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README.md

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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).
+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](https://huggingface.co/neuralmind/bert-base-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 Large)[https://huggingface.co/neuralmind/bert-large-portuguese-cased]: (bert-large-pt-ner-enamex)[https://huggingface.co/marcosgg/bert-large-pt-ner-enamex].
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 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](https://ceur-ws.org/Vol-2421/NER_Portuguese_paper_6.pdf). In _Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2019)