--- 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.