Adopt framework-specific blocks for content (#16342)

* ✨ refactor code samples with framework-specific blocks

* ✨ update training.mdx

* 🖍 apply feedback

docs/source/training.mdx

@@ -63,8 +63,10 @@ If you like, you can create a smaller subset of the full dataset to fine-tune on
 
 <a id='trainer'></a>
 
-## Fine-tune with `Trainer`
+## Train
 
+<frameworkcontent>
+<pt>
 <Youtube id="nvBXf7s7vTI"/>
 
 🤗 Transformers provides a [`Trainer`] class optimized for training 🤗 Transformers models, making it easier to start training without manually writing your own training loop. The [`Trainer`] API supports a wide range of training options and features such as logging, gradient accumulation, and mixed precision.
@@ -143,14 +145,13 @@ Then fine-tune your model by calling [`~transformers.Trainer.train`]:
 ```py
 >>> trainer.train()
 ```
-
+</pt>
+<tf>
 <a id='keras'></a>
 
-## Fine-tune with Keras
-
 <Youtube id="rnTGBy2ax1c"/>
 
-🤗 Transformers models also supports training in TensorFlow with the Keras API. You only need to make a few changes before you can fine-tune.
+🤗 Transformers models also supports training in TensorFlow with the Keras API.
 
 ### Convert dataset to TensorFlow format
 
@@ -210,11 +211,15 @@ Then compile and fine-tune your model with [`fit`](https://keras.io/api/models/m
 
 >>> model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)
 ```
+</tf>
+</frameworkcontent>
 
 <a id='pytorch_native'></a>
 
-## Fine-tune in native PyTorch
+## Train in native PyTorch
 
+<frameworkcontent>
+<pt>
 <Youtube id="Dh9CL8fyG80"/>
 
 [`Trainer`] takes care of the training loop and allows you to fine-tune a model in a single line of code. For users who prefer to write their own training loop, you can also fine-tune a 🤗 Transformers model in native PyTorch.
@@ -354,6 +359,8 @@ Just like how you need to add an evaluation function to [`Trainer`], you need to
 
 >>> metric.compute()
 ```
+</pt>
+</frameworkcontent>
 
 <a id='additional-resources'></a>