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, created using the methodology proposed in FineWEB-Edu.
Usage
from datasets import load_dataset
ds = load_dataset("latam-gpt/red_pajama_es_hq")
Filtering by quality score
Documents in this corpus are scored on academic quality from 2.5 to 5, with higher scores indicating better quality. The dataset can be filtered by score using standard filtering methods.
from datasets import load_dataset
ds = load_dataset("latam-gpt/red_pajama_es_hq")
# filter the dataset for scores > 3
filtered_ds = ds.filter(lambda x: x['score'] > 3)
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.
License
Please refer to the Common Crawl Foundation Terms of Use for the data. The code used to load and process the dataset is licensed under the Apache 2.0 license.