legal-nlp/04.5.Classifying_with_WindowSplitting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWoEwxGWo1iI"
      },
      "source": [
        "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5NVFZYOIs_A"
      },
      "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/04.5.Classifying_with_WindowSplitting.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vpPa8JDRKJ6"
      },
      "source": [
        "# **Splitting with Contextual Window Embeddings**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "gk3kZHmNj51v"
      },
      "source": [
        "#🎬 Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_914itZsj51v",
        "pycharm": {
          "is_executing": true
        }
      },
      "outputs": [],
      "source": [
        "! pip install -q johnsnowlabs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPsbAnNoPt0Z"
      },
      "source": [
        "##🔗 Automatic Installation\n",
        "Using my.johnsnowlabs.com SSO"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fY0lcShkj51w",
        "pycharm": {
          "is_executing": true
        }
      },
      "outputs": [],
      "source": [
        "from johnsnowlabs import nlp, legal\n",
        "\n",
        "# nlp.install(force_browser=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "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": null,
      "metadata": {
        "id": "i57QV3-_P2sQ"
      },
      "outputs": [],
      "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",
      "metadata": {
        "id": "xGgNdFzZP_hQ"
      },
      "source": [
        "- Install it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OfmmPqknP4rR"
      },
      "outputs": [],
      "source": [
        "nlp.install()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCl5ErZkNNLk"
      },
      "source": [
        "#📌 Starting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wRXTnNl3j51w"
      },
      "outputs": [],
      "source": [
        "spark = nlp.start()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74b31459-0f72-47f1-90f1-1e4941c0e269"
      },
      "source": [
        "##📃 Sentence Splitting with Contextual Window Embeddings\n",
        "Classifiers, in order to properly manage the text length, require to split big documents into smaller chunks. This can be done at sentence, paragraph, section or page level.\n",
        "\n",
        "However, after splitting, the Classifier will only see the splits, and the rest of the text becomes out of reach for them to be taken into account.\n",
        "\n",
        "This creates several issues:\n",
        "\n",
        "1️⃣- Small splits may not have a meaning of their own if not combined with the surroundings.\n",
        "\n",
        "2️⃣- Splits may be ambiguous, and disambiguation may only happen taking into account the surroundings.\n",
        "\n",
        "Fortunately, `WindowedSentenceModel` can help to provide some context from the surroundings."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Comparing Classification of isolated sentences with contexualized"
      ],
      "metadata": {
        "id": "XOU8wnB2xvaB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, let's take a document and split it by sentences. Then, we are going to run a classifier and see the results."
      ],
      "metadata": {
        "id": "LQ1_PvUxx85B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/legal-nlp/data/mnda_example.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xx-DUy5Dx1or",
        "outputId": "70fa9878-d041-4ecb-bbeb-9d4091863bbc"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-04-25 09:44:06--  https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/legal-nlp/data/mnda_example.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10836 (11K) [text/plain]\n",
            "Saving to: ‘mnda_example.txt’\n",
            "\n",
            "\rmnda_example.txt      0%[                    ]       0  --.-KB/s               \rmnda_example.txt    100%[===================>]  10.58K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-04-25 09:44:06 (75.9 MB/s) - ‘mnda_example.txt’ saved [10836/10836]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!head mnda_example.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFG-WAxOzJOd",
        "outputId": "396750ce-1e57-4930-b005-39c9c02fd714"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1  \r\n",
            "MUTUAL NONDISCLOSURE AGREEMENT  \r\n",
            "This Mutual Nondisclosure Agreement (the “Agreement”) is made on _________ (“Effective  \r\n",
            "Date”) by and between:  \r\n",
            "(1) John Snow Labs, a Delaware corporation, registered at 16192 Coastal Highway,  \r\n",
            "Lewes, Delaware 19958 (“John Snow Labs”), and   \r\n",
            "(2) Achiles, S.L, a Spanish corporation, registered at Gran Via, 2º floor, Offices 9\r\n",
            "and 10.(“Company”),  \r\n",
            "(each a “party” and together the “parties”).  \r\n",
            "Recitals:  \r\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is an NDA text. We have a classifier for it which identifies which clause the text belongs to.\n",
        "\n",
        "However, it works much better with more text, so Context Windows will come in handy. Let's see them in action."
      ],
      "metadata": {
        "id": "Ybob9i1kzQOb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sentence splitting"
      ],
      "metadata": {
        "id": "KQFvdt2vzOI8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's get some sentences"
      ],
      "metadata": {
        "id": "1W0LwIPV0BTr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('mnda_example.txt', 'r') as f:\n",
        "  text = f.read()\n",
        "\n",
        "df = spark.createDataFrame([[text]]).toDF(\"text\")"
      ],
      "metadata": {
        "id": "8QYW8qGA0LXt"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create first just a Sentence Splitting pipeline"
      ],
      "metadata": {
        "id": "y2A3aAAv0EYc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "doc_assembler = nlp.DocumentAssembler()\\\n",
        "  .setInputCol(\"text\")\\\n",
        "  .setOutputCol(\"document\")\n",
        "\n",
        "sentence_detector = nlp.SentenceDetector()\\\n",
        "  .setInputCols([\"document\"])\\\n",
        "  .setOutputCol(\"isolated_sentence\")\n",
        "\n",
        "sentence_pipeline = nlp.Pipeline(stages=[doc_assembler, sentence_detector])\n",
        "\n",
        "sentence_pipeline_model = sentence_pipeline.fit(df)\n",
        "\n",
        "sentence_pipeline_lp = nlp.LightPipeline(sentence_pipeline_model)"
      ],
      "metadata": {
        "id": "HW3_DWuwzPfc"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql import functions as F\n",
        "\n",
        "isolated_sentences = sentence_pipeline_lp.annotate(text)['isolated_sentence'][:25]\n",
        "isolated_sentences"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mpEowas_0AI8",
        "outputId": "f205a415-c62a-40be-c8ef-688bcbcf4bd3"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1  \\nMUTUAL NONDISCLOSURE AGREEMENT  \\nThis Mutual Nondisclosure Agreement (the “Agreement”) is made on _________ (“Effective  \\nDate”) by and between:  \\n(1) John Snow Labs, a Delaware corporation, registered at 16192 Coastal Highway,  \\nLewes, Delaware 19958 (“John Snow Labs”), and   \\n(2) Achiles, S.L, a Spanish corporation, registered at Gran Via, 2º floor, Offices 9\\nand 10.(“Company”),  \\n(each a “party” and together the “parties”).',\n",
              " 'Recitals:  \\nJohn Snow Labs and Company intend to explore the possibility of a business relationship  \\nbetween each other, whereby each party (“Discloser”) may disclose sensitive information to the  \\nother party (“Recipient”).',\n",
              " 'The parties agree as follows:',\n",
              " '1. Definition.',\n",
              " '1.1 The term “Confidential Information” means, to the extent previously, presently or  \\nsubsequently disclosed by or for Discloser to Recipient (whether in writing, orally or by  \\nanother means), all financial, business, economic, legal, competitively sensitive and technical  \\ninformation of Discloser or any of its affiliates, suppliers, customers and employees  \\n(including, without limitation, information about research, development, operations,  \\nmarketing, transactions, regulatory affairs, discoveries, inventions, methods, processes,  \\narticles, materials, algorithms, software, specifications, designs, drawings, data, strategies,  \\ncodes, patterns, plans, prospects, know-how, formulas, prototypes, procedures, concepts and  \\nideas, whether tangible or intangible, and including all copies, abstracts, summaries, analyses  \\nand other derivatives thereof), that is marked, traditionally recognized or otherwise identified  \\nas proprietary or confidential at the time of disclosure, or that by its nature would be  \\nunderstood by a reasonable person to be proprietary or confidential.',\n",
              " '1.2 Confidential Information shall not include any information that:',\n",
              " '(a)  was rightfully known to Recipient without restriction before receipt from Discloser;',\n",
              " '(b)  is rightfully disclosed to Recipient without restriction by a third party;',\n",
              " '(c)  is or becomes generally known to the public without violation of this Agreement by  \\nRecipient;',\n",
              " 'or',\n",
              " '(d)  is independently developed by Recipient or its employees without access to or reliance  \\non such information.',\n",
              " 'June 8, 2022 \\n  \\n 2  \\n1.3 Discloser represents and warrants to Recipient that it is authorized to disclose any and all  \\nConfidential Information made available to Recipient under this Agreement.',\n",
              " '2. Restrictions.',\n",
              " '2.1 As to the Discloser of Confidential Information, Recipient agrees:',\n",
              " '(a)  to use the Confidential Information only for the purpose set out in this Agreement and  \\nfor its consideration internally of a business relationship or transaction between the  \\nparties, and its performance in any resulting arrangement, but not for any other  \\npurpose;',\n",
              " '(b)  to strictly maintain the Confidential Information as confidential, and exercise  \\nreasonable precautions to prevent any unauthorized access, use or disclosure;',\n",
              " '(c)  not to copy the Confidential Information without a prior written approval of Discloser  \\nand for any purpose other than set out in this Agreement;',\n",
              " '(d)  not to disclose the Confidential Information to any third party other than Recipient’s  \\nemployees and agents who have a need to know for the permitted purpose and who are  \\napprised of the confidential nature of the Confidential Information and all of the  \\nrestrictions in this Agreement;',\n",
              " '(e)  not to decompile, disassemble or otherwise reverse engineer of any Confidential  \\nInformation, or use any similar means to discover its underlying composition,  \\nstructure, source code or trade secrets;',\n",
              " 'and',\n",
              " '(f)  not to export or re-export any Confidential Information or product thereof in violation  \\nof U.S. or other export control laws or regulations.',\n",
              " '2.2 Recipient acknowledges the confidential character of the Confidential Information, therefore  \\nundertakes to protect and maintain strictly such information in the same grade of protection  \\nas its own Confidential Information, but in no case any less than reasonable care.',\n",
              " '2.3 The terms and conditions of any transaction or possible transaction between the parties, the  \\nfact that disclosures, evaluations or discussions are taking place, and the status and results  \\nthereof will also be held in confidence by both parties and not disclosed to any third party.',\n",
              " 'Each party shall be responsible for any breach of its confidentiality obligations by its  \\nrespective employees and agents.',\n",
              " '3. Compelled Disclosures.']"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's store the embeddings in a separate object to use it for classification purposes"
      ],
      "metadata": {
        "id": "edSnyEvZFymc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Now, let's see our classifier in action with those splits"
      ],
      "metadata": {
        "id": "nC0ZTQiZ0vwJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings = nlp.UniversalSentenceEncoder.pretrained()\\\n",
        "                    .setInputCols([\"document\"])\\\n",
        "                    .setOutputCol(\"embeddings\")\n",
        "\n",
        "classifier = nlp.MultiClassifierDLModel.pretrained(\"legmulticlf_mnda_sections_paragraph_other\", \"en\", \"legal/models\")\\\n",
        "                    .setInputCols([\"embeddings\"])\\\n",
        "                    .setOutputCol(\"label\")\n",
        "\n",
        "prediction_pipeline = nlp.Pipeline(stages=[doc_assembler, embeddings, classifier])\n",
        "\n",
        "prediction_model = prediction_pipeline.fit(df)\n",
        "\n",
        "prediction_lp = nlp.LightPipeline(prediction_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GRqVsL8S0eNX",
        "outputId": "a005f51b-e833-429f-c08c-87988e80835e"
      },
      "execution_count": 121,
      "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",
            "legmulticlf_mnda_sections_paragraph_other download started this may take some time.\n",
            "Approximate size to download 12.7 MB\n",
            "[OK!]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's create a dataframe where we will store the sentences and the labels to compare the different methods"
      ],
      "metadata": {
        "id": "fjdKjy3EB0Ve"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "comparison_table = []\n",
        "\n",
        "for s in isolated_sentences:\n",
        "  label = prediction_lp.annotate(s)['label']\n",
        "\n",
        "  if len(label) == 0:\n",
        "    label = ['OTHER']\n",
        "  comparison_table.append(        (s, label)   )\n",
        "\n",
        "comparison_table_df = pd.DataFrame(comparison_table, columns=['sentence', 'label_no_context'])"
      ],
      "metadata": {
        "id": "ZEZDIKhw9OxP"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "comparison_table_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "XtKZa9FNCDXD",
        "outputId": "9682c589-f229-4ec3-83d8-a08137dcc21f"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             sentence    label_no_context\n",
              "0   1  \\nMUTUAL NONDISCLOSURE AGREEMENT  \\nThis Mu...  [NAMES_OF_PARTIES]\n",
              "1   Recitals:  \\nJohn Snow Labs and Company intend...          [PREAMBLE]\n",
              "2                       The parties agree as follows:       [TERMINATION]\n",
              "3                                      1. Definition.  [DEF_OF_CONF_INFO]\n",
              "4   1.1 The term “Confidential Information” means,...  [DEF_OF_CONF_INFO]\n",
              "5   1.2 Confidential Information shall not include...             [OTHER]\n",
              "6   (a)  was rightfully known to Recipient without...             [OTHER]\n",
              "7   (b)  is rightfully disclosed to Recipient with...             [OTHER]\n",
              "8   (c)  is or becomes generally known to the publ...        [EXCEPTIONS]\n",
              "9                                                  or             [OTHER]\n",
              "10  (d)  is independently developed by Recipient o...        [EXCEPTIONS]\n",
              "11  June 8, 2022 \\n  \\n 2  \\n1.3 Discloser represe...             [OTHER]\n",
              "12                                   2. Restrictions.             [OTHER]\n",
              "13  2.1 As to the Discloser of Confidential Inform...             [OTHER]\n",
              "14  (a)  to use the Confidential Information only ...          [PREAMBLE]\n",
              "15  (b)  to strictly maintain the Confidential Inf...  [USE_OF_CONF_INFO]\n",
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              "17  (d)  not to disclose the Confidential Informat...  [USE_OF_CONF_INFO]\n",
              "18  (e)  not to decompile, disassemble or otherwis...  [USE_OF_CONF_INFO]\n",
              "19                                                and             [OTHER]\n",
              "20  (f)  not to export or re-export any Confidenti...             [OTHER]\n",
              "21  2.2 Recipient acknowledges the confidential ch...  [REQ_DISCL, OTHER]\n",
              "22  2.3 The terms and conditions of any transactio...             [OTHER]\n",
              "23  Each party shall be responsible for any breach...  [USE_OF_CONF_INFO]\n",
              "24                          3. Compelled Disclosures.             [OTHER]"
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>June 8, 2022 \\n  \\n 2  \\n1.3 Discloser represe...</td>\n",
              "      <td>[OTHER]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2. Restrictions.</td>\n",
              "      <td>[OTHER]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2.1 As to the Discloser of Confidential Inform...</td>\n",
              "      <td>[OTHER]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(a)  to use the Confidential Information only ...</td>\n",
              "      <td>[PREAMBLE]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>(b)  to strictly maintain the Confidential Inf...</td>\n",
              "      <td>[USE_OF_CONF_INFO]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>(c)  not to copy the Confidential Information ...</td>\n",
              "      <td>[OTHER]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>(d)  not to disclose the Confidential Informat...</td>\n",
              "      <td>[USE_OF_CONF_INFO]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(e)  not to decompile, disassemble or otherwis...</td>\n",
              "      <td>[USE_OF_CONF_INFO]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>and</td>\n",
              "      <td>[OTHER]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>(f)  not to export or re-export any Confidenti...</td>\n",
              "      <td>[OTHER]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2.2 Recipient acknowledges the confidential ch...</td>\n",
              "      <td>[REQ_DISCL, OTHER]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2.3 The terms and conditions of any transactio...</td>\n",
              "      <td>[OTHER]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Each party shall be responsible for any breach...</td>\n",
              "      <td>[USE_OF_CONF_INFO]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>3. Compelled Disclosures.</td>\n",
              "      <td>[OTHER]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0a9b6e0-15b1-4a0f-b602-48611fcd6f5d')\"\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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              "  </svg>\n",
              "      </button>\n",
              "      \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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d0a9b6e0-15b1-4a0f-b602-48611fcd6f5d 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-d0a9b6e0-15b1-4a0f-b602-48611fcd6f5d');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Now, let's use Context Windows.\n",
        "Context windows give to each sentence, a context of ```[-n, +n]``` so that the model cna classify them with more information.\n",
        "\n",
        "```n``` is a number of sentences which is configurable."
      ],
      "metadata": {
        "id": "_A0qQppY1xQy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from johnsnowlabs import legal\n",
        "\n",
        "context_window = legal.WindowedSentenceModel()\\\n",
        "                    .setInputCols([\"isolated_sentence\"])\\\n",
        "                    .setOutputCol(\"window\")\\\n",
        "                    .setWindowSize(1)\n",
        "\n",
        "window_splitting_pipeline = nlp.Pipeline(stages=[doc_assembler, sentence_detector, context_window])\n",
        "\n",
        "window_splitting_model = window_splitting_pipeline.fit(df)\n",
        "\n",
        "window_splitting_lp = nlp.LightPipeline(window_splitting_model)"
      ],
      "metadata": {
        "id": "SoznAphM4oQg"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql import functions as F\n",
        "\n",
        "windows = window_splitting_lp.annotate(text)['window'][:25]\n",
        "windows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zMDZJ9QtM-Gz",
        "outputId": "fc5e74e3-e8b0-4a9a-d54d-30d610ee6c28"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1  \\nMUTUAL NONDISCLOSURE AGREEMENT  \\nThis Mutual Nondisclosure Agreement (the “Agreement”) is made on _________ (“Effective  \\nDate”) by and between:  \\n(1) John Snow Labs, a Delaware corporation, registered at 16192 Coastal Highway,  \\nLewes, Delaware 19958 (“John Snow Labs”), and   \\n(2) Achiles, S.L, a Spanish corporation, registered at Gran Via, 2º floor, Offices 9\\nand 10.(“Company”),  \\n(each a “party” and together the “parties”). Recitals:  \\nJohn Snow Labs and Company intend to explore the possibility of a business relationship  \\nbetween each other, whereby each party (“Discloser”) may disclose sensitive information to the  \\nother party (“Recipient”).',\n",
              " '1  \\nMUTUAL NONDISCLOSURE AGREEMENT  \\nThis Mutual Nondisclosure Agreement (the “Agreement”) is made on _________ (“Effective  \\nDate”) by and between:  \\n(1) John Snow Labs, a Delaware corporation, registered at 16192 Coastal Highway,  \\nLewes, Delaware 19958 (“John Snow Labs”), and   \\n(2) Achiles, S.L, a Spanish corporation, registered at Gran Via, 2º floor, Offices 9\\nand 10.(“Company”),  \\n(each a “party” and together the “parties”). Recitals:  \\nJohn Snow Labs and Company intend to explore the possibility of a business relationship  \\nbetween each other, whereby each party (“Discloser”) may disclose sensitive information to the  \\nother party (“Recipient”). The parties agree as follows:',\n",
              " 'Recitals:  \\nJohn Snow Labs and Company intend to explore the possibility of a business relationship  \\nbetween each other, whereby each party (“Discloser”) may disclose sensitive information to the  \\nother party (“Recipient”). The parties agree as follows: 1. Definition.',\n",
              " 'The parties agree as follows: 1. Definition. 1.1 The term “Confidential Information” means, to the extent previously, presently or  \\nsubsequently disclosed by or for Discloser to Recipient (whether in writing, orally or by  \\nanother means), all financial, business, economic, legal, competitively sensitive and technical  \\ninformation of Discloser or any of its affiliates, suppliers, customers and employees  \\n(including, without limitation, information about research, development, operations,  \\nmarketing, transactions, regulatory affairs, discoveries, inventions, methods, processes,  \\narticles, materials, algorithms, software, specifications, designs, drawings, data, strategies,  \\ncodes, patterns, plans, prospects, know-how, formulas, prototypes, procedures, concepts and  \\nideas, whether tangible or intangible, and including all copies, abstracts, summaries, analyses  \\nand other derivatives thereof), that is marked, traditionally recognized or otherwise identified  \\nas proprietary or confidential at the time of disclosure, or that by its nature would be  \\nunderstood by a reasonable person to be proprietary or confidential.',\n",
              " '1. Definition. 1.1 The term “Confidential Information” means, to the extent previously, presently or  \\nsubsequently disclosed by or for Discloser to Recipient (whether in writing, orally or by  \\nanother means), all financial, business, economic, legal, competitively sensitive and technical  \\ninformation of Discloser or any of its affiliates, suppliers, customers and employees  \\n(including, without limitation, information about research, development, operations,  \\nmarketing, transactions, regulatory affairs, discoveries, inventions, methods, processes,  \\narticles, materials, algorithms, software, specifications, designs, drawings, data, strategies,  \\ncodes, patterns, plans, prospects, know-how, formulas, prototypes, procedures, concepts and  \\nideas, whether tangible or intangible, and including all copies, abstracts, summaries, analyses  \\nand other derivatives thereof), that is marked, traditionally recognized or otherwise identified  \\nas proprietary or confidential at the time of disclosure, or that by its nature would be  \\nunderstood by a reasonable person to be proprietary or confidential. 1.2 Confidential Information shall not include any information that:',\n",
              " '1.1 The term “Confidential Information” means, to the extent previously, presently or  \\nsubsequently disclosed by or for Discloser to Recipient (whether in writing, orally or by  \\nanother means), all financial, business, economic, legal, competitively sensitive and technical  \\ninformation of Discloser or any of its affiliates, suppliers, customers and employees  \\n(including, without limitation, information about research, development, operations,  \\nmarketing, transactions, regulatory affairs, discoveries, inventions, methods, processes,  \\narticles, materials, algorithms, software, specifications, designs, drawings, data, strategies,  \\ncodes, patterns, plans, prospects, know-how, formulas, prototypes, procedures, concepts and  \\nideas, whether tangible or intangible, and including all copies, abstracts, summaries, analyses  \\nand other derivatives thereof), that is marked, traditionally recognized or otherwise identified  \\nas proprietary or confidential at the time of disclosure, or that by its nature would be  \\nunderstood by a reasonable person to be proprietary or confidential. 1.2 Confidential Information shall not include any information that: (a)  was rightfully known to Recipient without restriction before receipt from Discloser;',\n",
              " '1.2 Confidential Information shall not include any information that: (a)  was rightfully known to Recipient without restriction before receipt from Discloser; (b)  is rightfully disclosed to Recipient without restriction by a third party;',\n",
              " '(a)  was rightfully known to Recipient without restriction before receipt from Discloser; (b)  is rightfully disclosed to Recipient without restriction by a third party; (c)  is or becomes generally known to the public without violation of this Agreement by  \\nRecipient;',\n",
              " '(b)  is rightfully disclosed to Recipient without restriction by a third party; (c)  is or becomes generally known to the public without violation of this Agreement by  \\nRecipient; or',\n",
              " '(c)  is or becomes generally known to the public without violation of this Agreement by  \\nRecipient; or (d)  is independently developed by Recipient or its employees without access to or reliance  \\non such information.',\n",
              " 'or (d)  is independently developed by Recipient or its employees without access to or reliance  \\non such information. June 8, 2022 \\n  \\n 2  \\n1.3 Discloser represents and warrants to Recipient that it is authorized to disclose any and all  \\nConfidential Information made available to Recipient under this Agreement.',\n",
              " '(d)  is independently developed by Recipient or its employees without access to or reliance  \\non such information. June 8, 2022 \\n  \\n 2  \\n1.3 Discloser represents and warrants to Recipient that it is authorized to disclose any and all  \\nConfidential Information made available to Recipient under this Agreement. 2. Restrictions.',\n",
              " 'June 8, 2022 \\n  \\n 2  \\n1.3 Discloser represents and warrants to Recipient that it is authorized to disclose any and all  \\nConfidential Information made available to Recipient under this Agreement. 2. Restrictions. 2.1 As to the Discloser of Confidential Information, Recipient agrees:',\n",
              " '2. Restrictions. 2.1 As to the Discloser of Confidential Information, Recipient agrees: (a)  to use the Confidential Information only for the purpose set out in this Agreement and  \\nfor its consideration internally of a business relationship or transaction between the  \\nparties, and its performance in any resulting arrangement, but not for any other  \\npurpose;',\n",
              " '2.1 As to the Discloser of Confidential Information, Recipient agrees: (a)  to use the Confidential Information only for the purpose set out in this Agreement and  \\nfor its consideration internally of a business relationship or transaction between the  \\nparties, and its performance in any resulting arrangement, but not for any other  \\npurpose; (b)  to strictly maintain the Confidential Information as confidential, and exercise  \\nreasonable precautions to prevent any unauthorized access, use or disclosure;',\n",
              " '(a)  to use the Confidential Information only for the purpose set out in this Agreement and  \\nfor its consideration internally of a business relationship or transaction between the  \\nparties, and its performance in any resulting arrangement, but not for any other  \\npurpose; (b)  to strictly maintain the Confidential Information as confidential, and exercise  \\nreasonable precautions to prevent any unauthorized access, use or disclosure; (c)  not to copy the Confidential Information without a prior written approval of Discloser  \\nand for any purpose other than set out in this Agreement;',\n",
              " '(b)  to strictly maintain the Confidential Information as confidential, and exercise  \\nreasonable precautions to prevent any unauthorized access, use or disclosure; (c)  not to copy the Confidential Information without a prior written approval of Discloser  \\nand for any purpose other than set out in this Agreement; (d)  not to disclose the Confidential Information to any third party other than Recipient’s  \\nemployees and agents who have a need to know for the permitted purpose and who are  \\napprised of the confidential nature of the Confidential Information and all of the  \\nrestrictions in this Agreement;',\n",
              " '(c)  not to copy the Confidential Information without a prior written approval of Discloser  \\nand for any purpose other than set out in this Agreement; (d)  not to disclose the Confidential Information to any third party other than Recipient’s  \\nemployees and agents who have a need to know for the permitted purpose and who are  \\napprised of the confidential nature of the Confidential Information and all of the  \\nrestrictions in this Agreement; (e)  not to decompile, disassemble or otherwise reverse engineer of any Confidential  \\nInformation, or use any similar means to discover its underlying composition,  \\nstructure, source code or trade secrets;',\n",
              " '(d)  not to disclose the Confidential Information to any third party other than Recipient’s  \\nemployees and agents who have a need to know for the permitted purpose and who are  \\napprised of the confidential nature of the Confidential Information and all of the  \\nrestrictions in this Agreement; (e)  not to decompile, disassemble or otherwise reverse engineer of any Confidential  \\nInformation, or use any similar means to discover its underlying composition,  \\nstructure, source code or trade secrets; and',\n",
              " '(e)  not to decompile, disassemble or otherwise reverse engineer of any Confidential  \\nInformation, or use any similar means to discover its underlying composition,  \\nstructure, source code or trade secrets; and (f)  not to export or re-export any Confidential Information or product thereof in violation  \\nof U.S. or other export control laws or regulations.',\n",
              " 'and (f)  not to export or re-export any Confidential Information or product thereof in violation  \\nof U.S. or other export control laws or regulations. 2.2 Recipient acknowledges the confidential character of the Confidential Information, therefore  \\nundertakes to protect and maintain strictly such information in the same grade of protection  \\nas its own Confidential Information, but in no case any less than reasonable care.',\n",
              " '(f)  not to export or re-export any Confidential Information or product thereof in violation  \\nof U.S. or other export control laws or regulations. 2.2 Recipient acknowledges the confidential character of the Confidential Information, therefore  \\nundertakes to protect and maintain strictly such information in the same grade of protection  \\nas its own Confidential Information, but in no case any less than reasonable care. 2.3 The terms and conditions of any transaction or possible transaction between the parties, the  \\nfact that disclosures, evaluations or discussions are taking place, and the status and results  \\nthereof will also be held in confidence by both parties and not disclosed to any third party.',\n",
              " '2.2 Recipient acknowledges the confidential character of the Confidential Information, therefore  \\nundertakes to protect and maintain strictly such information in the same grade of protection  \\nas its own Confidential Information, but in no case any less than reasonable care. 2.3 The terms and conditions of any transaction or possible transaction between the parties, the  \\nfact that disclosures, evaluations or discussions are taking place, and the status and results  \\nthereof will also be held in confidence by both parties and not disclosed to any third party. Each party shall be responsible for any breach of its confidentiality obligations by its  \\nrespective employees and agents.',\n",
              " '2.3 The terms and conditions of any transaction or possible transaction between the parties, the  \\nfact that disclosures, evaluations or discussions are taking place, and the status and results  \\nthereof will also be held in confidence by both parties and not disclosed to any third party. Each party shall be responsible for any breach of its confidentiality obligations by its  \\nrespective employees and agents. 3. Compelled Disclosures.',\n",
              " 'Each party shall be responsible for any breach of its confidentiality obligations by its  \\nrespective employees and agents. 3. Compelled Disclosures. These restrictions will not prevent either party from complying with any law, regulation,  \\ncourt order or other legal requirement that purports to compel disclosure of any Confidential  \\nInformation.']"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And now, let's classify the sentences with their window, to compare the impact of them in an isolated way from the context:"
      ],
      "metadata": {
        "id": "CcIBXXedNeHq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "window_labels = []\n",
        "for w in windows:\n",
        "  label = prediction_lp.annotate(w)['label']\n",
        "  if len(label) == 0:\n",
        "    label = ['OTHER']\n",
        "  window_labels.append(label)"
      ],
      "metadata": {
        "id": "_4c81-1ZNZQK"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Now, let's visualize the difference!"
      ],
      "metadata": {
        "id": "7NBK86JqN-a2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "comparison_table_df['label_context'] = window_labels\n",
        "comparison_table_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "bjYZfX1VN-BB",
        "outputId": "d01656ae-4755-43d3-bfb1-72d6e8c50c24"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             sentence    label_no_context  \\\n",
              "0   1  \\nMUTUAL NONDISCLOSURE AGREEMENT  \\nThis Mu...  [NAMES_OF_PARTIES]   \n",
              "1   Recitals:  \\nJohn Snow Labs and Company intend...          [PREAMBLE]   \n",
              "2                       The parties agree as follows:       [TERMINATION]   \n",
              "3                                      1. Definition.  [DEF_OF_CONF_INFO]   \n",
              "4   1.1 The term “Confidential Information” means,...  [DEF_OF_CONF_INFO]   \n",
              "5   1.2 Confidential Information shall not include...             [OTHER]   \n",
              "6   (a)  was rightfully known to Recipient without...             [OTHER]   \n",
              "7   (b)  is rightfully disclosed to Recipient with...             [OTHER]   \n",
              "8   (c)  is or becomes generally known to the publ...        [EXCEPTIONS]   \n",
              "9                                                  or             [OTHER]   \n",
              "10  (d)  is independently developed by Recipient o...        [EXCEPTIONS]   \n",
              "11  June 8, 2022 \\n  \\n 2  \\n1.3 Discloser represe...             [OTHER]   \n",
              "12                                   2. Restrictions.             [OTHER]   \n",
              "13  2.1 As to the Discloser of Confidential Inform...             [OTHER]   \n",
              "14  (a)  to use the Confidential Information only ...          [PREAMBLE]   \n",
              "15  (b)  to strictly maintain the Confidential Inf...  [USE_OF_CONF_INFO]   \n",
              "16  (c)  not to copy the Confidential Information ...             [OTHER]   \n",
              "17  (d)  not to disclose the Confidential Informat...  [USE_OF_CONF_INFO]   \n",
              "18  (e)  not to decompile, disassemble or otherwis...  [USE_OF_CONF_INFO]   \n",
              "19                                                and             [OTHER]   \n",
              "20  (f)  not to export or re-export any Confidenti...             [OTHER]   \n",
              "21  2.2 Recipient acknowledges the confidential ch...  [REQ_DISCL, OTHER]   \n",
              "22  2.3 The terms and conditions of any transactio...             [OTHER]   \n",
              "23  Each party shall be responsible for any breach...  [USE_OF_CONF_INFO]   \n",
              "24                          3. Compelled Disclosures.             [OTHER]   \n",
              "\n",
              "         label_context  \n",
              "0   [NAMES_OF_PARTIES]  \n",
              "1   [NAMES_OF_PARTIES]  \n",
              "2           [PREAMBLE]  \n",
              "3   [DEF_OF_CONF_INFO]  \n",
              "4   [DEF_OF_CONF_INFO]  \n",
              "5   [DEF_OF_CONF_INFO]  \n",
              "6         [EXCEPTIONS]  \n",
              "7         [EXCEPTIONS]  \n",
              "8         [EXCEPTIONS]  \n",
              "9         [EXCEPTIONS]  \n",
              "10             [OTHER]  \n",
              "11             [OTHER]  \n",
              "12             [OTHER]  \n",
              "13             [OTHER]  \n",
              "14  [USE_OF_CONF_INFO]  \n",
              "15             [OTHER]  \n",
              "16  [USE_OF_CONF_INFO]  \n",
              "17  [USE_OF_CONF_INFO]  \n",
              "18  [USE_OF_CONF_INFO]  \n",
              "19         [NON_SOLIC]  \n",
              "20             [OTHER]  \n",
              "21  [USE_OF_CONF_INFO]  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "T-yRAS_mPy-4"
      }
    },
    {
      "cell_type": "markdown",
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)"
      ],
      "metadata": {
        "id": "tSOIOaw1Px5J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Although this is not a recipe you can use at any case, and highly depends on how your classifier was trained, it can really help to:\n",
        "✅ Standardise blocks of predictions, capturing and grouping sentences together\n",
        "✅ Carry out the meaning to the surroundings to resolve short sentences without much meaning\n",
        "\n",
        "❌However, this does not come without a caveat. Sometimes, short sentences with no meaningful text may get affected by the surroundings."
      ],
      "metadata": {
        "id": "26TM2CK9P0pg"
      }
    }
  ]
}