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-3-0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+task_ids:
+- explanation-generation
+- table-to-text
+---
+
+# Dataset Card for [Dataset Name]
+
+## 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
+
+- **Repository:** https://github.com/DavidGrangier/wikipedia-biography-dataset
+- **Paper:** https://arxiv.org/pdf/1603.07771.pdf
+- **GoogleDrive:** https://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil
+
+### Dataset Summary
+
+This Dataset contains 728321 biographies extracted from Wikipedia containing the first paragraph of the biography and the tabular infobox.
+### Supported Tasks and Leaderboards
+
+The main purpose of this dataset is developing text generation models.
+
+### Languages
+
+English.
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+The structure of a single sample is the following:
+```json
+{
+   "input_text":{
+      "context":"pope michael iii of alexandria\n",
+      "table":{
+         "column_header":[
+            "type",
+            "ended",
+            "death_date",
+            "title",
+            "enthroned",
+            "name",
+            "buried",
+            "religion",
+            "predecessor",
+            "nationality",
+            "article_title",
+            "feast_day",
+            "birth_place",
+            "residence",
+            "successor"
+         ],
+         "content":[
+            "pope",
+            "16 march 907",
+            "16 march 907",
+            "56th of st. mark pope of alexandria & patriarch of the see",
+            "25 april 880",
+            "michael iii of alexandria",
+            "monastery of saint macarius the great",
+            "coptic orthodox christian",
+            "shenouda i",
+            "egyptian",
+            "pope michael iii of alexandria\n",
+            "16 -rrb- march -lrb- 20 baramhat in the coptic calendar",
+            "egypt",
+            "saint mark 's church",
+            "gabriel i"
+         ],
+         "row_number":[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+      }
+   },
+   "target_text":"pope michael iii of alexandria -lrb- also known as khail iii -rrb- was the coptic pope of alexandria and patriarch of the see of st. mark -lrb- 880 -- 907 -rrb- .\nin 882 , the governor of egypt , ahmad ibn tulun , forced khail to pay heavy contributions , forcing him to sell a church and some attached properties to the local jewish community .\nthis building was at one time believed to have later become the site of the cairo geniza .\n"
+}
+```
+where, in the `"table"` field, all the information of the Wikpedia infobox is stored (the header of the infobox is stored in `"column_header"` and the information in the `"content"` field).
+### Data Splits
+
+- Train: 582659 samples.
+- Test: 72831 samples.
+- Validation: 72831 samples.
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+This dataset was announced in the paper <em>Neural Text Generation from Structured Data with Application to the Biography Domain</em> [(arxiv link)](https://arxiv.org/pdf/1603.07771.pdf) and is stored both in [this](https://github.com/DavidGrangier/wikipedia-biography-dataset) repo (owned by DavidGrangier) and in [Google Drive](https://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil) (zipped and mantained by the TensorFlow team).
+#### 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
+
+[More Information Needed]
+
+### Licensing Information
+
+This dataset is ditributed under Creative Comons CC BY-SA 3.0 License.
+### Citation Information
+
+For refering the original paper in BibTex format:
+
+```
+@article{DBLP:journals/corr/LebretGA16,
+  author    = {R{\'{e}}mi Lebret and
+               David Grangier and
+               Michael Auli},
+  title     = {Generating Text from Structured Data with Application to the Biography
+               Domain},
+  journal   = {CoRR},
+  volume    = {abs/1603.07771},
+  year      = {2016},
+  url       = {http://arxiv.org/abs/1603.07771},
+  archivePrefix = {arXiv},
+  eprint    = {1603.07771},
+  timestamp = {Mon, 13 Aug 2018 16:48:30 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/LebretGA16.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```

dataset_infos.json

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+{"default": {"description": "This dataset gathers 728,321 biographies from wikipedia. It aims at evaluating text generation\nalgorithms. For each article, we provide the first paragraph and the infobox (both tokenized).\nFor each article, we extracted the first paragraph (text), the infobox (structured data). Each\ninfobox is encoded as a list of (field name, field value) pairs. We used Stanford CoreNLP\n(http://stanfordnlp.github.io/CoreNLP/) to preprocess the data, i.e. we broke the text into\nsentences and tokenized both the text and the field values. The dataset was randomly split in\nthree subsets train (80%), valid (10%), test (10%).\n", "citation": "@article{DBLP:journals/corr/LebretGA16,\n  author    = {R{'{e}}mi Lebret and\n               David Grangier and\n               Michael Auli},\n  title     = {Generating Text from Structured Data with Application to the Biography\n               Domain},\n  journal   = {CoRR},\n  volume    = {abs/1603.07771},\n  year      = {2016},\n  url       = {http://arxiv.org/abs/1603.07771},\n  archivePrefix = {arXiv},\n  eprint    = {1603.07771},\n  timestamp = {Mon, 13 Aug 2018 16:48:30 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/LebretGA16.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://github.com/DavidGrangier/wikipedia-biography-dataset", "license": "CC BY-SA 3.0", "features": {"input_text": {"table": {"feature": {"column_header": {"dtype": "string", "id": null, "_type": "Value"}, "row_number": {"dtype": "int16", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "context": {"dtype": "string", "id": null, "_type": "Value"}}, "target_text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "input_text", "output": "target_text"}, "builder_name": "wiki_bio", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 618362475, "num_examples": 582659, "dataset_name": "wiki_bio"}, "test": {"name": "test", "num_bytes": 77151324, "num_examples": 72831, "dataset_name": "wiki_bio"}, "val": {"name": "val", "num_bytes": 77221530, "num_examples": 72831, "dataset_name": "wiki_bio"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil": {"num_bytes": 333998704, "checksum": "0de0fef4cc6c9182138939134b81b6ac33ffbc989b6d23a2d9ef1e50c49b8032"}}, "download_size": 333998704, "post_processing_size": null, "dataset_size": 772735329, "size_in_bytes": 1106734033}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9d32518d755ccc45802a799dec27896c33fca9fbdffcf3952e94ebf29ca1badf
+size 6579

wiki_bio.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.
+"""\
+This dataset gathers 728,321 biographies from Wikipedia. It aims at evaluating text generation
+algorithms. For each article, we provide the first paragraph and the infobox.
+"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+_CITATION = """\
+@article{DBLP:journals/corr/LebretGA16,
+  author    = {R{\'{e}}mi Lebret and
+               David Grangier and
+               Michael Auli},
+  title     = {Generating Text from Structured Data with Application to the Biography
+               Domain},
+  journal   = {CoRR},
+  volume    = {abs/1603.07771},
+  year      = {2016},
+  url       = {http://arxiv.org/abs/1603.07771},
+  archivePrefix = {arXiv},
+  eprint    = {1603.07771},
+  timestamp = {Mon, 13 Aug 2018 16:48:30 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/LebretGA16.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DESCRIPTION = """\
+This dataset gathers 728,321 biographies from wikipedia. It aims at evaluating text generation
+algorithms. For each article, we provide the first paragraph and the infobox (both tokenized).
+For each article, we extracted the first paragraph (text), the infobox (structured data). Each
+infobox is encoded as a list of (field name, field value) pairs. We used Stanford CoreNLP
+(http://stanfordnlp.github.io/CoreNLP/) to preprocess the data, i.e. we broke the text into
+sentences and tokenized both the text and the field values. The dataset was randomly split in
+three subsets train (80%), valid (10%), test (10%).
+"""
+
+_HOMEPAGE = "https://github.com/DavidGrangier/wikipedia-biography-dataset"
+
+_LICENSE = "CC BY-SA 3.0"
+
+# 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 = "https://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil"
+
+
+def _get_table(infobox_line):
+    """Converts the infobox into a one row table."""
+    cells = infobox_line.split("\t")
+    # remove empty cells
+    cells = list(filter(lambda x: x.find("<none>") == -1, cells))
+    columns = set([cell[0 : cell.split(":")[0].rfind("_")] for cell in cells])
+    table = {col: dict() for col in columns}
+    for cell in cells:
+        delimiter_position_value = cell.find(":")
+        column_index = cell[0:delimiter_position_value]
+        value = cell[delimiter_position_value + 1 :]
+        delimiter_column_index = column_index.rfind("_")
+        column = column_index[0:delimiter_column_index]
+        index = column_index[delimiter_column_index + 1 :]
+        table[column][index] = value
+    infobox_line_as_table = []
+    for column in table.keys():
+        row_value = " ".join([table[column][index] for index in sorted(table[column].keys())])
+        infobox_line_as_table.append(
+            {
+                "column_header": column,
+                "row_number": 1,
+                "content": row_value,
+            }
+        )
+    return infobox_line_as_table
+
+
+class WikiBio(datasets.GeneratorBasedBuilder):
+    """Infoboxes and first paragraph from Wikipedia biography pages."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "input_text": {
+                    "table": datasets.Sequence(
+                        {
+                            "column_header": datasets.Value("string"),
+                            "row_number": datasets.Value("int16"),
+                            "content": datasets.Value("string"),
+                        }
+                    ),
+                    "context": datasets.Value("string"),
+                },
+                "target_text": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("input_text", "target_text"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        my_urls = _URL
+        data_dir = dl_manager.download_and_extract(my_urls)
+        data_path = os.path.join(data_dir, "wikipedia-biography-dataset")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split("train"),
+                gen_kwargs={
+                    "id_file": os.path.join(data_path, "train", "train.id"),
+                    "infobox_file": os.path.join(data_path, "train", "train.box"),
+                    "nb_lines_file": os.path.join(data_path, "train", "train.nb"),
+                    "sentences_file": os.path.join(data_path, "train", "train.sent"),
+                    "article_title_file": os.path.join(data_path, "train", "train.title"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("test"),
+                gen_kwargs={
+                    "id_file": os.path.join(data_path, "test", "test.id"),
+                    "infobox_file": os.path.join(data_path, "test", "test.box"),
+                    "nb_lines_file": os.path.join(data_path, "test", "test.nb"),
+                    "sentences_file": os.path.join(data_path, "test", "test.sent"),
+                    "article_title_file": os.path.join(data_path, "test", "test.title"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("val"),
+                gen_kwargs={
+                    "id_file": os.path.join(data_path, "valid", "valid.id"),
+                    "infobox_file": os.path.join(data_path, "valid", "valid.box"),
+                    "nb_lines_file": os.path.join(data_path, "valid", "valid.nb"),
+                    "sentences_file": os.path.join(data_path, "valid", "valid.sent"),
+                    "article_title_file": os.path.join(data_path, "valid", "valid.title"),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, id_file, infobox_file, nb_lines_file, sentences_file, article_title_file):
+        """ Yields examples."""
+        with open(id_file, "r", encoding="utf-8") as id_src, open(
+            infobox_file, "r", encoding="utf-8"
+        ) as infobox_src, open(nb_lines_file, "r", encoding="utf-8") as nb_lines_src, open(
+            sentences_file, "r", encoding="utf-8"
+        ) as sentences_src, open(
+            article_title_file, "r", encoding="utf-8"
+        ) as article_title_src:
+            for id_, infobox, nb_lines, article_title in zip(id_src, infobox_src, nb_lines_src, article_title_src):
+                target_text = []
+                for _ in range(int(nb_lines)):
+                    target_text.append(sentences_src.readline())
+                yield id_, {
+                    "input_text": {"table": _get_table(infobox), "context": article_title},
+                    "target_text": "".join(target_text),
+                }