Modelo para Reconhecimento de Entidade Nomeadas em português utilizando o modelo spaCy pt_core_news_sm
Link do trabalho no Kaggle: https://www.kaggle.com/datasets/flaviagg/lenerbr .
Criei um Web App que proporciona a comparação dos modelos sm e lg: https://huggingface.co/spaces/flaviaggp/Streamlit_Lener .
Métricas por entidade
| Feature | Description |
|---|---|
| Name | pt_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.4.4,<3.5.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (6 labels for 1 components)
| Component | Labels |
|---|---|
ner |
JURISPRUDENCIA, LEGISLACAO, LOCAL, ORGANIZACAO, PESSOA, TEMPO |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
82.73 |
ENTS_P |
80.14 |
ENTS_R |
81.42 |
TOK2VEC_LOSS |
66943.07 |
NER_LOSS |
124326.31 |
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Dataset used to train flaviaggp/pt_pipeline
Space using flaviaggp/pt_pipeline 1
Evaluation results
- NER Precisionself-reported82.730
- NER Recallself-reported80.140
- NER F Scoreself-reported81.420