legal-nlp/10.0.Data_Augmentation_with_ChunkMappers.ipynb

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      "metadata": {
        "id": "db5f4f9a-7776-42b3-8758-85624d4c15ea"
      },
      "source": [
        "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "21e9eafb",
      "metadata": {
        "id": "21e9eafb"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/legal-nlp/10.0.Data_Augmentation_with_ChunkMappers.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cfbbcfc0-e0b7-4c25-8bd7-c64d90f836d1",
      "metadata": {
        "id": "cfbbcfc0-e0b7-4c25-8bd7-c64d90f836d1"
      },
      "source": [
        "# Legal Data Augmentation with Chunk Mappers"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "gk3kZHmNj51v",
      "metadata": {
        "collapsed": false,
        "id": "gk3kZHmNj51v"
      },
      "source": [
        "# Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "_914itZsj51v",
      "metadata": {
        "id": "_914itZsj51v",
        "pycharm": {
          "is_executing": true
        }
      },
      "outputs": [],
      "source": [
        "! pip install -q johnsnowlabs"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "YPsbAnNoPt0Z",
      "metadata": {
        "id": "YPsbAnNoPt0Z"
      },
      "source": [
        "## Automatic Installation\n",
        "Using my.johnsnowlabs.com SSO"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "fY0lcShkj51w",
      "metadata": {
        "id": "fY0lcShkj51w",
        "pycharm": {
          "is_executing": true
        }
      },
      "outputs": [],
      "source": [
        "from johnsnowlabs import nlp, legal\n",
        "\n",
        "# nlp.install(force_browser=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "hsJvn_WWM2GL",
      "metadata": {
        "id": "hsJvn_WWM2GL"
      },
      "source": [
        "## Manual downloading\n",
        "If you are not registered in my.johnsnowlabs.com, you received a license via e-email or you are using Safari, you may need to do a manual update of the license.\n",
        "\n",
        "- Go to my.johnsnowlabs.com\n",
        "- Download your license\n",
        "- Upload it using the following command"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "i57QV3-_P2sQ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "i57QV3-_P2sQ",
        "outputId": "803c0345-5921-4689-98e8-f01c6ee2e4f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please Upload your John Snow Labs License using the button below\n"
          ]
        },
        {
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              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// 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, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
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              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
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              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
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              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
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              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
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              "}\n",
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              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
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              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
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              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
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              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
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              "      promise: fileDataPromise,\n",
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              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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              "\n",
              "scope.google = scope.google || {};\n",
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              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving spark_nlp_for_healthcare_spark_ocr_6538.json to spark_nlp_for_healthcare_spark_ocr_6538 (2).json\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "print('Please Upload your John Snow Labs License using the button below')\n",
        "license_keys = files.upload()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "xGgNdFzZP_hQ",
      "metadata": {
        "id": "xGgNdFzZP_hQ"
      },
      "source": [
        "- Install it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "OfmmPqknP4rR",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfmmPqknP4rR",
        "outputId": "dd2eefd2-3d09-444a-9964-b3b510597e81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "👌 Detected license file /content/spark_nlp_for_healthcare_spark_ocr_6538.json\n",
            "👌 Detected license file /content/spark_nlp_for_healthcare_spark_ocr_6538.json\n",
            "👌 JSL-Home is up to date! \n",
            "👌 Everything is already installed, no changes made\n"
          ]
        }
      ],
      "source": [
        "nlp.install()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "DCl5ErZkNNLk",
      "metadata": {
        "id": "DCl5ErZkNNLk"
      },
      "source": [
        "# Starting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "wRXTnNl3j51w",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wRXTnNl3j51w",
        "outputId": "d9d12ed0-cdd5-4f61-cb5d-767be6b033fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Spark Session already created, some configs may not take.\n",
            "👌 Detected license file /content/spark_nlp_for_healthcare_spark_ocr_6538.json\n"
          ]
        }
      ],
      "source": [
        "spark = nlp.start()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d2cd4221-fbca-4ca1-86a9-65e6264c4ad1",
      "metadata": {
        "id": "d2cd4221-fbca-4ca1-86a9-65e6264c4ad1"
      },
      "source": [
        "# About Data Augmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bf9835fd-9def-44e4-b022-e8db0f045fec",
      "metadata": {
        "id": "bf9835fd-9def-44e4-b022-e8db0f045fec"
      },
      "source": [
        "__Data Augmentation__ is the process of increase an extracted datapoint with external sources. \n",
        "\n",
        "For example, let's suppose I work with a document which mentions the company _Apple_. We can extract that entity using using NER.\n",
        "\n",
        "But we can do much more than that! Public companies have a lot of information published in the Internet about them. We can check legal and financial information from those companies in Legal NLP, in an offline-mode!\n",
        "\n",
        "With __Chunk Mappers__, we can use external sources, as _SEC Edgar, Nasdaq_ or even _Wikidata_, to enrich `Apple` with much more information, allowing us to take better decisions.\n",
        "\n",
        "Let's see how to do it."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eef8c0e5-6793-4db5-ab39-f6381c9e500d",
      "metadata": {
        "id": "eef8c0e5-6793-4db5-ab39-f6381c9e500d"
      },
      "source": [
        "# Step 1: Name Entity Recognition"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "612f9fe0-4d7c-4d6e-afc1-47a59b99f529",
      "metadata": {
        "id": "612f9fe0-4d7c-4d6e-afc1-47a59b99f529"
      },
      "source": [
        "Let's suppose we get this news from scrapping the Internet, or from Wikipedia."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "ExkBVodzxYqf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "ExkBVodzxYqf",
        "outputId": "50a5dac7-b0ee-4c18-8965-81d6329009f3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'APPLE, INC. became the first publicly traded U.S. company to be valued at over $1 trillion in August 2018, then $2 trillion in August 2020, and most recently $3 trillion in January 2022. '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "text = \"\"\"APPLE, INC. became the first publicly traded U.S. company to be valued at over $1 trillion in August 2018, then $2 trillion in August 2020, and most recently $3 trillion in January 2022. \"\"\"\n",
        "\n",
        "text"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "71a5e806-9659-4b41-8ab6-38f81b26797f",
      "metadata": {
        "id": "71a5e806-9659-4b41-8ab6-38f81b26797f"
      },
      "source": [
        "Firstly, We use NER model to extract the companies name from the text."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "id": "cb765952-24c2-48b6-8d86-5413b13bd9fa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cb765952-24c2-48b6-8d86-5413b13bd9fa",
        "outputId": "ee7be705-044f-49ed-9d50-b53e9498bf97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sentence_detector_dl download started this may take some time.\n",
            "Approximate size to download 514.9 KB\n",
            "[OK!]\n",
            "bert_embeddings_sec_bert_base download started this may take some time.\n",
            "Approximate size to download 390.4 MB\n",
            "[OK!]\n",
            "legner_orgs_prods_alias download started this may take some time.\n",
            "[OK!]\n"
          ]
        }
      ],
      "source": [
        "documentAssembler = nlp.DocumentAssembler()\\\n",
        "    .setInputCol(\"text\")\\\n",
        "    .setOutputCol(\"document\")\n",
        "    \n",
        "sentenceDetector = nlp.SentenceDetectorDLModel.pretrained(\"sentence_detector_dl\",\"xx\")\\\n",
        "    .setInputCols([\"document\"])\\\n",
        "    .setOutputCol(\"sentence\")\n",
        "\n",
        "tokenizer = nlp.Tokenizer()\\\n",
        "    .setInputCols([\"sentence\"])\\\n",
        "    .setOutputCol(\"token\")\n",
        "\n",
        "embeddings = nlp.BertEmbeddings.pretrained(\"bert_embeddings_sec_bert_base\",\"en\") \\\n",
        "    .setInputCols([\"sentence\", \"token\"]) \\\n",
        "    .setOutputCol(\"embeddings\")\n",
        "\n",
        "ner_model = legal.NerModel.pretrained(\"legner_orgs_prods_alias\", \"en\", \"legal/models\")\\\n",
        "    .setInputCols([\"sentence\", \"token\", \"embeddings\"])\\\n",
        "    .setOutputCol(\"ner\")\n",
        "        \n",
        "ner_converter = nlp.NerConverter()\\\n",
        "    .setInputCols([\"sentence\",\"token\",\"ner\"])\\\n",
        "    .setOutputCol(\"ner_chunk\")\n",
        "\n",
        "nlpPipeline = nlp.Pipeline(stages=[\n",
        "        documentAssembler,\n",
        "        sentenceDetector,\n",
        "        tokenizer,\n",
        "        embeddings,\n",
        "        ner_model,\n",
        "        ner_converter,\n",
        "])\n",
        "\n",
        "empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n",
        "\n",
        "model = nlpPipeline.fit(empty_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "37eae9a4-52e1-400e-a1dd-effc6ed1da35",
      "metadata": {
        "id": "37eae9a4-52e1-400e-a1dd-effc6ed1da35"
      },
      "source": [
        "## We use LightPipelines to get the result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "id": "76a05a20-b9b1-4868-a198-0e950c05a786",
      "metadata": {
        "id": "76a05a20-b9b1-4868-a198-0e950c05a786"
      },
      "outputs": [],
      "source": [
        "lp_ner = nlp.LightPipeline(model)\n",
        "\n",
        "ner_result = lp_ner.fullAnnotate(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "id": "k9fWYB1Nptid",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "k9fWYB1Nptid",
        "outputId": "3651f935-a2a6-48f1-9210-3c8da53b73ba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        chunks  begin  end entities\n",
              "0  APPLE, INC.      0   10      ORG"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e6bf4c7-099d-4a14-8160-bc83811af32e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>chunks</th>\n",
              "      <th>begin</th>\n",
              "      <th>end</th>\n",
              "      <th>entities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>APPLE, INC.</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>ORG</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e6bf4c7-099d-4a14-8160-bc83811af32e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0e6bf4c7-099d-4a14-8160-bc83811af32e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0e6bf4c7-099d-4a14-8160-bc83811af32e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "chunks = []\n",
        "entities = []\n",
        "begin = []\n",
        "end = []\n",
        "\n",
        "for n in ner_result[0]['ner_chunk']:\n",
        "        \n",
        "    begin.append(n.begin)\n",
        "    end.append(n.end)\n",
        "    chunks.append(n.result)\n",
        "    entities.append(n.metadata['entity']) \n",
        "    \n",
        "df = pd.DataFrame({'chunks':chunks, 'begin': begin, 'end':end, 'entities':entities})\n",
        "\n",
        "df.head(20)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9fe41161-c8fd-467e-9fff-5d4fe1cb5160",
      "metadata": {
        "id": "9fe41161-c8fd-467e-9fff-5d4fe1cb5160"
      },
      "source": [
        "Alright! Company names has been detected as an organization. "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 2: Mapping NER to External Data Using Chunk Mappers (offline)"
      ],
      "metadata": {
        "id": "GUqa_TuekdvE"
      },
      "id": "GUqa_TuekdvE"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Very often, the name of the organizations we find in texts are not their official name. For example, if we train to find `APPLE, INC` in SEC Edgar, we won't find it as it is.\n",
        "\n",
        "Every data provider may have different versions of the official names. Most of them will include organization types as `Inc`, `Corp` etc. But again, Some others may have `Inc.`, `Incorporated`, etc.\n",
        "\n",
        "**ChunkMappers** by default work with exact matches, so before being able to map our ORG detected by NER to Chunk Mappers, we need to:\n",
        "1. Either enable Fuzzy Matching in Chunk Mappers (Section 2a);\n",
        "2. Or normalize the company name with Entity Resolvers (Section 2n);"
      ],
      "metadata": {
        "id": "edJ23P6tkfd0"
      },
      "id": "edJ23P6tkfd0"
    },
    {
      "cell_type": "markdown",
      "source": [
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)"
      ],
      "metadata": {
        "id": "qNT8JNpVlm7C"
      },
      "id": "qNT8JNpVlm7C"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's suppose we want to manually get information about these companies.\n",
        "\n",
        "Since it's a public US company, we can go to [SEC Edgar's database](https://www.sec.gov/edgar/searchedgar/companysearch) and look for it.\n",
        "\n",
        "THere is no `APPLE, INC.` in Edgar, but another variation of it (`APPLE INC`). Let's check the two methods we can apply to do the mapping from one to another."
      ],
      "metadata": {
        "id": "Kqu-on0TpJeq"
      },
      "id": "Kqu-on0TpJeq"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2a) ChunkMappers Fuzzy Matching\n",
        "\n"
      ],
      "metadata": {
        "id": "mrgcumq6lL30"
      },
      "id": "mrgcumq6lL30"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's get several variations of Apple to see how we can use Fuzzy Matching to get the official name of Apple in Edgar."
      ],
      "metadata": {
        "id": "GbSxKALIl-0n"
      },
      "id": "GbSxKALIl-0n"
    },
    {
      "cell_type": "code",
      "source": [
        "ORG = df['chunks'].tolist()\n",
        "\n",
        "ORG"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPio13sfl6AJ",
        "outputId": "57877e97-359d-4a15-e364-066789693033"
      },
      "id": "xPio13sfl6AJ",
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['APPLE, INC.']"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "document_assembler = nlp.DocumentAssembler() \\\n",
        "    .setInputCol(\"text\") \\\n",
        "    .setOutputCol(\"document\")\n",
        "\n",
        "# Posible distance metrics: ['levenshtein', 'longest-common-subsequence', 'cosine']\n",
        "CM = legal.ChunkMapperModel().pretrained(\"legmapper_edgar_companyname\", \"en\", \"legal/models\")\\\n",
        "    .setInputCols([\"document\"])\\\n",
        "    .setOutputCol(\"mappings\")\\\n",
        "    .setEnableFuzzyMatching(True)\\\n",
        "    .setEnableCharFingerprintMatching(False)\\\n",
        "    .setFuzzyMatchingDistances(['cosine'])\\\n",
        "    .setFuzzyMatchingDistanceThresholds([5])\n",
        "\n",
        "cm_pipeline = nlp.Pipeline(stages=[document_assembler, CM])\n",
        "\n",
        "empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n",
        "\n",
        "fit_cm_pipeline = cm_pipeline.fit(empty_data)\n",
        "\n",
        "lp = nlp.LightPipeline(fit_cm_pipeline)\n",
        "\n",
        "res = lp.fullAnnotate(ORG)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8o7VyhQwlyih",
        "outputId": "f236f0bf-ac60-4c0d-a8f2-6b63a340dc9a"
      },
      "id": "8o7VyhQwlyih",
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "legmapper_edgar_companyname download started this may take some time.\n",
            "[OK!]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for r in res:\n",
        "  for map in r['mappings']:\n",
        "    print(map)\n",
        "  print('\\n')"
      ],
      "metadata": {
        "id": "QmVdXHasmIOQ"
      },
      "id": "QmVdXHasmIOQ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**We have been able to successfully retrieve the information in Edgar using different variations of the Company Name `Apple`!**"
      ],
      "metadata": {
        "id": "Np8qn9z4mehf"
      },
      "id": "Np8qn9z4mehf"
    },
    {
      "cell_type": "markdown",
      "id": "eb3e2808-3550-46d9-835b-f747cac4123c",
      "metadata": {
        "id": "eb3e2808-3550-46d9-835b-f747cac4123c"
      },
      "source": [
        "## Step 2b: Using Entity Resolvers for Company Names Normalization"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6eb8eb7f-1bda-454c-8318-bb4df34f0b6a",
      "metadata": {
        "id": "6eb8eb7f-1bda-454c-8318-bb4df34f0b6a"
      },
      "source": [
        "`Company Name Normalization` is the process of obtaining the name of the company used by data providers, usually the **\"official\"** name of the company.\n",
        "\n",
        "Sometimes, some data providers may have different versions of the name with different punctuation. For example, for Meta:\n",
        "- Meta Platforms, Inc.\n",
        "- Meta Platforms Inc.\n",
        "- Meta Platforms, Inc\n",
        "- etc\n",
        "\n",
        "So, it's mandatory we do `Company Normalization` taking into account the database / datasource provider we want to extract data from. The data providers we have are:\n",
        "- SEC Edgar\n",
        "- Wikidata\n",
        "- etc.\n",
        "\n",
        "Let's normalize `APPLE INC` to the official name in _SEC Edgar_."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "2564fd32-99ec-451c-ae34-2792cf3036ef",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2564fd32-99ec-451c-ae34-2792cf3036ef",
        "outputId": "ae413a23-a54d-4816-d181-ae7db8c48dc9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tfhub_use download started this may take some time.\n",
            "Approximate size to download 923.7 MB\n",
            "[OK!]\n",
            "legel_edgar_company_name download started this may take some time.\n",
            "[OK!]\n"
          ]
        }
      ],
      "source": [
        "embeddings = nlp.UniversalSentenceEncoder.pretrained(\"tfhub_use\", \"en\") \\\n",
        "      .setInputCols(\"document\") \\\n",
        "      .setOutputCol(\"sentence_embeddings\")\n",
        "    \n",
        "resolver = legal.SentenceEntityResolverModel.pretrained(\"legel_edgar_company_name\", \"en\", \"legal/models\")\\\n",
        "      .setInputCols([\"sentence_embeddings\"]) \\\n",
        "      .setOutputCol(\"resolution\")\\\n",
        "      .setDistanceFunction(\"EUCLIDEAN\")\n",
        "\n",
        "pipelineModel = nlp.PipelineModel(\n",
        "      stages = [\n",
        "          documentAssembler,\n",
        "          embeddings,\n",
        "          resolver])\n",
        "\n",
        "lp_res = nlp.LightPipeline(pipelineModel)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "id": "36a6d5f7-6477-4219-acf7-53a95d1ebea3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36a6d5f7-6477-4219-acf7-53a95d1ebea3",
        "outputId": "5fb81c5b-5801-4669-9719-0eca77375553"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['APPLE, INC.']"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "ORG = df['chunks'].tolist()\n",
        "\n",
        "ORG"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "id": "c319f7b8-fe7e-4408-9960-15e7675a36c1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c319f7b8-fe7e-4408-9960-15e7675a36c1",
        "outputId": "cc7d1981-c2e6-4731-daa3-8c7924eb334a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'document': ['APPLE, INC.'],\n",
              "  'sentence_embeddings': ['APPLE, INC.'],\n",
              "  'resolution': ['APPLE INC']}]"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "el_res = lp_res.annotate(ORG)\n",
        "\n",
        "el_res"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "109efb72-bfae-413b-b1cb-ef1c57b9b66d",
      "metadata": {
        "id": "109efb72-bfae-413b-b1cb-ef1c57b9b66d"
      },
      "source": [
        "Here is our normalized name for:\n",
        "- Apple: `APPLE INC`.\n",
        "\n",
        "Now, let's see which information is available in Edgar database for `APPLE INC` company"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "id": "DzcZS7pLuYJz",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzcZS7pLuYJz",
        "outputId": "3289fa28-38f7-4aa6-fe85-36248087bb4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['APPLE INC']"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "NORM_ORG = el_res[0][\"resolution\"]\n",
        "\n",
        "NORM_ORG"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "85e51e16-56a2-4dad-a27c-1047a36ecea3",
      "metadata": {
        "id": "85e51e16-56a2-4dad-a27c-1047a36ecea3"
      },
      "source": [
        "## And now, we do exact match with the normalized version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "id": "b246357e-0ab7-489b-9dc0-6d74d3eb97ef",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b246357e-0ab7-489b-9dc0-6d74d3eb97ef",
        "outputId": "ad3831e7-7543-429a-9e93-b5e5f6426a86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "legmapper_edgar_companyname download started this may take some time.\n",
            "[OK!]\n"
          ]
        }
      ],
      "source": [
        "document_assembler = nlp.DocumentAssembler() \\\n",
        "    .setInputCol(\"text\") \\\n",
        "    .setOutputCol(\"document\")\n",
        "\n",
        "# Posible distance metrics: ['levenshtein', 'longest-common-subsequence', 'cosine']\n",
        "CM = legal.ChunkMapperModel().pretrained(\"legmapper_edgar_companyname\", \"en\", \"legal/models\")\\\n",
        "    .setInputCols([\"document\"])\\\n",
        "    .setOutputCol(\"mappings\")\\\n",
        "\n",
        "cm_pipeline = nlp.Pipeline(stages=[document_assembler, CM])\n",
        "\n",
        "empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n",
        "\n",
        "fit_cm_pipeline = cm_pipeline.fit(empty_data)\n",
        "\n",
        "lp = nlp.LightPipeline(fit_cm_pipeline)\n",
        "\n",
        "res = lp.fullAnnotate(NORM_ORG)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "id": "340f193f-dabd-4e41-96e0-0afe0a22ed8b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "340f193f-dabd-4e41-96e0-0afe0a22ed8b",
        "outputId": "e561c120-128c-420a-8592-bb0554cd7308"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Annotation(labeled_dependency, 0, 8, APPLE INC, {'__trained__': 'APPLE INC', 'relation': 'name', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'name'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, ELECTRONIC COMPUTERS [3571], {'__trained__': 'APPLE INC', 'relation': 'sic', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'sic'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, 3571, {'__trained__': 'APPLE INC', 'relation': 'sic_code', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '0', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'sic_code'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, 942404110, {'__trained__': 'APPLE INC', 'relation': 'irs_number', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '0', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'irs_number'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, 930, {'__trained__': 'APPLE INC', 'relation': 'fiscal_year_end', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '924:::927:::0:::929:::925:::926', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'fiscal_year_end'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, CA, {'__trained__': 'APPLE INC', 'relation': 'state_location', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'state_location'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, CA, {'__trained__': 'APPLE INC', 'relation': 'state_incorporation', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'state_incorporation'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, ONE INFINITE LOOP, {'__trained__': 'APPLE INC', 'relation': 'business_street', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': 'ONE APPLE PARK WAY:::1 INFINITE LOOP', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'business_street'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, CUPERTINO, {'__trained__': 'APPLE INC', 'relation': 'business_city', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'business_city'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, CA, {'__trained__': 'APPLE INC', 'relation': 'business_state', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'business_state'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, 95014, {'__trained__': 'APPLE INC', 'relation': 'business_zip', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'business_zip'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, (408) 996-1010, {'__trained__': 'APPLE INC', 'relation': 'business_phone', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '4089961010', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'business_phone'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, APPLE COMPUTER INC, {'__trained__': 'APPLE INC', 'relation': 'former_name', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'former_name'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, 19970808, {'__trained__': 'APPLE INC', 'relation': 'former_name_date', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'former_name_date'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, 2017-02-01, {'__trained__': 'APPLE INC', 'relation': 'date', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '2017-11-03:::2016-01-27:::2016-07-27:::2016-10-26:::2019-01-30:::2018-05-02:::2018-08-01:::2018-11-05:::2015-01-27:::2015-01-28:::2015-04-28:::2015-07-22:::2015-10-02:::2015-10-28:::2015-12-14:::2015-12-17:::2014-01-03:::2015-12-28:::2015-12-22:::2015-12-11:::2013-12-17:::2014-01-10:::2014-01-27:::2014-02-04:::2014-01-23:::2014-02-12:::2014-01-28:::2014-01-31:::2014-02-10:::2014-02-07:::2014-01-08:::2014-03-05:::2014-03-06:::2014-03-18:::2014-03-04:::2014-03-11:::2014-02-27:::2014-03-31:::2013-01-24:::2013-10-30:::2013-12-26:::2012-01-25:::2012-10-31:::2012-07-25:::2011-07-20:::2011-10-26:::2010-07-21:::2010-10-27:::2009-07-22:::2009-10-27:::2008-05-01:::2008-02-01:::2008-07-23:::2007-02-02:::2008-11-05:::2007-11-15', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'date'}, [])\n",
            "Annotation(labeled_dependency, 0, 8, 320193, {'__trained__': 'APPLE INC', 'relation': 'company_id', '__distance_function__': 'levenshtein', 'ops': '0.0', 'all_relations': '', 'entity': 'APPLE INC', 'distance': '0.0', '__relation_name__': 'company_id'}, [])\n"
          ]
        }
      ],
      "source": [
        "for r in res:\n",
        "  for map in r['mappings']:\n",
        "    print(map)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "UP1E0vZOpZ3h",
      "metadata": {
        "id": "UP1E0vZOpZ3h"
      },
      "source": [
        "# Train Your Own ChunkMapper Model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "34f517bb-adde-4daa-b12d-921b37dd6d38",
      "metadata": {
        "id": "34f517bb-adde-4daa-b12d-921b37dd6d38"
      },
      "source": [
        "Here, we will train a ChunkMapper model with 1000 samples"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Zu2TOFZ65AEg",
      "metadata": {
        "id": "Zu2TOFZ65AEg"
      },
      "source": [
        "### Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "id": "mvHfQ12f5Ef4",
      "metadata": {
        "id": "mvHfQ12f5Ef4"
      },
      "outputs": [],
      "source": [
        "! wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/legal-nlp/data/sample_openedgar.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "id": "8be8c43c-ebf2-4d1e-b98b-31c0fe68cabc",
      "metadata": {
        "id": "8be8c43c-ebf2-4d1e-b98b-31c0fe68cabc"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "with open('sample_openedgar.json', 'r') as f:\n",
        "    company_json = json.load(f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "be07f846-109f-4834-89ff-483bd00c5ab5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "be07f846-109f-4834-89ff-483bd00c5ab5",
        "outputId": "5f8ea398-3fb2-48a7-f29f-bc491379087e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'key': 'StepOne Personal Health, Inc.',\n",
              " 'relations': [{'key': 'name', 'values': ['StepOne Personal Health, Inc.']},\n",
              "  {'key': 'sic',\n",
              "   'values': ['SERVICES-COMPUTER PROCESSING & DATA PREPARATION [7374]']},\n",
              "  {'key': 'sic_code', 'values': [7374, 0]},\n",
              "  {'key': 'irs_number', 'values': [900785095, 0]},\n",
              "  {'key': 'fiscal_year_end', 'values': [1231, 0]},\n",
              "  {'key': 'state_location', 'values': ['MO', 'CA']},\n",
              "  {'key': 'state_incorporation', 'values': ['DE']},\n",
              "  {'key': 'business_street',\n",
              "   'values': ['509 SOUGH WALL AVENUE', 'C/O: ALTERNATIVE SECURITIES MARKET']},\n",
              "  {'key': 'business_city', 'values': ['JOPLIN', 'BEVERLY HILLS']},\n",
              "  {'key': 'business_state', 'values': ['MO', 'CA']},\n",
              "  {'key': 'business_zip', 'values': ['64801', '90210']},\n",
              "  {'key': 'business_phone', 'values': ['650-454-0862', '213-407-4386']},\n",
              "  {'key': 'former_name', 'values': ['']},\n",
              "  {'key': 'former_name_date', 'values': ['']},\n",
              "  {'key': 'date',\n",
              "   'values': ['2017-01-24',\n",
              "    '2015-03-23',\n",
              "    '2015-08-18',\n",
              "    '2015-08-25',\n",
              "    '2015-10-13',\n",
              "    '2015-10-16',\n",
              "    '2015-10-29',\n",
              "    '2015-10-30',\n",
              "    '2015-12-10',\n",
              "    '2015-12-22']},\n",
              "  {'key': 'company_id', 'values': [1606811]}]}"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "company_json['mappings'][1]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "jiN1I0L_vqPK",
      "metadata": {
        "id": "jiN1I0L_vqPK"
      },
      "source": [
        "### Check a sample company"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "id": "80cd73e4-288c-44fc-8ff3-fdded39ba25a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "80cd73e4-288c-44fc-8ff3-fdded39ba25a",
        "outputId": "9b5db762-37b8-4dc2-b96a-f378fa2d9275"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'key': 'StepOne Personal Health, Inc.', 'relations': [{'key': 'name', 'values': ['StepOne Personal Health, Inc.']}, {'key': 'sic', 'values': ['SERVICES-COMPUTER PROCESSING & DATA PREPARATION [7374]']}, {'key': 'sic_code', 'values': [7374, 0]}, {'key': 'irs_number', 'values': [900785095, 0]}, {'key': 'fiscal_year_end', 'values': [1231, 0]}, {'key': 'state_location', 'values': ['MO', 'CA']}, {'key': 'state_incorporation', 'values': ['DE']}, {'key': 'business_street', 'values': ['509 SOUGH WALL AVENUE', 'C/O: ALTERNATIVE SECURITIES MARKET']}, {'key': 'business_city', 'values': ['JOPLIN', 'BEVERLY HILLS']}, {'key': 'business_state', 'values': ['MO', 'CA']}, {'key': 'business_zip', 'values': ['64801', '90210']}, {'key': 'business_phone', 'values': ['650-454-0862', '213-407-4386']}, {'key': 'former_name', 'values': ['']}, {'key': 'former_name_date', 'values': ['']}, {'key': 'date', 'values': ['2017-01-24', '2015-03-23', '2015-08-18', '2015-08-25', '2015-10-13', '2015-10-16', '2015-10-29', '2015-10-30', '2015-12-10', '2015-12-22']}, {'key': 'company_id', 'values': [1606811]}]}\n"
          ]
        }
      ],
      "source": [
        "for x in company_json['mappings']:\n",
        "    if 'StepOne Personal Health, Inc.' in x['key']:\n",
        "        print(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "loOpl4LqvvPi",
      "metadata": {
        "id": "loOpl4LqvvPi"
      },
      "source": [
        "### Check all keys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "id": "e9fe7709-868d-453f-a162-7fa737f50989",
      "metadata": {
        "id": "e9fe7709-868d-453f-a162-7fa737f50989"
      },
      "outputs": [],
      "source": [
        "all_rels = [x['key'] for x in company_json['mappings'][0]['relations']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "id": "c47604d3-9028-41a1-a0be-a669d105beb3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c47604d3-9028-41a1-a0be-a669d105beb3",
        "outputId": "eecf2a27-3a94-44fb-b797-06c490ae565b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['name',\n",
              " 'sic',\n",
              " 'sic_code',\n",
              " 'irs_number',\n",
              " 'fiscal_year_end',\n",
              " 'state_location',\n",
              " 'state_incorporation',\n",
              " 'business_street',\n",
              " 'business_city',\n",
              " 'business_state',\n",
              " 'business_zip',\n",
              " 'business_phone',\n",
              " 'former_name',\n",
              " 'former_name_date',\n",
              " 'date',\n",
              " 'company_id']"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "all_rels"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7opshMS1vx1H",
      "metadata": {
        "id": "7opshMS1vx1H"
      },
      "source": [
        "### Create ChunkMapperApproach"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "id": "b5d078b5-bd12-4d4a-ade7-3200a278e061",
      "metadata": {
        "id": "b5d078b5-bd12-4d4a-ade7-3200a278e061",
        "tags": []
      },
      "outputs": [],
      "source": [
        "chunkerMapper = legal.ChunkMapperApproach()\\\n",
        "      .setInputCols([\"ner_chunk\"])\\\n",
        "      .setOutputCol(\"mappings\")\\\n",
        "      .setDictionary(\"sample_openedgar.json\")\\\n",
        "      .setRels(all_rels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "id": "940ca6b5-a603-4060-a112-d1f407834d4f",
      "metadata": {
        "id": "940ca6b5-a603-4060-a112-d1f407834d4f"
      },
      "outputs": [],
      "source": [
        "empty_dataset = spark.createDataFrame([[\"\"]]).toDF(\"text\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "6bd7485a-6f3c-442a-8d74-a07ae0385d56",
      "metadata": {
        "id": "6bd7485a-6f3c-442a-8d74-a07ae0385d56"
      },
      "outputs": [],
      "source": [
        "fit_CM = chunkerMapper.fit(empty_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "id": "d3336018-e50c-4cb9-a07f-755bae236d80",
      "metadata": {
        "id": "d3336018-e50c-4cb9-a07f-755bae236d80"
      },
      "outputs": [],
      "source": [
        "# Save model\n",
        "fit_CM.write().overwrite().save('openedgar_2000_2022_company_mapper')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Cg0oUxTbv3oE",
      "metadata": {
        "id": "Cg0oUxTbv3oE"
      },
      "source": [
        "### Let's test our ChunkMapper model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "DlxWvIcTsqTd",
      "metadata": {
        "id": "DlxWvIcTsqTd"
      },
      "outputs": [],
      "source": [
        "text = [\"\"\"StepOne Personal Health, Inc. is an American solar cell and engineered wafer manufacturer.\"\"\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "id": "_E5Szc8ast4n",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_E5Szc8ast4n",
        "outputId": "4dafb808-6132-40ba-973f-eaefad1672fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'document': [Annotation(document, 0, 89, StepOne Personal Health, Inc. is an American solar cell and engineered wafer manufacturer., {}, [])],\n",
              "  'ner_chunk': [Annotation(chunk, 0, 27, StepOne Personal Health, Inc, {'entity': 'ORG', 'sentence': '0', 'chunk': '0', 'confidence': '0.88218004'}, [])],\n",
              "  'token': [Annotation(token, 0, 6, StepOne, {'sentence': '0'}, []),\n",
              "   Annotation(token, 8, 15, Personal, {'sentence': '0'}, []),\n",
              "   Annotation(token, 17, 22, Health, {'sentence': '0'}, []),\n",
              "   Annotation(token, 23, 23, ,, {'sentence': '0'}, []),\n",
              "   Annotation(token, 25, 27, Inc, {'sentence': '0'}, []),\n",
              "   Annotation(token, 28, 28, ., {'sentence': '0'}, []),\n",
              "   Annotation(token, 30, 31, is, {'sentence': '0'}, []),\n",
              "   Annotation(token, 33, 34, an, {'sentence': '0'}, []),\n",
              "   Annotation(token, 36, 43, American, {'sentence': '0'}, []),\n",
              "   Annotation(token, 45, 49, solar, {'sentence': '0'}, []),\n",
              "   Annotation(token, 51, 54, cell, {'sentence': '0'}, []),\n",
              "   Annotation(token, 56, 58, and, {'sentence': '0'}, []),\n",
              "   Annotation(token, 60, 69, engineered, {'sentence': '0'}, []),\n",
              "   Annotation(token, 71, 75, wafer, {'sentence': '0'}, []),\n",
              "   Annotation(token, 77, 88, manufacturer, {'sentence': '0'}, []),\n",
              "   Annotation(token, 89, 89, ., {'sentence': '0'}, [])],\n",
              "  'ner': [Annotation(named_entity, 0, 6, B-ORG, {'word': 'StepOne', 'confidence': '0.7094', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 8, 15, I-ORG, {'word': 'Personal', 'confidence': '0.7036', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 17, 22, I-ORG, {'word': 'Health', 'confidence': '0.9988', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 23, 23, I-ORG, {'word': ',', 'confidence': '0.9995', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 25, 27, I-ORG, {'word': 'Inc', 'confidence': '0.9996', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 28, 28, O, {'word': '.', 'confidence': '0.6455', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 30, 31, O, {'word': 'is', 'confidence': '1.0', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 33, 34, O, {'word': 'an', 'confidence': '1.0', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 36, 43, O, {'word': 'American', 'confidence': '0.9938', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 45, 49, O, {'word': 'solar', 'confidence': '1.0', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 51, 54, O, {'word': 'cell', 'confidence': '1.0', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 56, 58, O, {'word': 'and', 'confidence': '1.0', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 60, 69, O, {'word': 'engineered', 'confidence': '1.0', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 71, 75, O, {'word': 'wafer', 'confidence': '1.0', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 77, 88, O, {'word': 'manufacturer', 'confidence': '1.0', 'sentence': '0'}, []),\n",
              "   Annotation(named_entity, 89, 89, O, {'word': '.', 'confidence': '1.0', 'sentence': '0'}, [])],\n",
              "  'embeddings': [Annotation(word_embeddings, 0, 6, StepOne, {'isOOV': 'false', 'pieceId': '101', 'isWordStart': 'true', 'token': 'StepOne', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 8, 15, Personal, {'isOOV': 'false', 'pieceId': '101', 'isWordStart': 'true', 'token': 'Personal', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 17, 22, Health, {'isOOV': 'false', 'pieceId': '101', 'isWordStart': 'true', 'token': 'Health', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 23, 23, ,, {'isOOV': 'false', 'pieceId': '116', 'isWordStart': 'true', 'token': ',', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 25, 27, Inc, {'isOOV': 'false', 'pieceId': '101', 'isWordStart': 'true', 'token': 'Inc', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 28, 28, ., {'isOOV': 'false', 'pieceId': '118', 'isWordStart': 'true', 'token': '.', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 30, 31, is, {'isOOV': 'false', 'pieceId': '686', 'isWordStart': 'true', 'token': 'is', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 33, 34, an, {'isOOV': 'false', 'pieceId': '620', 'isWordStart': 'true', 'token': 'an', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 36, 43, American, {'isOOV': 'false', 'pieceId': '101', 'isWordStart': 'true', 'token': 'American', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 45, 49, solar, {'isOOV': 'false', 'pieceId': '5634', 'isWordStart': 'true', 'token': 'solar', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 51, 54, cell, {'isOOV': 'false', 'pieceId': '3318', 'isWordStart': 'true', 'token': 'cell', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 56, 58, and, {'isOOV': 'false', 'pieceId': '624', 'isWordStart': 'true', 'token': 'and', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 60, 69, engineered, {'isOOV': 'false', 'pieceId': '8109', 'isWordStart': 'true', 'token': 'engineered', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 71, 75, wafer, {'isOOV': 'false', 'pieceId': '8326', 'isWordStart': 'true', 'token': 'wafer', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 77, 88, manufacturer, {'isOOV': 'false', 'pieceId': '4382', 'isWordStart': 'true', 'token': 'manufacturer', 'sentence': '0'}, []),\n",
              "   Annotation(word_embeddings, 89, 89, ., {'isOOV': 'false', 'pieceId': '118', 'isWordStart': 'true', 'token': '.', 'sentence': '0'}, [])],\n",
              "  'sentence': [Annotation(document, 0, 89, StepOne Personal Health, Inc. is an American solar cell and engineered wafer manufacturer., {'sentence': '0'}, [])]}]"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "# We get company name from sample text\n",
        "\n",
        "ner_result = lp_ner.fullAnnotate(text)\n",
        "\n",
        "ner_result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "id": "uGcQcPIntV5e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "uGcQcPIntV5e",
        "outputId": "9a12db3d-1579-40dc-e4d0-6b84562c8b44"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'StepOne Personal Health, Inc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "ORG = ner_result[0][\"ner_chunk\"][0].result\n",
        "\n",
        "ORG"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "id": "vFFo5P9Nttss",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFFo5P9Nttss",
        "outputId": "aeb0f0f9-3f73-4e31-f175-bed87a55c4af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'document': ['StepOne Personal Health, Inc'],\n",
              " 'sentence_embeddings': ['StepOne Personal Health, Inc'],\n",
              " 'resolution': ['StepOne Personal Health, Inc.']}"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "# We normalize company name\n",
        "\n",
        "el_res = lp_res.annotate(ORG)\n",
        "\n",
        "el_res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "id": "EbCbbGZ9ttvP",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbCbbGZ9ttvP",
        "outputId": "c008793a-f2ae-46bd-a53a-59678fb15fe7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['StepOne Personal Health, Inc.']"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "NORM_ORG = el_res[\"resolution\"]\n",
        "\n",
        "NORM_ORG"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "z-2Q7nnywUaH",
      "metadata": {
        "id": "z-2Q7nnywUaH"
      },
      "source": [
        "### Let's load our ChunkMapper model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "id": "Vx3_V33_ttyC",
      "metadata": {
        "id": "Vx3_V33_ttyC"
      },
      "outputs": [],
      "source": [
        "documentAssembler = nlp.DocumentAssembler()\\\n",
        "    .setInputCol(\"text\")\\\n",
        "    .setOutputCol(\"document\")\n",
        "\n",
        "chunkAssembler = nlp.Doc2Chunk() \\\n",
        "    .setInputCols(\"document\") \\\n",
        "    .setOutputCol(\"chunk\") \\\n",
        "    .setIsArray(False)\n",
        "\n",
        "CM =legal.ChunkMapperModel().load(\"openedgar_2000_2022_company_mapper\")\\\n",
        "      .setInputCols([\"chunk\"])\\\n",
        "      .setOutputCol(\"mappings\")\n",
        "\n",
        "cm_pipeline = nlp.Pipeline(stages=[documentAssembler, \n",
        "                                   chunkAssembler, \n",
        "                                   CM])\n",
        "\n",
        "fit_cm_pipeline = cm_pipeline.fit(empty_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "id": "w5I4muUkvJG7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w5I4muUkvJG7",
        "outputId": "bab9b3a3-779e-425d-ecb5-c11b171715da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+\n",
            "|                text|\n",
            "+--------------------+\n",
            "|StepOne Personal ...|\n",
            "+--------------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# LightPipelines don't support Doc2Chunk, so we will use here usual transform\n",
        "\n",
        "df = spark.createDataFrame([NORM_ORG]).toDF(\"text\")\n",
        "\n",
        "df.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "zS0Q-zamvOsT",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zS0Q-zamvOsT",
        "outputId": "eab618c1-89f4-4fd8-8318-a8a9f5602aa0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+--------------------+--------------------+--------------------+\n",
            "|                text|            document|               chunk|            mappings|\n",
            "+--------------------+--------------------+--------------------+--------------------+\n",
            "|StepOne Personal ...|[{document, 0, 28...|[{chunk, 0, 28, S...|[{labeled_depende...|\n",
            "+--------------------+--------------------+--------------------+--------------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "res = fit_cm_pipeline.transform(df)\n",
        "\n",
        "res.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "id": "e7b2c22c-49f6-4277-8636-62b876b0bf08",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7b2c22c-49f6-4277-8636-62b876b0bf08",
        "outputId": "d028fde3-8327-4f7a-8330-8bb30aed848b",
        "tags": []
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
            "|result                                                                                                                                                                                                 |\n",
            "+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
            "|[StepOne Personal Health, Inc., SERVICES-COMPUTER PROCESSING & DATA PREPARATION [7374], 7374, 900785095, 1231, MO, DE, 509 SOUGH WALL AVENUE, JOPLIN, MO, 64801, 650-454-0862, , , 2017-01-24, 1606811]|\n",
            "+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "res.select(\"mappings.result\").show(truncate=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "id": "fqeUMOeDuVUx",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqeUMOeDuVUx",
        "outputId": "f9c16c2b-6d39-48e6-ddcb-db5ac8cc99b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Row(mappings=[Row(annotatorType='labeled_dependency', begin=0, end=28, result='StepOne Personal Health, Inc.', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': '', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'name', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'name'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='SERVICES-COMPUTER PROCESSING & DATA PREPARATION [7374]', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': '', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'sic', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'sic'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='7374', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': '0', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'sic_code', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'sic_code'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='900785095', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': '0', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'irs_number', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'irs_number'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='1231', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': '0', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'fiscal_year_end', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'fiscal_year_end'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='MO', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': 'CA', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'state_location', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'state_location'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='DE', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': '', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'state_incorporation', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'state_incorporation'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='509 SOUGH WALL AVENUE', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': 'C/O: ALTERNATIVE SECURITIES MARKET', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'business_street', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'business_street'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='JOPLIN', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': 'BEVERLY HILLS', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'business_city', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'business_city'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='MO', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': 'CA', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'business_state', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'business_state'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='64801', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': '90210', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'business_zip', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'business_zip'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='650-454-0862', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': '213-407-4386', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'business_phone', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'business_phone'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': '', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'former_name', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'former_name'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': '', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'former_name_date', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'former_name_date'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='2017-01-24', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': '2015-03-23:::2015-08-18:::2015-08-25:::2015-10-13:::2015-10-16:::2015-10-29:::2015-10-30:::2015-12-10:::2015-12-22', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'date', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'date'}, embeddings=[]), Row(annotatorType='labeled_dependency', begin=0, end=28, result='1606811', metadata={'sentence': '0', 'ops': '0.0', 'distance': '0.0', 'all_relations': '', 'chunk': '0', '__trained__': 'StepOne Personal Health, Inc.', '__distance_function__': 'levenshtein', '__relation_name__': 'company_id', 'entity': 'StepOne Personal Health, Inc.', 'relation': 'company_id'}, embeddings=[])])]"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "r = res.select(\"mappings\").collect()\n",
        "r"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "id": "3ed118db-d108-4637-8d1f-67c7e5106458",
      "metadata": {
        "id": "3ed118db-d108-4637-8d1f-67c7e5106458"
      },
      "outputs": [],
      "source": [
        "json_dict = dict()\n",
        "for n in r[0]['mappings']:\n",
        "    json_dict[n.metadata['relation']] = str(n.result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "id": "0bee659b-9446-4255-9c67-2a685de56c56",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bee659b-9446-4255-9c67-2a685de56c56",
        "outputId": "0fd3c2d6-54c2-4c11-999d-2da95e2e7814"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "    \"business_city\": \"JOPLIN\",\n",
            "    \"business_phone\": \"650-454-0862\",\n",
            "    \"business_state\": \"MO\",\n",
            "    \"business_street\": \"509 SOUGH WALL AVENUE\",\n",
            "    \"business_zip\": \"64801\",\n",
            "    \"company_id\": \"1606811\",\n",
            "    \"date\": \"2017-01-24\",\n",
            "    \"fiscal_year_end\": \"1231\",\n",
            "    \"former_name\": \"\",\n",
            "    \"former_name_date\": \"\",\n",
            "    \"irs_number\": \"900785095\",\n",
            "    \"name\": \"StepOne Personal Health, Inc.\",\n",
            "    \"sic\": \"SERVICES-COMPUTER PROCESSING & DATA PREPARATION [7374]\",\n",
            "    \"sic_code\": \"7374\",\n",
            "    \"state_incorporation\": \"DE\",\n",
            "    \"state_location\": \"MO\"\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "print(json.dumps(json_dict, indent=4, sort_keys=True))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "toc_visible": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "tf-gpu",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7 (default, Sep 16 2021, 16:59:28) [MSC v.1916 64 bit (AMD64)]"
    },
    "vscode": {
      "interpreter": {
        "hash": "3f47d918ae832c68584484921185f5c85a1760864bf927a683dc6fb56366cc77"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}