Refine docs (#12)

Author: timctho

README.md

@@ -12,9 +12,9 @@ Azure AI Content Understanding is a new Generative AI-based [Azure AI service](h
 
 | File | Description |
 | --- | --- |
-| [field_extraction.ipynb](notebooks/field_extraction.ipynb) | Extract custom fields with sample analyzer templates |
-| [content_extraction.ipynb](notebooks/content_extraction.ipynb) | Extract structured content from your input files |
-| [analyzer_training.ipynb](notebooks/analyzer_training.ipynb) | Provide training data to improve the quality of your analyzer |
+| [content_extraction.ipynb](notebooks/content_extraction.ipynb) | In this sample we will show content understanding API can help you get semantic information from your file. For example OCR with table in document, audio transcription, and face analysis in video. |
+| [field_extraction.ipynb](notebooks/field_extraction.ipynb) | In this sample we will show how to create an analyzer to extract fields in your file. For example invoice amount in the document, how many people in an image, names mentioned in an audio, or summary of a video. You can customize the fields by creating your own analyzer template.  |
+| [analyzer_training.ipynb](notebooks/analyzer_training.ipynb) | If you want to futher boost the performance for field extraction, we can do training when you provide few labeled samples to the API. Note: This feature is available to document scenario now. |
 
 
 ## Getting started

notebooks/analyzer_training.ipynb

@@ -6,13 +6,19 @@
    "source": [
     "# Enhance your analyzer with labeled data\n",
     "\n",
+    "\n",
+    "> #################################################################################\n",
+    ">\n",
+    "> Note: Currently this feature is only available for analyzer scenario is `document`\n",
+    ">\n",
+    "> #################################################################################\n",
+    "\n",
     "Labeled data is a group of samples that have been tagged with one or more labels to add context or meaning, which is used to improve analyzer's performance.\n",
     "\n",
     "Please go to [Azure AI Foundry]() to use the labling tool to annotate your data.\n",
     "\n",
     "In this notebook we will demonstrate after you have the labeled data, how to create analyzer with them and analyze your files.\n",
     "\n",
-    "> Note: Currently this feature is only available for analyzer scenario is `document`\n",
     "\n",
     "\n",
     "## Prerequisites\n",
@@ -354,7 +360,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,

notebooks/content_extraction.ipynb

@@ -237,7 +237,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Video Content with Face"
+    "## Video Content with Face\n",
+    "This is a gated feature, please go through process [Azure AI Resource Face Gating](https://learn.microsoft.com/en-us/legal/cognitive-services/computer-vision/limited-access-identity?context=%2Fazure%2Fai-services%2Fcomputer-vision%2Fcontext%2Fcontext#registration-process) Select `[Video Indexer] Facial Identification (1:N or 1:1 matching) to search for a face in a media or entertainment video archive to find a face within a video and generate metadata for media or entertainment use cases only` in the registration form."
    ]
   },
   {

notebooks/field_extraction.ipynb

@@ -440,7 +440,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,