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---
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:
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.
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