Update README.md

README.md

@@ -51,6 +51,8 @@ Due to the small size of BERTimbau base and finetuning dataset, the model overfi
   - **recall**: 0.8993548387096775
   - **accuracy**: 0.9759397808828684
   - **loss**: 0.10249536484479904
+  
+Check as well the [large version of this model](https://huggingface.co/pierreguillou/ner-bert-large-cased-pt-lenerbr) with a f1 of 0.908.
 
 **Note**: the model [pierreguillou/bert-base-cased-pt-lenerbr](https://huggingface.co/pierreguillou/bert-base-cased-pt-lenerbr) is a language model that was created through the finetuning of the model [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the dataset [LeNER-Br language modeling](https://huggingface.co/datasets/pierreguillou/lener_br_finetuning_language_model) by using a MASK objective. This first specialization of the language model before finetuning on the NER task improved a bit the model quality. To prove it, here are the results of the NER model finetuned from the model [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased) (a non-specialized language model):
   - **f1**: 0.8716487228203504
@@ -63,6 +65,8 @@ Due to the small size of BERTimbau base and finetuning dataset, the model overfi
 
 You can test this model into the widget of this page.
 
+Use as well the [NER App](https://huggingface.co/spaces/pierreguillou/ner-bert-pt-lenerbr) that allows comparing the 2 BERT models (base and large) fitted in the NER task with the legal LeNER-Br dataset.
+
 ## Using the model for inference in production
 ````
 # install pytorch: check https://pytorch.org/