[TYPO] fix typo/format in quicktour.md (#25519)

* fix_all_language_quicktour

* give up ! before bash command

---------
Co-authored-by: lishukan <lishukan@dxy.cn>

docs/source/de/quicktour.md

@@ -68,11 +68,13 @@ Installieren Sie die folgenden Abhängigkeiten, falls Sie dies nicht bereits get
 
 <frameworkcontent>
 <pt>
+
 ```bash
 pip install torch
 ```
 </pt>
 <tf>
+
 ```bash
 pip install tensorflow
 ```
@@ -226,6 +228,7 @@ Genau wie die [`pipeline`] akzeptiert der Tokenizer eine Liste von Eingaben. Dar
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> pt_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -237,6 +240,7 @@ Genau wie die [`pipeline`] akzeptiert der Tokenizer eine Liste von Eingaben. Dar
 ```
 </pt>
 <tf>
+
 ```py
 >>> tf_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -375,6 +379,7 @@ Ein besonders cooles 🤗 Transformers-Feature ist die Möglichkeit, ein Modell
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> from transformers import AutoModel
 
@@ -383,6 +388,7 @@ Ein besonders cooles 🤗 Transformers-Feature ist die Möglichkeit, ein Modell
 ```
 </pt>
 <tf>
+
 ```py
 >>> from transformers import TFAutoModel
 

docs/source/en/quicktour.md

@@ -30,11 +30,13 @@ You'll also need to install your preferred machine learning framework:
 
 <frameworkcontent>
 <pt>
+
 ```bash
 pip install torch
 ```
 </pt>
 <tf>
+
 ```bash
 pip install tensorflow
 ```
@@ -208,6 +210,7 @@ A tokenizer can also accept a list of inputs, and pad and truncate the text to r
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> pt_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -219,6 +222,7 @@ A tokenizer can also accept a list of inputs, and pad and truncate the text to r
 ```
 </pt>
 <tf>
+
 ```py
 >>> tf_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -352,6 +356,7 @@ One particularly cool 🤗 Transformers feature is the ability to save a model a
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> from transformers import AutoModel
 
@@ -360,6 +365,7 @@ One particularly cool 🤗 Transformers feature is the ability to save a model a
 ```
 </pt>
 <tf>
+
 ```py
 >>> from transformers import TFAutoModel
 

docs/source/es/quicktour.md

@@ -68,11 +68,13 @@ Instala las siguientes dependencias si aún no lo has hecho:
 
 <frameworkcontent>
 <pt>
+
 ```bash
 pip install torch
 ```
 </pt>
 <tf>
+
 ```bash
 pip install tensorflow
 ```
@@ -224,6 +226,7 @@ Como con el [`pipeline`], el tokenizador aceptará una lista de inputs. Además,
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> pt_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -235,6 +238,7 @@ Como con el [`pipeline`], el tokenizador aceptará una lista de inputs. Además,
 ```
 </pt>
 <tf>
+
 ```py
 >>> tf_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -377,6 +381,7 @@ Una característica particularmente interesante de 🤗 Transformers es la habil
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> from transformers import AutoModel
 
@@ -385,6 +390,7 @@ Una característica particularmente interesante de 🤗 Transformers es la habil
 ```
 </pt>
 <tf>
+
 ```py
 >>> from transformers import TFAutoModel
 

docs/source/fr/quicktour.md

@@ -30,11 +30,13 @@ Vous aurez aussi besoin d'installer votre bibliothèque d'apprentissage profond
 
 <frameworkcontent>
 <pt>
+
 ```bash
 pip install torch
 ```
 </pt>
 <tf>
+
 ```bash
 pip install tensorflow
 ```
@@ -203,6 +205,7 @@ Un tokenizer peut également accepter une liste de textes, et remplir et tronque
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> pt_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -214,6 +217,7 @@ Un tokenizer peut également accepter une liste de textes, et remplir et tronque
 ```
 </pt>
 <tf>
+
 ```py
 >>> tf_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -346,6 +350,7 @@ Une fonctionnalité particulièrement cool 🤗 Transformers est la possibilité
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> from transformers import AutoModel
 
@@ -354,6 +359,7 @@ Une fonctionnalité particulièrement cool 🤗 Transformers est la possibilité
 ```
 </pt>
 <tf>
+
 ```py
 >>> from transformers import TFAutoModel
 

docs/source/it/quicktour.md

@@ -68,11 +68,13 @@ Installa le seguenti dipendenze se non lo hai già fatto:
 
 <frameworkcontent>
 <pt>
+
 ```bash
 pip install torch
 ```
 </pt>
 <tf>
+
 ```bash
 pip install tensorflow
 ```
@@ -379,6 +381,7 @@ Una caratteristica particolarmente interessante di 🤗 Transformers è la sua a
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> from transformers import AutoModel
 
@@ -387,6 +390,7 @@ Una caratteristica particolarmente interessante di 🤗 Transformers è la sua a
 ```
 </pt>
 <tf>
+
 ```py
 >>> from transformers import TFAutoModel
 

docs/source/ko/quicktour.md

@@ -30,11 +30,13 @@ rendered properly in your Markdown viewer.
 
 <frameworkcontent>
 <pt>
+
 ```bash
 pip install torch
 ```
 </pt>
 <tf>
+
 ```bash
 pip install tensorflow
 ```
@@ -210,6 +212,7 @@ label: NEGATIVE, with score: 0.5309
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> pt_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -221,6 +224,7 @@ label: NEGATIVE, with score: 0.5309
 ```
 </pt>
 <tf>
+
 ```py
 >>> tf_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -353,6 +357,7 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> from transformers import AutoModel
 
@@ -361,6 +366,7 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
 ```
 </pt>
 <tf>
+
 ```py
 >>> from transformers import TFAutoModel
 

docs/source/pt/quicktour.md

@@ -228,6 +228,7 @@ Assim como o [`pipeline`], o tokenizer aceitará uma lista de entradas. Além di
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> pt_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 transformers library.", "We hope you don't hate it."],
@@ -239,6 +240,7 @@ Assim como o [`pipeline`], o tokenizer aceitará uma lista de entradas. Além di
 ```
 </pt>
 <tf>
+
 ```py
 >>> tf_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -377,6 +379,7 @@ Um recurso particularmente interessante dos 🤗 Transformers é a capacidade de
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> from transformers import AutoModel
 
@@ -385,6 +388,7 @@ Um recurso particularmente interessante dos 🤗 Transformers é a capacidade de
 ```
 </pt>
 <tf>
+
 ```py
 >>> from transformers import TFAutoModel
 

docs/source/zh/quicktour.md

@@ -30,11 +30,13 @@ rendered properly in your Markdown viewer.
 
 <frameworkcontent>
 <pt>
+
 ```bash
 pip install torch
 ```
 </pt>
 <tf>
+
 ```bash
 pip install tensorflow
 ```
@@ -203,6 +205,7 @@ label: NEGATIVE, with score: 0.5309
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> pt_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -214,6 +217,7 @@ label: NEGATIVE, with score: 0.5309
 ```
 </pt>
 <tf>
+
 ```py
 >>> tf_batch = tokenizer(
 ...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
@@ -347,6 +351,7 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
 
 <frameworkcontent>
 <pt>
+
 ```py
 >>> from transformers import AutoModel
 
@@ -355,6 +360,7 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
 ```
 </pt>
 <tf>
+
 ```py
 >>> from transformers import TFAutoModel