metadata
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
Description
Summary: Dataset of posts and comments from pikabu.ru, a website that is Russian Reddit/9gag.
Script: convert_pikabu.py
Point of Contact: Ilya Gusev
Languages: Mostly Russian.
Usage
Prerequisites:
pip install datasets zstandard jsonlines pysimdjson
Dataset iteration:
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:
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 website.
- An original dump can be found here: pikastat
- Processing script is here.
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.