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---
dataset_info:
features:
- name: id
dtype: uint32
- name: language
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text_markdown
dtype: string
- name: text_html
dtype: string
- name: author
dtype: string
- name: original_author
dtype: string
- name: original_url
dtype: string
- name: lead_html
dtype: string
- name: lead_markdown
dtype: string
- name: type
dtype: string
- name: time_published
dtype: uint64
- name: statistics
struct:
- name: commentsCount
dtype: uint32
- name: favoritesCount
dtype: uint32
- name: readingCount
dtype: uint32
- name: score
dtype: int32
- name: votesCount
dtype: int32
- name: votesCountPlus
dtype: int32
- name: votesCountMinus
dtype: int32
- name: labels
sequence: string
- name: hubs
sequence: string
- name: flows
sequence: string
- name: tags
sequence: string
- name: reading_time
dtype: uint32
- name: format
dtype: string
- name: complexity
dtype: string
- name: comments
sequence:
- name: id
dtype: uint64
- name: parent_id
dtype: uint64
- name: level
dtype: uint32
- name: time_published
dtype: uint64
- name: score
dtype: int32
- name: votes
dtype: uint32
- name: message_html
dtype: string
- name: message_markdown
dtype: string
- name: author
dtype: string
- name: children
sequence: uint64
splits:
- name: train
num_bytes: 19968161329
num_examples: 302049
download_size: 3485570346
dataset_size: 19968161329
task_categories:
- text-generation
language:
- ru
- en
size_categories:
- 100K<n<1M
---
# Habr 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)
## Description
**Summary:** Dataset of posts and comments from [habr.com](https://habr.com/ru/all/), a Russian collaborative blog about IT, computer science and anything related to the Internet.
**Script:** [create_habr.py](https://github.com/IlyaGusev/rulm/blob/master/data_processing/create_habr.py)
**Point of Contact:** [Ilya Gusev](ilya.gusev@phystech.edu)
**Languages:** Russian, English, some programming code.
## Usage
Prerequisites:
```bash
pip install datasets zstandard jsonlines pysimdjson
```
Dataset iteration:
```python
from datasets import load_dataset
dataset = load_dataset('IlyaGusev/habr', split="train", streaming=True)
for example in dataset:
print(example["text_markdown"])
```
## Data Instances
```
{
"id": 12730,
"language": "ru",
"url": "https://habr.com/ru/post/12730/",
"text_markdown": "...",
"text_html": "...",
"lead_markdown": "...",
"lead_html": "...",
"type": "article",
"labels": [],
"original_author": null,
"original_url": null,
"time_published": 1185962380,
"author": "...",
"title": "Хочешь в университет — сделай презентацию",
"statistics": {
"commentsCount": 23,
"favoritesCount": 1,
"readingCount": 1542,
"score": 7,
"votesCount": 15,
"votesCountPlus": 11,
"votesCountMinus": 4
},
"hubs": [
"itcompanies"
],
"flows": [
"popsci"
],
"tags": [
"PowerPoint",
"презентация",
"абитуриенты",
],
"reading_time": 1,
"format": null,
"complexity": null,
"comments": {
"id": [11653537, 11653541],
"parent_id": [null, 11653537],
"level": [0, 1],
"time_published": [1185963192, 1185967886],
"score": [-1, 0],
"votes": [1, 0],
"message_html": ["...", "..."],
"author": ["...", "..."],
"children": [[11653541], []]
}
}
```
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 [Habr](https://habr.com/) website.
* API call example: [post 709430](https://habr.com/kek/v2/articles/709430).
* Processing script is [here](https://github.com/IlyaGusev/rulm/blob/master/data_processing/create_habr.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.