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
+*.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
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README.md

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+---
+annotations_creators:
+- crowdsourced
+- expert-generated
+language_creators:
+- crowdsourced
+- expert-generated
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|natural_questions
+- extended|other-Common-Crawl
+- original
+task_categories:
+- other
+- question-answering
+task_ids:
+- abstractive-qa
+- extractive-qa
+- other-other-query-based-multi-document-summarization
+---
+
+# Dataset Card for AQuaMuSe
+## 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-fields)
+  - [Data Splits](#data-splits)
+- [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:** https://github.com/google-research-datasets/aquamuse
+- **Repository:** https://github.com/google-research-datasets/aquamuse
+- **Paper:** https://arxiv.org/pdf/2010.12694.pdf
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)
+
+This dataset contains versions of automatically generated datasets for abstractive and extractive query-based multi-document summarization as described in [AQuaMuSe paper](https://arxiv.org/pdf/2010.12694.pdf).
+### Supported Tasks and Leaderboards
+
+- **Abstractive** and **Extractive** query-based multi-document summarization
+- Question Answering
+
+### Languages
+
+en : English
+
+## Dataset Structure
+
+### Data Instances
+
+- `input_urls`: a `list` of `string` features. 
+- `query`: a `string` feature.
+- `target`: a `string` feature
+
+
+Example: 
+
+```
+{
+    'input_urls': ['https://boxofficebuz.com/person/19653-charles-michael-davis'],
+     'query': 'who is the actor that plays marcel on the originals',
+     'target': "In February 2013, it was announced that Davis was cast in a lead role on The CW's new show The 
+Originals, a spinoff of The Vampire Diaries, centered on the Original Family as they move to New Orleans, where 
+Davis' character (a vampire named Marcel) currently rules."
+}
+```
+
+### Data Fields
+
+ - `input_urls`: a `list` of `string` features. 
+  - List of URLs to input documents pointing to  [Common Crawl](https://commoncrawl.org/2017/07/june-2017-crawl-archive-now-available)  to be summarized. 
+  - Dependencies: Documents URLs references the  [Common Crawl June 2017 Archive](https://commoncrawl.org/2017/07/june-2017-crawl-archive-now-available).
+  
+ - `query`: a `string` feature.
+  - Input query to be used as summarization context. This is  derived from  [Natural Questions](https://ai.google.com/research/NaturalQuestions/)  user queries.
+  
+ -  `target`: a `string` feature
+  - Summarization target, derived from  [Natural Questions](https://ai.google.com/research/NaturalQuestions/)  long answers.
+### Data Splits
+ - This dataset has two high-level configurations `abstractive` and `extractive`
+ - Each configuration has the data splits of `train`, `dev` and `test`
+ - The original format of the data was in [TFrecords](https://www.tensorflow.org/tutorials/load_data/tfrecord), which has been parsed to the format as specified in [Data Instances](#data-instances)
+ 
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset is automatically generated datasets for abstractive and extractive query-based multi-document summarization as described in [AQuaMuSe paper](https://arxiv.org/pdf/2010.12694.pdf).
+### 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 curator is [sayalikulkarni](https://github.com/google-research-datasets/aquamuse/commits?author=sayalikulkarni), who is the contributor for the official GitHub repository for this dataset and also one of the authors of this dataset’s paper. As the account handles of other authors are not available currently who were also part of the curation of this dataset, the authors of the paper are mentioned here as follows, Sayali Kulkarni, Sheide Chammas, Wan Zhu, Fei Sha, and Eugene Ie.
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+@misc{kulkarni2020aquamuse,
+      title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization}, 
+      author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie},
+      year={2020},
+      eprint={2010.12694},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}

aquamuse.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.
+"""AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+from os import listdir
+from os.path import isfile, join
+
+import tensorflow as tf
+
+import datasets
+
+
+_CITATION = """\
+@misc{kulkarni2020aquamuse,
+      title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization},
+      author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie},
+      year={2020},
+      eprint={2010.12694},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)"""
+
+_HOMEPAGE = "https://github.com/google-research-datasets/aquamuse"
+
+_LICENSE = ""
+
+zipped_data_url = "https://github.com/google-research-datasets/aquamuse/raw/main/v2/aquamuse_v2.zip"
+
+
+class Aquamuse(datasets.GeneratorBasedBuilder):
+    """Dataset for Query-based Multi-Document Summarization"""
+
+    VERSION = datasets.Version("2.3.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="abstractive", version=VERSION, description="Abstractive query-based multi-document summarization"
+        ),
+        datasets.BuilderConfig(
+            name="extractive", version=VERSION, description="Extractive query-based multi-document summarization"
+        ),
+    ]
+
+    # DEFAULT_CONFIG_NAME = "abstractive"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "query": datasets.Value("string"),
+                "input_urls": datasets.Sequence(datasets.Value("string")),
+                "target": 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."""
+
+        if self.config.name == "abstractive":
+            data_dir = dl_manager.download_and_extract(zipped_data_url)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "v2.3/abstractive/train/"),
+                        "split": "train",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "v2.3/abstractive/test/"),
+                        "split": "test",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "v2.3/abstractive/dev/"),
+                        "split": "dev",
+                    },
+                ),
+            ]
+
+        else:
+            data_dir = dl_manager.download_and_extract(zipped_data_url)
+            print(data_dir)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "v2.3/extractive/train/"),
+                        "split": "train",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "v2.3/extractive/test/"),
+                        "split": "test",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "v2.3/extractive/dev/"),
+                        "split": "dev",
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+        filepath = [join(filepath, f) for f in listdir(filepath) if isfile(join(filepath, f))]
+        filepath = sorted(filepath)
+        raw_dataset = tf.data.TFRecordDataset(filepath)
+        for id_, raw_record in enumerate(raw_dataset):
+            example = tf.train.Example()
+            example.ParseFromString(raw_record.numpy())
+            yield id_, {
+                "query": example.features.feature["query"].bytes_list.value[0].decode(),
+                "input_urls": example.features.feature["input_urls"].bytes_list.value[0].decode().split("<EOD>"),
+                "target": example.features.feature["target"].bytes_list.value[0].decode(),
+            }

dataset_infos.json

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+{"abstractive": {"description": "AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)", "citation": "@misc{kulkarni2020aquamuse,title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization}, author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie}, year={2020}, eprint={2010.12694}, archivePrefix={arXiv}, primaryClass={cs.CL}}", "homepage": "https://github.com/google-research-datasets/aquamuse", "license": "", "features": {"query": {"dtype": "string", "id": null, "_type": "Value"}, "input_urls": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "target": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "aquamuse", "config_name": "abstractive", "version": {"version_str": "2.3.0", "description": null, "major": 2, "minor": 3, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 6434909, "num_examples": 6253, "dataset_name": "aquamuse"}, "test": {"name": "test", "num_bytes": 843181, "num_examples": 811, "dataset_name": "aquamuse"}, "validation": {"name": "validation", "num_bytes": 689109, "num_examples": 661, "dataset_name": "aquamuse"}}, "download_checksums": {"https://github.com/google-research-datasets/aquamuse/raw/main/v2/aquamuse_v2.zip": {"num_bytes": 7755161, "checksum": "f2b4d9523031a986e545a7c0fdc8180670519696340d09179a39514fc76466d0"}}, "download_size": 7755161, "post_processing_size": null, "dataset_size": 7967199, "size_in_bytes": 15722360}, "extractive": {"description": "AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)", "citation": "@misc{kulkarni2020aquamuse,title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization}, author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie}, year={2020}, eprint={2010.12694}, archivePrefix={arXiv}, primaryClass={cs.CL}}", "homepage": "https://github.com/google-research-datasets/aquamuse", "license": "", "features": {"query": {"dtype": "string", "id": null, "_type": "Value"}, "input_urls": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "target": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "aquamuse", "config_name": "extractive", "version": {"version_str": "2.3.0", "description": null, "major": 2, "minor": 3, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 6434909, "num_examples": 6253, "dataset_name": "aquamuse"}, "test": {"name": "test", "num_bytes": 843181, "num_examples": 811, "dataset_name": "aquamuse"}, "validation": {"name": "validation", "num_bytes": 689109, "num_examples": 661, "dataset_name": "aquamuse"}}, "download_checksums": {"https://github.com/google-research-datasets/aquamuse/raw/main/v2/aquamuse_v2.zip": {"num_bytes": 7755161, "checksum": "f2b4d9523031a986e545a7c0fdc8180670519696340d09179a39514fc76466d0"}}, "download_size": 7755161, "post_processing_size": null, "dataset_size": 7967199, "size_in_bytes": 15722360}}

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dummy/extractive/2.3.0/dummy_data.zip

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