| 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: | |
|  | |
| 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. | |