diff --git a/src/transformers/models/bart/tokenization_bart.py b/src/transformers/models/bart/tokenization_bart.py
index 5dc9357810..22ee1a0db6 100644
--- a/src/transformers/models/bart/tokenization_bart.py
+++ b/src/transformers/models/bart/tokenization_bart.py
@@ -105,12 +105,14 @@ class BartTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import BartTokenizer
+
     >>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 
diff --git a/src/transformers/models/bart/tokenization_bart_fast.py b/src/transformers/models/bart/tokenization_bart_fast.py
index 6d6e29986b..f05ed1b7a8 100644
--- a/src/transformers/models/bart/tokenization_bart_fast.py
+++ b/src/transformers/models/bart/tokenization_bart_fast.py
@@ -75,12 +75,14 @@ class BartTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import BartTokenizerFast
+
     >>> tokenizer = BartTokenizerFast.from_pretrained("facebook/bart-base")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 
diff --git a/src/transformers/models/blenderbot/tokenization_blenderbot.py b/src/transformers/models/blenderbot/tokenization_blenderbot.py
index 208ced46bc..cb4a33a3c2 100644
--- a/src/transformers/models/blenderbot/tokenization_blenderbot.py
+++ b/src/transformers/models/blenderbot/tokenization_blenderbot.py
@@ -96,13 +96,15 @@ class BlenderbotTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import BlenderbotTokenizer
+
     >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B")
     >>> tokenizer.add_prefix_space = False
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [47, 921, 86, 1085, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [6950, 1085, 2]
     ```
 
diff --git a/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py b/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py
index 7c4e060e5d..4737e92617 100644
--- a/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py
+++ b/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py
@@ -55,12 +55,14 @@ class BlenderbotTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import BlenderbotTokenizerFast
+
     >>> tokenizer = BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [6950, 1085, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [6950, 1085, 2]
     ```
 
diff --git a/src/transformers/models/bloom/tokenization_bloom_fast.py b/src/transformers/models/bloom/tokenization_bloom_fast.py
index 1c8efb10cb..800c73f025 100644
--- a/src/transformers/models/bloom/tokenization_bloom_fast.py
+++ b/src/transformers/models/bloom/tokenization_bloom_fast.py
@@ -54,13 +54,15 @@ class BloomTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import BloomTokenizerFast
+
     >>> tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom")
-    >>> tokenizer("Hello world")['input_ids']
-    [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
-    [18435, 995]
+    >>> tokenizer("Hello world")["input_ids"]
+    [59414, 8876]
+
+    >>> tokenizer(" Hello world")["input_ids"]
+    [86153, 8876]
     ```
 
     You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since
diff --git a/src/transformers/models/codegen/tokenization_codegen.py b/src/transformers/models/codegen/tokenization_codegen.py
index c09a816bfb..0c01e63471 100644
--- a/src/transformers/models/codegen/tokenization_codegen.py
+++ b/src/transformers/models/codegen/tokenization_codegen.py
@@ -102,12 +102,14 @@ class CodeGenTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import CodeGenTokenizer
+
     >>> tokenizer = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [18435, 995]
     ```
 
diff --git a/src/transformers/models/codegen/tokenization_codegen_fast.py b/src/transformers/models/codegen/tokenization_codegen_fast.py
index 332f0ed934..83e5e30d0b 100644
--- a/src/transformers/models/codegen/tokenization_codegen_fast.py
+++ b/src/transformers/models/codegen/tokenization_codegen_fast.py
@@ -68,12 +68,14 @@ class CodeGenTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import CodeGenTokenizerFast
+
     >>> tokenizer = CodeGenTokenizerFast.from_pretrained("Salesforce/codegen-350M-mono")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [18435, 995]
     ```
 
diff --git a/src/transformers/models/deberta/tokenization_deberta.py b/src/transformers/models/deberta/tokenization_deberta.py
index bcaaaa4421..8a778a947c 100644
--- a/src/transformers/models/deberta/tokenization_deberta.py
+++ b/src/transformers/models/deberta/tokenization_deberta.py
@@ -116,13 +116,15 @@ class DebertaTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import DebertaTokenizer
+
     >>> tokenizer = DebertaTokenizer.from_pretrained("microsoft/deberta-base")
-    >>> tokenizer("Hello world")['input_ids']
-    [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
-    [18435, 995]
+    >>> tokenizer("Hello world")["input_ids"]
+    [1, 31414, 232, 2]
+
+    >>> tokenizer(" Hello world")["input_ids"]
+    [1, 20920, 232, 2]
     ```
 
     You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
diff --git a/src/transformers/models/deberta/tokenization_deberta_fast.py b/src/transformers/models/deberta/tokenization_deberta_fast.py
index 959bcae470..c05cf25761 100644
--- a/src/transformers/models/deberta/tokenization_deberta_fast.py
+++ b/src/transformers/models/deberta/tokenization_deberta_fast.py
@@ -79,13 +79,15 @@ class DebertaTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import DebertaTokenizerFast
+
     >>> tokenizer = DebertaTokenizerFast.from_pretrained("microsoft/deberta-base")
-    >>> tokenizer("Hello world")['input_ids']
-    [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
-    [18435, 995]
+    >>> tokenizer("Hello world")["input_ids"]
+    [1, 31414, 232, 2]
+
+    >>> tokenizer(" Hello world")["input_ids"]
+    [1, 20920, 232, 2]
     ```
 
     You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since
diff --git a/src/transformers/models/gpt2/tokenization_gpt2.py b/src/transformers/models/gpt2/tokenization_gpt2.py
index c462e45d01..9a8ce3a4fa 100644
--- a/src/transformers/models/gpt2/tokenization_gpt2.py
+++ b/src/transformers/models/gpt2/tokenization_gpt2.py
@@ -108,12 +108,14 @@ class GPT2Tokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import GPT2Tokenizer
+
     >>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [18435, 995]
     ```
 
diff --git a/src/transformers/models/gpt2/tokenization_gpt2_fast.py b/src/transformers/models/gpt2/tokenization_gpt2_fast.py
index 7d7500ee9c..cf2b8b2cb2 100644
--- a/src/transformers/models/gpt2/tokenization_gpt2_fast.py
+++ b/src/transformers/models/gpt2/tokenization_gpt2_fast.py
@@ -75,12 +75,14 @@ class GPT2TokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import GPT2TokenizerFast
+
     >>> tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [18435, 995]
     ```
 
diff --git a/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py b/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py
index 1d4c1cec3a..570b2abaa4 100644
--- a/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py
+++ b/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py
@@ -49,12 +49,14 @@ class GPTNeoXTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import GPTNeoXTokenizerFast
+
     >>> tokenizer = GPTNeoXTokenizerFast.from_pretrained("gpt2")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [18435, 995]
     ```
 
diff --git a/src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py b/src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
index f982c5b6b1..422cb07220 100644
--- a/src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
+++ b/src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
@@ -47,10 +47,11 @@ class GPTSw3Tokenizer(PreTrainedTokenizer):
     this superclass for more information regarding those methods.
 
     Example usage:
-    ```
+    ```python
     >>> from transformers import GPTSw3Tokenizer
+
     >>> tokenizer = GPTSw3Tokenizer.from_pretrained("AI-Sweden/gpt-sw3-126m")
-    >>> tokenizer("Svenska är kul!")['input_ids']
+    >>> tokenizer("Svenska är kul!")["input_ids"]
     [1814, 377, 3617, 63504]
     ```
 
diff --git a/src/transformers/models/jukebox/tokenization_jukebox.py b/src/transformers/models/jukebox/tokenization_jukebox.py
index 63399adf16..9a45f6bd65 100644
--- a/src/transformers/models/jukebox/tokenization_jukebox.py
+++ b/src/transformers/models/jukebox/tokenization_jukebox.py
@@ -68,13 +68,13 @@ class JukeboxTokenizer(PreTrainedTokenizer):
     as the conditioning of the model can be done on the three different queries. If None is provided, defaults values will be used.:
 
     Depending on the number of genres on which the model should be conditioned (`n_genres`).
-    ```
+    ```python
     >>> from transformers import JukeboxTokenizer
+
     >>> tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics")
-    >>> tokenizer("Alan Jackson", "Country Rock", "old town road")['input_ids']
+    >>> tokenizer("Alan Jackson", "Country Rock", "old town road")["input_ids"]
     [tensor([[   0,    0,    0, 6785,  546,   41,   38,   30,   76,   46,   41,   49,
                40,   76,   44,   41,   27,   30]]), tensor([[  0,   0,   0, 145,   0]]), tensor([[  0,   0,   0, 145,   0]])]
-
     ```
 
     You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
diff --git a/src/transformers/models/led/tokenization_led.py b/src/transformers/models/led/tokenization_led.py
index 5b22701a22..1cdb524301 100644
--- a/src/transformers/models/led/tokenization_led.py
+++ b/src/transformers/models/led/tokenization_led.py
@@ -97,12 +97,14 @@ class LEDTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import LEDTokenizer
+
     >>> tokenizer = LEDTokenizer.from_pretrained("allenai/led-base-16384")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 
diff --git a/src/transformers/models/led/tokenization_led_fast.py b/src/transformers/models/led/tokenization_led_fast.py
index 153b32c296..51b8ab4aaa 100644
--- a/src/transformers/models/led/tokenization_led_fast.py
+++ b/src/transformers/models/led/tokenization_led_fast.py
@@ -55,12 +55,14 @@ class LEDTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import LEDTokenizerFast
+
     >>> tokenizer = LEDTokenizerFast.from_pretrained("allenai/led-base-16384")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 
diff --git a/src/transformers/models/longformer/tokenization_longformer.py b/src/transformers/models/longformer/tokenization_longformer.py
index 69bc505953..fea949658a 100644
--- a/src/transformers/models/longformer/tokenization_longformer.py
+++ b/src/transformers/models/longformer/tokenization_longformer.py
@@ -120,12 +120,14 @@ class LongformerTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import LongformerTokenizer
+
     >>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 
diff --git a/src/transformers/models/longformer/tokenization_longformer_fast.py b/src/transformers/models/longformer/tokenization_longformer_fast.py
index dfe1b08e14..1460f2f2cc 100644
--- a/src/transformers/models/longformer/tokenization_longformer_fast.py
+++ b/src/transformers/models/longformer/tokenization_longformer_fast.py
@@ -96,12 +96,14 @@ class LongformerTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import LongformerTokenizerFast
+
     >>> tokenizer = LongformerTokenizerFast.from_pretrained("allenai/longformer-base-4096")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 
diff --git a/src/transformers/models/luke/tokenization_luke.py b/src/transformers/models/luke/tokenization_luke.py
index 89fb9b63e8..8b47ced1d3 100644
--- a/src/transformers/models/luke/tokenization_luke.py
+++ b/src/transformers/models/luke/tokenization_luke.py
@@ -197,12 +197,14 @@ class LukeTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import LukeTokenizer
+
     >>> tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 
diff --git a/src/transformers/models/mvp/tokenization_mvp.py b/src/transformers/models/mvp/tokenization_mvp.py
index 98d373188e..2d497c23d1 100644
--- a/src/transformers/models/mvp/tokenization_mvp.py
+++ b/src/transformers/models/mvp/tokenization_mvp.py
@@ -93,12 +93,14 @@ class MvpTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import MvpTokenizer
+
     >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 
diff --git a/src/transformers/models/mvp/tokenization_mvp_fast.py b/src/transformers/models/mvp/tokenization_mvp_fast.py
index 28dd1ea942..fd6abd1700 100644
--- a/src/transformers/models/mvp/tokenization_mvp_fast.py
+++ b/src/transformers/models/mvp/tokenization_mvp_fast.py
@@ -58,12 +58,14 @@ class MvpTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import MvpTokenizerFast
+
     >>> tokenizer = MvpTokenizerFast.from_pretrained("RUCAIBox/mvp")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 
diff --git a/src/transformers/models/roberta/tokenization_roberta.py b/src/transformers/models/roberta/tokenization_roberta.py
index e8d4a751bc..24b9748c3d 100644
--- a/src/transformers/models/roberta/tokenization_roberta.py
+++ b/src/transformers/models/roberta/tokenization_roberta.py
@@ -111,12 +111,14 @@ class RobertaTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import RobertaTokenizer
+
     >>> tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 
diff --git a/src/transformers/models/roberta/tokenization_roberta_fast.py b/src/transformers/models/roberta/tokenization_roberta_fast.py
index 32cc66d750..c2c479da09 100644
--- a/src/transformers/models/roberta/tokenization_roberta_fast.py
+++ b/src/transformers/models/roberta/tokenization_roberta_fast.py
@@ -81,12 +81,14 @@ class RobertaTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import RobertaTokenizerFast
+
     >>> tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 
diff --git a/utils/documentation_tests.txt b/utils/documentation_tests.txt
index 7f3535eb55..bfd87a5c0b 100644
--- a/utils/documentation_tests.txt
+++ b/utils/documentation_tests.txt
@@ -444,3 +444,26 @@ src/transformers/models/wav2vec2/processing_wav2vec2.py
 src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py
 src/transformers/models/whisper/processing_whisper.py
 src/transformers/models/x_clip/processing_x_clip.py
+src/transformers/models/bart/tokenization_bart.py
+src/transformers/models/bart/tokenization_bart_fast.py
+src/transformers/models/blenderbot/tokenization_blenderbot.py
+src/transformers/models/blenderbot/tokenization_blenderbot_fast.py
+src/transformers/models/bloom/tokenization_bloom_fast.py
+src/transformers/models/codegen/tokenization_codegen.py
+src/transformers/models/codegen/tokenization_codegen_fast.py
+src/transformers/models/deberta/tokenization_deberta.py
+src/transformers/models/deberta/tokenization_deberta_fast.py
+src/transformers/models/gpt2/tokenization_gpt2.py
+src/transformers/models/gpt2/tokenization_gpt2_fast.py
+src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py
+src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
+src/transformers/models/jukebox/tokenization_jukebox.py
+src/transformers/models/led/tokenization_led.py
+src/transformers/models/led/tokenization_led_fast.py
+src/transformers/models/longformer/tokenization_longformer.py
+src/transformers/models/longformer/tokenization_longformer_fast.py
+src/transformers/models/luke/tokenization_luke.py
+src/transformers/models/mvp/tokenization_mvp.py
+src/transformers/models/mvp/tokenization_mvp_fast.py
+src/transformers/models/roberta/tokenization_roberta.py
+src/transformers/models/roberta/tokenization_roberta_fast.py