BERT_PDF_to_Text.ipynb

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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ludwigwittgenstein2/Research/blob/master/BERT_PDF_to_Text.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fziWS-WAscb9",
        "outputId": "08660d68-421a-4cf5-f694-54a0be55d58a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install PyPDF2"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: PyPDF2 in /usr/local/lib/python3.6/dist-packages (1.26.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_A5Rm0HZOfT"
      },
      "source": [
        "import PyPDF2"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiCW_mRNsriy"
      },
      "source": [
        "pdfFileObject = open(r\"/content/sample_data/Deep_Learing.pdf\", 'rb')"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EF1aeXauZLGY"
      },
      "source": [
        "pdfReader = PyPDF2.PdfFileReader(pdfFileObject)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TciVfnkXZROV",
        "outputId": "79208263-f715-4f2b-c3be-9a04bf668428",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(\" No. Of Pages :\", pdfReader.numPages)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " No. Of Pages : 328\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBTakzuFZTrA"
      },
      "source": [
        "pageObject = pdfReader.getPage(0)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pV42vhqZfmU"
      },
      "source": [
        "reader = PyPDF2.PdfFileReader(pdfFileObject)\n",
        "for page in reader.pages:\n",
        "  pass"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dx9O9_9TbpJQ",
        "outputId": "85152c1a-b2bf-4537-930f-0d3ff8c7595b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "outside = reader.getPage(0)\n",
        "first_text = (page.extractText())\n",
        "print(page.extractText())"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tommaso TeofiliISBN-13: 978-1-61729-479-2ISBN-10: 1-61729-479-9Deep learning handles the toughest search challenges, \n",
            "including imprecise search terms, badly indexed data, \n",
            "\n",
            "and retrieving images with minimal metadata. And \n",
            "with modern tools like DL4J and TensorFlow, you can apply \n",
            "powerful \n",
            "DL techniques without a deep background in data \n",
            "science or natural language processing \n",
            "(NLP). This book will \n",
            "show you how\n",
            ".Deep Learning for Search teaches you to improve your search \n",
            "results with neural networks. You™ll review how \n",
            "DL relates \n",
            "to search basics like indexing and ranking. Then, you™ll walk \n",
            "\n",
            "through in-depth examples to upgrade your search with \n",
            "DL techniques using Apache Lucene and Deeplearning4j. As the \n",
            "\n",
            "book progresses, you™ll explore advanced topics like searching \n",
            "\n",
            "through images, translating user queries, and designing search \n",
            "\n",
            "engines that improve as they learn!\n",
            " What’s InsideGenerating synonyms\n",
            "Accurate and relevant rankings\n",
            "Searching across languages\n",
            "Content-based image search\n",
            "Search with recommendations\n",
            "For developers comfortable with Java or a similar language \n",
            "and search basics. No experience with deep learning or \n",
            "NLP needed.Tommaso Teofili is a software engineer with a passion for open \n",
            "source and arti˚ cial intelligence. He™s a long-time member of \n",
            "\n",
            "the Apache Software Foundation, contributing to projects for \n",
            "\n",
            "information retrieval, \n",
            "NLP, and distributed computing.To download their free eBook in PDF, ePub, and Kindle formats, \n",
            "owners of this book should visit \n",
            "www.manning.com/books/deep-learning-for-search\n",
            "$59.99 / Can $79.99  [INCLUDING eBOOK]\n",
            "Deep Learning for SearchSEARCH/MACHINE LEARNING\n",
            "MANNINGﬁA practical approach that \n",
            "shows you the state of the art \n",
            "in using neural networks, \n",
            "AI, and deep learning in the development \n",
            "of search engines.\n",
            "ﬂŠFrom the Foreword by \n",
            "Chris Mattmann, \n",
            "NASA JPLﬁA thorough and thoughtful \n",
            "synthesis of traditional search \n",
            "and the latest advancements \n",
            "in deep learning.ﬂŠGreg Zanotti, Marquette Partners\n",
            "ﬁA well-laid-out deep dive \n",
            "into the latest technologies that will take your search \n",
            "engine to the next level.\n",
            "ﬂŠAndrew Wyllie, Thynk Health \n",
            "ﬁHands-on exercises teach \n",
            "you how to master \n",
            "deep learning for   search-based products.\n",
            "ﬂŠAntonio Magnaghi, System1 \n",
            "See first page\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpMG9icBdoy4"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IA2gyP_RZVct",
        "outputId": "f6a1bb9d-ebeb-4e12-e805-385354754e92",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(pageObject.extractText())"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MANNINGTommaso TeofiliForeword by\n",
            " Chris Mattmann\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxTF5BdeZXf-"
      },
      "source": [
        "pdfFileObject.close()"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1Okv6QAu4Ff",
        "outputId": "109904eb-409a-47bf-c95d-9b691be659bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9c/34/fb092588df61bf33f113ade030d1cbe74fb73a0353648f8dd938a223dce7/transformers-3.5.0-py3-none-any.whl (1.3MB)\n",
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            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893257 sha256=314c2d780d39192fce193e328f6f2624e8d7218fc6247846878dbb620ca04175\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sentencepiece, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.43 sentencepiece-0.1.91 tokenizers-0.9.3 transformers-3.5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCphYfJvd4gb",
        "outputId": "427d7c94-290b-4842-cd1d-3e3583f93813",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164,
          "referenced_widgets": [
            "e20e597ce033487d900dcfd9e2941387",
            "20e79ea24b374fe496899d332d105a51",
            "14194be3f8e946fcbf526b6d21cf80ee",
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            "262ea86043c446a29a4aac97201e12b2",
            "2b5a08547f5f4f6faeaa9bea681cbe49",
            "899a63f6f7694375b16f94ade7134a50",
            "d36056feaa0f4e1fb881bfbd2121f60e",
            "fb7a2bb2eca941089856db329c741e51",
            "c7035b054d1240aabba790d6a9d80534",
            "a3a3bfeb194d4f28a52b0d3870231c4a",
            "d174cdbfc8484d61ad46cc2a15d23f2a",
            "7060a66e2599490e9a3a0b8821c10cdf",
            "eab27c63d2724b868195198326fc1fd3",
            "95dd8670bad84efb9dc5144be6e190e0",
            "807cc28aab76489ea282c209bcf77a51",
            "7ff832cc4b344f33a03e1311a5d12b2b",
            "bd0ef509b0a8479ea3db357e73677dba",
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            "8633d2dc0a4e4157873536d0974e932b",
            "75e451934ec04ce29906d5745c2554a5",
            "70f8d5349ff44dfe854cbe8e7ff5b976"
          ]
        }
      },
      "source": [
        "from transformers import AutoTokenizer, AutoModelForQuestionAnswering\n",
        "import torch\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n",
        "model = AutoModelForQuestionAnswering.from_pretrained(\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e20e597ce033487d900dcfd9e2941387",
              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=443.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "899a63f6f7694375b16f94ade7134a50",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "95dd8670bad84efb9dc5144be6e190e0",
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1340675298.0, style=ProgressStyle(descr…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fAmoZBaud9c5"
      },
      "source": [
        "text = first_text"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5r-VwFSetlT"
      },
      "source": [
        "questions = [\"How does your Search improve with Deep Learning?\", \"What will this Book show me?\"]\n"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhLCRKyvfAbK",
        "outputId": "b09de98b-2370-4f00-9a63-dbaa83d15641",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "for question in questions:\n",
        "    inputs = tokenizer(question, text, add_special_tokens=True, return_tensors=\"pt\")\n",
        "    input_ids = inputs[\"input_ids\"].tolist()[0]\n",
        "    text_tokens = tokenizer.convert_ids_to_tokens(input_ids)\n",
        "    answer_start_scores, answer_end_scores = model(**inputs)\n",
        "    answer_start = torch.argmax(\n",
        "        answer_start_scores\n",
        "    )  # Get the most likely beginning of answer with the argmax of the score\n",
        "    answer_end = torch.argmax(answer_end_scores) + 1  # Get the most likely end of answer with the argmax of the score\n",
        "    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))\n",
        "    print(f\"Question: {question}\")\n",
        "    print(f\"Answer: {answer}\")"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Question: How does your Search improve with Deep Learning?\n",
            "Answer: \n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}