Datasets:
Tasks:
Text Generation
Size:
10K - 100K
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/). | |
**Script:** [save_habr.py](https://github.com/IlyaGusev/rulm/blob/master/data_processing/save_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. | |