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+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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README.md

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+---
+languages:
+- ru
+multilinguality:
+- monolingual
+pretty_name: RuREBus
+task_categories:
+- structure-prediction
+task_ids:
+- named-entity-recognition
+---
+
+# RuREBus dataset
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+- [Dataset Structure](#dataset-structure)
+- [Citation Information](#citation-information)
+- [Contacts](#contacts)
+
+## Dataset Description
+RuREBus dataset (https://github.com/dialogue-evaluation/RuREBus) is
+a Russian dataset for named entity recognition and relation extraction.
+
+## Dataset Structure
+There are two subsets of the dataset.
+
+Using 
+`load_dataset('MalakhovIlya/RuREBus')` 
+you can download annotated data (DatasetDict) for named entity recognition task and
+relation extraction tasks. 
+This subset consists of two splits: "train" and "test".
+ 
+Using 
+`load_dataset('MalakhovIlya/NEREL', 'raw_txt')['raw_txt']` 
+you can download (Dataset) large corpus (~3gb) raw texts of the same subject
+area, but without any annotations.
+
+"entities" are used in named-entity recognition task (see https://en.wikipedia.org/wiki/Named-entity_recognition).  
+"relations" are used in relationship extraction task (see https://en.wikipedia.org/wiki/Relationship_extraction).
+
+Each entity is represented by a string of the following format:  
+`"<id>\t<type> <start> <stop>\t<text>"`, where  
+`<id>` is an entity id,  
+`<type>` is one of entity types,  
+`<start>` is a position of the first symbol of entity in text,  
+`<stop>` is the last symbol position in text +1.
+
+Each relation is represented by a string of the following format:  
+`"<id>\t<type> Arg1:<arg1_id> Arg2:<arg2_id>"`, where  
+`<id>` is a relation id,  
+`<arg1_id>` and `<arg2_id>` are entity ids.
+
+## Citation Information
+@inproceedings{rurebus,
+  Address = {Moscow, Russia},
+  Author = {Ivanin, Vitaly and Artemova, Ekaterina and Batura, Tatiana and Ivanov, Vladimir and Sarkisyan, Veronika and Tutubalina, Elena and Smurov, Ivan},
+  Title = {RuREBus-2020 Shared Task: Russian Relation Extraction for Business},
+  Booktitle = {Computational  Linguistics  and  Intellectual  Technologies:  Proceedings of the International Conference “Dialog” [Komp’iuternaia Lingvistika  i  Intellektual’nye  Tehnologii:  Trudy  Mezhdunarodnoj  Konferentsii  “Dialog”]},
+  Year = {2020}
+}
+
+## Contacts
+Malakhov Ilya  
+Telegram - https://t.me/noname_4710

RuREBus.py

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+import datasets
+import json
+import requests
+from urllib.parse import urlencode
+from pathlib import Path
+import zipfile
+import os
+import shutil
+
+_NAME = 'RuREBus'
+_CITATION = '''
+@inproceedings{rurebus,
+  Address = {Moscow, Russia},
+  Author = {Ivanin, Vitaly and Artemova, Ekaterina and Batura, Tatiana and Ivanov, Vladimir and Sarkisyan, Veronika and Tutubalina, Elena and Smurov, Ivan},
+  Title = {RuREBus-2020 Shared Task: Russian Relation Extraction for Business},
+  Booktitle = {Computational  Linguistics  and  Intellectual  Technologies:  Proceedings of the International Conference “Dialog” [Komp’iuternaia Lingvistika  i  Intellektual’nye  Tehnologii:  Trudy  Mezhdunarodnoj  Konferentsii  “Dialog”]},
+  Year = {2020}
+}
+'''.strip()
+_DESCRIPTION = 'Russian Relation Extraction for Business'
+_HOMEPAGE = 'https://github.com/dialogue-evaluation/RuREBus'
+_VERSION = '1.0.0'
+
+
+def decode_file_names(folder):
+    for root, dirs, files in os.walk(folder, topdown=False):
+        root = Path(root)
+        for file in files:
+            old_name = root / Path(file)
+            new_name = root / Path(
+                file.encode('cp437').decode('cp866'))
+            old_name.rename(new_name)
+        for dir in dirs:
+            old_name = root / Path(dir)
+            new_name = root / Path(dir.encode('cp437').decode('cp866'))
+            old_name.rename(new_name)
+
+
+class RuREBusBuilder(datasets.GeneratorBasedBuilder):
+    base_url = 'https://cloud-api.yandex.net/v1/disk/public/resources/download?'
+    public_key = 'https://disk.yandex.ru/d/t1WakmYXlL6jBw'
+    final_url = base_url + urlencode(dict(public_key=public_key))
+    response = requests.get(final_url)
+    raw_txt_url = response.json()['href']
+
+    _DATA_URLS = {
+        'train': 'data/train.jsonl',
+        'test': f'data/test.jsonl',
+    }
+    _RAW_TXT_URLS = {
+        'raw_txt': raw_txt_url
+    }
+    VERSION = datasets.Version(_VERSION)
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig('data',
+                               version=VERSION,
+                               description='Annotated data'),
+        datasets.BuilderConfig('raw_txt',
+                               version=VERSION,
+                               description='Raw texts without annotations'),
+    ]
+    DEFAULT_CONFIG_NAME = 'data'
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.name == 'data':
+            features = datasets.Features({
+                'id': datasets.Value('int32'),
+                'text': datasets.Value('string'),
+                'entities': datasets.Sequence(datasets.Value('string')),
+                'relations': datasets.Sequence(datasets.Value('string'))
+            })
+        else:
+            features = datasets.Features({
+                'region': datasets.Value('string'),
+                'district': datasets.Value('string'),
+                'title': datasets.Value('string'),
+                'text': datasets.Value('string')
+            })
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        if self.config.name == 'data':
+            files = dl_manager.download(self._DATA_URLS)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={'filepath': files['train']},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={'filepath': files['test']},
+                ),
+            ]
+        else:
+            folder = dl_manager.download_and_extract(self._RAW_TXT_URLS)['raw_txt']
+            decode_file_names(folder)
+            return [
+                datasets.SplitGenerator(
+                    name='raw_txt',
+                    gen_kwargs={'filepath': folder,
+                                'raw_txt': True},
+                )
+            ]
+
+    def _generate_examples(self, filepath, raw_txt=False):
+        if not raw_txt:
+            with open(filepath, encoding='utf-8') as f:
+                for i, line in enumerate(f):
+                    yield i, json.loads(line)
+        else:
+            path = Path(filepath) / 'MED_txt/unparsed_txt'
+            i = 0
+            for root, dirs, files in os.walk(path):
+                if files:
+                    root = Path(root)
+                    region = root.parent.name
+                    district = root.name
+                    titles = {}
+                    with open(root / 'name_dict.txt', encoding='utf-8') as f_titles:
+                        for line in f_titles:
+                            key, title = line.split(maxsplit=1)[1].split('_', maxsplit=1)
+                            titles[key] = title.strip()
+                    for file in files:
+                        if file != 'name_dict.txt':
+                            file = Path(file)
+                            key = file.name.split('_', maxsplit=1)[0]
+                            title = titles[key]
+                            with open(root / file, encoding='utf-8') as f:
+                                text = f.read()
+                            item = {
+                                'region': region,
+                                'district': district,
+                                'title': title,
+                                'text': text
+                            }
+                            yield i, item
+                            i += 1