Update README.md

README.md

@@ -34,45 +34,16 @@ You can test this model into the widget of this page.
 ````
 # install pytorch: check https://pytorch.org/
 # !pip install transformers 
-from transformers import AutoModelForTokenClassification, AutoTokenizer
-import torch
+from transformers import AutoTokenizer, AutoModelForMaskedLM
 
-# parameters
-model_name = "ner-bert-base-portuguese-cased-lenebr"
-model = AutoModelForTokenClassification.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-input_text = "EMENTA: APELAÇÃO CÍVEL - AÇÃO DE INDENIZAÇÃO POR DANOS MORAIS - PRELIMINAR - ARGUIDA PELO MINISTÉRIO PÚBLICO EM GRAU RECURSAL - NULIDADE - AUSÊNCIA DE IN- TERVENÇÃO DO PARQUET NA INSTÂNCIA A QUO - PRESENÇA DE INCAPAZ - PREJUÍZO EXISTENTE - PRELIMINAR ACOLHIDA - NULIDADE RECONHECIDA."
-
-# tokenization
-inputs = tokenizer(input_text, max_length=512, truncation=True, return_tensors="pt")
-tokens = inputs.tokens()
-
-# get predictions
-outputs = model(**inputs).logits
-predictions = torch.argmax(outputs, dim=2)
-
-# print predictions
-for token, prediction in zip(tokens, predictions[0].numpy()):
-    print((token, model.config.id2label[prediction]))
-````
-You can use pipeline, too. However, it seems to have an issue regarding to the max_length of the input sequence.
+tokenizer = AutoTokenizer.from_pretrained("pierreguillou/bert-base-cased-pt-lenerbr")
+model = AutoModelForMaskedLM.from_pretrained("pierreguillou/bert-base-cased-pt-lenerbr")
 ````
-!pip install transformers
-import transformers
-from transformers import pipeline
 
-model_name = "ner-bert-base-portuguese-cased-lenebr"
-
-ner = pipeline(
-    "ner",
-    model=model_name
-) 
-
-ner(input_text)
-````
 ## Training procedure
 
+See the notebook...
+
 ### Training results
 
 ````