.zip",
+    "test": "https://download.microsoft.com/download/0/c/4/0c4a8893-cbf9-4a43-a44a-09bab9539234/metalwoz-test-v1.zip",
+}
+
+
+class MetaWoz(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="dialogues", description="The dataset of dialogues from various domains."),
+        datasets.BuilderConfig(
+            name="tasks", description="The metadata for tasks corresponding to dialogues from " "various domains."
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "dialogues"
+
+    def _info(self):
+        if self.config.name == "tasks":
+            features = datasets.Features(
+                {
+                    "task_id": datasets.Value("string"),
+                    "domain": datasets.Value("string"),
+                    "bot_prompt": datasets.Value("string"),
+                    "bot_role": datasets.Value("string"),
+                    "user_prompt": datasets.Value("string"),
+                    "user_role": datasets.Value("string"),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "user_id": datasets.Value("string"),
+                    "bot_id": datasets.Value("string"),
+                    "domain": datasets.Value("string"),
+                    "task_id": datasets.Value("string"),
+                    "turns": datasets.Sequence(datasets.Value("string")),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URLs)
+        data_dir["test"] = dl_manager.extract(os.path.join(data_dir["test"], "dstc8_metalwoz_heldout.zip"))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"data_dir": data_dir["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"data_dir": data_dir["test"]},
+            ),
+        ]
+
+    def _generate_examples(self, data_dir):
+        """ Yields examples. """
+        if self.config.name == "tasks":
+            filepath = os.path.join(data_dir, "tasks.txt")
+            with open(filepath, encoding="utf-8") as f:
+                for id_, row in enumerate(f):
+                    data = json.loads(row)
+                    yield id_, {
+                        "task_id": data["task_id"],
+                        "domain": data["domain"],
+                        "bot_prompt": data["bot_prompt"],
+                        "bot_role": data["bot_role"],
+                        "user_prompt": data["user_prompt"],
+                        "user_role": data["user_role"],
+                    }
+        else:
+            id_ = -1
+            base_path = os.path.join(data_dir, "dialogues")
+            file_list = sorted(
+                [os.path.join(base_path, file) for file in os.listdir(base_path) if file.endswith(".txt")]
+            )
+            for filepath in file_list:
+                with open(filepath, encoding="utf-8") as f:
+                    for row in f:
+                        id_ += 1
+                        data = json.loads(row)
+                        yield id_, {
+                            "id": data["id"],
+                            "user_id": data["user_id"],
+                            "bot_id": data["bot_id"],
+                            "domain": data["domain"],
+                            "task_id": data["task_id"],
+                            "turns": data["turns"],
+                        }