remove transition links for deprecated (openvinotoolkit#2080)

Author: eaidova

notebooks/104-model-tools/104-model-tools.ipynb

@@ -8,7 +8,6 @@
    "source": [
     "# Working with Open Model Zoo Models\n",
     "\n",
-    "> **Note:** This notebook has been moved to a new branch named \"latest\". [Click here](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/model-tools/model-tools.ipynb) to get the most updated version of the notebook. This branch is deprecated.\n",
     "This tutorial shows how to download a model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo), convert it to OpenVINO™ IR format, show information about the model, and benchmark the model.\n",
     "\n",
     "\n",
@@ -793,4 +792,4 @@
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notebooks/215-image-inpainting/215-image-inpainting.ipynb

@@ -8,7 +8,6 @@
    "source": [
     "## Image In-painting with OpenVINO™\n",
     "\n",
-    "> **Note:** This notebook has been moved to a new branch named \"latest\". [Click here](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/image-inpainting/image-inpainting.ipynb) to get the most updated version of the notebook. This branch is deprecated.\n",
     "This notebook demonstrates how to use an image in-painting model with OpenVINO, using [GMCNN model](https://github.com/shepnerd/inpainting_gmcnn) from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/). This model, given a tampered image, is able to create something very similar to the original image. The Following pipeline will be used in this notebook.\n",
     "![pipeline](https://user-images.githubusercontent.com/4547501/165792473-ba784c0d-0a37-409f-a5f6-bb1849c1d140.png)\n",
     "\n",
@@ -569,4 +568,4 @@
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notebooks/223-text-prediction/223-text-prediction.ipynb

@@ -1,13 +1,12 @@
 {
  "cells": [
   {
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    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Text Prediction with OpenVINO™\n",
     "\n",
-    "> **Note:** This notebook has been moved to a new branch named \"latest\". [Click here](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/text-prediction/text-prediction.ipynb) to get the most updated version of the notebook. This branch is deprecated.\n",
-    "\n",
     "This notebook shows text prediction with OpenVINO. This notebook can work in two different modes, Text Generation and Conversation, which the user can select via selecting the model in the Model Selection Section. We use three models [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf), [GPT-Neo](https://zenodo.org/record/5297715#.ZAmpsXZBztU), and [PersonaGPT](https://arxiv.org/abs/2110.12949v1), which are a part of the Generative Pre-trained Transformer (GPT) family. GPT-2 and GPT-Neo can be used for text generation, whereas PersonaGPT is trained for the downstream task of conversation.\n",
     "\n",
     "GPT-2 and GPT-Neo are pre-trained on a large corpus of English text using unsupervised training. They both display a broad set of capabilities, including the ability to generate conditional synthetic text samples of unprecedented quality, where we prime the model with an input and have it generate a lengthy continuation.\n",
@@ -35,7 +34,8 @@
     "For Conversation, User Input is tokenized with `eos_token` concatenated in the end. Then, the text gets generated as detailed above. The Generated response is added to the history with the `eos_token` at the end. Additional user input is added to the history, and the sequence is passed back into the model.\n",
     "\n",
     "\n",
-    "#### Table of contents:\n\n",
+    "#### Table of contents:\n",
+    "\n",
     "- [Model Selection](#Model-Selection)\n",
     "- [Load Model](#Load-Model)\n",
     "- [Convert Pytorch Model to OpenVINO IR](#Convert-Pytorch-Model-to-OpenVINO-IR)\n",
@@ -56,6 +56,7 @@
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@@ -112,6 +113,7 @@
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@@ -171,6 +173,7 @@
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@@ -225,6 +228,7 @@
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@@ -235,6 +239,7 @@
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@@ -305,13 +310,15 @@
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    "source": [
     "Input keys are the names of the input nodes and output keys contain names of the output nodes of the network. In the case of GPT-Neo, we have `batch size` and `sequence length` as inputs and `batch size`, `sequence length` and `vocab size` as outputs."
    ]
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@@ -322,6 +329,7 @@
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