updating datasource and instructions

Author: dereklegenzoff

AzureML-Custom-Skill/notebook/custom_skill_azureml.ipynb

@@ -21,7 +21,7 @@
     "\n",
     "To train the aspect-based sentiment model, you will be using the [nlp recipes repository](https://github.com/microsoft/nlp-recipes/tree/master/examples/sentiment_analysis/absa). The model will then be deployed as an endpoint on an Azure Kubernetes cluster. Once deployed, the model is added to the enrichment pipeline as a custom skill for use by the Cognitive Search service.\n",
     "\n",
-    "There are two datasets provided. To train the model the larger of the two datasets, the hotel_reviews_1000.csv, is required. Prefer to skip the training step? Download the hotel_reviews_100.csv.\n",
+    "There are two datasets provided. To train the model the larger of the two datasets, the `hotel_reviews_1000.csv`, is required. Prefer to skip the training step? Download the `hotel_reviews_100.csv`.\n",
     "\n",
     "## Prerequisites\n",
     "\n",
@@ -177,16 +177,17 @@
     "\n",
     "headers = {'api-key': api_key, 'Content-Type': content_type}\n",
     "# Test out the URLs to ensure that the configuration works\n",
-    "print(construct_Url(search_service, \"indexes\", \"custom-ner\", \"analyze\", api_version))\n",
-    "print(construct_Url(search_service, \"indexes\", \"custom-ner\", None, api_version))\n",
+    "print(construct_Url(search_service, \"indexes\", \"azureml-sentiment\", \"analyze\", api_version))\n",
+    "print(construct_Url(search_service, \"indexes\", \"azureml-sentiment\", None, api_version))\n",
     "print(construct_Url(search_service, \"indexers\", None, None, api_version))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 1.1 Create datasource"
+    "## 1.1 Create datasource\n",
+    "Be sure to upload `hotel_reviews_1000.csv` or `hotel_reviews_100.csv` to your storage account before continuing."
    ]
   },
   {
@@ -195,13 +196,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Replace with the container name, storage account name, storage account connection string\n",
     "container = datasource_container\n",
     "\n",
-    "\n",
     "datsource_def = {\n",
     "    'name': f'{datasource_container}-ds',\n",
-    "    'description': f'km-aml solution accelerator- Datasource to label and extract custom entities from a corpus in STORAGEACCOUNTNAME, container datasource_container',\n",
+    "    'description': f'Datasource with hotel reviews',\n",
     "    'type': 'azureblob',\n",
     "    'subtype': None,\n",
     "    'credentials': {\n",