language: | |
- es | |
dataset_info: | |
features: | |
- name: texto | |
dtype: string | |
- name: score | |
dtype: float64 | |
- name: int_score | |
dtype: int64 | |
- name: eval_prompt | |
dtype: string | |
splits: | |
- name: train | |
num_bytes: 1326892816808 | |
num_examples: 157608923 | |
download_size: 783538217743 | |
dataset_size: 1326892816808 | |
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. | |