language:
- pt
license: apache-2.0
metrics:
- f1
- precision
- recall
pipeline_tag: token-classification
tags:
- ner
- named-entity-recognition
- gliner
- portuguese
- information-extraction
- public-security
- crime
base_model:
- urchade/gliner_large-v1
NERDD (Portuguese)
NERDD is a fine-tuned version of GLiNER Large for Named Entity Recognition (NER) in Brazilian Portuguese criminal reports collected from the Disque Denúncia anonymous crime reporting system.
The model recognizes three entity types:
- PER — Person
- LOC — Location
- ORG — Organization
Unlike general-purpose NER models, this model was trained on highly informal criminal reports containing abbreviations, slang, nicknames, misspellings and domain-specific terminology commonly found in Brazilian public security datasets.
Quick Start
pip install gliner
from gliner import GLiNER
model = GLiNER.from_pretrained("BIRDRED/NERDD")
text = """
Traficantes do Comando Vermelho estão escondidos na comunidade Cidade Alta.
Segundo a denúncia, Biel foi visto armado próximo à Avenida Brasil.
"""
labels = ["PER","LOC","ORG"]
entities = model.predict_entities(
text,
labels
)
print(entities)
Example output
[
{'text': 'Comando Vermelho', 'label': 'ORG', 'score': 0.97},
{'text': 'Cidade Alta', 'label': 'LOC', 'score': 0.95},
{'text': 'Biel', 'label': 'PER', 'score': 0.94},
{'text': 'Avenida Brasil', 'label': 'LOC', 'score': 0.96}
]
Supported Labels
| Label | Description |
|---|---|
| PER | Persons, suspects, nicknames and aliases |
| LOC | Streets, neighborhoods, communities, cities and geographic locations |
| ORG | Criminal organizations, police units, institutions and companies |
Benchmark Results
The best model was obtained after the fourth pseudo-labeling iteration, using metadata during the self-training process.
| Metric | Score |
|---|---|
| F1-score | 67.0% |
| Precision | 66.2% |
| Recall | 67.9% |
These results were obtained on the held-out test set after fine-tuning GLiNER and applying iterative self-training with metadata-enhanced pseudo-labeling.
Dataset
The model was fine-tuned using a manually annotated corpus of criminal reports.
Dataset statistics:
| Item | Value |
|---|---|
| Reports | 5,230 |
| Annotated entities | 49,069 |
| Person entities | 20,903 |
| Location entities | 23,213 |
| Organization entities | 4,953 |
The original corpus is not publicly available because it contains sensitive criminal intelligence information.
Training Details
| Detail | Value |
|---|---|
| Base model | GLiNER Large |
| Learning Rate | 3e-5 |
| Weight Decay | 0.01 |
| Batch Size | 8 |
| Random Seed | 42 |
| Max Length | 384 tokens |
| Validation | 5-fold Nested Cross Validation |
When to Use This Model
| Use case | Description |
|---|---|
| Criminal intelligence | Extract entities from crime reports |
| Public security | Information extraction from anonymous reports |
| Academic research | Portuguese NER evaluation |
| Information extraction | Structured entity extraction from unstructured reports |
Limitations
- The model was trained specifically on criminal reports from Rio de Janeiro.
- Performance may decrease on other Portuguese domains.
- The training dataset is not publicly available.
- Organization entities are less frequent than location entities, which may slightly affect performance on this class.
Citation
If you use this model, please cite:
@mastersthesis{melo2026,
author={Gustavo Melo},
title={Named Entity Recognition in Brazilian Criminal Reports Using GLiNER, Metadata Integration and Iterative Self-Training},
school={CEFET/RJ},
year={2026}
}
Related work
@inproceedings{zaratiana2024gliner,
title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer},
author={Zaratiana, Urchade and others},
booktitle={NAACL},
year={2024}
}
Acknowledgements
This model was developed as part of a Master's dissertation at CEFET/RJ focused on Named Entity Recognition for Brazilian Public Security applications using the Disque Denúncia dataset.
License
Apache 2.0