Update README.md

README.md

@@ -14,14 +14,14 @@ Pretrained model on English language using a causal language modeling (CLM) obje
 and first released at [this page](https://openai.com/blog/better-language-models/).
 
 ## Model description
-mk-gpt2 is a transformers model pretrained on a very large corpus of Macedonian data in a self-supervised fashion. This
+gr-gpt2 is a transformers model pretrained on a very large corpus of Greek data in a self-supervised fashion. This
 means it was pretrained 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. More precisely,
 it was trained to guess the next word in sentences.
 More precisely, inputs are sequences of the continuous text of a certain length and the targets are the same sequence,
 shifted one token (word or piece of a word) to the right. The model uses internally a mask-mechanism to make sure the
 predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
-This way, the model learns an inner representation of the Macedonian language that can then be used to extract features
+This way, the model learns an inner representation of the Greek language that can then be used to extract features
 useful for downstream tasks. The model is best at what it was pretrained for, however, which is generating texts from a
 prompt.
 
@@ -37,7 +37,7 @@ input_text = 'Η Αθήνα είναι'
 
 if len(input_text) == 0: \
     encoded_input = tokenizer(input_text, return_tensors="pt") \
-        output = model.generate( \
+    output = model.generate( \
         bos_token_id=random.randint(1, 50000), \
         do_sample=True, \
         top_k=50, \