Datasets:
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_examples: 142178930
- name: validation
num_examples: 71208
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
task_categories:
- text-generation
language:
- ru
size_categories:
- 100M<n<1B
Cultura-Ru-Edu
The Cultura-Ru-Edu
dataset consists of Russian educational web pages filtered from the uonlp/CulturaX
dataset.
The dataset creation was inspired by HuggingFaceFW/fineweb-edu
, but with a focus on the Russian language.
By filtering the dataset based on educational criteria, the Cultura-Ru-Edu
dataset is both high-quality and large enough to train a Russian-focused language model for tasks requiring knowledge of the world.
Dataset curation
To create this dataset, we annotated a subset with the Meta-Llama-3-70B-Instruct
model, trained a classifier on it, and then applied it to the entire dataset, keeping only the high-quality samples.
Annotation
Follow deepvk/cultura_ru_edu_llama3_annotations
to see details about creating the annotation dataset.
Training classifier
We trained a classifier based on the USER-base
model.
Unlike the original FineWeb-Edu pipeline, we used binary classification, where the positive class includes samples with a score of 3 and higher.
We found this approach more stable due to the high imbalance in the annotation dataset.
Dataset scoring
We converted the classifier to ONNX format and applied it to the Russian part of the uonlp/CulturaX
dataset.
The original dataset contained approximately 800 million documents, and after filtration, only 140 million documents remained (~17.5% of the original dataset).
Dataset information
Each sample contains only one property — text
, the original text document.
Some notes:
- This dataset is a filtered version of the larger, multilingual
uonlp/CulturaX
dataset. No other information was added or removed. - Since the original dataset consists of parsed web pages, there may still be artifacts in the text header or footer. Future work may include detecting and removing such blocks.
Usage
To use this dataset, one may simply use the datasets
API.
from datasets import load_dataset
cultura_ru_edu = load_dataset("deepvk/cultura_ru_edu", split="train", streaming=True)
Note that the dataset size is approximately 500GB, so it is better to use streaming or download it directly via Git LFS.
Citations
@misc{deepvk2024cultura-ru-edu,
title={Cultura-Ru-Edu},
author={Spirin, Egor and Sokolov, Andrey},
url={https://huggingface.co./datasets/deepvk/cultura_ru_edu},
publisher={Hugging Face}
year={2024},
}