README.md

---
language:
- es
dataset_info:
  features:
  - name: text
    dtype: string
  - name: meta
    dtype: string
  - name: score
    dtype: float64
  - name: int_score
    dtype: int64
  splits:
  - name: train
    num_bytes: 1201679966776
    num_examples: 128920537
  download_size: 700567029628
  dataset_size: 1201679966776
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# RedPajama'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 in [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 550k samples from the original dataset. Then, we used these samples to train a encoder-based classifier, so that it learns to assign a score from 0 to 5. Since this model is cheaper to use than an GPT, we can run it at scale over the entire dataset, thus allowing us to filter 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 open 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.