Update README.md

README.md

@@ -1,5 +1,5 @@
 # MK-RoBERTa base model
-Pretrained model on Macedonia language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.
+Pretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.
 
 # Model description
 RoBERTa is a transformers model pre-trained on a large corpus of мацед data in a self-supervised fashion. This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
@@ -13,12 +13,12 @@ You can use the raw model for masked language modeling, but it's mostly intended
 
 Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification, or question answering. For tasks such as text generation, you should look at models like GPT2.
 
-How to use
+# How to use
 You can use this model directly with a pipeline for masked language modeling: \
 
 from transformers import pipeline \
 unmasker = pipeline('fill-mask', model='macedonizer/mk-roberta-base') \
-unmasker("Скопје е \<mask\> град на Македонија.") \
+unmasker("Скопје е \\<mask\\> град на Македонија.") \
 
 [{'sequence': 'Скопје е главен град на Македонија.', \
     'score': 0.5900368094444275, \