|
--- |
|
license: agpl-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 corpora (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. |
