{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I08sFJYCxR0Z"
      },
      "source": [
        "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FwJ-P56kq6FU"
      },
      "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/finance-nlp/02.Sentence_Splitting_Tokenization.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "6qPFEQN241i-"
      },
      "source": [
        "#🎬 Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hPwo4Czy3pqq",
        "pycharm": {
          "is_executing": true
        }
      },
      "outputs": [],
      "source": [
        "! pip install -q johnsnowlabs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9jwg5rw44K3"
      },
      "source": [
        "##🔗 Automatic Installation\n",
        "Using my.johnsnowlabs.com SSO"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "_L-7mLYp3pqr",
        "pycharm": {
          "is_executing": true
        }
      },
      "outputs": [],
      "source": [
        "from johnsnowlabs import nlp, finance\n",
        "\n",
        "# nlp.install(force_browser=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "URf6ZPiT47Q_"
      },
      "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": "pzUVhjeb4-iM"
      },
      "source": [
        "#📌 Starting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "x3jVICoa3pqr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d3e114a-728f-4698-c204-63725cc6fddd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "👌 Detected license file /content/spark_nlp_for_healthcare_spark_ocr_7187 (2).json\n",
            "👌 Launched \u001b[92mcpu optimized\u001b[39m session with with: 🚀Spark-NLP==4.3.0, 💊Spark-Healthcare==4.3.0, running on ⚡ PySpark==3.1.2\n"
          ]
        }
      ],
      "source": [
        "spark = nlp.start()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KcFQ-pqcRhA3"
      },
      "source": [
        "#🔎 2. Splitting for training word-based classifiers (as Named Entity Recognition)\n",
        "\n",
        "In the previous notebook we have seen how splitting documents and pages is important for **Text Classification**: `you may leave in too much information, or too little`.\n",
        "\n",
        "For **Word (token) Classification** splitting is also very important. We usually split **by sentences**, although they may be other use cases where you may need information from the context.\n",
        "\n",
        "This is the usual TextSplitter pipeline which will be used by default for training our NER models.\n",
        "\n",
        "📚Default `TextSplitter()` breaks texts by lines using the following patterns:\n",
        "\n",
        "```\n",
        "- Lists (\"(i), (ii)\", \"(a), (b)\", \"1., 2.\")\n",
        "- Numbers\n",
        "- Abbreviations\n",
        "- Punctuations\n",
        "- Multiple Periods\n",
        "- Geo-Locations/Coordinates (\"N°. 1026.253.553.\")\n",
        "- Ellipsis (\"...\")\n",
        "- In-between punctuations\n",
        "- Quotation marks\n",
        "- Exclamation Points\n",
        "- Basic Breakers (\".\", \";\")\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "R3BQpJr5SOiV"
      },
      "outputs": [],
      "source": [
        "document_assembler = nlp.DocumentAssembler() \\\n",
        "        .setInputCol(\"text\") \\\n",
        "        .setOutputCol(\"document\")\n",
        "\n",
        "text_splitter = finance.TextSplitter() \\\n",
        "    .setInputCols([\"document\"]) \\\n",
        "    .setOutputCol(\"sentences\") \\\n",
        "    .setExplodeSentences(True)\n",
        "\n",
        "nlp_pipeline = nlp.Pipeline(stages=[\n",
        "    document_assembler,\n",
        "    text_splitter])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "SjlATAuCeYld"
      },
      "outputs": [],
      "source": [
        "text = \"\"\"The IC and SoC design excellence requires technologies for custom IC, digital IC design and signoff, and functional verification, and leverages pre-built semiconductor IP. These tools, IP and associated services are specifically designed to meet the growing requirements of engineers designing increasingly complex chips across analog, digital and mixed-signal domains, and perform the associated verification tasks, including validation of low-level software running on the silicon model, thereby enabling design teams to manage complexity and verification throughput without commensurately increasing the team size or extending the project schedule, while reducing technical risks. The second layer of our strategy centers around system innovation. It includes tools and services used for system design of the packages that encapsulate the ICs and the PCBs, system simulation which includes electromagnetic, electro-thermal and other multi-physics analysis necessary as part of optimizing the full system’s performance, radio frequency (“RF”) and microwave systems, and embedded software. The third layer of our strategy addresses pervasive intelligence in new electronics. It starts with providing solutions and services to develop AI-enhanced systems and includes machine learning and deep learning capabilities being added to the Cadence\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "GEcyheA0fHYM",
        "outputId": "d08c8ff9-8254-4222-ba80-e2c41f6e909c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The IC and SoC design excellence requires technologies for custom IC, digital IC design and signoff, and functional verification, and leverages pre-built semiconductor IP. These tools, IP and associated services are specifically designed to meet the growing requirements of engineers designing increasingly complex chips across analog, digital and mixed-signal domains, and perform the associated verification tasks, including validation of low-level software running on the silicon model, thereby enabling design teams to manage complexity and verification throughput without commensurately increasing the team size or extending the project schedule, while reducing technical risks. The second layer of our strategy centers around system innovation. It includes tools and services used for system design of the packages that encapsulate the ICs and the PCBs, system simulation which includes electromagnetic, electro-thermal and other multi-physics analysis necessary as part of optimizing the full system’s performance, radio frequency (“RF”) and microwave systems, and embedded software. The third layer of our strategy addresses pervasive intelligence in new electronics. It starts with providing solutions and services to develop AI-enhanced systems and includes machine learning and deep learning capabilities being added to the Cadence'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "KmSErdcHeb4S"
      },
      "outputs": [],
      "source": [
        "df = spark.createDataFrame([[text]]).toDF(\"text\")\n",
        "\n",
        "res = nlp_pipeline.fit(df).transform(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8OxpbPyne2OC",
        "outputId": "77bfd932-43d5-4302-96e4-96faafc18b9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+\n",
            "|              result|\n",
            "+--------------------+\n",
            "|[The IC and SoC d...|\n",
            "|[These tools, IP ...|\n",
            "|[The second layer...|\n",
            "|[It includes tool...|\n",
            "|[The third layer ...|\n",
            "|[It starts with p...|\n",
            "+--------------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "res.select('sentences.result').show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OaZcQ_rGUb10"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XQDh09qS070"
      },
      "source": [
        "But you already know from previous notebook that you can customize those boundaries using `setCustomBounds()`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tmb58fnnS9mc"
      },
      "source": [
        "In addition, you have a Deep Learning pretrained TextSplitter, called `SentenceDetectorDLModel`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "mHjCa_fJSef1"
      },
      "outputs": [],
      "source": [
        "document_assembler = nlp.DocumentAssembler() \\\n",
        "        .setInputCol(\"text\") \\\n",
        "        .setOutputCol(\"document\")\n",
        "\n",
        "sentence_detector = nlp.SentenceDetectorDLModel()\\\n",
        "    .setInputCols([\"document\"]) \\\n",
        "    .setOutputCol(\"sentence\")\n",
        "\n",
        "nlp_pipeline = nlp.Pipeline(stages=[\n",
        "    document_assembler,\n",
        "    sentence_detector])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6Gj9aEWTOy8"
      },
      "source": [
        "That model is `pretrained`, which means it learnt during training time the boundaries of sentences and it can't be modified.\n",
        "\n",
        "You can train your SentenceDetector using DeepLearning as explained here: https://nlp.johnsnowlabs.com/docs/en/annotators#sentencedetectordl"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSdIMxrzaSii"
      },
      "source": [
        "###✔ Length restriction of Transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yS1cbkpxaVEb"
      },
      "source": [
        "📚Transformers have restrictions in terms of the number of tokens they can process, as shown in the list below. Any subsequent token after that limit of subwords will be discarded from the sentence.\n",
        "\n",
        "| Transformer | Limit of tokens |\n",
        "|-------------|-----------------|\n",
        "| BERT        | 512             |\n",
        "| XLNet       | 512             |\n",
        "| RoBERTa     | 512             |\n",
        "| XLM-RoBERTa | 512             |\n",
        "| Electra     | 512             |\n",
        "| Longformers*| 4096 (8 x BERT) |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sk_5xbOWcsdo"
      },
      "source": [
        "As you can see, most of the transformers work with a token limit of 512. There are several exceptions, as Longformer and BigBird, which allow up to 4096."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t52Dd93odNLb"
      },
      "source": [
        "#🔎 Tokenization\n",
        "Tokenization is the NLP task in charge of splitting sentences in smaller pieces, usually words. Althought it's a `splitting` task, we don't call it splitting, we call it `tokenization`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "erc2n91dVuI9"
      },
      "source": [
        "The main component to do tokenization is the `Tokenizer`. It will get the `tokens` from the piece of text you pass to it.\n",
        "\n",
        "You can tokenize at `whole-text` level:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "sDbc0JMsV60q"
      },
      "outputs": [],
      "source": [
        "document_assembler = nlp.DocumentAssembler() \\\n",
        "    .setInputCol(\"text\") \\\n",
        "    .setOutputCol(\"document\")\n",
        "\n",
        "tokenizer = nlp.Tokenizer()\\\n",
        "    .setInputCols([\"document\"]) \\\n",
        "    .setOutputCol(\"tokens\")\n",
        "\n",
        "nlp_pipeline = nlp.Pipeline(stages=[\n",
        "    document_assembler,\n",
        "    tokenizer])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VSDMKf_rV6dL"
      },
      "source": [
        "... but more frequently we do it at `sentence` or `paragraph` level, using it after TextSplitter."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "BM9dnrthWHJL"
      },
      "outputs": [],
      "source": [
        "document_assembler = nlp.DocumentAssembler() \\\n",
        "    .setInputCol(\"text\") \\\n",
        "    .setOutputCol(\"document\")\n",
        "\n",
        "text_splitter = finance.TextSplitter() \\\n",
        "    .setInputCols([\"document\"])\\\n",
        "    .setOutputCol(\"sentences\")\\\n",
        "    .setExplodeSentences(True)\n",
        "\n",
        "tokenizer = nlp.Tokenizer()\\\n",
        "    .setInputCols([\"sentences\"]) \\\n",
        "    .setOutputCol(\"tokens\")\n",
        "\n",
        "nlp_pipeline = nlp.Pipeline(stages=[\n",
        "    document_assembler,\n",
        "    text_splitter,\n",
        "    tokenizer])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "uykzI5FZgF0h"
      },
      "outputs": [],
      "source": [
        "fit = nlp_pipeline.fit(df)\n",
        "\n",
        "lp = nlp.LightPipeline(fit)\n",
        "\n",
        "res = lp.fullAnnotate(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-t53ZXI7hJ-C",
        "outputId": "676c6034-a178-41d1-aa59-dc10ed143522"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence: The IC and SoC design excellence requires technologies for custom IC, digital IC design and signoff, and functional verification, and leverages pre-built semiconductor IP.\n",
            "Tokens: ['The', 'IC', 'and', 'SoC', 'design', 'excellence', 'requires', 'technologies', 'for', 'custom', 'IC', ',', 'digital', 'IC', 'design', 'and', 'signoff', ',', 'and', 'functional', 'verification', ',', 'and', 'leverages', 'pre-built', 'semiconductor', 'IP', '.']\n",
            "\n",
            "Sentence: These tools, IP and associated services are specifically designed to meet the growing requirements of engineers designing increasingly complex chips across analog, digital and mixed-signal domains, and perform the associated verification tasks, including validation of low-level software running on the silicon model, thereby enabling design teams to manage complexity and verification throughput without commensurately increasing the team size or extending the project schedule, while reducing technical risks.\n",
            "Tokens: ['These', 'tools', ',', 'IP', 'and', 'associated', 'services', 'are', 'specifically', 'designed', 'to', 'meet', 'the', 'growing', 'requirements', 'of', 'engineers', 'designing', 'increasingly', 'complex', 'chips', 'across', 'analog', ',', 'digital', 'and', 'mixed-signal', 'domains', ',', 'and', 'perform', 'the', 'associated', 'verification', 'tasks', ',', 'including', 'validation', 'of', 'low-level', 'software', 'running', 'on', 'the', 'silicon', 'model', ',', 'thereby', 'enabling', 'design', 'teams', 'to', 'manage', 'complexity', 'and', 'verification', 'throughput', 'without', 'commensurately', 'increasing', 'the', 'team', 'size', 'or', 'extending', 'the', 'project', 'schedule', ',', 'while', 'reducing', 'technical', 'risks', '.']\n",
            "\n",
            "Sentence: The second layer of our strategy centers around system innovation.\n",
            "Tokens: ['The', 'second', 'layer', 'of', 'our', 'strategy', 'centers', 'around', 'system', 'innovation', '.']\n",
            "\n",
            "Sentence: It includes tools and services used for system design of the packages that encapsulate the ICs and the PCBs, system simulation which includes electromagnetic, electro-thermal and other multi-physics analysis necessary as part of optimizing the full system’s performance, radio frequency (“RF”) and microwave systems, and embedded software.\n",
            "Tokens: ['It', 'includes', 'tools', 'and', 'services', 'used', 'for', 'system', 'design', 'of', 'the', 'packages', 'that', 'encapsulate', 'the', 'ICs', 'and', 'the', 'PCBs', ',', 'system', 'simulation', 'which', 'includes', 'electromagnetic', ',', 'electro-thermal', 'and', 'other', 'multi-physics', 'analysis', 'necessary', 'as', 'part', 'of', 'optimizing', 'the', 'full', 'system’s', 'performance', ',', 'radio', 'frequency', '(', '“RF”', ')', 'and', 'microwave', 'systems', ',', 'and', 'embedded', 'software', '.']\n",
            "\n",
            "Sentence: The third layer of our strategy addresses pervasive intelligence in new electronics.\n",
            "Tokens: ['The', 'third', 'layer', 'of', 'our', 'strategy', 'addresses', 'pervasive', 'intelligence', 'in', 'new', 'electronics', '.']\n",
            "\n",
            "Sentence: It starts with providing solutions and services to develop AI-enhanced systems and includes machine learning and deep learning capabilities being added to the Cadence\n",
            "Tokens: ['It', 'starts', 'with', 'providing', 'solutions', 'and', 'services', 'to', 'develop', 'AI-enhanced', 'systems', 'and', 'includes', 'machine', 'learning', 'and', 'deep', 'learning', 'capabilities', 'being', 'added', 'to', 'the', 'Cadence']\n",
            "\n"
          ]
        }
      ],
      "source": [
        "sentences = res[0]['sentences']\n",
        "tokens = res[0]['tokens']\n",
        "\n",
        "for i in range(len(sentences)):\n",
        "  sen_tokens = [x.result for x in tokens if x.metadata['sentence'] == str(i)]\n",
        "  print(f\"Sentence: {sentences[i].result}\")\n",
        "  print(f\"Tokens: {sen_tokens}\")\n",
        "  print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Px9SMGZbiGuv"
      },
      "source": [
        "##✔ The problem of financial verbosity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Niy3mZAjoayg"
      },
      "source": [
        "Financial models are usually `verbose`. Sentences have much more words than in conversational or informal versions of the language. Just take a look at this piece of text, extracted from a SEC 10-K filing.\n",
        "\n",
        "> _Under Nevada law, we are prohibited from paying dividends if the distribution would result in our Company not being able to pay its debts as they become due in the normal course of business if our total assets would be less than the sum of our total liabilities plus the amount that would be needed to pay the dividends, or if we were to be dissolved at the time of distribution to satisfy the preferential rights upon dissolution of stockholders whose preferential rights are superior to those receiving the distribution._\n",
        "\n",
        "Often times, you can find inline lists:\n",
        "\n",
        "> _In addition to these objective standards, the NYSE American may delist the securities of any issuer (i) if, in its opinion, the issuer’s financial condition and/or operating results appear unsatisfactory; (ii) if it appears that the extent of public distribution or the aggregate market value of the security has become so reduced as to make continued listing on the NYSE American inadvisable; (iii) if the issuer sells or disposes of principal operating assets or ceases to be an operating company; (iv) if an issuer fails to comply with the NYSE American’s listing requirements; (v) if an issuer’s securities sell at what the NYSE American considers a “low selling price” which the exchange generally considers $0.20 per share and the issuer fails to correct this via a reverse split of shares after notification by the NYSE American; or (vi) if any other event occurs or any condition exists which makes continued listing on the NYSE American, in its opinion, inadvisable._\n",
        "\n",
        "And sometimes, very long enumerations...\n",
        "\n",
        "> _On June 20, 2019, Inuvo entered into an Agreement and Plan of Merger Termination Agreement (the “Merger Termination Agreement”) with ConversionPoint Technologies Inc., a Delaware corporation (“CPT”), ConversionPoint Holdings, Inc., a Delaware corporation (“Parent”), CPT Merger Sub, Inc., a Delaware corporation, (“CPT Merger Sub”), and CPT Cigar Merger Sub, Inc., a Nevada corporation (“Inuvo Merger Sub”)_\n",
        "\n",
        "... and what is worse, some of them combined.\n",
        "\n",
        "> _On June 20, 2019, Inuvo entered into an Agreement and Plan of Merger Termination Agreement (the “Merger Termination Agreement”) with ConversionPoint Technologies Inc., a Delaware corporation (“CPT”), ConversionPoint Holdings, Inc., a Delaware corporation (“Parent”), CPT Merger Sub, Inc., a Delaware corporation, (“CPT Merger Sub”), and CPT Cigar Merger Sub, Inc., a Nevada corporation (“Inuvo Merger Sub”) which, among other things, terminated the Agreement and Plan of Merger, dated November 2, 2018, by and among Inuvo, CPT, Parent, CPT Merger Sub, and Inuvo Merger Sub, as amended (the “Merger Agreement”), pursuant to which Inuvo would have merged with and into Inuvo Merger Sub and become a wholly-owned subsidiary of Parent, and CPT would have merged with and into CPT Merger Sub and become a wholly-owned subsidiary of Parent (the “Mergers”), and (2) terminated each of the Support Agreements that were entered into by certain officers and directors of Inuvo and the parties to the Merger Agreement_\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y9ZW-hOqiWDI"
      },
      "source": [
        "If reading those long sentences is already a challenge for humans, just imagine how challenging it may be for a machine. Let's talk about these restrictions.\n",
        "\n",
        "TextSplitter, without custom bounds, will not split what is inside a sentence. As a result, some sentences may end up having `a high amount of words` being last tokens not taken into account due to Transformers Restrictions (512 tokens) or getting the information deluded."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "PRvOXTIkjYgD"
      },
      "outputs": [],
      "source": [
        "text = \"On June 20, 2019, Inuvo entered into an Agreement and Plan of Merger Termination Agreement (the “Merger Termination Agreement”) with ConversionPoint Technologies Inc., a Delaware corporation (“CPT”), ConversionPoint Holdings, Inc., a Delaware corporation (“Parent”), CPT Merger Sub, Inc., a Delaware corporation, (“CPT Merger Sub”), and CPT Cigar Merger Sub, Inc., a Nevada corporation (“Inuvo Merger Sub”) which, among other things, terminated the Agreement and Plan of Merger, dated November 2, 2018, by and among Inuvo, CPT, Parent, CPT Merger Sub, and Inuvo Merger Sub, as amended (the “Merger Agreement”), pursuant to which Inuvo would have merged with and into Inuvo Merger Sub and become a wholly-owned subsidiary of Parent, and CPT would have merged with and into CPT Merger Sub and become a wholly-owned subsidiary of Parent (the “Mergers”), and (2) terminated each of the Support Agreements that were entered into by certain officers and directors of Inuvo and the parties to the Merger Agreement\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7rt4BqcqjjBv",
        "outputId": "b0d3db84-7b49-41ba-fe24-6440114f724d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence: On June 20, 2019, Inuvo entered into an Agreement and Plan of Merger Termination Agreement (the “Merger Termination Agreement”) with ConversionPoint Technologies Inc., a Delaware corporation (“CPT”), ConversionPoint Holdings, Inc., a Delaware corporation (“Parent”), CPT Merger Sub, Inc., a Delaware corporation, (“CPT Merger Sub”), and CPT Cigar Merger Sub, Inc., a Nevada corporation (“Inuvo Merger Sub”) which, among other things, terminated the Agreement and Plan of Merger, dated November 2, 2018, by and among Inuvo, CPT, Parent, CPT Merger Sub, and Inuvo Merger Sub, as amended (the “Merger Agreement”), pursuant to which Inuvo would have merged with and into Inuvo Merger Sub and become a wholly-owned subsidiary of Parent, and CPT would have merged with and into CPT Merger Sub and become a wholly-owned subsidiary of Parent (the “Mergers”), and (2) terminated each of the Support Agreements that were entered into by certain officers and directors of Inuvo and the parties to the Merger Agreement\n",
            "Tokens 192: ['On', 'June', '20', ',', '2019', ',', 'Inuvo', 'entered', 'into', 'an', 'Agreement', 'and', 'Plan', 'of', 'Merger', 'Termination', 'Agreement', '(', 'the', '“Merger', 'Termination', 'Agreement”', ')', 'with', 'ConversionPoint', 'Technologies', 'Inc', '.,', 'a', 'Delaware', 'corporation', '(', '“CPT”', '),', 'ConversionPoint', 'Holdings', ',', 'Inc', '.,', 'a', 'Delaware', 'corporation', '(', '“Parent”', '),', 'CPT', 'Merger', 'Sub', ',', 'Inc', '.,', 'a', 'Delaware', 'corporation', ',', '(', '“CPT', 'Merger', 'Sub”', '),', 'and', 'CPT', 'Cigar', 'Merger', 'Sub', ',', 'Inc', '.,', 'a', 'Nevada', 'corporation', '(', '“Inuvo', 'Merger', 'Sub”', ')', 'which', ',', 'among', 'other', 'things', ',', 'terminated', 'the', 'Agreement', 'and', 'Plan', 'of', 'Merger', ',', 'dated', 'November', '2', ',', '2018', ',', 'by', 'and', 'among', 'Inuvo', ',', 'CPT', ',', 'Parent', ',', 'CPT', 'Merger', 'Sub', ',', 'and', 'Inuvo', 'Merger', 'Sub', ',', 'as', 'amended', '(', 'the', '“Merger', 'Agreement”', '),', 'pursuant', 'to', 'which', 'Inuvo', 'would', 'have', 'merged', 'with', 'and', 'into', 'Inuvo', 'Merger', 'Sub', 'and', 'become', 'a', 'wholly-owned', 'subsidiary', 'of', 'Parent', ',', 'and', 'CPT', 'would', 'have', 'merged', 'with', 'and', 'into', 'CPT', 'Merger', 'Sub', 'and', 'become', 'a', 'wholly-owned', 'subsidiary', 'of', 'Parent', '(', 'the', '“Mergers”', '),', 'and', '(', '2', ')', 'terminated', 'each', 'of', 'the', 'Support', 'Agreements', 'that', 'were', 'entered', 'into', 'by', 'certain', 'officers', 'and', 'directors', 'of', 'Inuvo', 'and', 'the', 'parties', 'to', 'the', 'Merger', 'Agreement']\n",
            "\n"
          ]
        }
      ],
      "source": [
        "res = lp.fullAnnotate(text)\n",
        "\n",
        "sentences = res[0]['sentences']\n",
        "tokens = res[0]['tokens']\n",
        "\n",
        "for i in range(len(sentences)):\n",
        "  sen_tokens = [x.result for x in tokens if x.metadata['sentence'] == str(i)]\n",
        "  print(f\"Sentence: {sentences[i].result}\")\n",
        "  print(f\"Tokens {len(sen_tokens)}: {sen_tokens}\")\n",
        "  print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETQAtbuvj1u4"
      },
      "source": [
        "##✔ What can we expect from a sentence with 192 tokens?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Whmbjhzj9kf"
      },
      "source": [
        "We can't say for sure, but these are the possible consequences:\n",
        "- But the longer the sentence is, the more diluted the information may be.\n",
        "- Issues may arise about the `token` number limitations for transformer-based models.\n",
        "\n",
        "You may think - well, we are still very far from 512 tokens, right?\n",
        "\n",
        "Well, I have bad news for you...."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjNY7V7SQovC"
      },
      "source": [
        "##✔ About Out of Vocabulary and Subwording in Transformers Tokenization\n",
        "The number of words in a vocabulary used in a transformer can't be infinite. Better to say, the number of `tokens`. A token is the smallest piece of text we woprk with, obtained after a `tokenization` process. \n",
        "\n",
        "`Tokenizing` means separating a sentence into these smaller pieces. In languages with words separated by white spaces, there are at least 3 ways to do that:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOCq_4aLUuOr"
      },
      "source": [
        "\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXImjfMmTHmu"
      },
      "source": [
        "Let's simplify and think we just separate the tokens by using punctuation. In a sentence like:\n",
        "`Let's tokenize! Isn't this easy?`\n",
        "\n",
        "We would be getting:\n",
        "`Let ' s tokenize ! Isn ' t this easy ?`\n",
        "\n",
        "That's already 11 tokens to go to the vocabulary. The traditional way to build a vocabulary is was just that:\n",
        "- 1) Tokenizing the corpus;\n",
        "- 2) Getting each token;\n",
        "- 3) Adding it to the vocabulary\n",
        "\n",
        "**Problem**: languages may consist of 500K to 1M words, if we take into consideration different forms (is not, is, isn ' t ...) . That's something unbearable for real-time NLP architectures to cope with."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCRaL2K7XQL1"
      },
      "source": [
        "###📌 Out of Vocabulary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4AsP_S1PW-Xu"
      },
      "source": [
        "**1st solution**: OOV (Out of Vocabulary). The first ML/DL algorithms used to prune the vocabulary, leaving the top-N most frequent tokens seen in the corpus and discarding the rest\n",
        "\n",
        "This approach soon run short, as the necesities to capture all the meaning of a text without discardign anything became crucial"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WhwHt0G_XR_k"
      },
      "source": [
        "###📌 Subword tokenization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hPt1aLiEXaME"
      },
      "source": [
        "The **2nd solution** is _subword tokenization_. The idea behind this is that we can split words into sequences of characters, if we see a probability of that sequence of characters being frequently used to form other words. For example, let's suppose we have the following sentence:\n",
        "\n",
        "`My favourite Natural Language Processing library is clearly Spark NLP!`\n",
        "\n",
        "The subword tokenization algorithm used by `BERT` transformers is called WordPiece. The result WordPiece returns for that sentence is:\n",
        "\n",
        "```\n",
        "['My', 'favourite', 'Natural', 'Language', 'Process', '##ing', 'library', 'is', 'clearly', 'Spa', '##rk', 'NL', '##P', '!']\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VcGkVPlKZJ5a"
      },
      "source": [
        "As we can see, we got:\n",
        "- `Processing` split into 2 subwords: `Process` and `##ing`. That's because `Process` seems a very common subword in the texts used to train BERT, and `ing` a very common subword (suffix) as well. The `##` means that subwords goes attached to the previous word (`Process`).\n",
        "- `Spark` split into `Spa`(again, `Spa` is a common subword, for example, `Spa` in Wellness) and `##rk`\n",
        "- `NLP` into `NL` and `#P`\n",
        "\n",
        "As you have probably noticed, proper nouns are usually split in more than one subword, as also derivative words (Process - Process+ing).\n",
        "\n",
        "This second approach is the most used in State of the Art NLP architectures, including in all the Spark NLP Transformers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ryVm3i9KVEJC"
      },
      "source": [
        "#🔎 So, what happened to our previous 192 tokens❓\n",
        "When we use a Transformer model, those 192 tokens will also be split into different `subwords`, which counts as 1 for the token restriction. Example:\n",
        "- `Spark NLP` is not 2 tokens, it is 4 tokens: `Spa ##rk NL ##P`\n",
        "\n",
        "📚So our previous 192 tokens probably are already about ~400. Still inside the boundaries of Bert, but we are very close already...\n",
        "\n",
        "| Transformer | Limit of tokens |\n",
        "|-------------|-----------------|\n",
        "| BERT        | 512             |\n",
        "| XLNet       | 512             |\n",
        "| RoBERTa     | 512             |\n",
        "| XLM-RoBERTa | 512             |\n",
        "| Electra     | 512             |\n",
        "| Longformers*| 4096 (8 x BERT) |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ApfIWT39kWgv"
      },
      "source": [
        "#🔎 Solutions: Let's try go find sub-sentences using splitting!\n",
        "As we do for paragraphs inside pages, or pages inside documents."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6a-2L7mugB_O"
      },
      "source": [
        "##✔ Default non-aggressive text splitting\n",
        "Let's use several characters which don't drastically change the structure of the sentence. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "VTL_ZMjjgP5Q"
      },
      "outputs": [],
      "source": [
        "text = \"\"\"In addition to these objective standards, the NYSE American may delist the securities of any issuer (i) if, in its opinion, the issuer’s financial condition and/or operating results appear unsatisfactory; (ii) if it appears that the extent of public distribution or the aggregate market value of the security has become so reduced as to make continued listing on the NYSE American inadvisable; (iii) if the issuer sells or disposes of principal operating assets or ceases to be an operating company; (iv) if an issuer fails to comply with the NYSE American’s listing requirements; (v) if an issuer’s securities sell at what the NYSE American considers a “low selling price” which the exchange generally considers $0.20 per share and the issuer fails to correct this via a reverse split of shares after notification by the NYSE American; or (vi) if any other event occurs or any condition exists which makes continued listing on the NYSE American, in its opinion, inadvisable. This is another sentence.\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yE-aJoE-mRCv"
      },
      "source": [
        "📚For this case, just the default strategy of the Text Splitter works well:\n",
        "\n",
        "```\n",
        "Lists (“(i), (ii)”, “(a), (b)”, “1., 2.”)\n",
        "Numbers\n",
        "Abbreviations\n",
        "Punctuations\n",
        "Multiple Periods\n",
        "Geo-Locations/Coordinates (“N°. 1026.253.553.”)\n",
        "Ellipsis (”…”)\n",
        "In-between punctuations\n",
        "Quotation marks\n",
        "Exclamation Points\n",
        "Basic Breakers (“.”, “;”)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "DEa5SITBxmY0"
      },
      "outputs": [],
      "source": [
        "documentAssembler = nlp.DocumentAssembler()\\\n",
        "    .setInputCol(\"text\")\\\n",
        "    .setOutputCol(\"document\")\n",
        "        \n",
        "textSplitter = finance.TextSplitter()\\\n",
        "    .setInputCols([\"document\"])\\\n",
        "    .setOutputCol(\"sentence\")\\\n",
        "    .setCustomBounds([\";\"])\\\n",
        "    .setUseCustomBoundsOnly(False)\n",
        "\n",
        "nlpPipeline = nlp.Pipeline(stages=[\n",
        "        documentAssembler,\n",
        "        textSplitter])\n",
        "\n",
        "empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n",
        "\n",
        "model = nlpPipeline.fit(empty_data)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "eFVN6txZhqwL"
      },
      "outputs": [],
      "source": [
        "lp = nlp.LightPipeline(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LmmdDTXnhuOZ",
        "outputId": "40d9748a-974b-4f94-9559-4a59d98c930f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['In addition to these objective standards, the NYSE American may delist the securities of any issuer',\n",
              " '(i) if, in its opinion, the issuer’s financial condition and/or operating results appear unsatisfactory',\n",
              " '(ii) if it appears that the extent of public distribution or the aggregate market value of the security has become so reduced as to make continued listing on the NYSE American inadvisable',\n",
              " '(iii) if the issuer sells or disposes of principal operating assets or ceases to be an operating company',\n",
              " '(iv) if an issuer fails to comply with the NYSE American’s listing requirements',\n",
              " '(v) if an issuer’s securities sell at what the NYSE American considers a “low selling price” which the exchange generally considers $0.20 per share and the issuer fails to correct this via a reverse split of shares after notification by the NYSE American',\n",
              " 'or',\n",
              " '(vi) if any other event occurs or any condition exists which makes continued listing on the NYSE American, in its opinion, inadvisable.',\n",
              " 'This is another sentence.']"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "lp.annotate(text)['sentence']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-IKuzTqmhV5"
      },
      "source": [
        "This splitting seems good to me, except maybe a a couple of long sentences.\n",
        "\n",
        "However, let's take a look at another example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "nMiJAU29mv8L"
      },
      "outputs": [],
      "source": [
        "text = 'Taiwan. Speaking only hours after Chinese state media said the time was right to engage in political talks with Taiwan, Foreign Ministry spokesman Shen Guofang told Reuters: \"The necessary atmosphere for the opening of the talks has been disrupted by the Taiwan authorities.\"'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "XpqHH7H3mw4h"
      },
      "outputs": [],
      "source": [
        "documentAssembler = nlp.DocumentAssembler()\\\n",
        "    .setInputCol(\"text\")\\\n",
        "    .setOutputCol(\"document\")\n",
        "        \n",
        "textSplitter = finance.TextSplitter()\\\n",
        "    .setInputCols([\"document\"])\\\n",
        "    .setOutputCol(\"sentence\")\n",
        "\n",
        "nlpPipeline = nlp.Pipeline(stages=[\n",
        "        documentAssembler,\n",
        "        textSplitter])\n",
        "\n",
        "empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n",
        "\n",
        "model = nlpPipeline.fit(empty_data)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "qIgGqn-9FXEy"
      },
      "outputs": [],
      "source": [
        "lp = nlp.LightPipeline(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QOm6weDFFYBq",
        "outputId": "52aa28ff-4c9c-42f3-986f-f06f426672c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Taiwan.',\n",
              " 'Speaking only hours after Chinese state media said the time was right to engage in political talks with Taiwan, Foreign Ministry spokesman Shen Guofang told Reuters: \"The necessary atmosphere for the opening of the talks has been disrupted by the Taiwan authorities.\"']"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "lp.annotate(text)['sentence']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AA83VrL6FWvL"
      },
      "source": [
        "That second sentence is definitely too long to process. It has much information and many tokens. Let's try to subsplit it with  `setCustomBounds()`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "ideh7r1Xl4Ey"
      },
      "outputs": [],
      "source": [
        "documentAssembler = nlp.DocumentAssembler()\\\n",
        "    .setInputCol(\"text\")\\\n",
        "    .setOutputCol(\"document\")\n",
        "        \n",
        "textSplitter = finance.TextSplitter()\\\n",
        "    .setInputCols([\"document\"])\\\n",
        "    .setOutputCol(\"sentence\")\\\n",
        "    .setCustomBounds([\";\", \":\", \"\\\"\"])\\\n",
        "    .setUseCustomBoundsOnly(False)\n",
        "\n",
        "nlpPipeline = nlp.Pipeline(stages=[\n",
        "        documentAssembler,\n",
        "        textSplitter])\n",
        "\n",
        "empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n",
        "\n",
        "model = nlpPipeline.fit(empty_data)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "JjDqfVaml7p7"
      },
      "outputs": [],
      "source": [
        "lp = nlp.LightPipeline(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vHImhFfWl8lx",
        "outputId": "e6c30f66-bfc3-4ec2-f18d-8eae1fcc2398"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Taiwan.',\n",
              " 'Speaking only hours after Chinese state media said the time was right to engage in political talks with Taiwan, Foreign Ministry spokesman Shen Guofang told Reuters',\n",
              " 'The necessary atmosphere for the opening of the talks has been disrupted by the Taiwan authorities.']"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "lp.annotate(text)['sentence']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dm_bz41hFlgS"
      },
      "source": [
        "Much better!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V2iA9ICro4z4"
      },
      "source": [
        "##✔ Aggressive sentence-splitting\n",
        "Sometimes the splitting with \"conservative\" techniques is not enough. In that case, you may considering breaking the sentence structure. \n",
        "\n",
        "This has the main caveat that you may lose context by breaking the sentence into smaller pieces.\n",
        "\n",
        "However, if there is no other way you can split a sentence, it's better to lose some context but using all the words, than just not splitting and cutting off the sentence on the 512nd token.\n",
        "\n",
        "Even when you are within the limits, sometimes splitting into smaller chunks helps the model repetitive patterns which may get deluded in big sentences.\n",
        "\n",
        "Also, the performance degrades the longer the sentence is. Sometimes splitting in smaller sentences may provide with an performance improvement."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "czon3nRSo34R"
      },
      "outputs": [],
      "source": [
        "text = 'On June 20, 2019, Inuvo entered into an Agreement and Plan of Merger Termination Agreement (the “Merger Termination Agreement”) with ConversionPoint Technologies Inc., a Delaware corporation (“CPT”), ConversionPoint Holdings, Inc., a Delaware corporation (“Parent”), CPT Merger Sub, Inc., a Delaware corporation, (“CPT Merger Sub”), and CPT Cigar Merger Sub, Inc., a Nevada corporation (“Inuvo Merger Sub”) which, among other things, terminated the Agreement and Plan of Merger, dated November 2, 2018, by and among Inuvo, CPT, Parent, CPT Merger Sub, and Inuvo Merger Sub, as amended (the “Merger Agreement”), pursuant to which Inuvo would have merged with and into Inuvo Merger Sub and become a wholly-owned subsidiary of Parent, and CPT would have merged with and into CPT Merger Sub and become a wholly-owned subsidiary of Parent (the “Mergers”), and (2) terminated each of the Support Agreements that were entered into by certain officers and directors of Inuvo and the parties to the Merger Agreement\"'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "iKqUu3NbpwXf"
      },
      "outputs": [],
      "source": [
        "documentAssembler = nlp.DocumentAssembler()\\\n",
        "    .setInputCol(\"text\")\\\n",
        "    .setOutputCol(\"document\")\n",
        "        \n",
        "textSplitter = finance.TextSplitter()\\\n",
        "    .setInputCols([\"document\"])\\\n",
        "    .setOutputCol(\"sentence\")\\\n",
        "    .setCustomBounds([\";\",\"\\\"\"])\\\n",
        "    .setUseCustomBoundsOnly(False)\n",
        "\n",
        "nlpPipeline = nlp.Pipeline(stages=[\n",
        "        documentAssembler,\n",
        "        textSplitter])\n",
        "\n",
        "empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n",
        "\n",
        "model = nlpPipeline.fit(empty_data)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "7xjMN7CDrcL7"
      },
      "outputs": [],
      "source": [
        "lp = nlp.LightPipeline(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJylSx4XrdWd",
        "outputId": "89f28765-b8d4-449e-ac0b-dfa59de5da23"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['On June 20, 2019, Inuvo entered into an Agreement and Plan of Merger Termination Agreement (the “Merger Termination Agreement”) with ConversionPoint Technologies Inc., a Delaware corporation (“CPT”), ConversionPoint Holdings, Inc., a Delaware corporation (“Parent”), CPT Merger Sub, Inc., a Delaware corporation, (“CPT Merger Sub”), and CPT Cigar Merger Sub, Inc., a Nevada corporation (“Inuvo Merger Sub”) which, among other things, terminated the Agreement and Plan of Merger, dated November 2, 2018, by and among Inuvo, CPT, Parent, CPT Merger Sub, and Inuvo Merger Sub, as amended (the “Merger Agreement”), pursuant to which Inuvo would have merged with and into Inuvo Merger Sub and become a wholly-owned subsidiary of Parent, and CPT would have merged with and into CPT Merger Sub and become a wholly-owned subsidiary of Parent (the “Mergers”), and (2) terminated each of the Support Agreements that were entered into by certain officers and directors of Inuvo and the parties to the Merger Agreement']"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "lp.annotate(text)['sentence']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e_NiCwXIrhg1"
      },
      "source": [
        "Conservative splitting does not help"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATyJ0jSasF72"
      },
      "source": [
        "Splitting by parethensis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "dyJs-OaHrvxI"
      },
      "outputs": [],
      "source": [
        "documentAssembler = nlp.DocumentAssembler()\\\n",
        "    .setInputCol(\"text\")\\\n",
        "    .setOutputCol(\"document\")\n",
        "        \n",
        "textSplitter = finance.TextSplitter()\\\n",
        "    .setInputCols([\"document\"])\\\n",
        "    .setOutputCol(\"sentence\")\\\n",
        "    .setCustomBounds([\"\\(\",\"\\)\"])\\\n",
        "    .setUseCustomBoundsOnly(False)\n",
        "\n",
        "nlpPipeline = nlp.Pipeline(stages=[\n",
        "        documentAssembler,\n",
        "        textSplitter])\n",
        "\n",
        "empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n",
        "\n",
        "model = nlpPipeline.fit(empty_data)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "LVQog7uGr1jO"
      },
      "outputs": [],
      "source": [
        "lp = nlp.LightPipeline(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MGVv04ohr2j-",
        "outputId": "1410408b-a6f9-421b-b63f-c1c5b2b56591"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['On June 20, 2019, Inuvo entered into an Agreement and Plan of Merger Termination Agreement',\n",
              " 'the “Merger Termination Agreement”',\n",
              " 'with ConversionPoint Technologies Inc., a Delaware corporation',\n",
              " '“CPT”',\n",
              " ', ConversionPoint Holdings, Inc., a Delaware corporation',\n",
              " '“Parent”',\n",
              " ', CPT Merger Sub, Inc., a Delaware corporation,',\n",
              " '“CPT Merger Sub”',\n",
              " ', and CPT Cigar Merger Sub, Inc., a Nevada corporation',\n",
              " '“Inuvo Merger Sub”',\n",
              " 'which, among other things, terminated the Agreement and Plan of Merger, dated November 2, 2018, by and among Inuvo, CPT, Parent, CPT Merger Sub, and Inuvo Merger Sub, as amended',\n",
              " 'the “Merger Agreement”',\n",
              " ', pursuant to which Inuvo would have merged with and into Inuvo Merger Sub and become a wholly-owned subsidiary of Parent, and CPT would have merged with and into CPT Merger Sub and become a wholly-owned subsidiary of Parent',\n",
              " 'the “Mergers”',\n",
              " ', and',\n",
              " '2',\n",
              " 'terminated each of the Support Agreements that were entered into by certain officers and directors of Inuvo and the parties to the Merger Agreement\"']"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "lp.annotate(text)['sentence']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V68b5p3MsIZU"
      },
      "source": [
        "We can use **REGEX** or just several characters in a row in our `setCustomBounds`to be able to have finegrain splits."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "8lSHIvMLsazd"
      },
      "outputs": [],
      "source": [
        "documentAssembler = nlp.DocumentAssembler()\\\n",
        "    .setInputCol(\"text\")\\\n",
        "    .setOutputCol(\"document\")\n",
        "        \n",
        "textSplitter = finance.TextSplitter()\\\n",
        "    .setInputCols([\"document\"])\\\n",
        "    .setOutputCol(\"sentence\")\\\n",
        "    .setUseCustomBoundsOnly(False)\\\n",
        "    .setCustomBounds([\", and\"])\n",
        "\n",
        "nlpPipeline = nlp.Pipeline(stages=[\n",
        "        documentAssembler,\n",
        "        textSplitter])\n",
        "\n",
        "empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n",
        "\n",
        "model = nlpPipeline.fit(empty_data)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "f9gwpiw8scXF"
      },
      "outputs": [],
      "source": [
        "lp = nlp.LightPipeline(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BcIcDQCpsdGO",
        "outputId": "ee25e366-1570-441c-9a9f-68ec534d7f2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['On June 20, 2019, Inuvo entered into an Agreement and Plan of Merger Termination Agreement (the “Merger Termination Agreement”) with ConversionPoint Technologies Inc., a Delaware corporation (“CPT”), ConversionPoint Holdings, Inc., a Delaware corporation (“Parent”), CPT Merger Sub, Inc., a Delaware corporation, (“CPT Merger Sub”)',\n",
              " 'CPT Cigar Merger Sub, Inc., a Nevada corporation (“Inuvo Merger Sub”) which, among other things, terminated the Agreement and Plan of Merger, dated November 2, 2018, by and among Inuvo, CPT, Parent, CPT Merger Sub',\n",
              " 'Inuvo Merger Sub, as amended (the “Merger Agreement”), pursuant to which Inuvo would have merged with and into Inuvo Merger Sub and become a wholly-owned subsidiary of Parent',\n",
              " 'CPT would have merged with and into CPT Merger Sub and become a wholly-owned subsidiary of Parent (the “Mergers”)',\n",
              " '(2) terminated each of the Support Agreements that were entered into by certain officers and directors of Inuvo and the parties to the Merger Agreement\"']"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "lp.annotate(text)['sentence']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsa9w7audkfy"
      },
      "source": [
        "##📜 Conclusions\n",
        "In this notebook we will show you several techniques and their caveats to properly split Sentences so that you reduce your sentence length after tokenization.\n",
        "\n",
        "Doing this has proved to be better even when you are within the limits of your sentence token length (512) - smaller chunks of pieces of text are better managed than long sequences."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "xsa9w7audkfy"
      ],
      "provenance": [],
      "toc_visible": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.6 (default, Oct 18 2022, 12:41:40) \n[Clang 14.0.0 (clang-1400.0.29.202)]"
    },
    "vscode": {
      "interpreter": {
        "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}