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:
+- found
+language_creators:
+- found
+languages:
+- en
+licenses:
+- cc-by-sa-2-0
+multilinguality:
+- monolingual
+size_categories:
+- n>1M
+source_datasets:
+- original
+task_categories:
+- other
+task_ids:
+- other-other-data-to-text-generation
+---
+
+# Dataset Card for Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)
+
+## 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:** https://github.com/google-research-datasets/KELM-corpus
+- **Repository:** https://github.com/google-research-datasets/KELM-corpus
+- **Paper:** https://arxiv.org/abs/2010.12688
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into
+a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text.
+The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations.
+
+### Supported Tasks and Leaderboards
+
+The intended task is data-to-text generation, taking in a knowledge graph tuple and generating a natural language
+representation from it. Specifically, the data is in the format the authors used to train a seq2seq language model
+with the tuples concatenated into a single sequence.
+
+### Languages
+
+The dataset is in English.
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance consists of one KG triple paired with corresponding natural language.
+
+### Data Fields
+
+- `triple`: Wikipedia triples of the form `<subject> <relation> <object>` where some subjects have multiple
+relations, e.g. `<subject> <relation1> <object1> <relation2> <object2> <relation3> <object3>`. For more details on
+how these relations are grouped, please refer to the paper.
+- `sentence`: The corresponding Wikipedia sentence.
+
+### Data Splits
+
+The dataset includes a pre-determined train, validation, and test split.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The goal of the dataset's curation and the associated modeling work discussed in the paper is to be able to generate
+natural text from a knowledge graph.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+The data is sourced from English Wikipedia and it's associated knowledge graph.
+
+### 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
+
+From the paper:
+
+> Wikipedia has documented ideological, gender6, and racial biases in its text. While the KELM corpus may still
+contain some of these biases, certain types of biases may be reduced.
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+This dataset has been released under the [CC BY-SA 2.0 license](https://creativecommons.org/licenses/by-sa/2.0/).
+
+### Citation Information
+
+```
+@misc{agarwal2020large,
+      title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training}, 
+      author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},
+      year={2020},
+      eprint={2010.12688},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

dataset_infos.json

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+{"default": {"description": "Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into\na natural language sentence(s). This dataset consists of English KG data converted into paired natural language text.\nThe generated corpus consists of \u223c18M sentences spanning \u223c45M triples with \u223c1500 distinct relations.\n", "citation": "@misc{agarwal2020large,\n      title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training},\n      author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},\n      year={2020},\n      eprint={2010.12688},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/google-research-datasets/KELM-corpus", "license": "", "features": {"triple": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "kelm", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1343187306, "num_examples": 6371131, "dataset_name": "kelm"}, "validation": {"name": "validation", "num_bytes": 167790917, "num_examples": 796471, "dataset_name": "kelm"}, "test": {"name": "test", "num_bytes": 167921750, "num_examples": 796493, "dataset_name": "kelm"}}, "download_checksums": {"https://storage.googleapis.com/gresearch/kelm-corpus/quadruples-train.tsv": {"num_bytes": 1305075939, "checksum": "55c1d4e1beaccda979fc7193e192bc48af05bf3357bd7c14b93ba750fca91c55"}, "https://storage.googleapis.com/gresearch/kelm-corpus/quadruples-validation.tsv": {"num_bytes": 163026560, "checksum": "802c26a7856b16f09e5380e54f115bee66d83539cc2f41bb39fbf651b99a31ed"}, "https://storage.googleapis.com/gresearch/kelm-corpus/quadruples-test.tsv": {"num_bytes": 163157370, "checksum": "d41be1cb6feed48d938136b8783ba67a9bfc262fc0614df6208937381de11e36"}}, "download_size": 1631259869, "post_processing_size": null, "dataset_size": 1678899973, "size_in_bytes": 3310159842}}

dummy/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2068588539a4f366e8aa40fe80791e20e8fa75270f9fc91ed12f59c3018bd719
+size 2349

kelm.py

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+# coding=utf-8
+# Copyright 2020 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.
+
+# Lint as: python3
+"""Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import datasets
+
+
+_DESCRIPTION = """\
+Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into
+a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text.
+The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations.
+"""
+
+_CITATION = """\
+@misc{agarwal2020large,
+      title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training},
+      author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},
+      year={2020},
+      eprint={2010.12688},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DOWNLOAD_URL = "https://storage.googleapis.com/gresearch/kelm-corpus/quadruples-{}.tsv"
+_WEBPAGE = "https://github.com/google-research-datasets/KELM-corpus"
+
+
+class KELM(datasets.GeneratorBasedBuilder):
+    """Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "triple": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                }
+            ),
+            homepage=_WEBPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = dl_manager.download_and_extract(_DOWNLOAD_URL.format("train"))
+        validation_path = dl_manager.download_and_extract(_DOWNLOAD_URL.format("validation"))
+        test_path = dl_manager.download_and_extract(_DOWNLOAD_URL.format("test"))
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, "r", encoding="utf-8") as csv_file:
+            csv_reader = csv.DictReader(csv_file, delimiter="\t", fieldnames=["triple", "sentence"])
+            for irow, row in enumerate(csv_reader):
+                yield irow, row