Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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README.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- expert-generated
+- found
+languages:
+- en
+- pl
+- ru
+licenses:
+- unknown
+multilinguality:
+- translation
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+task_ids:
+- machine-translation
+---
+
+# Dataset Card for poleval2019_mt
+
+## 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:** PolEval-2019 competition. http://2019.poleval.pl/
+- **Repository:** Links available [in this page](http://2019.poleval.pl/index.php/tasks/task4)
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish.
+Submitted solutions compete against one another within certain tasks selected by organizers, using available data and are evaluated according to
+pre-established procedures. One of the tasks in PolEval-2019 was Machine Translation (Task-4).
+
+The task is to train as good as possible machine translation system, using any technology,with limited textual resources.
+The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced
+Russian-Polish (in both directions).
+
+Here, Polish-English is also made available to allow for training in both directions. However, the test data is ONLY available for English-Polish
+
+### Supported Tasks and Leaderboards
+Supports Machine Translation between Russian to Polish and English to Polish (and vice versa). 
+
+### Languages
+- Polish (pl)
+- Russian (ru)
+- English (en) 
+
+## Dataset Structure
+
+### Data Instances
+As the training data set, a set of bi-lingual corpora aligned at the sentence level has been prepared. The corpora are saved in UTF-8 encoding as plain text, one language per file.
+
+### Data Fields
+One example of the translation is as below:
+```
+{
+  'translation': {'ru': 'не содержала в себе моделей. Модели это сравнительно новое явление. ', 
+                  'pl': 'nie miała w sobie modeli. Modele to względnie nowa dziedzina. Tak więc, jeśli '}
+}
+```
+
+### Data Splits
+
+The dataset is divided into two splits. All the headlines are scraped from news websites on the internet.
+|       | Tain   | Valid | Test  |
+| ----- | ------ | ----- | ----- |
+| ru-pl |  20001 | 3001  | 2969  |
+| pl-ru |  20001 | 3001  | 2969  |
+| en-pl | 129255 | 1000  | 9845  |
+
+## Dataset Creation
+
+### Curation Rationale
+
+This data was curated as a task for the PolEval-2019. The task is to train as good as possible machine translation system, using any technology, with limited textual resources. The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced Russian-Polish (in both directions).
+
+PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish. Submitted tools compete against one another within certain tasks selected by organizers, using available data and are evaluated according to pre-established procedures.
+
+PolEval 2019-related papers were presented at AI & NLP Workshop Day (Warsaw, May 31, 2019).
+The links for the top performing models on various tasks (including the Task-4: Machine Translation) is present in [this](http://2019.poleval.pl/index.php/publication) link
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+The organization details of PolEval is present in this [link](http://2019.poleval.pl/index.php/organizers)
+
+### 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
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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poleval2019_mt.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.
+"""Poleval 2019 dataset for Polish Translation"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = ""
+
+
+_DESCRIPTION = """\
+PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish.\
+Submitted solutions compete against one another within certain tasks selected by organizers, using available data and are evaluated according to\
+pre-established procedures. One of the tasks in PolEval-2019 was Machine Translation (Task-4).\
+
+The task is to train as good as possible machine translation system, using any technology,with limited textual resources.\
+The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced\
+Russian-Polish (in both directions).
+
+Here, Polish-English is also made available to allow for training in both directions. However, the test data is ONLY available for English-Polish.
+"""
+
+# Official homepage for the dataset
+_HOMEPAGE = "http://2019.poleval.pl/"
+
+# Licence
+_LICENSE = ""
+
+# All the urls for train data download
+_TRAIN_URL = {
+    "ru-pl": {
+        "dev.pl": "https://drive.google.com/u/0/uc?id=1mwx_zyQeTZzkXEWMPoj4yghcbFq4ETWx&export=download",
+        "dev.ru": "https://drive.google.com/u/0/uc?id=1-z09ntfDYo6j3TBTpxqu6htE_a7IAWte&export=download",
+        "train.pl": "https://drive.google.com/u/0/uc?id=11EBGHMAswT5JDO60xh7gnZfYjpMQs7h7&export=download",
+        "train.ru": "https://drive.google.com/u/0/uc?id=1H7FphKVVCYoH49sUXl79CuztEfJLaKoF&export=download",
+    },
+    "en-pl": {
+        "dev.en": "https://drive.google.com/u/0/uc?id=1L6qQiO6kPLFj8BUK9XFNUH7bNyJVA7FC&export=download",
+        "dev.pl": "https://drive.google.com/u/0/uc?id=1CP3oHL04qE1nfu3h_zmaxz5fmEtlwzLs&export=download",
+        "train.en": "https://drive.google.com/u/0/uc?id=1NAeuWLgYBzLwU5jCdkrtj4_PRUocuvlb&export=download",
+        "train.pl": "https://drive.google.com/u/0/uc?id=13ZyFc2qepAYSg9WIFaeJ9y402gblsl2e&export=download",
+    },
+}
+
+
+# All the tsv files are present in the below link.
+_TEST_URL = "http://2019.poleval.pl/task4/task4_test.zip"
+
+# These are the supported languages in the parallel corpora in the PolEval-2019 MT task
+_SUPPORTED_LANGUAGES = {
+    "ru": "Russian",
+    "en": "English",
+}
+
+
+class PolevalMTConfig(datasets.BuilderConfig):
+    """BuilderConfig for PolEval-2019 MT corpus."""
+
+    def __init__(self, language_pair=(None, None), **kwargs):
+        """BuilderConfig for PolEval-2019.
+        Args:
+            for the `datasets.features.text.TextEncoder` used for the features feature.
+            language_pair: pair of languages that will be used for translation. Should
+            contain 2-letter coded strings. First will be used at source and second
+            as target in supervised mode. For example: ("pl", "en").
+          **kwargs: keyword arguments forwarded to super.
+        """
+        # Validate language pair.
+        name = "%s-%s" % (language_pair[0], language_pair[1])
+        assert "pl" in language_pair, ("Config language pair must contain `pl` (Polish), got: %s", language_pair)
+        source, target = language_pair
+        non_pl = source if target == "pl" else target
+        assert non_pl in _SUPPORTED_LANGUAGES.keys(), ("Invalid non-polish language in pair: %s", non_pl)
+
+        description = ("Translation dataset between Polish and %s") % (_SUPPORTED_LANGUAGES[non_pl])
+        super(PolevalMTConfig, self).__init__(
+            name=name,
+            description=description,
+            version=datasets.Version("1.0.0", ""),
+            **kwargs,
+        )
+
+        self.language_pair = language_pair
+
+
+class Poleval2019Mt(datasets.GeneratorBasedBuilder):
+    """Polish Translation Dataset"""
+
+    BUILDER_CONFIGS = [PolevalMTConfig(language_pair=(key, "pl")) for key, val in _SUPPORTED_LANGUAGES.items()] + [
+        PolevalMTConfig(language_pair=("pl", key)) for key, val in _SUPPORTED_LANGUAGES.items()
+    ]
+
+    def _info(self):
+        source, target = self.config.language_pair
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {"translation": datasets.features.Translation(languages=self.config.language_pair)}
+            ),
+            supervised_keys=(source, target),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        source, target = self.config.language_pair
+
+        if "en" in self.config.language_pair:
+            urls = _TRAIN_URL["en-pl"]
+        else:
+            urls = _TRAIN_URL["ru-pl"]
+
+        # Test path templates
+        test_tmpl = "tst_to_{target}.{source}"  # Hardcode alert
+
+        files = {}
+        for split in ("train", "dev"):
+            dl_file_src = dl_manager.download_and_extract(urls[split + "." + source])
+            dl_file_dst = dl_manager.download_and_extract(urls[split + "." + target])
+
+            files[split] = {
+                "source_file": dl_file_src,
+                "target_file": dl_file_dst,
+                "split": split,
+            }
+
+        # To handle test split when english is the target language.
+        # This is because there is no Polish to English test file that is available in the default set
+        if "en" == source:
+            dl_dir_test = dl_manager.download_and_extract(_TEST_URL)
+            test_file = os.path.join(dl_dir_test, "task4_test", "tst.en")
+        elif "en" == target:
+            test_file = ""
+        else:
+            dl_dir_test = dl_manager.download_and_extract(_TEST_URL)
+            test_file = os.path.join(dl_dir_test, "task4_test", test_tmpl.format(target=target.upper(), source=source))
+
+        files["test"] = {"source_file": test_file, "target_file": "", "split": "test"}
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=files["train"]),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=files["dev"]),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=files["test"]),
+        ]
+
+    def _generate_examples(self, source_file, target_file, split):
+        """This function returns the examples in the raw (text) form."""
+        source, target = self.config.language_pair
+
+        # Returning an empty source and target just to handle the test file absence when English is the target
+        if split == "test":
+            if target == "en":
+                # Returning dummy info
+                result = {"translation": {source: "", target: ""}}
+                yield 0, result
+            else:  # Handling cases for Polish and Russian languages
+                with open(source_file, encoding="utf-8") as f:
+                    source_sentences = f.read().split("\n")
+
+                for idx, sent in enumerate(source_sentences):
+                    if sent.strip() != "":
+                        result = {"translation": {source: sent, target: ""}}
+                        yield idx, result
+        else:
+            # Training and Dev sets examples
+            with open(source_file, encoding="utf-8") as f:
+                source_sentences = f.read().split("\n")
+            with open(target_file, encoding="utf-8") as f:
+                target_sentences = f.read().split("\n")
+
+            assert len(target_sentences) == len(source_sentences), "Sizes do not match: %d vs %d for %s vs %s." % (
+                len(source_sentences),
+                len(target_sentences),
+                source_file,
+                target_file,
+            )
+
+            for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
+                result = {"translation": {source: l1, target: l2}}
+                # Make sure that both translations are non-empty.
+                yield idx, result