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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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|natural_questions
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+- question-answering-other-explanations-in-question-answering
+---
+
+# 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:** N/A
+- **Repository:** [GitHub](https://github.com/google-research-datasets/QED)
+- **Paper:** [QED: A Framework and Dataset for Explanations in Question Answering](https://arxiv.org/abs/2009.06354)
+- **Leaderboard:** N/A
+- **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
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+## 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
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"qed": {"description": "QED, is a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. It is an expertannotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset.\n", "citation": "@misc{lamm2020qed,\n    title={QED: A Framework and Dataset for Explanations in Question Answering},\n    author={Matthew Lamm and Jennimaria Palomaki and Chris Alberti and Daniel Andor and Eunsol Choi and Livio Baldini Soares and Michael Collins},\n    year={2020},\n    eprint={2009.06354},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/google-research-datasets/QED", "license": "", "features": {"example_id": {"dtype": "int64", "id": null, "_type": "Value"}, "title_text": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "paragraph_text": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_starts": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "original_nq_answers": [[{"start": {"dtype": "int32", "id": null, "_type": "Value"}, "end": {"dtype": "int32", "id": null, "_type": "Value"}, "string": {"dtype": "string", "id": null, "_type": "Value"}}]], "annotation": {"referential_equalities": [{"question_reference": {"start": {"dtype": "int32", "id": null, "_type": "Value"}, "end": {"dtype": "int32", "id": null, "_type": "Value"}, "string": {"dtype": "string", "id": null, "_type": "Value"}}, "sentence_reference": {"start": {"dtype": "int32", "id": null, "_type": "Value"}, "end": {"dtype": "int32", "id": null, "_type": "Value"}, "bridge": {"dtype": "bool_", "id": null, "_type": "Value"}, "string": {"dtype": "string", "id": null, "_type": "Value"}}}], "answer": [{"sentence_reference": {"start": {"dtype": "int32", "id": null, "_type": "Value"}, "end": {"dtype": "int32", "id": null, "_type": "Value"}, "bridge": {"dtype": "bool_", "id": null, "_type": "Value"}, "string": {"dtype": "string", "id": null, "_type": "Value"}}, "paragraph_reference": {"start": {"dtype": "int32", "id": null, "_type": "Value"}, "end": {"dtype": "int32", "id": null, "_type": "Value"}, "string": {"dtype": "string", "id": null, "_type": "Value"}}}], "explanation_type": {"dtype": "string", "id": null, "_type": "Value"}, "selected_sentence": {"start": {"dtype": "int32", "id": null, "_type": "Value"}, "end": {"dtype": "int32", "id": null, "_type": "Value"}, "string": {"dtype": "string", "id": null, "_type": "Value"}}}}, "post_processed": null, "supervised_keys": null, "builder_name": "qed", "config_name": "qed", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 8560864, "num_examples": 7638, "dataset_name": "qed"}, "validation": {"name": "validation", "num_bytes": 1615171, "num_examples": 1355, "dataset_name": "qed"}}, "download_checksums": {"https://raw.githubusercontent.com/google-research-datasets/QED/master/qed-train.jsonlines": {"num_bytes": 11839736, "checksum": "b5cf65414defef8d42f6778dbd3cf0fa710adcdcb86fc693ab8edec8f0be7faf"}, "https://raw.githubusercontent.com/google-research-datasets/QED/master/qed-dev.jsonlines": {"num_bytes": 2244232, "checksum": "2ea322b71a333023380c3954083b81af2d5670c8ac47ddec58c843233895c429"}}, "download_size": 14083968, "post_processing_size": null, "dataset_size": 10176035, "size_in_bytes": 24260003}}

dummy/qed/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:71bc5583a7f20c2180e00fd10beedcf31e986453078b3393c1329465cd1195ec
+size 2981

qed.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors.
+#
+# 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.
+"""QED: A Dataset for Explanations in Question Answering"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+
+import datasets
+
+
+_CITATION = """\
+@misc{lamm2020qed,
+    title={QED: A Framework and Dataset for Explanations in Question Answering},
+    author={Matthew Lamm and Jennimaria Palomaki and Chris Alberti and Daniel Andor and Eunsol Choi and Livio Baldini Soares and Michael Collins},
+    year={2020},
+    eprint={2009.06354},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+QED, is a linguistically informed, extensible framework for explanations in question answering. \
+A QED explanation specifies the relationship between a question and answer according to formal semantic notions \
+such as referential equality, sentencehood, and entailment. It is an expertannotated dataset of QED explanations \
+built upon a subset of the Google Natural Questions dataset.
+"""
+
+_HOMEPAGE = "https://github.com/google-research-datasets/QED"
+
+_BASE_URL = "https://raw.githubusercontent.com/google-research-datasets/QED/master/"
+_URLS = {
+    "train": _BASE_URL + "qed-train.jsonlines",
+    "dev": _BASE_URL + "qed-dev.jsonlines",
+}
+
+
+class Qed(datasets.GeneratorBasedBuilder):
+    """QED: A Dataset for Explanations in Question Answering"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="qed", version=datasets.Version("1.0.0")),
+    ]
+
+    def _info(self):
+        span_features = {
+            "start": datasets.Value("int32"),
+            "end": datasets.Value("int32"),
+            "string": datasets.Value("string"),
+        }
+        reference_features = {
+            "start": datasets.Value("int32"),
+            "end": datasets.Value("int32"),
+            "bridge": datasets.Value("bool_"),
+            "string": datasets.Value("string"),
+        }
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "example_id": datasets.Value("int64"),
+                    "title_text": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "paragraph_text": datasets.Value("string"),
+                    "sentence_starts": datasets.Sequence(datasets.Value("int32")),
+                    "original_nq_answers": [span_features],
+                    "annotation": {
+                        "referential_equalities": [
+                            {
+                                "question_reference": span_features,
+                                "sentence_reference": reference_features,
+                            }
+                        ],
+                        "answer": [
+                            {
+                                "sentence_reference": reference_features,
+                                "paragraph_reference": span_features,
+                            }
+                        ],
+                        "explanation_type": datasets.Value("string"),
+                        "selected_sentence": span_features,
+                    },
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_paths = dl_manager.download(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": downloaded_paths["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": downloaded_paths["dev"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            examples = f.readlines()
+            for example in examples:
+                example = json.loads(example.strip())
+                example["question"] = example.pop("question_text")
+
+                # some examples have missing annotation, assign empty values to such examples
+                if "answer" not in example["annotation"]:
+                    example["annotation"]["answer"] = []
+                if "selected_sentence" not in example["annotation"]:
+                    example["annotation"]["selected_sentence"] = {
+                        "start": -1,
+                        "end": -1,
+                        "string": "",
+                    }
+                if "referential_equalities" not in example["annotation"]:
+                    example["annotation"]["referential_equalities"] = []
+
+                # remove the nested list
+                example["original_nq_answers"] = example["original_nq_answers"][0]
+
+                yield example["example_id"], example