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---
license: gpl-3.0
language:
- pt
- gl
widget:
- text: "A minha amiga Rosa, de São Paulo, estudou en Montreal. Agora trabalha em Santiago de Compostela com o Mário."
---
# Named Entity Recognition (NER) model for Portuguese
This is a NER model for Portuguese which uses the standard 'enamex' classes: LOC (geographical locations); PER (people); ORG (organizations); MISC (other entities).
The model is based on [BERTimbau Large](https://huggingface.co/neuralmind/bert-large-portuguese-cased), which has been fine-tuned using a combination of available corpus (see [1] for details).
There is an alternative model trained using (BERTimbau Base)[https://huggingface.co/neuralmind/bert-base-portuguese-cased]: (bert-base-pt-ner-enamex)[https://huggingface.co/marcosgg/bert-base-pt-ner-enamex].
It was trained with a batch size of 32 and a learning rate of 3e-5 during 3 epochs. It achieved the following results on the test set (Precision/Recall/F1): 0.919/0.925/0.922.
[1] Pablo Gamallo, Marcos Garcia & Patricia Martín-Rodilla, 2019. [NER and open information extraction for Portuguese notebook for IberLEF 2019 Portuguese named entity recognition and relation extraction tasks](https://ceur-ws.org/Vol-2421/NER_Portuguese_paper_6.pdf). In _Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2019)
co-located with 35th Conference of the Spanish Society for Natural Language Processing (SEPLN 2019)_: 457-467. |