update readme after moving repo

README.md

@@ -7,7 +7,7 @@ language:
 
 This model card aims to simplify the use of the [portuguese Bert, a.k.a, Bertimbau](https://github.com/neuralmind-ai/portuguese-bert) for the Named Entity Recognition task. 
 
-For this model card the we used the BERT-CRF (selective scenario, 5 classes) model available in the [ner_evalutaion](https://github.com/neuralmind-ai/portuguese-bert/tree/master/ner_evaluation) folder of the original Bertimbau repo.
+For this model card the we used the `BERT-CRF (selective scenario, 5 classes)` model available in the [ner_evaluation](https://github.com/neuralmind-ai/portuguese-bert/tree/master/ner_evaluation) folder of the original Bertimbau repo.
 
 ## Usage
 
@@ -15,8 +15,8 @@ For this model card the we used the BERT-CRF (selective scenario, 5 classes) mod
 # Load model directly
 from transformers import AutoTokenizer, AutoModelForTokenClassification
 
-tokenizer = AutoTokenizer.from_pretrained("marquesafonso/bertimbau-large-ner")
-model = AutoModelForTokenClassification.from_pretrained("marquesafonso/bertimbau-large-ner")
+tokenizer = AutoTokenizer.from_pretrained("marquesafonso/bertimbau-large-ner-selective")
+model = AutoModelForTokenClassification.from_pretrained("marquesafonso/bertimbau-large-ner-selective")
 
 ```
 
@@ -25,7 +25,7 @@ model = AutoModelForTokenClassification.from_pretrained("marquesafonso/bertimbau
 ```
 from transformers import pipeline
 
-pipe = pipeline("ner", model="marquesafonso/bertimbau-large-ner", aggregation_strategy='simple')
+pipe = pipeline("ner", model="marquesafonso/bertimbau-large-ner-selective", aggregation_strategy='simple')
 
 sentence = "Acima de Ederson, abaixo de Rúben Dias. É entre os dois jogadores do Manchester City que se vai colocar Gonçalo Ramos no ranking de vendas mais avultadas do Benfica."