Update files from the datasets library (from 1.7.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.7.0

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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en-US
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-answering
+- sequence-modeling
+task_ids:
+- open-domain-qa
+- dialogue-modeling
+---
+
+# Dataset Card for ConvQuestions
+
+## 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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [ConvQuestions page](https://convex.mpi-inf.mpg.de)
+- **Repository:** [GitHub](https://github.com/PhilippChr/CONVEX)
+- **Paper:** [Look before you hop: Conversational question answering over knowledge graphs using judicious context expansion](https://arxiv.org/abs/1910.03262)
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Philipp Christmann](mailto:pchristm@mpi-inf.mpg.de)
+
+### Dataset Summary
+
+ConvQuestions is the first realistic benchmark for conversational question answering over
+knowledge graphs. It contains 11,200 conversations which can be evaluated over Wikidata.
+They are compiled from the inputs of 70 Master crowdworkers on Amazon Mechanical Turk,
+with conversations from five domains: Books, Movies, Soccer, Music, and TV Series.
+The questions feature a variety of complex question phenomena like comparisons, aggregations,
+compositionality, and temporal reasoning. Answers are grounded in Wikidata entities to enable
+fair comparison across diverse methods. The data gathering setup was kept as natural as
+possible, with the annotators selecting entities of their choice from each of the five domains,
+and formulating the entire conversation in one session. All questions in a conversation are
+from the same Turker, who also provided gold answers to the questions. For suitability to knowledge
+graphs, questions were constrained to be objective or factoid in nature, but no other restrictive
+guidelines were set. A notable property of ConvQuestions is that several questions are not
+answerable by Wikidata alone (as of September 2019), but the required facts can, for example,
+be found in the open Web or in Wikipedia. For details, please refer to the CIKM 2019 full paper
+(https://dl.acm.org/citation.cfm?id=3358016).
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+en
+
+## Dataset Structure
+
+### Data Instances
+
+An example of 'train' looks as follows.
+```
+{
+  'domain': 'music',
+  'seed_entity': 'https://www.wikidata.org/wiki/Q223495',
+  'seed_entity_text': 'The Carpenters', 
+  'questions': [
+    'When did The Carpenters sign with A&M Records?',
+    'What song was their first hit?',
+    'When did Karen die?',
+    'Karen had what eating problem?',
+    'and how did she die?'
+  ],
+  'answers': [
+    [
+      '1969'
+    ],
+    [
+      'https://www.wikidata.org/wiki/Q928282'
+    ],
+    [
+      '1983'
+    ],
+    [
+      'https://www.wikidata.org/wiki/Q131749'
+    ],
+    [
+      'https://www.wikidata.org/wiki/Q181754'
+    ]
+  ],
+  'answer_texts': [
+    '1969',
+    '(They Long to Be) Close to You',
+    '1983',
+    'anorexia nervosa',
+    'heart failure'
+  ]
+}
+```
+
+### Data Fields
+
+- `domain`: a `string` feature. Any of: ['books', 'movies', 'music', 'soccer', 'tv_series']
+- `seed_entity`: a `string` feature. Wikidata ID of the topic entity.
+- `seed_entity_text`: a `string` feature. Surface form of the topic entity.
+- `questions`: a `list` of `string` features. List of questions (initial question and follow-up questions).
+- `answers`: a `list` of `lists` of `string` features. List of answers, given as Wikidata IDs or literals (e.g. timestamps or names).
+- `answer_texts`: a `list` of `string` features. List of surface forms of the answers.
+
+### Data Splits
+
+|train|validation|tests|
+|----:|---------:|----:|
+| 6720|      2240| 2240|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+With insights from a meticulous in-house pilot study with ten students over two weeks, the authors posed the conversation generation task on Amazon Mechanical Turk (AMT) in the most natural setup: Each crowdworker was asked to build a conversation by asking five sequential questions starting from any seed entity of his/her choice, as this is an intuitive mental model that humans may have when satisfying their real information needs via their search assistants.
+
+#### Who are the annotators?
+
+Local students (Saarland Informatics Campus) and AMT Master Workers.
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+The ConvQuestions benchmark is licensed under a Creative Commons Attribution 4.0 International License.
+
+### Citation Information
+
+```
+@InProceedings{christmann2019look,
+  title={Look before you hop: Conversational question answering over knowledge graphs using judicious context expansion},
+  author={Christmann, Philipp and Saha Roy, Rishiraj and Abujabal, Abdalghani and Singh, Jyotsna and Weikum, Gerhard},
+  booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
+  pages={729--738},
+  year={2019}
+}
+```
+
+### Contributions
+
+Thanks to [@PhilippChr](https://github.com/PhilippChr) for adding this dataset.

conv_questions.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.
+"""
+ConvQuestions is the first realistic benchmark for conversational question answering over
+knowledge graphs. It contains 11,200 conversations which can be evaluated over Wikidata.
+They are compiled from the inputs of 70 Master crowdworkers on Amazon Mechanical Turk,
+with conversations from five domains: Books, Movies, Soccer, Music, and TV Series.
+The questions feature a variety of complex question phenomena like comparisons, aggregations,
+compositionality, and temporal reasoning. Answers are grounded in Wikidata entities to enable
+fair comparison across diverse methods. The data gathering setup was kept as natural as
+possible, with the annotators selecting entities of their choice from each of the five domains,
+and formulating the entire conversation in one session. All questions in a conversation are
+from the same Turker, who also provided gold answers to the questions. For suitability to knowledge
+graphs, questions were constrained to be objective or factoid in nature, but no other restrictive
+guidelines were set. A notable property of ConvQuestions is that several questions are not
+answerable by Wikidata alone (as of September 2019), but the required facts can, for example,
+be found in the open Web or in Wikipedia. For details, please refer to our CIKM 2019 full paper
+(https://dl.acm.org/citation.cfm?id=3358016).
+"""
+
+
+import json
+import os
+
+import datasets
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{christmann2019look,
+  title={Look before you hop: Conversational question answering over knowledge graphs using judicious context expansion},
+  author={Christmann, Philipp and Saha Roy, Rishiraj and Abujabal, Abdalghani and Singh, Jyotsna and Weikum, Gerhard},
+  booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
+  pages={729--738},
+  year={2019}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+ConvQuestions is the first realistic benchmark for conversational question answering over knowledge graphs.
+It contains 11,200 conversations which can be evaluated over Wikidata. The questions feature a variety of complex
+question phenomena like comparisons, aggregations, compositionality, and temporal reasoning."""
+
+_HOMEPAGE = "https://convex.mpi-inf.mpg.de"
+
+_LICENSE = "CC BY 4.0"
+
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL = "http://qa.mpi-inf.mpg.de/convex/"
+_URLs = {
+    "train": _URL + "ConvQuestions_train.zip",
+    "dev": _URL + "ConvQuestions_dev.zip",
+    "test": _URL + "ConvQuestions_test.zip",
+}
+
+
+class ConvQuestions(datasets.GeneratorBasedBuilder):
+    """ConvQuestions is a realistic benchmark for conversational question answering over knowledge graphs."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = datasets.Features(
+            {
+                "domain": datasets.Value("string"),
+                "seed_entity": datasets.Value("string"),
+                "seed_entity_text": datasets.Value("string"),
+                "questions": datasets.features.Sequence(datasets.Value("string")),
+                "answers": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
+                "answer_texts": datasets.features.Sequence(datasets.Value("string")),
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        data_dir = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir["train"], "train_set/train_set_ALL.json"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir["dev"], "dev_set/dev_set_ALL.json"),
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir["test"], "test_set/test_set_ALL.json"), "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(
+        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """Yields examples as (key, example) tuples."""
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for id_, instance in enumerate(data):
+                yield id_, {
+                    "domain": instance["domain"],
+                    "seed_entity": instance["seed_entity"],
+                    "seed_entity_text": instance["seed_entity_text"],
+                    "questions": [turn["question"] for turn in instance["questions"]],
+                    "answers": [turn["answer"].split(";") for turn in instance["questions"]],
+                    "answer_texts": [turn["answer_text"] for turn in instance["questions"]],
+                }

dataset_infos.json

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+{"default": {"description": "ConvQuestions is the first realistic benchmark for conversational question answering over knowledge graphs.\nIt contains 11,200 conversations which can be evaluated over Wikidata. The questions feature a variety of complex\nquestion phenomena like comparisons, aggregations, compositionality, and temporal reasoning.", "citation": "@InProceedings{christmann2019look,\n  title={Look before you hop: Conversational question answering over knowledge graphs using judicious context expansion},\n  author={Christmann, Philipp and Saha Roy, Rishiraj and Abujabal, Abdalghani and Singh, Jyotsna and Weikum, Gerhard},\n  booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},\n  pages={729--738},\n  year={2019}\n}\n", "homepage": "https://convex.mpi-inf.mpg.de", "license": "CC BY 4.0", "features": {"domain": {"dtype": "string", "id": null, "_type": "Value"}, "seed_entity": {"dtype": "string", "id": null, "_type": "Value"}, "seed_entity_text": {"dtype": "string", "id": null, "_type": "Value"}, "questions": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answers": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "answer_texts": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "conv_questions", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3589880, "num_examples": 6720, "dataset_name": "conv_questions"}, "validation": {"name": "validation", "num_bytes": 1241778, "num_examples": 2240, "dataset_name": "conv_questions"}, "test": {"name": "test", "num_bytes": 1175656, "num_examples": 2240, "dataset_name": "conv_questions"}}, "download_checksums": {"http://qa.mpi-inf.mpg.de/convex/ConvQuestions_train.zip": {"num_bytes": 2139687, "checksum": "093b7ea4106501035e5954213fda6111d0e4747011e8efa558765f2a9705d651"}, "http://qa.mpi-inf.mpg.de/convex/ConvQuestions_dev.zip": {"num_bytes": 594329, "checksum": "91faf376a5f702734c78033e2f357c507291cc3c85d9fda39e65c366f0abc7fd"}, "http://qa.mpi-inf.mpg.de/convex/ConvQuestions_test.zip": {"num_bytes": 542001, "checksum": "698e2a1761b9a0bff6490ccc735df8a1be9b85a7bbd8ed451a1b81ff5a1df28d"}}, "download_size": 3276017, "post_processing_size": null, "dataset_size": 6007314, "size_in_bytes": 9283331}}

dummy/1.0.0/dummy_data.zip

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