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:
+- crowdsourced
+- machine-generated
+language_creators:
+- crowdsourced
+- machine-generated
+languages:
+- en
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|wikitable_questions
+- extended|wikisql
+- extended|web_nlg
+- extended|cleaned_e2e
+task_categories:
+- conditional-text-generation
+task_ids:
+- conditional-text-generation-other-rdf-to-text
+---
+
+# 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:** [homepahe](https://github.com/Yale-LILY/dart)
+- **Repository:** [github](https://github.com/Yale-LILY/dart)
+- **Paper:** [paper](https://arxiv.org/abs/2007.02871)
+- **Leaderboard:** [leaderboard](https://github.com/Yale-LILY/dart#leaderboard)
+
+### Dataset Summary
+
+DART is a large dataset for open-domain structured data record to text generation. We consider the structured data record input as a set of RDF entity-relation triples, a format widely used for knowledge representation and semantics description. DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set. This hierarchical, structured format with its open-domain nature differentiates DART from other existing table-to-text corpora.
+
+### Supported Tasks and Leaderboards
+
+The task associated to DART is text generation from data records that are RDF triplets:
+
+- `conditional-text-generation-other-rdf-to-text`: The dataset can be used to train a model for text generation from RDF triplets, which consists in generating textual description of structured data. Success on this task is typically measured by achieving a *high* [BLEU](https://huggingface.co/metrics/bleu), [METEOR](https://huggingface.co/metrics/meteor), [BLEURT](https://huggingface.co/metrics/bleurt), [TER](https://huggingface.co/metrics/ter), [MoverScore](https://huggingface.co/metrics/mover_score), and [BERTScore](https://huggingface.co/metrics/bert_score). The ([BART-large model](https://huggingface.co/facebook/bart-large) from [BART](https://huggingface.co/transformers/model_doc/bart.html)) model currently achieves the following scores:
+
+|            | BLEU  | METEOR | TER  | MoverScore  | BERTScore  | BLEURT |
+| -----      | ----- | ------ | ---- | ----------- | ---------- | ------ |
+| BART       | 37.06 | 0.36   | 0.57 | 0.44        | 0.92       | 0.22   |
+
+This task has an active leaderboard which can be found [here](https://github.com/Yale-LILY/dart#leaderboard) and ranks models based on the above metrics while also reporting.
+
+### Languages
+
+The dataset is in english (en).
+
+## Dataset Structure
+
+### Data Instances
+
+Here is an example from the dataset:
+
+```
+{'annotations': {'source': ['WikiTableQuestions_mturk'],
+  'text': ['First Clearing\tbased on Callicoon, New York and location at On NYS 52 1 Mi. Youngsville']},
+ 'subtree_was_extended': False,
+ 'tripleset': [['First Clearing', 'LOCATION', 'On NYS 52 1 Mi. Youngsville'],
+  ['On NYS 52 1 Mi. Youngsville', 'CITY_OR_TOWN', 'Callicoon, New York']]}
+```
+
+It contains one annotation where the textual description is 'First Clearing\tbased on Callicoon, New York and location at On NYS 52 1 Mi. Youngsville'. The RDF triplets considered to generate this description are in tripleset and are formatted as subject, predicate, object.
+
+### Data Fields
+
+The different fields are:
+
+- `annotations`:
+  - `text`: list of text descriptions of the triplets
+  - `source`: list of sources of the RDF triplets (WikiTable, e2e, etc.)
+- `subtree_was_extended`: boolean, if the subtree condidered during the dataset construction was extended. Sometimes this field is missing, and therefore set to `None`
+- `tripleset`: RDF triplets as a list of triplets of strings (subject, predicate, object)
+
+### Data Splits
+
+There are three splits, train, validation and test:
+
+|                  | Tain    | Valid | Test |
+| -----            | ------- | ----- | ---- |
+| N. Examples      | 30526   | 2768  | 6959 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+Automatically generating textual descriptions from structured data inputs is crucial to improving the accessibility of knowledge bases to lay users.
+
+### Source Data
+
+DART comes from existing datasets that cover a variety of different domains while allowing to build a tree ontology and form RDF triple sets as semantic representations. The datasets used are WikiTableQuestions, WikiSQL, WebNLG and Cleaned E2E.
+
+#### Initial Data Collection and Normalization
+
+DART is constructed using multiple complementary methods: (1) human annotation on open-domain Wikipedia tables
+from WikiTableQuestions (Pasupat and Liang, 2015) and WikiSQL (Zhong et al., 2017), (2) automatic conversion of questions in WikiSQL to declarative sentences, and (3) incorporation of existing datasets including WebNLG 2017 (Gardent et al., 2017a,b; Shimorina and Gardent, 2018) and Cleaned E2E (Novikova et al., 2017b; Dušek et al., 2018, 2019)
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+DART is constructed using multiple complementary methods: (1) human annotation on open-domain Wikipedia tables
+from WikiTableQuestions (Pasupat and Liang, 2015) and WikiSQL (Zhong et al., 2017), (2) automatic conversion of questions in WikiSQL to declarative sentences, and (3) incorporation of existing datasets including WebNLG 2017 (Gardent et al., 2017a,b; Shimorina and Gardent, 2018) and Cleaned E2E (Novikova et al., 2017b; Dušek et al., 2018, 2019)
+
+#### Annotation process
+
+The two stage annotation process for constructing tripleset sentence pairs is based on a tree-structured ontology of each table.
+First, internal skilled annotators denote the parent column for each column header.
+Then, a larger number of annotators provide a sentential description of an automatically-chosen subset of table cells in a row.
+
+#### 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
+
+Under MIT license (see [here](https://github.com/Yale-LILY/dart/blob/master/LICENSE))
+
+### Citation Information
+
+```
+@article{radev2020dart,
+  title={DART: Open-Domain Structured Data Record to Text Generation},
+  author={Dragomir Radev and Rui Zhang and Amrit Rau and Abhinand Sivaprasad and Chiachun Hsieh and Nazneen Fatema Rajani and Xiangru Tang and Aadit Vyas and Neha Verma and Pranav Krishna and Yangxiaokang Liu and Nadia Irwanto and Jessica Pan and Faiaz Rahman and Ahmad Zaidi and Murori Mutuma and Yasin Tarabar and Ankit Gupta and Tao Yu and Yi Chern Tan and Xi Victoria Lin and Caiming Xiong and Richard Socher},
+  journal={arXiv preprint arXiv:2007.02871},
+  year={2020}
+```

dart.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
+"""DART: Open-Domain Structured Data Record to Text Generation"""
+
+import json
+
+import datasets
+
+
+_CITATION = """\
+@article{radev2020dart,
+  title={DART: Open-Domain Structured Data Record to Text Generation},
+  author={Dragomir Radev and Rui Zhang and Amrit Rau and Abhinand Sivaprasad and Chiachun Hsieh and Nazneen Fatema Rajani and Xiangru Tang and Aadit Vyas and Neha Verma and Pranav Krishna and Yangxiaokang Liu and Nadia Irwanto and Jessica Pan and Faiaz Rahman and Ahmad Zaidi and Murori Mutuma and Yasin Tarabar and Ankit Gupta and Tao Yu and Yi Chern Tan and Xi Victoria Lin and Caiming Xiong and Richard Socher},
+  journal={arXiv preprint arXiv:2007.02871},
+  year={2020}
+"""
+
+_DESCRIPTION = """\
+DART is a large and open-domain structured DAta Record to Text generation corpus with high-quality
+sentence annotations with each input being a set of entity-relation triples following a tree-structured ontology.
+It consists of 82191 examples across different domains with each input being a semantic RDF triple set derived
+from data records in tables and the tree ontology of table schema, annotated with sentence description that
+covers all facts in the triple set.
+
+DART is released in the following paper where you can find more details and baseline results:
+https://arxiv.org/abs/2007.02871
+"""
+
+_URL = "https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/"
+_TRAINING_FILE = "dart-v1.1.1-full-train.json"
+_DEV_FILE = "dart-v1.1.1-full-dev.json"
+_TEST_FILE = "dart-v1.1.1-full-test.json"
+
+
+class Dart(datasets.GeneratorBasedBuilder):
+    """Dart dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "tripleset": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
+                    "subtree_was_extended": datasets.Value("bool"),
+                    "annotations": datasets.Sequence(
+                        {
+                            "source": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                        }
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/Yale-LILY/dart",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            data = json.loads(f.read())
+            for example_idx, example in enumerate(data):
+                yield example_idx, {
+                    "tripleset": example["tripleset"],
+                    "subtree_was_extended": example.get("subtree_was_extended", None),  # some are missing
+                    "annotations": {
+                        "source": [annotation["source"] for annotation in example["annotations"]],
+                        "text": [annotation["text"] for annotation in example["annotations"]],
+                    },
+                }

dataset_infos.json

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+{"default": {"description": "DART is a large and open-domain structured DAta Record to Text generation corpus with high-quality\nsentence annotations with each input being a set of entity-relation triples following a tree-structured ontology.\nIt consists of 82191 examples across different domains with each input being a semantic RDF triple set derived\nfrom data records in tables and the tree ontology of table schema, annotated with sentence description that\ncovers all facts in the triple set.\n\nDART is released in the following paper where you can find more details and baseline results:\nhttps://arxiv.org/abs/2007.02871\n", "citation": "@article{radev2020dart,\n  title={DART: Open-Domain Structured Data Record to Text Generation},\n  author={Dragomir Radev and Rui Zhang and Amrit Rau and Abhinand Sivaprasad and Chiachun Hsieh and Nazneen Fatema Rajani and Xiangru Tang and Aadit Vyas and Neha Verma and Pranav Krishna and Yangxiaokang Liu and Nadia Irwanto and Jessica Pan and Faiaz Rahman and Ahmad Zaidi and Murori Mutuma and Yasin Tarabar and Ankit Gupta and Tao Yu and Yi Chern Tan and Xi Victoria Lin and Caiming Xiong and Richard Socher},\n  journal={arXiv preprint arXiv:2007.02871},\n  year={2020}\n", "homepage": "https://github.com/Yale-LILY/dart", "license": "", "features": {"tripleset": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "subtree_was_extended": {"dtype": "bool", "id": null, "_type": "Value"}, "annotations": {"feature": {"source": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "dart", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 12960061, "num_examples": 30526, "dataset_name": "dart"}, "validation": {"name": "validation", "num_bytes": 1457414, "num_examples": 2768, "dataset_name": "dart"}, "test": {"name": "test", "num_bytes": 2989087, "num_examples": 6959, "dataset_name": "dart"}}, "download_checksums": {"https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-train.json": {"num_bytes": 22001131, "checksum": "0671b56f4b090ccf1c0187364d45c6f1214421d6f1081a21800596860f314e70"}, "https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-dev.json": {"num_bytes": 2370637, "checksum": "5038f3543b6d59b94ac4e3f69d15a0b01d8578913f862142e7c560200dd6e434"}, "https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-test.json": {"num_bytes": 5001020, "checksum": "c772553b482dd5fc7b8ad90d68889062a2603e28d4449ee1f162006819e0565e"}}, "download_size": 29372788, "post_processing_size": null, "dataset_size": 17406562, "size_in_bytes": 46779350}}

dummy/0.0.0/dummy_data.zip

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