---
language:
- es
dataset_info:
  features:
  - name: text
    dtype: string
  - name: score
    dtype: float64
  - name: int_score
    dtype: int64
  splits:
  - name: train
    num_bytes: 1322871803543
    num_examples: 157608923
  download_size: 781860036534
  dataset_size: 1322871803543
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Red Pajama's High Quality Spanish subset

## What is this?

The following is a high-quality dataset distilled from the Spanish subsection of [RedPajama-Data-v2](https://github.com/togethercomputer/RedPajama-Data), created using the methodology proposed for [FineWEB-Edu](https://arxiv.org/abs/2406.17557).

## Dataset creation

In a nutshell, we use Llama-3.1-70B to grade the educational quality of various samples from the original dataset. Then, we used these 500K samples to train a classifier using an encoder-based model, so that it learns to assign a score from 0 to 5. Since this model is way cheaper to use than an LLM, we run it over the entire dataset, thus getting a high-quality section from it.

Here is an overview of the architecture:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b15c3f20037ec5d7c91aa6/H5xPOHy_4RhMEDtGvsnTE.png)

For more detailed information on how this dataset was created, refer to [our implementation](https://github.com/latam-gpt/llm-data-eval).

## License

Please refer to the [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use) for the data. The code used to load and process the dataset is licensed under the Apache 2.0 license.