| ---
|
| language:
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| - pt
|
|
|
| license: apache-2.0
|
|
|
| metrics:
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| - f1
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| - precision
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| - recall
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|
|
| pipeline_tag: token-classification
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|
|
| tags:
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| - ner
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| - named-entity-recognition
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| - gliner
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| - portuguese
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| - information-extraction
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| - public-security
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| - crime
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|
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| base_model:
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| - 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.
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|
|
| The model recognizes three entity types:
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|
|
| - **PER** — Person
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| - **LOC** — Location
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| - **ORG** — Organization
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|
|
| 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
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| pip install gliner
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| ```
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|
|
| ```python
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| from gliner import GLiNER
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|
|
| model = GLiNER.from_pretrained("BIRDRED/NERDD")
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|
|
| text = """
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| Traficantes do Comando Vermelho estão escondidos na comunidade Cidade Alta.
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| Segundo a denúncia, Biel foi visto armado próximo à Avenida Brasil.
|
| """
|
|
|
| labels = ["PER","LOC","ORG"]
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|
|
| entities = model.predict_entities(
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| text,
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| labels
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| )
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|
|
| print(entities)
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| ```
|
|
|
| Example output
|
|
|
| ```python
|
| [
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| {'text': 'Comando Vermelho', 'label': 'ORG', 'score': 0.97},
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| {'text': 'Cidade Alta', 'label': 'LOC', 'score': 0.95},
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| {'text': 'Biel', 'label': 'PER', 'score': 0.94},
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| {'text': 'Avenida Brasil', 'label': 'LOC', 'score': 0.96}
|
| ]
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| ```
|
|
|
| ---
|
|
|
| # Supported Labels
|
|
|
| | Label | Description |
|
| |-------|-------------|
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| | **PER** | Persons, suspects, nicknames and aliases |
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| | **LOC** | Streets, neighborhoods, communities, cities and geographic locations |
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| | **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 |
|
| |---------|:-----:|
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| | **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:
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|
|
| | Item | Value |
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| |------|------:|
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| | Reports | 5,230 |
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| | Annotated entities | 49,069 |
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| | Person entities | 20,903 |
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| | Location entities | 23,213 |
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| | 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 |
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| | Random Seed | 42 |
|
| | Max Length | 384 tokens |
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| | 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,
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| author={Gustavo Melo},
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| title={Named Entity Recognition in Brazilian Criminal Reports Using GLiNER, Metadata Integration and Iterative Self-Training},
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| school={CEFET/RJ},
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| 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 |
