first commit

README.md

@@ -1,49 +1,258 @@
 ---
-task_categories:
-- text-generation
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
 language:
 - ru
+license:
+- apache-2.0
+multilinguality:
+- monolingual
 size_categories:
-- 1K<n<10K
+- 1K<n<10k
+task_categories:
+- text-generation
+task_ids:
+- automatic-spelling-correction
+pretty_name: Russian Spellcheck Benchmark
+language_bcp47:
+- ru-RU
+tags:
+- spellcheck
+- russian
 ---
 
-# Spellcheck Benchmark
+# Dataset Card for [Russian Spellcheck Benchmark]
 
-## Dataset Description
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#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)
+  - [Contributions](#contributions)
 
-Spellcheck Benchmark is a collection of datasets dedicated to spelling correction problem for Russian language. 
-
-**GitHub** - <link>
+## Dataset Description
 
-**Paper** - <link>
+- **Repository:** # TODO: insert link to SpellKit may be?
+- **Paper:** # TODO: insert paper to Dialog / EMNLP paper
+- **Point of Contact:** nikita.martynov.98@list.ru
 
-## Dataset Summary
+### Dataset Summary
 
 Spellcheck Benchmark includes four datasets, each of which consists of pairs of sentences in Russian language. 
 Each pair embodies sentence, which may contain spelling errors, and its corresponding correction. 
-Datasets were gathered from various sources and domains including social networks, internet blogs, github commits, medical anamnesis, literature, news, reviews and more. 
+Datasets were gathered from various sources and domains including social networks, internet blogs, github commits, medical anamnesis, literature, news, reviews and more.
+
+All datasets were passed through two-stage manual labeling pipeline.
+ The correction of a sentence is defined by an agreement of at least two human annotators. 
+ Manual labeling scheme accounts for jargonisms, collocations and common language, hence in some cases it encourages 
+ annotators not to amend a word in favor of preserving style of a text.
+
+### Supported Tasks and Leaderboards
 
-All datasets were passed through two-stage manual labeling pipeline. The correction of a sentence is defined by an agreement of at least two human annotators. 
-Manual labeling scheme accounts for jargonisms, collocations and common language, hence in some cases it encourages annotators not to amend a word
-in favor of preserving style of a text. 
+- **Task:** automatic spelling correction.
+- **Metrics:** https://www.dialog-21.ru/media/3427/sorokinaaetal.pdf.
 
-## Supported Tasks and Leaderboards
 
-- Automatic spelling correction
+### Languages
+
+Russian.
 
 ## Dataset Structure
 
 ### Data Instances
 
-...
+#### RUSpellRU
+
+- **Size of downloaded dataset files:** # TODO
+- **Size of the generated dataset:** # TODO
+- **Total amount of disk used:** # TODO
+
+An example of "train" / "test" looks as follows
+```
+{
+    "source": "очень классная тетка ктобы что не говорил.",
+    "correction": "очень классная тетка кто бы что ни говорил",
+}
+```
+
+#### MultidomainGold
+
+- **Size of downloaded dataset files:** # TODO
+- **Size of the generated dataset:** # TODO
+- **Total amount of disk used:** # TODO
+
+An example of "test" looks as follows
+```
+{
+    "source": "Ну что могу сказать... Я заказала 2 вязанных платья: за 1000 руб (у др продавца) и это ща 1200. Это платье- голимая синтетика (в том платье в составе была шерсть). Это платье как очень плохая резинка. На свои параметры (83-60-85) я заказала С . Пока одевала/снимала - оно в горловине растянулось. Помимо этого в этом платье я выгляжу ну очень тоской. У меня вес 43 кг на 165 см роста.  Кстати, продавец отправлял платье очень долго. Я пыталась отказаться от заказа, но он постоянно отклонял мой запрос. В общем не советую.",
+    "correction": "Ну что могу сказать... Я заказала 2 вязаных платья: за 1000 руб (у др продавца) и это ща 1200. Это платье- голимая синтетика (в том платье в составе была шерсть). Это платье как очень плохая резинка. На свои параметры (83-60-85) я заказала С . Пока надевала/снимала - оно в горловине растянулось. Помимо этого в этом платье я выгляжу ну очень доской. У меня вес 43 кг на 165 см роста.  Кстати, продавец отправлял платье очень долго. Я пыталась отказаться от заказа, но он постоянно отклонял мой запрос. В общем не советую.",
+    "domain": "reviews",
+    
+}
+```
+
+#### MedSpellcheck
+
+- **Size of downloaded dataset files:**  # TODO
+- **Size of the generated dataset:**  # TODO
+- **Total amount of disk used:**  # TODO
+
+An example of "test" looks as follows
+```
+{
+    # TO DO
+}
+```
+
+
+#### GitHubTypoCorpusRu
+
+- **Size of downloaded dataset files:** # TODO
+- **Size of the generated dataset:** # TODO
+- **Total amount of disk used:** # TODO
+
+An example of "test" looks as follows
+```
+{
+    "source": "## Запросы и ответа содержат заголовки",
+    "correction": "## Запросы и ответы содержат заголовки",
+}
+```
+
+
+### Data Fields
+
+#### RUSpellRU
+
+- `source`: a `string` feature
+- `correction`: a `string` feature
+
+
+#### MultidomainGold
+
+- `source`: a `string` feature
+- `correction`: a `string` feature
+- `domain`: a `string` feature
+
+#### MedSpellcheck
+
+- `source`: a `string` feature
+- `correction`: a `string` feature
+
+#### GitHubTypoCorpusRu
+
+- `source`: a `string` feature
+- `correction`: a `string` feature
+
+
 
 ### Data Splits
 
-The benchmark consists of the following splits that reflect corresponding dataset:
+#### 	RUSpellRU
+|   |train|test|
+|---|---:|---:|
+|RUSpellRU|2000|2008|
+
+#### MultidomainGold
+
+|   |train|test|
+|---|---:|---:|
+|web|386|756|
+|news|361|245|
+|social_media|430|200|
+|reviews|584|586|
+|subtitles|1810|1810|
+|strategic_documents|-|250|
+|literature|-|260|
+
+#### MedSpellcheck
+
+|   |test|
+|---|---:|---:|
+|MedSpellcheck|2000|
+
+#### GitHubTypoCorpusRu
+
+|   |test|
+|---|---:|---:|
+|GitHubTypoCorpusRu|1136|
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### 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
+
+[More Information Needed]
+
+### Citation Information
 
-- **MultidomainGoldSet**: collected and annotated by SalutDevices Team;
-- **RUSpellRU**: by Sorokin et al.(2016);
-- **MedSpellCheck**: ...;
-- **GithubTypos**: ...;
+[More Information Needed]
 
-You can load dataset of your interest by passing # TODO

russian_spellcheck_benchmark.py

@@ -0,0 +1,217 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the 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
+"""The Russian Spellcheck Benchmark"""
+
+import os
+import json
+import pandas as pd
+from typing import List, Dict, Optional
+
+import datasets
+
+
+_RUSSIAN_SPELLCHECK_BENCHMARK_DESCRIPTION = """
+                Russian Spellcheck Benchmark is a new benchmark for spelling correction in Russian language.
+                It includes four datasets, each of which consists of pairs of sentences in Russian language. 
+                Each pair embodies sentence, which may contain spelling errors, and its corresponding correction. 
+                Datasets were gathered from various sources and domains including social networks, internet blogs, github commits, 
+                medical anamnesis, literature, news, reviews and more.
+                """
+
+_MULTIDOMAIN_GOLD_DESCRIPTION = """
+                MultidomainGold is a dataset of 3500 sentence pairs 
+                dedicated to a problem of automatic spelling correction in Russian language. 
+                The dataset is gathered from seven different domains including news, Russian classic literature,
+                social media texts, open web, strategic documents, subtitles and reviews.
+                It has been passed through two-stage manual labeling process with native speakers as annotators
+                to correct spelling violation and preserve original style of text at the same time.
+                """
+
+_GITHUB_TYPO_CORPUS_RU_DESCRIPTION = """
+                GitHubTypoCorpusRu is a manually labeled part of GitHub Typo Corpus https://arxiv.org/abs/1911.12893.
+                The sentences with "ru" tag attached to them have been extracted from GitHub Typo Corpus 
+                and pass them through manual labeling to ensure the corresponding corrections are right.
+                """
+
+_RUSPELLRU_DESCRIPTION = """
+                RUSpellRU is a first benchmark on the task of automatic spelling correction for Russian language 
+                introduced in https://www.dialog-21.ru/media/3427/sorokinaaetal.pdf. 
+                Original sentences are drawn from social media domain and labeled by
+                human annotators. 
+                """
+
+_MEDSPELLCHECK_DESCRIPTION = """
+                The dataset is taken from GitHub repo associated with eponymos project https://github.com/DmitryPogrebnoy/MedSpellChecker.
+                Original sentences are taken from anonymized medical anamnesis and passed through 
+                two-stage manual labeling pipeline. 
+                """
+
+_RUSSIAN_SPELLCHECK_BENCHMARK_CITATION = """ # TODO: add citation"""
+
+_MULTIDOMAIN_GOLD_CITATION = """ # TODO: add citation from Dialog"""
+
+_GITHUB_TYPO_CORPUS_RU_CITATION = """
+@article{DBLP:journals/corr/abs-1911-12893,
+                author       = {Masato Hagiwara and
+                                Masato Mita},
+                title        = {GitHub Typo Corpus: {A} Large-Scale Multilingual Dataset of Misspellings
+                                and Grammatical Errors},
+                journal      = {CoRR},
+                volume       = {abs/1911.12893},
+                year         = {2019},
+                url          = {http://arxiv.org/abs/1911.12893},
+                eprinttype    = {arXiv},
+                eprint       = {1911.12893},
+                timestamp    = {Wed, 08 Jan 2020 15:28:22 +0100},
+                biburl       = {https://dblp.org/rec/journals/corr/abs-1911-12893.bib},
+                bibsource    = {dblp computer science bibliography, https://dblp.org}
+                }
+"""
+
+_RUSPELLRU_CITATION = """
+@inproceedings{Shavrina2016SpellRuevalT,
+                title={SpellRueval : the FiRSt Competition on automatiC Spelling CoRReCtion FoR RuSSian},
+                author={Tatiana Shavrina and Россия Москва and Москва Яндекс and Россия and Россия Долгопрудный},
+                year={2016}
+                }
+"""
+
+_LICENSE = "apache-2.0"
+
+
+class RussianSpellcheckBenchmarkConfig(datasets.BuilderConfig):
+    """BuilderConfig for RussianSpellcheckBenchmark."""
+
+    def __init__(
+        self, 
+        data_urls: Dict[str,str], 
+        features: List[str],
+        citation: str,
+         **kwargs):
+        """BuilderConfig for RussianSpellcheckBenchmark.
+        Args:
+        features: *list[string]*, list of the features that will appear in the
+            feature dict. Should not include "label".
+        data_urls: *dict[string]*, urls to download the zip file from.
+        **kwargs: keyword arguments forwarded to super.
+        """
+        super(RussianSpellcheckBenchmarkConfig, self).__init__(version=datasets.Version("0.0.1"), **kwargs)
+        self.data_urls = data_urls
+        self.features = features
+        self.citation = citation
+
+
+class RussianSpellcheckBenchmark(datasets.GeneratorBasedBuilder):
+    """Russian Spellcheck Benchmark."""
+
+    BUILDER_CONFIGS = [
+        RussianSpellcheckBenchmarkConfig(
+            name="GitHubTypoCorpusRu",
+            description = _GITHUB_TYPO_CORPUS_RU_DESCRIPTION,
+            data_urls={
+                "test": "data/GitHubTypoCorpusRu/test.json",
+            },
+            features=["source", "correction"],
+            citation=_GITHUB_TYPO_CORPUS_RU_CITATION,
+        ),
+        RussianSpellcheckBenchmarkConfig(
+            name="MedSpellchecker",
+            description = _MEDSPELLCHECK_DESCRIPTION,
+            data_urls={
+                "test": "data/MedSpellchecker/test.json",
+            },
+            features=["source", "correction"],
+            citation="",
+        ),
+        RussianSpellcheckBenchmarkConfig(
+            name="MultidomainGold",
+            description = _MULTIDOMAIN_GOLD_DESCRIPTION,
+            data_urls={
+                "train": "data/MultidomainGold/train.json",
+                "test": "data/MultidomainGold/test.json",
+            },
+            features=["source", "correction", "domain"],
+            citation=_MULTIDOMAIN_GOLD_CITATION,
+        ),
+        RussianSpellcheckBenchmarkConfig(
+            name="RUSpellRU",
+            description = _RUSPELLRU_DESCRIPTION,
+            data_urls={
+                "test": "data/RUSpellRU/test.json",
+                "train": "data/RUSpellRU/train.json",
+            },
+            features=["source", "correction"],
+            citation=_RUSPELLRU_CITATION,
+        ),
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        features = {
+                "source": datasets.Value("string"),
+                "correction": datasets.Value("string"),
+            }
+        if self.config.name == "MultidomainGold":
+            features["domain"] = datasets.Value("string")
+
+        return datasets.DatasetInfo(
+            features=datasets.Features(features),
+            description=_RUSSIAN_SPELLCHECK_BENCHMARK_DESCRIPTION + self.config.description,
+            license=_LICENSE,
+            citation=self.config.citation + "\n" + _RUSSIAN_SPELLCHECK_BENCHMARK_CITATION,
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        urls_to_download = self.config.data_urls
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        if self.config.name == "GitHubTypoCorpusRu" or \
+           self.config.name == "MedSpellchecker":
+           return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_file": downloaded_files["test"],
+                    "split": datasets.Split.TEST,
+                },
+            )
+        ] 
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_file": downloaded_files["train"],
+                    "split": datasets.Split.TRAIN,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_file": downloaded_files["test"],
+                    "split": datasets.Split.TEST,
+                },
+            )
+        ]
+
+    def _generate_examples(self, data_file, split):
+        with open(data_file, encoding="utf-8") as f:
+            key = 0
+            for line in f:
+                row = json.loads(line)
+                example = {feature: row[feature] for feature in self.config.features}
+                yield key, example
+                key += 1