Update README.md

README.md

@@ -1,13 +1,15 @@
 ---
-language: 
-  - ja
+language:
+- ja
+- de
+- ru
 tags:
 - kenlm
 - perplexity
 - n-gram
 - kneser-ney
 - bigscience
-license: "mit"
+license: mit
 datasets:
 - wikipedia
 ---
@@ -42,4 +44,4 @@ model.get_perplexity("I am very perplexed")
 model.get_perplexity("im hella trippin")
 # 46793.5 (high perplexity, since the sentence is colloquial and contains grammar mistakes)
 ```
-In the example above we see that, since Wikipedia is a collection of encyclopedic articles, a KenLM model trained on it will naturally give lower perplexity scores to sentences with formal language and no grammar mistakes than colloquial sentences with grammar mistakes.
+In the example above we see that, since Wikipedia is a collection of encyclopedic articles, a KenLM model trained on it will naturally give lower perplexity scores to sentences with formal language and no grammar mistakes than colloquial sentences with grammar mistakes.