Update README.md

README.md

@@ -30,7 +30,7 @@ You can use this model directly with a pipeline for masked language modeling:
 
 ```python
 >>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='AI-Sweden-Models/roberta-large-550k')
+>>> unmasker = pipeline('fill-mask', model='AI-Sweden-Models/roberta-large-1160k')
 >>> unmasker("Huvudstaden i Sverige är <mask>.")
 [{'score': 0.5841221213340759,
   'token': 1945,
@@ -104,8 +104,8 @@ Here is how to use this model to get the features of a given text in PyTorch:
 
 ```python
 from transformers import RobertaTokenizer, RobertaModel
-tokenizer = RobertaTokenizer.from_pretrained('AI-Sweden-Models/roberta-large-550k')
-model = RobertaModel.from_pretrained('AI-Sweden-Models/roberta-large-550k')
+tokenizer = RobertaTokenizer.from_pretrained('AI-Sweden-Models/roberta-large-1160k')
+model = RobertaModel.from_pretrained('AI-Sweden-Models/roberta-large-1160k')
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)
@@ -120,7 +120,7 @@ The model was trained with the [optimum-habana](https://github.com/huggingface/o
 
 The weights from https://huggingface.co/FacebookAI/roberta-large are used as initialization, and the tokenizer is trained from scratch.
 
-This model is a checkpoint (1 160 000 / 1 350 790). The final run is 5 epochs.
+This model is a checkpoint (1 160 000 / 1 350 790). The final run is 5 epochs. This is epoch: 4.29.
 
 A batch size of 1536 was used.