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:
+- expert-generated
+- machine-generated
+language_creators:
+- expert-generated
+- machine-generated
+languages:
+- de
+- en
+- es
+- fr
+- ja
+- ko
+- zh
+licenses:
+- other
+multilinguality:
+- multilingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|other-paws
+task_categories:
+- text-classification
+- text-scoring
+task_ids:
+- semantic-similarity-classification
+- semantic-similarity-scoring
+- text-scoring-other-paraphrase-identification
+---
+
+# 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:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx)
+- **Repository:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx)
+- **Paper:** [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://arxiv.org/abs/1908.11828)
+- **Point of Contact:** [Yinfei Yang](yinfeiy@google.com)
+
+### Dataset Summary
+
+This dataset contains 23,659 **human** translated PAWS evaluation pairs and
+296,406 **machine** translated training pairs in six typologically distinct
+languages: French, Spanish, German, Chinese, Japanese, and Korean. All
+translated pairs are sourced from examples in
+[PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki).
+
+For further details, see the accompanying paper:
+[PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase
+Identification](https://arxiv.org/abs/1908.11828)
+
+### Supported Tasks and Leaderboards
+
+It has been majorly used for paraphrase identification for English and other 6 languages namely French, Spanish, German, Chinese, Japanese, and Korean
+
+### Languages
+
+The dataset is in English, French, Spanish, German, Chinese, Japanese, and Korean
+
+## Dataset Structure
+
+### Data Instances
+
+For en:
+```
+id		    :   1
+sentence1	:	In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland .
+sentence2	:	In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England .
+label       :   0
+```
+For fr:
+```
+id		    :   1
+sentence1	:	À Paris, en octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, lui demandant un passeport pour retourner en Angleterre en passant par l'Écosse.
+sentence2	:	En octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, à Paris, et lui demanda un passeport pour retourner en Écosse par l'Angleterre.
+label       :   0
+```
+
+### Data Fields
+
+All files are in tsv format with four columns:
+
+Column Name | Data
+:---------- | :--------------------------------------------------------
+id          | An ID that matches the ID of the source pair in PAWS-Wiki
+sentence1   | The first sentence
+sentence2   | The second sentence
+label       | Label for each pair
+
+The source text of each translation can be retrieved by looking up the ID in the
+corresponding file in PAWS-Wiki.
+
+### Data Splits
+
+The numbers of examples for each of the six languages are shown below:
+
+Language | Train   | Dev    | Test
+:------- | ------: | -----: | -----:
+fr       | 49,401  | 1,992  | 1,985
+es       | 49,401  | 1,962  | 1,999
+de       | 49,401  | 1,932  | 1,967
+zh       | 49,401  | 1,984  | 1,975
+ja       | 49,401  | 1,980  | 1,946
+ko       | 49,401  | 1,965  | 1,972
+Total    | 296,406 | 11,815 | 11,844
+
+> **Caveat**: please note that the dev and test sets of PAWS-X are both sourced
+> from the dev set of PAWS-Wiki. As a consequence, the same `sentence 1` may
+> appear in both the dev and test sets. Nevertheless our data split guarantees
+> that there is no overlap on sentence pairs (`sentence 1` + `sentence 2`)
+> between dev and test.
+
+## Dataset Creation
+
+### Curation Rationale
+
+Most existing work on adversarial data generation focuses on English. For example, PAWS (Paraphrase Adversaries from Word Scrambling) (Zhang et al., 2019) consists of challenging English paraphrase identification pairs from Wikipedia and Quora. They remedy this gap with PAWS-X, a new dataset of 23,659 human translated PAWS evaluation pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. They provide baseline numbers for three models with different capacity to capture non-local context and sentence structure, and using different multilingual training and evaluation regimes. Multilingual BERT (Devlin et al., 2019) fine-tuned on PAWS English plus machine-translated data performs the best, with a range of 83.1-90.8 accuracy across the non-English languages and an average accuracy gain of 23% over the next best model. PAWS-X shows the effectiveness of deep, multilingual pre-training while also leaving considerable headroom as a new challenge to drive multilingual research that better captures structure and contextual information.
+
+### Source Data
+
+PAWS (Paraphrase Adversaries from Word Scrambling)
+
+#### Initial Data Collection and Normalization
+
+All translated pairs are sourced from examples in [PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki)
+
+#### Who are the source language producers?
+
+This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean.
+
+### Annotations
+
+#### Annotation process
+
+If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.
+
+#### Who are the annotators?
+
+The paper mentions the translate team, especially Mengmeng Niu, for the help with the annotations.
+
+### 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
+
+List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
+
+### Licensing Information
+
+The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
+
+### Citation Information
+
+```
+@InProceedings{pawsx2019emnlp,
+  title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
+  author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
+  booktitle = {Proc. of EMNLP},
+  year = {2019}
+}
+```

dataset_infos.json

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paws-x.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.
+"""PAWS-X, a multilingual version of PAWS for six languages."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{pawsx2019emnlp,
+  title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
+  author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
+  booktitle = {Proc. of EMNLP},
+  year = {2019}
+}
+"""
+
+_DESCRIPTION = """\
+PAWS-X, a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages.
+
+This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine
+translated training pairs in six typologically distinct languages: French, Spanish, German,
+Chinese, Japanese, and Korean. English language is available by default. All translated
+pairs are sourced from examples in PAWS-Wiki.
+
+For further details, see the accompanying paper: PAWS-X: A Cross-lingual Adversarial Dataset
+for Paraphrase Identification (https://arxiv.org/abs/1908.11828)
+
+NOTE: There might be some missing or wrong labels in the dataset and we have replaced them with -1.
+"""
+
+_HOMEPAGE = "https://github.com/google-research-datasets/paws/tree/master/pawsx"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = 'The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.'
+
+# TODO: Add link to the official dataset URLs here
+# 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)
+_DATA_OPTIONS = [
+    "en",
+    "de",
+    "es",
+    "fr",
+    "ja",
+    "ko",
+    "zh",
+]
+
+_DATA_URL = "https://storage.googleapis.com/paws/pawsx/x-final.tar.gz"
+
+
+class PAWSXConfig(datasets.BuilderConfig):
+    """BuilderConfig for PAWSX."""
+
+    def __init__(self, **kwargs):
+        """Constructs a PAWSXConfig.
+        Args:
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(PAWSXConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs),
+
+
+class PAWSX(datasets.GeneratorBasedBuilder):
+    """PAWS-X, a multilingual version of PAWS for six languages."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        PAWSXConfig(
+            name=config_name,
+            description=(f"This config contains samples in {config_name}."),
+        )
+        for config_name in _DATA_OPTIONS
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("int32"),
+                "sentence1": datasets.Value("string"),
+                "sentence2": datasets.Value("string"),
+                "label": datasets.features.ClassLabel(names=["0", "1"]),
+            }
+        )
+        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=None,
+            # 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."""
+
+        data_dir = dl_manager.download_and_extract(_DATA_URL)
+
+        _TEST_FILE_NAME = os.path.join(data_dir, f"x-final/{self.config.name}/test_2k.tsv")
+        _VAL_FILE_NAME = os.path.join(data_dir, f"x-final/{self.config.name}/dev_2k.tsv")
+
+        if self.config.name == "en":
+            _TRAIN_FILE_NAME = os.path.join(data_dir, f"x-final/{self.config.name}/train.tsv")
+        else:
+            _TRAIN_FILE_NAME = os.path.join(data_dir, f"x-final/{self.config.name}/translated_train.tsv")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": _TRAIN_FILE_NAME,
+                    "split": datasets.Split.TRAIN,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": _TEST_FILE_NAME,
+                    "split": datasets.Split.TEST,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": _VAL_FILE_NAME,
+                    "split": datasets.Split.VALIDATION,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+
+        with open(filepath, encoding="utf-8") as f:
+            data = csv.DictReader(f, delimiter="\t")
+            for id_, row in enumerate(data):
+                if row["label"] not in ["0", "1"]:
+                    row["label"] = -1
+                yield id_, {
+                    "id": row["id"],
+                    "sentence1": row["sentence1"],
+                    "sentence2": row["sentence2"],
+                    "label": row["label"],
+                }