Addressing review feedback

Author: RyanMullins

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

@@ -105,6 +105,7 @@
       },
       "outputs": [],
       "source": [
+        "# @title install Hugging Face Transformers v4.50+\n",
         "! pip install -q 'transformers>=4.50.0'"
       ]
     },
@@ -116,7 +117,19 @@
       },
       "outputs": [],
       "source": [
-        "! huggingface-cli login"
+        "# @title Authenticate with Hugging Face Hub\n",
+        "# @markdown ShieldGemma is a gated model. To access the weights, you must accept\n",
+        "# @markdown the license on Hugging Face Hub under your account and then provide\n",
+        "# @markdown an [Access Token](https://huggingface.co/docs/hub/en/security-tokens)\n",
+        "# @markdown to authenticate with the Hugging Face Hub API. If using Colab, the\n",
+        "# @markdown easiest way to do this is by creating a read-only token specifically\n",
+        "# @markdown for Colab and setting this as the value of the `HF_TOKEN` secret;\n",
+        "# @markdown this token will then be reusable across all Colab notebooks. Other\n",
+        "# @markdown Python notebook platforms may provide a similar mechanism. For those\n",
+        "# @markdown that do not, un-comment the lines in this cell to install the\n",
+        "# @markdown Hugging Face Hub CLI and log in interactively.\n",
+        "# ! pip install -q 'huggingface_hub[cli]'\n",
+        "# ! huggingface-cli login"
       ]
     },
     {
@@ -146,10 +159,9 @@
         "from PIL import Image\n",
         "import requests\n",
         "\n",
-        "# The image included in this Colab is benign and will results in the prediction\n",
-        "# of a `No` token for all policies, meanign the image does not violate any\n",
-        "# content policies. Change this URL or otherwise update this code to use an\n",
-        "# image that may be violative.\n",
+        "# The image included in this Colab is benign and will not violate any of\n",
+        "# ShieldGemma's built-in content policies. Change this URL or otherwise update\n",
+        "# this code to use an image that may be violative.\n",
         "url = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg\"\n",
         "image = Image.open(requests.get(url, stream=True).raw)"
       ]
@@ -169,7 +181,7 @@
         "\n",
         "# `scores` is a `ShieldGemma2ImageClassifierOutputWithNoAttention` instance\n",
         "# continaing the logits and probabilities associated with the model predicting\n",
-        "# the `Yes` or `No` token as the response to the prompt batch, captured in the\n",
+        "# the `Yes` or `No` tokens as the response to the prompt batch, captured in the\n",
         "# following properties.\n",
         "#\n",
         "#   *   `logits` (`torch.Tensor` of shape `(batch_size, 2)`): The first position\n",
@@ -187,8 +199,8 @@
         "print(scores.probabilities)\n",
         "\n",
         "# ShieldGemma prompts are constructed such that predicting the `Yes` token means\n",
-        "# the content does violate the policy. If you are only interested in the\n",
-        "# violative condition, use to extract that slice from the output tensors.\n",
+        "# the content violates the policy. If you are only interested in the violative\n",
+        "# condition, you can extract only that slice from the output tensors.\n",
         "p_violated = scores.probabilities[:, 0]\n",
         "print(p_violated)\n"
       ]