diff --git a/docs/Documentation/model_introduction.md b/docs/Documentation/model_introduction.md
index cd24fccbec8..cadd407ba0b 100644
--- a/docs/Documentation/model_introduction.md
+++ b/docs/Documentation/model_introduction.md
@@ -221,7 +221,7 @@ The figure below illustrates the typical workflow for deploying a trained deep-l
 Convert a Model in CLI: ``ovc``
 ###############################
 
-Another option for model conversion is to use ``ovc`` command-line tool, which stands for OpenVINO Model Converter. The tool combines both ``openvino.convert_model`` and ``openvino.save_model`` functionalities. It is convenient to use when the original model is ready for inference and is in one of the supported file formats: ONNX, TensorFlow, TensorFlow Lite, or PaddlePaddle. As a result, ``ovc`` produces an OpenVINO IR, consisting of ``.xml`` and ``.bin`` files, which needs to be read with the ``ov.read_model()`` method. You can compile and infer the ``ov.Model`` later with :doc:`OpenVINO™ Runtime <openvino_docs_OV_UG_OV_Runtime_User_Guide>`
+Another option for model conversion is to use ``ovc`` command-line tool, which stands for OpenVINO Model Converter. The tool combines both ``openvino.convert_model`` and ``openvino.save_model`` functionalities. It is convenient to use when the original model is ready for inference and is in one of the supported file formats: ONNX, TensorFlow, TensorFlow Lite, or PaddlePaddle. As a result, ``ovc`` produces an OpenVINO IR, consisting of ``.xml`` and ``.bin`` files, which needs to be read with the ``openvino.Core.read_model`` method. You can compile and infer the ``ov.Model`` later with :doc:`OpenVINO™ Runtime <openvino_docs_OV_UG_OV_Runtime_User_Guide>`
 
 .. note::
    PyTorch models cannot be converted with ``ovc``, use ``openvino.convert_model`` instead.
diff --git a/docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_Paddle.md b/docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_Paddle.md
index dd3f821229b..8b4c549547e 100644
--- a/docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_Paddle.md
+++ b/docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_Paddle.md
@@ -89,7 +89,7 @@ Some PaddlePaddle models may require setting ``example_input`` or ``output`` for
 
 * Example of converting ``paddle.fluid.dygraph.layers.Layer`` format model:
 
-  ``example_input`` is required while ``output`` is optional, which accept the following formats:
+  ``example_input`` is required while ``output`` is optional.  ``example_input`` accepts the following formats:
 
   ``list`` with tensor (``paddle.Tensor``) or InputSpec (``paddle.static.input.InputSpec``)
 
diff --git a/docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_PyTorch.md b/docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_PyTorch.md
index 83005b7e978..6fcd6d7c03a 100644
--- a/docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_PyTorch.md
+++ b/docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_PyTorch.md
@@ -40,8 +40,8 @@ The value for the ``example_input`` parameter can be easily derived from knowing
    import torch
    import openvino as ov
 
-   model = torchvision.models.resnet50(pretrained=True)
-   ov_model = ov.convert_model(model, example_input=example_input=torch.rand(1, 3, 224, 224))
+   model = torchvision.models.resnet50(weights='DEFAULT')
+   ov_model = ov.convert_model(model, example_input=torch.rand(1, 3, 224, 224))
 
 In practice, the code to evaluate or test the PyTorch model is usually provided with the model itself and can be used to generate a proper ``example_input`` value. A modified example of using ``resnet50`` model from ``torchvision`` is presented below. It demonstrates how to switch inference in the existing PyTorch application to OpenVINO and how to get value for ``example_input``: