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marcosgg
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bert-base-pt-ner-enamex

Token Classification
Transformers
PyTorch
Portuguese
Galician
bert
Inference Endpoints
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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 Base, which has been fine-tuned using a combination of available corpora (see [1] for details).

There is an alternative model trained using BERTimbau Large: bert-large-pt-ner-enamex.

It was trained with a batch size of 8 and a learning rate of 2e-5 during 3 epochs. It achieved the following results on the test set (Precision/Recall/F1): 0.913/0.918/0.915.

[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. 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.

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