deepvk/cultura_ru_edu

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},
}