fixing linter errors

Author: RyanMullins

site/en/responsible/docs/safeguards/shieldgemma2_on_huggingface.ipynb

@@ -40,6 +40,25 @@
         "# Evaluating content safety with ShieldGemma 2 and Hugging Face Transformers"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2b40722aa1a9"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/responsible/docs/safeguards/shieldgemma2_on_huggingface\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "  </td>\n",
+        "    <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/responsible/docs/safeguards/shieldgemma2_on_huggingface.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/responsible/docs/safeguards/shieldgemma2_on_huggingface.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -78,14 +97,14 @@
       "source": [
         "# Supported Use Case\n",
         "\n",
-        "**We recommend using `ShieldGemma 2` as an input filter to vision language models or as an output filter of image generation systems or both.**  ShieldGemma 2 offers the following key advantages:\n",
+        "ShieldGemma 2 is should be used as an input filter to vision language models or as an output filter of image generation systems or both.**  ShieldGemma 2 offers the following key advantages:\n",
         "\n",
         "* **Policy-Aware Classification**: ShieldGemma 2 accepts both a user-defined safety policy and an image as input, providing classifications for both real and generated images, tailored to the specific policy guidelines.\n",
         "* **Probability-Based Output and Thresholding**: ShieldGemma 2 outputs a probability score for its predictions, allowing downstream users to flexibly tune the classification threshold based on their specific use cases and risk tolerance. This enables a more nuanced and adaptable approach to safety classification.\n",
         "\n",
         "The input/output format are as follows:\n",
         "* **Input**: Image + Prompt Instruction with policy definition\n",
-        "* **Output**: Probability of 'Yes'/'No' tokens, 'Yes' meaning that the image violated the specific policy. The higher the score, the higher the model's confidence that the image violates the specified policy."
+        "* **Output**: Probability of 'Yes'/'No' tokens, 'Yes' meaning that the image violated the specific policy. The higher the score for the 'Yes' token, the higher the model's confidence that the image violates the specified policy."
       ]
     },
     {
@@ -153,7 +172,9 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "a436de5a4e95"
+      },
       "outputs": [],
       "source": [
         "from PIL import Image\n",
@@ -209,16 +230,12 @@
   "metadata": {
     "accelerator": "GPU",
     "colab": {
-      "gpuType": "A100",
-      "machine_shape": "hm",
-      "provenance": []
+      "name": "shieldgemma2_on_huggingface.ipynb",
+      "toc_visible": true
     },
     "kernelspec": {
       "display_name": "Python 3",
       "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
     }
   },
   "nbformat": 4,