---
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

```bash
pip install gliner
```

```python
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

```python
[
 {'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:

```bibtex
@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

```bibtex
@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