diff --git a/README.md b/README.md
index 8c91b481..39744a36 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,7 @@ versatility.
 
 ## Main features:
 
- - Train new vocabularies and tokenize, using todays most used tokenizers.
+ - Train new vocabularies and tokenize, using today's most used tokenizers.
  - Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes
    less than 20 seconds to tokenize a GB of text on a server's CPU.
  - Easy to use, but also extremely versatile.
@@ -34,29 +34,29 @@ Start using in a matter of seconds:
 
 ```python
 # Tokenizers provides ultra-fast implementations of most current tokenizers:
-from tokenizers import (ByteLevelBPETokenizer,
-                        BPETokenizer,
-                        SentencePieceBPETokenizer,
-                        BertWordPieceTokenizer)
+>>> from tokenizers import (ByteLevelBPETokenizer,
+                            BPETokenizer,
+                            SentencePieceBPETokenizer,
+                            BertWordPieceTokenizer)
 # Ultra-fast => they can encode 1GB of text in ~20sec on a standard server's CPU
 # Tokenizers can be easily instantiated from standard files
-tokenizer = BertWordPieceTokenizer("bert-base-uncased-vocab.txt", lowercase=True)
->>> Tokenizer(vocabulary_size=30522, model=BertWordPiece, add_special_tokens=True, unk_token=[UNK], 
+>>> tokenizer = BertWordPieceTokenizer("bert-base-uncased-vocab.txt", lowercase=True)
+Tokenizer(vocabulary_size=30522, model=BertWordPiece, add_special_tokens=True, unk_token=[UNK], 
               sep_token=[SEP], cls_token=[CLS], clean_text=True, handle_chinese_chars=True, 
               strip_accents=True, lowercase=True, wordpieces_prefix=##)
 
 # Tokenizers provide exhaustive outputs: tokens, mapping to original string, attention/special token masks.
 # They also handle model's max input lengths as well as padding (to directly encode in padded batches)
-output = tokenizer.encode("Hello, y'all! How are you 😁 ?")
->>> Encoding(num_tokens=13, attributes=[ids, type_ids, tokens, offsets, attention_mask, special_tokens_mask, overflowing, original_str, normalized_str])
-print(output.ids, output.tokens, output.offsets)
->>> [101, 7592, 1010, 1061, 1005, 2035, 999, 2129, 2024, 2017, 100, 1029, 102]
->>> ['[CLS]', 'hello', ',', 'y', "'", 'all', '!', 'how', 'are', 'you', '[UNK]', '?', '[SEP]']
->>> [(0, 0), (0, 5), (5, 6), (7, 8 (8, 9), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (26, 27),
-     (28, 29), (0, 0)]
+>>> output = tokenizer.encode("Hello, y'all! How are you 😁 ?")
+Encoding(num_tokens=13, attributes=[ids, type_ids, tokens, offsets, attention_mask, special_tokens_mask, overflowing, original_str, normalized_str])
+>>> print(output.ids, output.tokens, output.offsets)
+[101, 7592, 1010, 1061, 1005, 2035, 999, 2129, 2024, 2017, 100, 1029, 102]
+['[CLS]', 'hello', ',', 'y', "'", 'all', '!', 'how', 'are', 'you', '[UNK]', '?', '[SEP]']
+[(0, 0), (0, 5), (5, 6), (7, 8), (8, 9), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (26, 27),
+ (28, 29), (0, 0)]
 # Here is an example using the offsets mapping to retrieve the string coresponding to the 10th token:
-output.original_str[output.offsets[10]]
->>> '😁'
+>>> output.original_str[output.offsets[10]]
+'😁'
 ```
 
 And training an new vocabulary is just as easy: