fix torchscript docs (#6740)

docs/source/serialization.rst

@@ -130,13 +130,12 @@ Pytorch's two modules `JIT and TRACE <https://pytorch.org/docs/stable/jit.html>`
 their model to be re-used in other programs, such as efficiency-oriented C++ programs.
 
 We have provided an interface that allows the export of 🤗 Transformers models to TorchScript so that they can
-be reused in a different environment than a Pytorch-based python program. Here we explain how to use our models so that
-they can be exported, and what to be mindful of when using these models with TorchScript.
+be reused in a different environment than a Pytorch-based python program. Here we explain how to export and use our models using TorchScript.
 
-Exporting a model needs two things:
+Exporting a model requires two things:
 
-* dummy inputs to execute a model forward pass.
-* the model needs to be instantiated with the ``torchscript`` flag.
+* a forward pass with dummy inputs.
+* model instantiation with the ``torchscript`` flag.
 
 These necessities imply several things developers should be careful about. These are detailed below.
 
@@ -147,8 +146,8 @@ Implications
 TorchScript flag and tied weights
 ------------------------------------------------
 This flag is necessary because most of the language models in this repository have tied weights between their
-``Embedding`` layer and their ``Decoding`` layer. TorchScript does not allow the export of models that have tied weights,
-it is therefore necessary to untie the weights beforehand.
+``Embedding`` layer and their ``Decoding`` layer. TorchScript does not allow the export of models that have tied weights, therefore
+it is necessary to untie and clone the weights beforehand.
 
 This implies that models instantiated with the ``torchscript`` flag have their ``Embedding`` layer and ``Decoding`` layer
 separate, which means that they should not be trained down the line. Training would de-synchronize the two layers,
@@ -181,7 +180,7 @@ when exporting varying sequence-length models.
 Using TorchScript in Python
 -------------------------------------------------
 
-Below are examples of using the Python to save, load models as well as how to use the trace for inference.
+Below is an example, showing how to save, load models as well as how to use the trace for inference.
 
 Saving a model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -237,10 +236,10 @@ We are re-using the previously initialised ``dummy_input``.
 
 .. code-block:: python
 
-    loaded_model = torch.jit.load("traced_model.pt")
+    loaded_model = torch.jit.load("traced_bert.pt")
     loaded_model.eval()
 
-    all_encoder_layers, pooled_output = loaded_model(dummy_input)
+    all_encoder_layers, pooled_output = loaded_model(*dummy_input)
 
 Using a traced model for inference
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^