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---
dataset_info:
features:
- name: id
dtype: int64
- name: title
dtype: string
- name: text_markdown
dtype: string
- name: timestamp
dtype: uint64
- name: author_id
dtype: int64
- name: username
dtype: string
- name: rating
dtype: int64
- name: pluses
dtype: int64
- name: minuses
dtype: int64
- name: url
dtype: string
- name: tags
sequence: string
- name: blocks
sequence:
- name: data
dtype: string
- name: type
dtype: string
- name: comments
sequence:
- name: id
dtype: int64
- name: timestamp
dtype: uint64
- name: parent_id
dtype: int64
- name: text_markdown
dtype: string
- name: text_html
dtype: string
- name: images
sequence: string
- name: rating
dtype: int64
- name: pluses
dtype: int64
- name: minuses
dtype: int64
- name: author_id
dtype: int64
- name: username
dtype: string
splits:
- name: train
num_bytes: 96105803658
num_examples: 6907622
download_size: 20196853689
dataset_size: 96105803658
task_categories:
- text-generation
language:
- ru
size_categories:
- 1M<n<10M
---
# Pikabu 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 [pikabu.ru](https://pikabu.ru/), a website that is Russian Reddit/9gag.
**Script:** [convert_pikabu.py](https://github.com/IlyaGusev/rulm/blob/master/data_processing/convert_pikabu.py)
**Point of Contact:** [Ilya Gusev](ilya.gusev@phystech.edu)
**Languages:** Mostly Russian.
## Usage
Prerequisites:
```bash
pip install datasets zstandard jsonlines pysimdjson
```
Dataset iteration:
```python
from datasets import load_dataset
dataset = load_dataset('IlyaGusev/pikabu', split="train", streaming=True)
for example in dataset:
print(example["text_markdown"])
```
## Data Instances
```
{
"id": 69911642,
"title": "Что можно купить в Китае за цену нового iPhone 11 Pro",
"text_markdown": "...",
"timestamp": 1571221527,
"author_id": 2900955,
"username": "chinatoday.ru",
"rating": -4,
"pluses": 9,
"minuses": 13,
"url": "...",
"tags": ["Китай", "AliExpress", "Бизнес"],
"blocks": {"data": ["...", "..."], "type": ["text", "text"]},
"comments": {
"id": [152116588, 152116426],
"text_markdown": ["...", "..."],
"text_html": ["...", "..."],
"images": [[], []],
"rating": [2, 0],
"pluses": [2, 0],
"minuses": [0, 0],
"author_id": [2104711, 2900955],
"username": ["FlyZombieFly", "chinatoday.ru"]
}
}
```
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
```
## Source Data
* The data source is the [Pikabu](https://pikabu.ru/) website.
* An original dump can be found here: [pikastat](https://pikastat.d3d.info/)
* Processing script is [here](https://github.com/IlyaGusev/rulm/blob/master/data_processing/convert_pikabu.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.
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