Datasets:

IlyaGusev
/

ru_stackoverflow

	---
	license: other
	task_categories:
	- text-generation
	- question-answering
	language:
	- ru
	size_categories:
	- 100K<n<1M
	dataset_info:
	features:
	- name: question_id
	dtype: uint32
	- name: url
	dtype: string
	- name: answer_count
	dtype: uint32
	- name: text_html
	dtype: string
	- name: text_markdown
	dtype: string
	- name: score
	dtype: int32
	- name: title
	dtype: string
	- name: tags
	sequence: string
	- name: views
	dtype: uint64
	- name: author
	dtype: string
	- name: timestamp
	dtype: uint64
	- name: comments
	sequence:
	- name: text
	dtype: string
	- name: author
	dtype: string
	- name: comment_id
	dtype: uint32
	- name: score
	dtype: int32
	- name: timestamp
	dtype: uint64
	- name: answers
	sequence:
	- name: answer_id
	dtype: uint32
	- name: is_accepted
	dtype: uint8
	- name: text_html
	dtype: string
	- name: text_markdown
	dtype: string
	- name: score
	dtype: int32
	- name: author
	dtype: string
	- name: timestamp
	dtype: uint64
	- name: comments
	sequence:
	- name: text
	dtype: string
	- name: author
	dtype: string
	- name: comment_id
	dtype: uint32
	- name: score
	dtype: int32
	- name: timestamp
	dtype: uint64
	splits:
	- name: train
	num_bytes: 3013377174
	num_examples: 437604
	download_size: 670468664
	dataset_size: 3013377174
	---

	# Russian StackOverflow dataset

	## Table of Contents
	- [Table of Contents](#table-of-contents)
	- [Description](#description)
	- [Usage](#usage)
	- [Data Instances](#data-instances)
	- [Source Data](#source-data)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Licensing Information](#licensing-information)

	## Description

	Summary: Dataset of questions, answers, and comments from [ru.stackoverflow.com](https://ru.stackoverflow.com/).

	Script: [create_stackoverflow.py](https://github.com/IlyaGusev/rulm/blob/hf/data_processing/create_stackoverflow.py)

	Point of Contact: [Ilya Gusev](ilya.gusev@phystech.edu)

	Languages: The dataset is in Russian with some programming code.


	## Usage

	Prerequisites:
	```bash
	pip install datasets zstandard jsonlines pysimdjson
	```

	Loading:
	```python
	from datasets import load_dataset
	dataset = load_dataset('IlyaGusev/ru_stackoverflow', split="train")
	for example in dataset:
	print(example["text_markdown"])
	print()
	```

	## Data Instances

	```
	{
	"question_id": 11235,
	"answer_count": 1,
	"url": "https://ru.stackoverflow.com/questions/11235",
	"score": 2,
	"tags": ["c++", "сериализация"],
	"title": "Извлечение из файла, запись в файл",
	"views": 1309,
	"author": "...",
	"timestamp": 1303205289,
	"text_html": "...",
	"text_markdown": "...",
	"comments": {
	"text": ["...", "...",
	"author": ["...", "..."],
	"comment_id": [11236, 11237],
	"score": [0, 0],
	"timestamp": [1303205411, 1303205678]
	},
	"answers": {
	"answer_id": [11243, 11245],
	"timestamp": [1303207791, 1303207792],
	"is_accepted": [1, 0],
	"text_html": ["...", "..."],
	"text_markdown": ["...", "..."],
	"score": [3, 0],
	"author": ["...", "..."],
	"comments": {
	"text": ["...", "..."],
	"author": ["...", "..."],
	"comment_id": [11246, 11249],
	"score": [0, 0],
	"timestamp": [1303207961, 1303207800]
	}
	}
	}
	```

	You can use this little helper to unflatten sequences:
	```python
	def revert_flattening(records):
	fixed_records = []
	for key, values in records.items():
	if not fixed_records:
	fixed_records = [{} for _ in range(len(values))]
	for i, value in enumerate(values):
	fixed_records[i][key] = value
	return fixed_records
	```

	The original JSONL is already unflattened.

	## Source Data

	* The data source is the [Russian StackOverflow](https://ru.stackoverflow.com/) website.
	* Original XMLs: [ru.stackoverflow.com.7z](https://ia600107.us.archive.org/27/items/stackexchange/ru.stackoverflow.com.7z).
	* Processing script is [here](https://github.com/IlyaGusev/rulm/blob/hf/data_processing/create_stackoverflow.py).

	## Personal and Sensitive Information

	The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible.

	## Licensing Information

	According to the license of original data, this dataset is distributed under [CC BY-SA 2.5](https://creativecommons.org/licenses/by-sa/2.5/).