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:
+- machine-generated
+language_creators:
+- expert-generated
+languages:
+- cs
+- de
+- el
+- en
+- es
+- fr
+- hu
+- ja
+- ko
+- pt
+- ro
+- ru
+- sk
+- uk
+- zh
+- hu
+licenses:
+- cc-by-4-0
+multilinguality:
+- translation
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+task_ids:
+- language-modeling
+---
+
+# 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-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:** [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://figshare.com/articles/ParaPat_The_Multi-Million_Sentences_Parallel_Corpus_of_Patents_Abstracts/12627632)
+- **Repository:** [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://github.com/soares-f/parapat)
+- **Paper:** [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://www.aclweb.org/anthology/2020.lrec-1.465/)
+- **Point of Contact:** [Felipe Soares](fs@felipesoares.net)
+
+### Dataset Summary
+
+ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts
+
+This dataset contains the developed parallel corpus from the open access Google Patents dataset in 74 language pairs, comprising more than 68 million sentences and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The dataset contains samples in cs, de, el, en, es, fr, hu, ja, ko, pt, ro, ru, sk, uk, zh, hu
+
+## Dataset Structure
+
+### Data Instances
+
+They are of 2 types depending on the dataset:
+
+First type
+{
+   "translation":{
+      "en":"A method for converting a series of m-bit information words to a modulated signal is described.",
+      "es":"Se describe un método para convertir una serie de palabras de informacion de bits m a una señal modulada."
+   }
+}
+
+Second type
+{
+   "family_id":10944407,
+   "index":844,
+   "translation":{
+      "el":"αφές ο οποίος παρασκευάζεται με χαρμάνι ελληνικού καφέ είτε σε συσκευή καφέ εσπρέσο είτε σε συσκευή γαλλικού καφέ (φίλτρου) είτε κατά τον παραδοσιακό τρόπο του ελληνικού καφέ και διυλίζεται, κτυπιέται στη συνέχεια με πάγο σε χειροκίνητο ή ηλεκτρικόμίξερ ώστε να παγώσει ομοιόμορφα και να αποκτήσει πλούσιο αφρό και σερβίρεται σε ποτήρι. ΰ",
+      "en":"offee prepared using the mix for Greek coffee either in an espresso - type coffee making machine, or in a filter coffee making machine or in the traditional way for preparing Greek coffee and is then filtered , shaken with ice manually or with an electric mixer so that it freezes homogeneously, obtains a rich froth and is served in a glass."
+   }
+}
+
+### Data Fields
+
+**index:** position in the corpus
+**family id:** for each abstract, such that researchers can use that information for other text mining purposes.
+**translation:** distionary containing source and target sentence for that example
+
+### Data Splits
+
+No official train/val/test splits given.
+
+Parallel corpora aligned into sentence level
+
+|Language Pair|# Sentences|# Unique Tokens|
+|--------|-----|------|
+|EN/ZH|4.9M|155.8M|
+|EN/JA|6.1M|189.6M|
+|EN/FR|12.2M|455M|
+|EN/KO|2.3M|91.4M|
+|EN/DE|2.2M|81.7M|
+|EN/RU|4.3M|107.3M|
+|DE/FR|1.2M|38.8M|
+|FR/JA|0.3M|9.9M|
+|EN/ES|0.6M|24.6M|
+
+Parallel corpora aligned into abstract level
+
+|Language Pair|# Abstracts|
+|--------|-----|
+|FR/KO|120,607|
+|EN/UK|89,227|
+|RU/UK|85,963|
+|CS/EN|78,978|
+|EN/RO|48,789|
+|EN/HU|42,629|
+|ES/FR|32,553|
+|EN/SK|23,410|
+|EN/PT|23,122|
+|BG/EN|16,177|
+|FR/RU|10,889|
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+The availability of parallel corpora is required by current Statistical and Neural Machine Translation systems (SMT and NMT). Acquiring a high-quality parallel corpus that is large enough to train MT systems, particularly NMT ones, is not a trivial task due to the need for correct alignment and, in many cases, human curation. In this context, the automated creation of parallel corpora from freely available resources is extremely important in Natural Language Pro- cessing (NLP).
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Google makes patents data available under the Google Cloud Public Datasets. BigQuery is a Google service that supports the efficient storage and querying of massive datasets which are usually a challenging task for usual SQL databases. For instance, filtering the September 2019 release of the dataset, which contains more than 119 million rows, can take less than 1 minute for text fields. The on-demand billing for BigQuery is based on the amount of data processed by each query run, thus for a single query that performs a full-scan, the cost can be over USD 15.00, since the cost per TB is currently USD 5.00.
+
+#### Who are the source language producers?
+
+BigQuery is a Google service that supports the efficient storage and querying of massive datasets which are usually a challenging task for usual SQL databases.
+
+### Annotations
+
+#### Annotation process
+
+The following steps describe the process of producing patent aligned abstracts:
+
+1. Load the nth individual file
+2. Remove rows where the number of abstracts with more than one language is less than 2 for a given family id. The family id attribute is used to group patents that refers to the same invention. By removing these rows, we remove abstracts that are available only in one language.
+3. From the resulting set, create all possible parallel abstracts from the available languages. For instance, an abstract may be available in English, French and German, thus, the possible language pairs are English/French, English/German, and French/German.
+4. Store the parallel patents into an SQL database for easier future handling and sampling.
+
+#### 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
+
+Funded by Google Tensorflow Research Cloud.
+
+### Licensing Information
+
+CC BY 4.0
+
+### Citation Information
+
+```
+@inproceedings{soares-etal-2020-parapat,
+    title = "{P}ara{P}at: The Multi-Million Sentences Parallel Corpus of Patents Abstracts",
+    author = "Soares, Felipe  and
+      Stevenson, Mark  and
+      Bartolome, Diego  and
+      Zaretskaya, Anna",
+    booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://www.aclweb.org/anthology/2020.lrec-1.465",
+    pages = "3769--3774",
+    language = "English",
+    ISBN = "979-10-95546-34-4",
+}
+```
+
+[DOI](https://doi.org/10.6084/m9.figshare.12627632)

dataset_infos.json

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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.
+"""ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{soares-etal-2020-parapat,
+    title = "{P}ara{P}at: The Multi-Million Sentences Parallel Corpus of Patents Abstracts",
+    author = "Soares, Felipe  and
+      Stevenson, Mark  and
+      Bartolome, Diego  and
+      Zaretskaya, Anna",
+    booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://www.aclweb.org/anthology/2020.lrec-1.465",
+    pages = "3769--3774",
+    language = "English",
+    ISBN = "979-10-95546-34-4",
+}
+"""
+
+_DESCRIPTION = """\
+ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts
+
+This dataset contains the developed parallel corpus from the open access Google
+Patents dataset in 74 language pairs, comprising more than 68 million sentences
+and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm
+for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.
+
+"""
+
+_HOMEPAGE = (
+    "https://figshare.com/articles/ParaPat_The_Multi-Million_Sentences_Parallel_Corpus_of_Patents_Abstracts/12627632"
+)
+
+_LICENSE = "CC BY 4.0"
+
+type1_datasets_file = ["el-en", "cs-en", "en-hu", "en-ro", "en-sk", "en-uk", "es-fr", "fr-ru"]
+type2_datasets_file = [
+    "de-fr",
+    "en-ja",
+    "en-es",
+    "en-fr",
+    "de-en",
+    "en-ko",
+    "fr-ja",
+    "en-zh",
+    "en-ru",
+    "fr-ko",
+    "ru-uk",
+    "en-pt",
+]
+
+type1_datasets_features = [
+    "el-en",
+    "cs-en",
+    "en-hu",
+    "en-ro",
+    "en-sk",
+    "en-uk",
+    "es-fr",
+    "fr-ru",
+    "fr-ko",
+    "ru-uk",
+    "en-pt",
+]
+type2_datasets_features = ["de-fr", "en-ja", "en-es", "en-fr", "de-en", "en-ko", "fr-ja", "en-zh", "en-ru"]
+
+
+class ParaPatConfig(datasets.BuilderConfig):
+    """BuilderConfig for ParaPat."""
+
+    def __init__(self, language_pair=(None, None), url=None, **kwargs):
+        """BuilderConfig for ParaPat."""
+        name = "%s-%s" % (language_pair[0], language_pair[1])
+
+        description = ("Translation dataset from %s to %s") % (language_pair[0], language_pair[1])
+
+        source, target = language_pair
+        super(ParaPatConfig, self).__init__(
+            name=name,
+            description=description,
+            version=datasets.Version("1.1.0", ""),
+            **kwargs,
+        )
+
+        self.language_pair = language_pair
+        self.url = url
+
+
+class ParaPat(datasets.GeneratorBasedBuilder):
+    """ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        ParaPatConfig(
+            language_pair=("el", "en"),
+            url="https://ndownloader.figshare.com/files/23748818",
+        ),
+        ParaPatConfig(
+            language_pair=("cs", "en"),
+            url="https://ndownloader.figshare.com/files/23748821",
+        ),
+        ParaPatConfig(
+            language_pair=("en", "hu"),
+            url="https://ndownloader.figshare.com/files/23748827",
+        ),
+        ParaPatConfig(
+            language_pair=("en", "ro"),
+            url="https://ndownloader.figshare.com/files/23748842",
+        ),
+        ParaPatConfig(
+            language_pair=("en", "sk"),
+            url="https://ndownloader.figshare.com/files/23748848",
+        ),
+        ParaPatConfig(
+            language_pair=("en", "uk"),
+            url="https://ndownloader.figshare.com/files/23748851",
+        ),
+        ParaPatConfig(
+            language_pair=("es", "fr"),
+            url="https://ndownloader.figshare.com/files/23748857",
+        ),
+        ParaPatConfig(
+            language_pair=("fr", "ru"),
+            url="https://ndownloader.figshare.com/files/23748863",
+        ),
+        ParaPatConfig(
+            language_pair=("de", "fr"),
+            url="https://ndownloader.figshare.com/files/23748872",
+        ),
+        ParaPatConfig(
+            language_pair=("en", "ja"),
+            url="https://ndownloader.figshare.com/files/23748626",
+        ),
+        ParaPatConfig(
+            language_pair=("en", "es"),
+            url="https://ndownloader.figshare.com/files/23748896",
+        ),
+        ParaPatConfig(
+            language_pair=("en", "fr"),
+            url="https://ndownloader.figshare.com/files/23748944",
+        ),
+        ParaPatConfig(
+            language_pair=("de", "en"),
+            url="https://ndownloader.figshare.com/files/23855657",
+        ),
+        ParaPatConfig(
+            language_pair=("en", "ko"),
+            url="https://ndownloader.figshare.com/files/23748689",
+        ),
+        ParaPatConfig(
+            language_pair=("fr", "ja"),
+            url="https://ndownloader.figshare.com/files/23748866",
+        ),
+        ParaPatConfig(
+            language_pair=("en", "zh"),
+            url="https://ndownloader.figshare.com/files/23748779",
+        ),
+        ParaPatConfig(
+            language_pair=("en", "ru"),
+            url="https://ndownloader.figshare.com/files/23748704",
+        ),
+        ParaPatConfig(
+            language_pair=("fr", "ko"),
+            url="https://ndownloader.figshare.com/files/23855408",
+        ),
+        ParaPatConfig(
+            language_pair=("ru", "uk"),
+            url="https://ndownloader.figshare.com/files/23855465",
+        ),
+        ParaPatConfig(
+            language_pair=("en", "pt"),
+            url="https://ndownloader.figshare.com/files/23855441",
+        ),
+    ]
+    BUILDER_CONFIG_CLASS = ParaPatConfig
+
+    def _info(self):
+        source, target = self.config.language_pair
+
+        if self.config.name in type1_datasets_features:
+            features = datasets.Features(
+                {
+                    "index": datasets.Value("int32"),
+                    "family_id": datasets.Value("int32"),
+                    "translation": datasets.features.Translation(languages=(source, target)),
+                }
+            )
+        elif self.config.name in type2_datasets_features:
+            features = datasets.Features(
+                {
+                    "translation": datasets.features.Translation(languages=(source, target)),
+                }
+            )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=(source, target),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        source, target = self.config.language_pair
+
+        data_dir = dl_manager.download_and_extract(self.config.url)
+
+        if self.config.name in type1_datasets_file:
+            _TRAIN_FILE_NAME = data_dir
+        else:
+            name = self.config.name.replace("-", "_")
+            _TRAIN_FILE_NAME = os.path.join(data_dir, f"{name}.tsv")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": _TRAIN_FILE_NAME,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+        source, target = self.config.language_pair
+        with open(filepath, encoding="utf-8") as f:
+            if self.config.name in type1_datasets_features:
+                data = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+                for id_, row in enumerate(data):
+                    if row["src_lang"] + "-" + row["tgt_lang"] != self.config.name:
+                        continue
+                    yield id_, {
+                        "index": row["index"],
+                        "family_id": row["family_id"],
+                        "translation": {source: row["src_abs"], target: row["tgt_abs"]},
+                    }
+            else:
+                data = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+                for id_, row in enumerate(data):
+                    yield id_, {
+                        "translation": {source: row[0], target: row[1]},
+                    }