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.gitattributes

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 *.webp filter=lfs diff=lfs merge=lfs -text
+test.jsonl filter=lfs diff=lfs merge=lfs -text
+train.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+annotations_creators:
+- machine-generated
+language_creators:
+- found
+language:
+- ru
+size_categories:
+- 10K<n<100K
+license:
+- unknown
+multilinguality:
+- monolingual
+source_datasets:
+- original
+---
+
+# Dataset Card
+
+## 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)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+### Dataset Summary
+
+Dataset for automatic text segmentation of Russian wiki. Text corpora based on May 2023 Wikipedia dump. Markup was generated automatically based on 2 methods: taking texts with ready division into paragraphs and random joining parts of different texts.
+
+### Supported Tasks and Leaderboards
+
+Dataset designed for text segmentation task.
+
+### Languages
+
+The dataset is in Russian.
+
+### Usage
+
+```python
+from datasets import load_dataset
+dataset = load_dataset('mlenjoyneer/RuTextSegWiki')
+```
+
+### Other datasets
+
+mlenjoyneer/RuTextSegNews - similar dataset based on news corpora
+
+## Dataset Structure
+
+### Data Instances
+
+For each instance, there is a list of strings for text sentences, a list of ints for labels (1 is new topic starting and 0 is previous topic continuation) and a string for sample generation method (base or random_joining).
+
+### Data Splits
+
+| Dataset Split | Number of Instances in Split |
+|:---------|:---------|
+| Train | 20000 |
+| Test | 4000 |
+
+## Additional Information
+
+### Licensing Information
+
+In progress
+
+### Citation Information
+
+```bibtex
+In progress
+```

rutextsegwiki.txt

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+# coding=utf-8
+# 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
+"""RuTextSegWiki: Dataset for automatic text semantic segmentation of Russian wiki"""
+
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """
+In progress
+"""
+
+_DESCRIPTION = "Dataset for automatic text semantic segmentation of Russian wiki"
+_URLS = {
+    "train": "train.jsonl",
+    "test": "test.jsonl"
+}
+
+
+class RuTextSegWikiDataset(datasets.GeneratorBasedBuilder):
+    """RuTextSegWiki Dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="default", version=VERSION, description=""),
+    ]
+
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "sentences": [datasets.Value("string")],
+                "labels": [datasets.Value("int8")],
+                "method": datasets.Value("string")
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                yield id_, data

test.jsonl

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+oid sha256:256d26f1ba96d0a01fe078ff13a130f8bbdf5513ed8461775b41c5dbd15e79eb
+size 60033505

train.jsonl

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+oid sha256:32fc8871c27d17cef2aad0fc3f88712ac3debbb76d8396e61559e5b69c8b894d
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