transformers safetensors deberta-v2 fill-mask portuguese financial bert deberta nlp masked-lm dataset:FAKE.BR dataset:CAROSIA dataset:BBRC dataset:OFFCOMBR-3 license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- FAKE.BR
- CAROSIA
- BBRC
- OFFCOMBR-3
library_name: transformers
license: mit
metrics:
- f1
- precision
- recall
- pr_auc
tags:
- portuguese
- financial
- bert
- deberta
- nlp
- fill-mask
- masked-lm
pt: pt-br
inference: true
model-index:
- name: DeB3RTa-base
  results:
  - task:
      type: text-classification
      name: Fake News Detection
    dataset:
      name: FAKE.BR
      type: FAKE.BR
    metrics:
    - type: f1
      value: 0.9598
  - task:
      type: text-classification
      name: Sentiment Analysis
    dataset:
      name: CAROSIA
      type: CAROSIA
    metrics:
    - type: f1
      value: 0.8722
  - task:
      type: text-classification
      name: Regulatory Classification
    dataset:
      name: BBRC
      type: BBRC
    metrics:
    - type: f1
      value: 0.6712
  - task:
      type: text-classification
      name: Hate Speech Detection
    dataset:
      name: OFFCOMBR-3
      type: OFFCOMBR-3
    metrics:
    - type: f1
      value: 0.546
---

# DeB3RTa: A Transformer-Based Model for the Portuguese Financial Domain

DeB3RTa is a family of transformer-based language models specifically designed for Portuguese financial text processing. These models are built on the DeBERTa-v2 architecture and trained using a comprehensive mixed-domain pretraining strategy that combines financial, political, business management, and accounting corpora.

## Model Variants

Two variants are available:

- **DeB3RTa-base**: 12 attention heads, 12 layers, intermediate size of 3072, hidden size of 768 (~426M parameters)
- **DeB3RTa-small**: 6 attention heads, 12 layers, intermediate size of 1536, hidden size of 384 (~70M parameters)

## Key Features

- First Portuguese financial domain-specific transformer model
- Mixed-domain pretraining incorporating finance, politics, business, and accounting texts
- Enhanced performance on financial NLP tasks compared to general-domain models
- Resource-efficient architecture with strong performance-to-parameter ratio
- Advanced fine-tuning techniques including layer reinitialization, mixout regularization, and layer-wise learning rate decay

## Performance

The models have been evaluated on multiple financial domain tasks:

| Task | Dataset | DeB3RTa-base F1 | DeB3RTa-small F1 |
|------|----------|-----------------|------------------|
| Fake News Detection | FAKE.BR | 0.9906 | 0.9598 |
| Sentiment Analysis | CAROSIA | 0.9207 | 0.8722 |
| Regulatory Classification | BBRC | 0.7609 | 0.6712 |
| Hate Speech Detection | OFFCOMBR-3 | 0.7539 | 0.5460 |

## Training Data

The models were trained on a diverse corpus of 1.05 billion tokens, including:
- Financial market relevant facts (2003-2023)
- Financial patents (2006-2021)
- Research articles from Brazilian Scielo
- Financial news articles (1999-2023)
- Wikipedia articles in Portuguese

## Usage

```python
from transformers import AutoModelForMaskedLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForMaskedLM.from_pretrained("higopires/DeB3RTa-[base/small]")
tokenizer = AutoTokenizer.from_pretrained("higopires/DeB3RTa-[base/small]")

# Example usage
text = "O mercado financeiro brasileiro apresentou [MASK] no último trimestre."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
```

## Citations

If you use this model in your research, please cite:

```bibtex
@article{pires2025deb3rta,
  AUTHOR = {Pires, Higo and Paucar, Leonardo and Carvalho, Joao Paulo},
  TITLE = {DeB3RTa: A Transformer-Based Model for the Portuguese Financial Domain},
  JOURNAL = {Big Data and Cognitive Computing},
  VOLUME = {9},
  YEAR = {2025},
  NUMBER = {3},
  ARTICLE-NUMBER = {51},
  URL = {https://www.mdpi.com/2504-2289/9/3/51},
  ISSN = {2504-2289},
  ABSTRACT = {The complex and specialized terminology of financial language in Portuguese-speaking markets create significant challenges for natural language processing (NLP) applications, which must capture nuanced linguistic and contextual information to support accurate analysis and decision-making. This paper presents DeB3RTa, a transformer-based model specifically developed through a mixed-domain pretraining strategy that combines extensive corpora from finance, politics, business management, and accounting to enable a nuanced understanding of financial language. DeB3RTa was evaluated against prominent models—including BERTimbau, XLM-RoBERTa, SEC-BERT, BusinessBERT, and GPT-based variants—and consistently achieved significant gains across key financial NLP benchmarks. To maximize adaptability and accuracy, DeB3RTa integrates advanced fine-tuning techniques such as layer reinitialization, mixout regularization, stochastic weight averaging, and layer-wise learning rate decay, which together enhance its performance across varied and high-stakes NLP tasks. These findings underscore the efficacy of mixed-domain pretraining in building high-performance language models for specialized applications. With its robust performance in complex analytical and classification tasks, DeB3RTa offers a powerful tool for advancing NLP in the financial sector and supporting nuanced language processing needs in Portuguese-speaking contexts.},
  DOI = {10.3390/bdcc9030051}
}
```

## Limitations

- Performance degradation on the smaller variant, particularly for hate speech detection
- May require task-specific fine-tuning for optimal performance
- Limited evaluation on multilingual financial tasks
- Model behavior on very long documents (>128 tokens) not extensively tested

## License

MIT License

Copyright (c) 2025 Higo Pires

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

## Acknowledgments

This work was supported by the Instituto Federal de Educação, Ciência e Tecnologia do Maranhão and the Human Language Technology Lab in Instituto de Engenharia de Sistemas e Computadores—Investigação e Desenvolvimento (INESC-ID).