damondouglas commented on code in PR #27516:
URL: https://github.com/apache/beam/pull/27516#discussion_r1266128537


##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,973 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a 
href=\"https://colab.research.google.com/github/svetakvsundhar/beam/blob/healthcarenlp/examples/notebooks/healthcare/beam_nlp.ipynb\";
 target=\"_parent\"><img 
src=\"https://colab.research.google.com/assets/colab-badge.svg\"; alt=\"Open In 
Colab\"/></a>"

Review Comment:
   Will this URL remain if this PR merged?



##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,973 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a 
href=\"https://colab.research.google.com/github/svetakvsundhar/beam/blob/healthcarenlp/examples/notebooks/healthcare/beam_nlp.ipynb\";
 target=\"_parent\"><img 
src=\"https://colab.research.google.com/assets/colab-badge.svg\"; alt=\"Open In 
Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title ###### Licensed to the Apache Software Foundation (ASF), 
Version 2.0 (the \"License\")\n",
+        "\n",
+        "# Licensed to the Apache Software Foundation (ASF) under one\n",
+        "# or more contributor license agreements. See the NOTICE file\n",
+        "# distributed with this work for additional information\n",
+        "# regarding copyright ownership. The ASF licenses this file\n",
+        "# to you under the Apache License, Version 2.0 (the\n",
+        "# \"License\"); you may not use this file except in compliance\n",
+        "# with the License. You may obtain a copy of the License at\n",
+        "#\n",
+        "#   http://www.apache.org/licenses/LICENSE-2.0\n";,
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing,\n",
+        "# software distributed under the License is distributed on an\n",
+        "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+        "# KIND, either express or implied. See the License for the\n",
+        "# specific language governing permissions and limitations\n",
+        "# under the License"
+      ],
+      "metadata": {
+        "id": "lBuUTzxD2mvJ"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Natural Language Processing Pipeline**\n",
+        "\n",
+        "**Note**: This example is used from 
[here](https://github.com/rasalt/healthcarenlp/blob/main/nlp_public.ipynb).\n",
+        "\n",
+        "\n",
+        "\n",
+        "This example demonstrates how to set up an Apache Beam pipeline that 
reads from a [Google Cloud Storage](https://https://cloud.google.com/storage) 
file containing text from Shakespeare's work King Lear, tokenizes the text 
lines into individual words, and performs a frequency count on each of those 
words.\n",

Review Comment:
   I see Healthcare NLP and a reference to Shakespeare's King Lear. May we 
consider editing this?



##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,973 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a 
href=\"https://colab.research.google.com/github/svetakvsundhar/beam/blob/healthcarenlp/examples/notebooks/healthcare/beam_nlp.ipynb\";
 target=\"_parent\"><img 
src=\"https://colab.research.google.com/assets/colab-badge.svg\"; alt=\"Open In 
Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title ###### Licensed to the Apache Software Foundation (ASF), 
Version 2.0 (the \"License\")\n",
+        "\n",
+        "# Licensed to the Apache Software Foundation (ASF) under one\n",
+        "# or more contributor license agreements. See the NOTICE file\n",
+        "# distributed with this work for additional information\n",
+        "# regarding copyright ownership. The ASF licenses this file\n",
+        "# to you under the Apache License, Version 2.0 (the\n",
+        "# \"License\"); you may not use this file except in compliance\n",
+        "# with the License. You may obtain a copy of the License at\n",
+        "#\n",
+        "#   http://www.apache.org/licenses/LICENSE-2.0\n";,
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing,\n",
+        "# software distributed under the License is distributed on an\n",
+        "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+        "# KIND, either express or implied. See the License for the\n",
+        "# specific language governing permissions and limitations\n",
+        "# under the License"
+      ],
+      "metadata": {
+        "id": "lBuUTzxD2mvJ"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Natural Language Processing Pipeline**\n",
+        "\n",
+        "**Note**: This example is used from 
[here](https://github.com/rasalt/healthcarenlp/blob/main/nlp_public.ipynb).\n",
+        "\n",
+        "\n",
+        "\n",
+        "This example demonstrates how to set up an Apache Beam pipeline that 
reads from a [Google Cloud Storage](https://https://cloud.google.com/storage) 
file containing text from Shakespeare's work King Lear, tokenizes the text 
lines into individual words, and performs a frequency count on each of those 
words.\n",
+        "\n",
+        "An Apache Beam pipeline is a pipeline that reads input data, 
transforms that data, and writes output data. It consists of PTransforms and 
PCollections. A PCollection represents a distributed data set that your Beam 
pipeline operates on. A PTransform represents a data processing operation, or a 
step, in your pipeline. It takes one or more PCollections as input, performs a 
processing function that you provide on the elements of that PCollection, and 
produces zero or more output PCollection objects.\n",
+        "\n",
+        "For details about Apache Beam pipelines, including PTransforms and 
PCollections, visit the [Beam Programming 
Guide](https://beam.apache.org/documentation/programming-guide/).\n",
+        "\n",
+        "You'll be able to use this notebook to explore the data in each 
PCollection."
+      ],
+      "metadata": {
+        "id": "nEUAYCTx4Ijj"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Instructions\n",
+        "1. Set the variables in the next cell based upon your project and 
preferences\n",
+        "2. The files referred to in this notebook nlpsample*.csv are in the 
format with one\n",
+        "blurb of clinical note."
+      ],
+      "metadata": {
+        "id": "ZLBB0PTG5CHw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install apache-beam[gcp]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/";
+        },
+        "id": "O7hq2sse8K4u",
+        "outputId": "c6f559a6-1419-43f2-b88d-49fedca1c686"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: apache-beam[gcp] in 
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+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Change this variable to True if you want to debug the Interactive 
Runner Pipeline else it uses Dataflow\n",
+        "debug = False\n",
+        "DATASET=\"<YOUR_DATASET_NAME>\"\n",
+        "TEMP_LOCATION=\"<YOUR_TEMP_LOCATION>\"\n",
+        "PROJECT='<YOUR_PROJECT_ID>'\n",
+        "LOCATION='us-central1'\n",
+        "URL='https://healthcare.googleapis.com/v1/projects/'+PROJECT+ 
'/locations/'+LOCATION+'/services/nlp:analyzeEntities'\n",
+        
"NLP_SERVICE='projects/'+PROJECT+'/locations/'+LOCATION+'/services/nlp'\n",

Review Comment:
   Could you use python's f-string syntax instead of concatenating using '+'?



##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,973 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a 
href=\"https://colab.research.google.com/github/svetakvsundhar/beam/blob/healthcarenlp/examples/notebooks/healthcare/beam_nlp.ipynb\";
 target=\"_parent\"><img 
src=\"https://colab.research.google.com/assets/colab-badge.svg\"; alt=\"Open In 
Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title ###### Licensed to the Apache Software Foundation (ASF), 
Version 2.0 (the \"License\")\n",
+        "\n",
+        "# Licensed to the Apache Software Foundation (ASF) under one\n",
+        "# or more contributor license agreements. See the NOTICE file\n",
+        "# distributed with this work for additional information\n",
+        "# regarding copyright ownership. The ASF licenses this file\n",
+        "# to you under the Apache License, Version 2.0 (the\n",
+        "# \"License\"); you may not use this file except in compliance\n",
+        "# with the License. You may obtain a copy of the License at\n",
+        "#\n",
+        "#   http://www.apache.org/licenses/LICENSE-2.0\n";,
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing,\n",
+        "# software distributed under the License is distributed on an\n",
+        "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+        "# KIND, either express or implied. See the License for the\n",
+        "# specific language governing permissions and limitations\n",
+        "# under the License"
+      ],
+      "metadata": {
+        "id": "lBuUTzxD2mvJ"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Natural Language Processing Pipeline**\n",
+        "\n",
+        "**Note**: This example is used from 
[here](https://github.com/rasalt/healthcarenlp/blob/main/nlp_public.ipynb).\n",
+        "\n",
+        "\n",
+        "\n",
+        "This example demonstrates how to set up an Apache Beam pipeline that 
reads from a [Google Cloud Storage](https://https://cloud.google.com/storage) 
file containing text from Shakespeare's work King Lear, tokenizes the text 
lines into individual words, and performs a frequency count on each of those 
words.\n",
+        "\n",
+        "An Apache Beam pipeline is a pipeline that reads input data, 
transforms that data, and writes output data. It consists of PTransforms and 
PCollections. A PCollection represents a distributed data set that your Beam 
pipeline operates on. A PTransform represents a data processing operation, or a 
step, in your pipeline. It takes one or more PCollections as input, performs a 
processing function that you provide on the elements of that PCollection, and 
produces zero or more output PCollection objects.\n",
+        "\n",
+        "For details about Apache Beam pipelines, including PTransforms and 
PCollections, visit the [Beam Programming 
Guide](https://beam.apache.org/documentation/programming-guide/).\n",
+        "\n",
+        "You'll be able to use this notebook to explore the data in each 
PCollection."
+      ],
+      "metadata": {
+        "id": "nEUAYCTx4Ijj"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Instructions\n",
+        "1. Set the variables in the next cell based upon your project and 
preferences\n",
+        "2. The files referred to in this notebook nlpsample*.csv are in the 
format with one\n",
+        "blurb of clinical note."
+      ],
+      "metadata": {
+        "id": "ZLBB0PTG5CHw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install apache-beam[gcp]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/";
+        },
+        "id": "O7hq2sse8K4u",
+        "outputId": "c6f559a6-1419-43f2-b88d-49fedca1c686"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: apache-beam[gcp] in 
/usr/local/lib/python3.10/dist-packages (2.49.0)\n",
+            "Requirement already satisfied: crcmod<2.0,>=1.7 in 
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/usr/local/lib/python3.10/dist-packages (from apache-beam[gcp]) (3.9.2)\n",
+            "Requirement already satisfied: dill<0.3.2,>=0.3.1.1 in 
/usr/local/lib/python3.10/dist-packages (from apache-beam[gcp]) (0.3.1.1)\n",
+            "Requirement already satisfied: cloudpickle~=2.2.1 in 
/usr/local/lib/python3.10/dist-packages (from apache-beam[gcp]) (2.2.1)\n",
+            "Requirement already satisfied: fastavro<2,>=0.23.6 in 
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+            "Requirement already satisfied: fasteners<1.0,>=0.3 in 
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+            "Requirement already satisfied: grpcio!=1.48.0,<2,>=1.33.1 in 
/usr/local/lib/python3.10/dist-packages (from apache-beam[gcp]) (1.56.0)\n",
+            "Requirement already satisfied: hdfs<3.0.0,>=2.1.0 in 
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+            "Requirement already satisfied: httplib2<0.23.0,>=0.8 in 
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+            "Requirement already satisfied: numpy<1.25.0,>=1.14.3 in 
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+            "Requirement already satisfied: requests<3.0.0,>=2.24.0 in 
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+            "Requirement already satisfied: typing-extensions>=3.7.0 in 
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+            "Requirement already satisfied: pyarrow<12.0.0,>=3.0.0 in 
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+            "Requirement already satisfied: cachetools<6,>=3.1.0 in 
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+            "Requirement already satisfied: google-apitools<0.5.32,>=0.5.31 in 
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+            "Requirement already satisfied: google-auth<3,>=1.18.0 in 
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+            "Requirement already satisfied: google-auth-httplib2<0.2.0,>=0.1.0 
in /usr/local/lib/python3.10/dist-packages (from apache-beam[gcp]) (0.1.0)\n",
+            "Requirement already satisfied: google-cloud-datastore<3,>=2.0.0 
in /usr/local/lib/python3.10/dist-packages (from apache-beam[gcp]) (2.15.2)\n",
+            "Requirement already satisfied: google-cloud-pubsub<3,>=2.1.0 in 
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+            "Requirement already satisfied: google-cloud-pubsublite<2,>=1.2.0 
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+            "Requirement already satisfied: google-cloud-bigquery<4,>=2.0.0 in 
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+            "Requirement already satisfied: 
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+            "Requirement already satisfied: google-cloud-core<3,>=2.0.0 in 
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+            "Requirement already satisfied: 
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+            "Requirement already satisfied: oauth2client>=1.4.12 in 
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+            "Requirement already satisfied: six>=1.12.0 in 
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+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in 
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+            "Requirement already satisfied: rsa<5,>=3.1.4 in 
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+            "Requirement already satisfied: 
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 in /usr/local/lib/python3.10/dist-packages (from 
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+            "Requirement already satisfied: packaging>=14.3 in 
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+            "Requirement already satisfied: 
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+            "Requirement already satisfied: 
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+            "Requirement already satisfied: sqlparse>=0.4.4 in 
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+            "Requirement already satisfied: docopt in 
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+            "Requirement already satisfied: 
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+            "Requirement already satisfied: 
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+            "Requirement already satisfied: pyasn1>=0.1.7 in 
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+            "Requirement already satisfied: h11<0.15,>=0.13 in 
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+            "Requirement already satisfied: anyio<5.0,>=3.0 in 
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+            "Requirement already satisfied: exceptiongroup in 
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 (1.1.2)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Change this variable to True if you want to debug the Interactive 
Runner Pipeline else it uses Dataflow\n",
+        "debug = False\n",
+        "DATASET=\"<YOUR_DATASET_NAME>\"\n",
+        "TEMP_LOCATION=\"<YOUR_TEMP_LOCATION>\"\n",
+        "PROJECT='<YOUR_PROJECT_ID>'\n",
+        "LOCATION='us-central1'\n",
+        "URL='https://healthcare.googleapis.com/v1/projects/'+PROJECT+ 
'/locations/'+LOCATION+'/services/nlp:analyzeEntities'\n",
+        
"NLP_SERVICE='projects/'+PROJECT+'/locations/'+LOCATION+'/services/nlp'\n",
+        "GCS_BUCKET=PROJECT"
+      ],
+      "metadata": {
+        "id": "s9lhe5CZ5F3o"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**BigQuery Setup**\n",
+        "\n",
+        "We will be using BigQuery to warehouse the structured data revealed 
in the output of the Healthcare NLP API. For this purpose I create 3 tables to 
organize the data"

Review Comment:
   In tutorial speech, might this say "For this purpose we create ..."



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