Update README.md

README.md

@@ -119,7 +119,7 @@ You can use this model directly with a pipeline for masked language modeling:
 
 ```python
 >>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='PORTULAN/albertina-pt-pt')
+>>> unmasker = pipeline('fill-mask', model='PORTULAN/albertina-ptpt')
 >>> unmasker("Países como [MASK] falam a língua portuguesa.")
 
 [{'score': 0.5964823365211487, 'token': 34214, 'token_str': 'Angola', 'sequence': 'Países como Angola falam a língua portuguesa.'},
@@ -136,8 +136,8 @@ The model can be used by fine-tuning it for a specific task:
 >>> from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
 >>> from datasets import load_dataset
 
->>> model = AutoModelForSequenceClassification.from_pretrained("PORTULAN/albertina-pt-pt", num_labels=2)
->>> tokenizer = AutoTokenizer.from_pretrained("PORTULAN/albertina-pt-pt")
+>>> model = AutoModelForSequenceClassification.from_pretrained("PORTULAN/albertina-ptpt", num_labels=2)
+>>> tokenizer = AutoTokenizer.from_pretrained("PORTULAN/albertina-ptpt")
 >>> dataset = load_dataset("PORTULAN/glueptpt", "rte")
 
 >>> def tokenize_function(examples):
@@ -145,7 +145,7 @@ The model can be used by fine-tuning it for a specific task:
 
 >>> tokenized_datasets = dataset.map(tokenize_function, batched=True)
 
->>> training_args = TrainingArguments(output_dir="albertina-pt-pt-rte", evaluation_strategy="epoch")
+>>> training_args = TrainingArguments(output_dir="albertina-ptpt-rte", evaluation_strategy="epoch")
 >>> trainer = Trainer(
 ...     model=model,
 ...     args=training_args,