script

spellcheck_benchmark.py

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+# 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", "domain"],
+            citation=_GITHUB_TYPO_CORPUS_RU_CITATION,
+        ),
+        RussianSpellcheckBenchmarkConfig(
+            name="MedSpellchecker",
+            description=_MEDSPELLCHECK_DESCRIPTION,
+            data_urls={
+                "test": "data/MedSpellchecker/test.json",
+            },
+            features=["source", "correction", "domain"],
+            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", "domain"],
+            citation=_RUSPELLRU_CITATION,
+        ),
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        features = {
+                "source": datasets.Value("string"),
+                "correction": datasets.Value("string"),
+                "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