spellcheck_benchmark_actualized

@@ -1,217 +0,0 @@
-# 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