dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: string
- name: url
dtype: string
- name: edu_score
dtype: float32
- name: stem_score
dtype: float32
- name: toxic_score
dtype: float32
splits:
- name: train
num_bytes: 285576324067
num_examples: 96975210
download_size: 164777356452
dataset_size: 285576324067
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
📚 ClassiCC-PT: Classified Common Crawl Corpus for Portuguese
📖 Overview
ClassiCC-PT (Classified Common Crawl – Portuguese) is a large-scale web corpus containing ~120B Portuguese tokens extracted from Common Crawl snapshots. It is specifically curated for training large language models in Portuguese, with a focus on data quality, language specificity, and targeted filtering.
This corpus was created as part of a study on continued pretraining for adapting English-trained LLMs to Portuguese.
🏗 Dataset Construction
Source Snapshots: CC-2021-31, CC-2021-39, CC-2022-40 Steps:
Language Filtering
Selected only pages tagged with Portuguese in Common Crawl metadata (~2% of each CC crawl).HTML to Text Extraction
Used Trafilatura to remove boilerplate and extract main content.Deduplication
Applied MinHash intra-crawl deduplication (removing ~40% duplicates).Neural-Based Filtering
Developed three BERTimbau-based classifiers for: Educational content (ClassiCC-PT-edu) STEM content (ClassiCC-PT-STEM) Toxic content (ClassiCC-PT-toxic) Classifiers were trained on GPT-4o-annotated Portuguese data.
Final Corpus
Retained ~106M documents / ~125B tokens ( Llama 2 tokenizer)
🚀 Performance Impact
When used for continued pretraining of TinyLlama-1.1B (1T EN tokens), ClassiCC-PT improved Portuguese benchmark performance (Poeta v1) significantly, outperforming mC4-PT and matching ClueWeb-22-PT. The model trained with ClassiCC-PT is called Curió 1.1B and is available at huggingface.
| Model | Training Regimen | Poeta v1 NPM |
|---|---|---|
| TinyLlama-1T (EN) | – | 17.4 |
| mC4-PT | cont. pretraining | ~20 |
| ClueWeb-22-PT | cont. pretraining | ~27 |
| ClassiCC-PT (Curió-1.1B) | cont. pretraining | 27.1 |
📥 Download & Usage
from datasets import load_dataset
ds = load_dataset("ClassiCC-Corpus/ClassiCC-PT", split="train")
print(ds[0])
# {
# 'text': '...',
# 'id': '...',
# 'url': '...',
# 'edu_score': 4.0,
# 'stem_score': 1.0,
# 'toxic_score': 0.0
# }
📜 Citation
If you use ClassiCC-PT, please cite:
@article{almeida2025building,
title={Building High-Quality Datasets for Portuguese LLMs: From Common Crawl Snapshots to Industrial-Grade Corpora},
author={Almeida, Thales Sales and Nogueira, Rodrigo and Pedrini, Helio},
journal={Journal of the Brazilian Computer Society},
volume={31},
number={1},
pages={1246--1262},
year={2025}
}
Acknowledgements
We thank the google TRC program, which generously granted us the necessary resources for the development of this research.