Update files from the datasets library (from 1.2.0)

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

.gitattributes

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- en
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- other
+task_ids:
+- other-other-Coached Conversation Preference
+---
+
+# Dataset Card for Coached Conversational Preference Elicitation
+
+## 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:** [Google Research](https://research.google/tools/datasets/coached-conversational-preference-elicitation/)
+- **Repository:**
+- **Paper:** [Aclweb](https://www.aclweb.org/anthology/W19-5941/)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+Each conversation has the following fields:
+
+* conversationId: A unique random ID for the conversation. The ID has no meaning.
+* utterances: An array of utterances by the workers.
+
+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 as written by the ASSISTANT, or transcribed from the spoken recording of USER.
+* segments: An array of semantic annotations of spans in the text.  
+
+Each semantic annotation segment has the following fields:
+
+* startIndex: The position of the start of the annotation in the utterance text.
+* endIndex: The position of the end of the annotation in the utterance text.
+* text: The raw text that has been annotated.
+* annotations: An array of annotation details for this segment.
+
+Each annotation has two fields:
+
+* annotationType: The class of annotation (see ontology below).
+* entityType: The class of the entity to which the text refers (see ontology below).
+
+**EXPLANATION OF ONTOLOGY**
+
+In the corpus, preferences and the entities that these preferences refer to are annotated with an annotation type as well as an entity type.
+
+Annotation types fall into four categories:
+
+* ENTITY_NAME: These mark the names of relevant entities mentioned.
+* ENTITY_PREFERENCE: These are defined as statements indicating that the dialog participant does or does not like the relevant entity in general, or that they do or do not like some aspect of the entity. This may also be thought of the participant having some sentiment about what is being discussed.
+* ENTITY_DESCRIPTION: Neutral descriptions that describe an entity but do not convey an explicit liking or disliking.
+* ENTITY_OTHER: Other relevant statements about an entity that convey relevant information of how the participant relates to the entity but do not provide a sentiment. Most often, these relate to whether a participant has seen a particular movie, or knows a lot about a given entity.
+
+Entity types are marked as belonging to one of four categories:
+
+* MOVIE_GENRE_OR_CATEGORY for genres or general descriptions that capture a particular type or style of movie.
+* MOVIE_OR_SERIES for the full or partial name of a movie or series of movies.
+* PERSON for the full or partial name of an actual person.
+* SOMETHING_ELSE for other important proper nouns, such as the names of characters or locations.
+
+
+### Data Splits
+
+[More Information Needed]
+
+## 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
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

coached_conv_pref.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.
+"""Coached Conversational Preference Elicitation Dataset to Understanding Movie Preferences"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{48414,
+title	= {Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences},
+author	= {Filip Radlinski and Krisztian Balog and Bill Byrne and Karthik Krishnamoorthi},
+year	= {2019},
+booktitle	= {Proceedings of the Annual SIGdial Meeting on Discourse and Dialogue}
+}
+"""
+
+_DESCRIPTION = """\
+A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing
+movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers,
+where one worker plays the role of an 'assistant', while the other plays the role of a 'user'. The 'assistant' elicits
+the 'user’s' preferences about movies following a Coached Conversational Preference Elicitation (CCPE) method. The
+assistant asks questions designed to minimize the bias in the terminology the 'user' employs to convey his or her
+preferences as much as possible, and to obtain these preferences in natural language. Each dialog is annotated with
+entity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of
+entities."""
+
+_HOMEPAGE = "https://research.google/tools/datasets/coached-conversational-preference-elicitation/"
+
+_LICENSE = "https://creativecommons.org/licenses/by-sa/4.0/"
+
+_URLs = {"dataset": "https://storage.googleapis.com/dialog-data-corpus/CCPE-M-2019/data.json"}
+
+
+class CoachedConvPrefConfig(datasets.BuilderConfig):
+    """BuilderConfig for DialogRE"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for DialogRE.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(CoachedConvPrefConfig, self).__init__(**kwargs)
+
+
+class CoachedConvPref(datasets.GeneratorBasedBuilder):
+    """Coached Conversational Preference Elicitation Dataset to Understanding Movie Preferences"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        CoachedConvPrefConfig(
+            name="coached_conv_pref",
+            version=datasets.Version("1.1.0"),
+            description="Coached Conversational Preference Elicitation Dataset to Understanding Movie Preferences",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "conversationId": datasets.Value("string"),
+                    "utterances": datasets.Sequence(
+                        {
+                            "index": datasets.Value("int32"),
+                            "speaker": datasets.features.ClassLabel(names=["USER", "ASSISTANT"]),
+                            "text": datasets.Value("string"),
+                            "segments": datasets.Sequence(
+                                {
+                                    "startIndex": datasets.Value("int32"),
+                                    "endIndex": datasets.Value("int32"),
+                                    "text": datasets.Value("string"),
+                                    "annotations": datasets.Sequence(
+                                        {
+                                            "annotationType": datasets.features.ClassLabel(
+                                                names=[
+                                                    "ENTITY_NAME",
+                                                    "ENTITY_PREFERENCE",
+                                                    "ENTITY_DESCRIPTION",
+                                                    "ENTITY_OTHER",
+                                                ]
+                                            ),
+                                            "entityType": datasets.features.ClassLabel(
+                                                names=[
+                                                    "MOVIE_GENRE_OR_CATEGORY",
+                                                    "MOVIE_OR_SERIES",
+                                                    "PERSON",
+                                                    "SOMETHING_ELSE",
+                                                ]
+                                            ),
+                                        }
+                                    ),
+                                }
+                            ),
+                        }
+                    ),
+                }
+            ),
+            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)
+
+        # Dataset is a single corpus (does not contain any split)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir["dataset"]),
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+
+        # Empty Segment list with annotations dictionary
+        # First prompt of a conversation does not contain the segment dictionary
+        # We are setting it to None values
+        segments_empty = [
+            {
+                "startIndex": 0,
+                "endIndex": 0,
+                "text": "",
+                "annotations": [],
+            }
+        ]
+
+        with open(filepath, encoding="utf-8") as f:
+            dataset = json.load(f)
+
+            for id_, data in enumerate(dataset):
+                conversationId = data["conversationId"]
+
+                utterances = data["utterances"]
+                for utterance in utterances:
+                    if "segments" not in utterance:
+                        utterance["segments"] = segments_empty.copy()
+
+                yield id_, {
+                    "conversationId": conversationId,
+                    "utterances": utterances,
+                }

dataset_infos.json

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+{"coached_conv_pref": {"description": "A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing\nmovie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers,\nwhere one worker plays the role of an 'assistant', while the other plays the role of a 'user'. The 'assistant' elicits\nthe 'user\u2019s' preferences about movies following a Coached Conversational Preference Elicitation (CCPE) method. The\nassistant asks questions designed to minimize the bias in the terminology the 'user' employs to convey his or her\npreferences as much as possible, and to obtain these preferences in natural language. Each dialog is annotated with\nentity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of\nentities.", "citation": "@inproceedings{48414,\ntitle\t= {Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences},\nauthor\t= {Filip Radlinski and Krisztian Balog and Bill Byrne and Karthik Krishnamoorthi},\nyear\t= {2019},\nbooktitle\t= {Proceedings of the Annual SIGdial Meeting on Discourse and Dialogue}\n}\n", "homepage": "https://research.google/tools/datasets/coached-conversational-preference-elicitation/", "license": "https://creativecommons.org/licenses/by-sa/4.0/", "features": {"conversationId": {"dtype": "string", "id": null, "_type": "Value"}, "utterances": {"feature": {"index": {"dtype": "int32", "id": null, "_type": "Value"}, "speaker": {"num_classes": 2, "names": ["USER", "ASSISTANT"], "names_file": null, "id": null, "_type": "ClassLabel"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "segments": {"feature": {"startIndex": {"dtype": "int32", "id": null, "_type": "Value"}, "endIndex": {"dtype": "int32", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "annotations": {"feature": {"annotationType": {"num_classes": 4, "names": ["ENTITY_NAME", "ENTITY_PREFERENCE", "ENTITY_DESCRIPTION", "ENTITY_OTHER"], "names_file": null, "id": null, "_type": "ClassLabel"}, "entityType": {"num_classes": 4, "names": ["MOVIE_GENRE_OR_CATEGORY", "MOVIE_OR_SERIES", "PERSON", "SOMETHING_ELSE"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "coached_conv_pref", "config_name": "coached_conv_pref", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2295579, "num_examples": 502, "dataset_name": "coached_conv_pref"}}, "download_checksums": {"https://storage.googleapis.com/dialog-data-corpus/CCPE-M-2019/data.json": {"num_bytes": 5191959, "checksum": "4ff051ea7ea60cf0f480c911c7e2cfed56434e2e2c9ea8965ac5e26365773f0a"}}, "download_size": 5191959, "post_processing_size": null, "dataset_size": 2295579, "size_in_bytes": 7487538}}

dummy/coached_conv_pref/1.1.0/dummy_data.zip

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