Video_Summarization.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9gVmPBMzQh6s"
      },
      "source": [
        "# **1. Install dependencies**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m47JndjZQh6z",
        "outputId": "07206675-f262-4e30-c9e9-b8099e15b9aa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://download.pytorch.org/whl/lts/1.8/torch_lts.html\n",
            "Collecting torch==1.8.2+cu111\n",
            "  Downloading https://download.pytorch.org/whl/lts/1.8/cu111/torch-1.8.2%2Bcu111-cp37-cp37m-linux_x86_64.whl (1982.2 MB)\n",
            "\u001b[K     |█████████████▌                  | 834.1 MB 1.4 MB/s eta 0:13:55tcmalloc: large alloc 1147494400 bytes == 0x657e8000 @  0x7fcaac0f1615 0x58ead6 0x4f355e 0x4d222f 0x51041f 0x5b4ee6 0x58ff2e 0x510325 0x5b4ee6 0x58ff2e 0x50d482 0x4d00fb 0x50cb8d 0x4d00fb 0x50cb8d 0x4d00fb 0x50cb8d 0x4bac0a 0x538a76 0x590ae5 0x510280 0x5b4ee6 0x58ff2e 0x50d482 0x5b4ee6 0x58ff2e 0x50c4fc 0x58fd37 0x50ca37 0x5b4ee6 0x58ff2e\n",
            "\u001b[K     |█████████████████               | 1055.7 MB 1.3 MB/s eta 0:11:58tcmalloc: large alloc 1434370048 bytes == 0x3010000 @  0x7fcaac0f1615 0x58ead6 0x4f355e 0x4d222f 0x51041f 0x5b4ee6 0x58ff2e 0x510325 0x5b4ee6 0x58ff2e 0x50d482 0x4d00fb 0x50cb8d 0x4d00fb 0x50cb8d 0x4d00fb 0x50cb8d 0x4bac0a 0x538a76 0x590ae5 0x510280 0x5b4ee6 0x58ff2e 0x50d482 0x5b4ee6 0x58ff2e 0x50c4fc 0x58fd37 0x50ca37 0x5b4ee6 0x58ff2e\n",
            "\u001b[K     |█████████████████████▋          | 1336.2 MB 1.3 MB/s eta 0:08:25tcmalloc: large alloc 1792966656 bytes == 0x587fc000 @  0x7fcaac0f1615 0x58ead6 0x4f355e 0x4d222f 0x51041f 0x5b4ee6 0x58ff2e 0x510325 0x5b4ee6 0x58ff2e 0x50d482 0x4d00fb 0x50cb8d 0x4d00fb 0x50cb8d 0x4d00fb 0x50cb8d 0x4bac0a 0x538a76 0x590ae5 0x510280 0x5b4ee6 0x58ff2e 0x50d482 0x5b4ee6 0x58ff2e 0x50c4fc 0x58fd37 0x50ca37 0x5b4ee6 0x58ff2e\n",
            "\u001b[K     |███████████████████████████▎    | 1691.1 MB 1.3 MB/s eta 0:03:50tcmalloc: large alloc 2241208320 bytes == 0xc35e4000 @  0x7fcaac0f1615 0x58ead6 0x4f355e 0x4d222f 0x51041f 0x5b4ee6 0x58ff2e 0x510325 0x5b4ee6 0x58ff2e 0x50d482 0x4d00fb 0x50cb8d 0x4d00fb 0x50cb8d 0x4d00fb 0x50cb8d 0x4bac0a 0x538a76 0x590ae5 0x510280 0x5b4ee6 0x58ff2e 0x50d482 0x5b4ee6 0x58ff2e 0x50c4fc 0x58fd37 0x50ca37 0x5b4ee6 0x58ff2e\n",
            "\u001b[K     |████████████████████████████████| 1982.2 MB 1.2 MB/s eta 0:00:01tcmalloc: large alloc 1982201856 bytes == 0x3010000 @  0x7fcaac0f01e7 0x4b2590 0x4b261c 0x58ead6 0x4f355e 0x4d222f 0x51041f 0x5b4ee6 0x58ff2e 0x50ca37 0x5b4ee6 0x58ff2e 0x50ca37 0x5b4ee6 0x58ff2e 0x50ca37 0x5b4ee6 0x58ff2e 0x50ca37 0x5b4ee6 0x58ff2e 0x50ca37 0x58fd37 0x50ca37 0x5b4ee6 0x58ff2e 0x50d482 0x5b4ee6 0x58ff2e 0x50d482 0x5b4ee6\n",
            "tcmalloc: large alloc 2477752320 bytes == 0x148f46000 @  0x7fcaac0f1615 0x58ead6 0x4f355e 0x4d222f 0x51041f 0x5b4ee6 0x58ff2e 0x50ca37 0x5b4ee6 0x58ff2e 0x50ca37 0x5b4ee6 0x58ff2e 0x50ca37 0x5b4ee6 0x58ff2e 0x50ca37 0x5b4ee6 0x58ff2e 0x50ca37 0x58fd37 0x50ca37 0x5b4ee6 0x58ff2e 0x50d482 0x5b4ee6 0x58ff2e 0x50d482 0x5b4ee6 0x4bad99 0x4d3249\n",
            "\u001b[K     |████████████████████████████████| 1982.2 MB 7.8 kB/s \n",
            "\u001b[?25hCollecting torchvision==0.9.2+cu111\n",
            "  Downloading https://download.pytorch.org/whl/lts/1.8/cu111/torchvision-0.9.2%2Bcu111-cp37-cp37m-linux_x86_64.whl (17.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 17.5 MB 353 kB/s \n",
            "\u001b[?25hCollecting torchaudio===0.8.2\n",
            "  Downloading https://download.pytorch.org/whl/lts/1.8/torchaudio-0.8.2-cp37-cp37m-linux_x86_64.whl (1.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.9 MB 37.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.2+cu111) (4.1.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==1.8.2+cu111) (1.21.6)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.9.2+cu111) (7.1.2)\n",
            "Installing collected packages: torch, torchvision, torchaudio\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.12.1+cu113\n",
            "    Uninstalling torch-1.12.1+cu113:\n",
            "      Successfully uninstalled torch-1.12.1+cu113\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.13.1+cu113\n",
            "    Uninstalling torchvision-0.13.1+cu113:\n",
            "      Successfully uninstalled torchvision-0.13.1+cu113\n",
            "  Attempting uninstall: torchaudio\n",
            "    Found existing installation: torchaudio 0.12.1+cu113\n",
            "    Uninstalling torchaudio-0.12.1+cu113:\n",
            "      Successfully uninstalled torchaudio-0.12.1+cu113\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchtext 0.13.1 requires torch==1.12.1, but you have torch 1.8.2+cu111 which is incompatible.\u001b[0m\n",
            "Successfully installed torch-1.8.2+cu111 torchaudio-0.8.2 torchvision-0.9.2+cu111\n"
          ]
        }
      ],
      "source": [
        "# Install PyTorch\n",
        "!pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Svf4USCRQh62",
        "outputId": "dae73515-2e87-4b81-b1bf-abe82207aa1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.24.0-py3-none-any.whl (5.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.5 MB 32.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Collecting huggingface-hub<1.0,>=0.10.0\n",
            "  Downloading huggingface_hub-0.11.0-py3-none-any.whl (182 kB)\n",
            "\u001b[K     |████████████████████████████████| 182 kB 74.5 MB/s \n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.6 MB 56.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.13.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.10.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.11.0 tokenizers-0.13.2 transformers-4.24.0\n"
          ]
        }
      ],
      "source": [
        "# Install transformers\n",
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mh9DTREUZWB8",
        "outputId": "485ba3ef-c641-44c4-bdf5-939da979bb15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tokenizers in /usr/local/lib/python3.7/dist-packages (0.13.2)\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.97-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 27.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (3.19.6)\n",
            "Installing collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.97\n"
          ]
        }
      ],
      "source": [
        "!pip install tokenizers sentencepiece protobuf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s7JJLo_yZZlV",
        "outputId": "8604ae03-37ba-43c4-99a0-045065694b59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (0.1.97)\n"
          ]
        }
      ],
      "source": [
        "!pip install sentencepiece"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yFtnEHPQh63"
      },
      "source": [
        "# **2. Import and Load Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H4cjaoyHD4Hy",
        "outputId": "857ab08f-57e6-4379-a786-6014e230ff9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: youtube-transcript-api in /usr/local/lib/python3.7/dist-packages (0.5.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from youtube-transcript-api) (2.23.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->youtube-transcript-api) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->youtube-transcript-api) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->youtube-transcript-api) (2022.9.24)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->youtube-transcript-api) (3.0.4)\n"
          ]
        }
      ],
      "source": [
        "!pip install youtube-transcript-api"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "LDWMg6oAEyHh"
      },
      "outputs": [],
      "source": [
        "import youtube_transcript_api\n",
        "from youtube_transcript_api import YouTubeTranscriptApi\n",
        "import nltk\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "import sklearn\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X74tOxc7E2Hz",
        "outputId": "bb38b7b8-4358-465c-89fe-68db900f1cf5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import sent_tokenize\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "NcaBQz-7E0M-"
      },
      "outputs": [],
      "source": [
        "link = \"https://www.youtube.com/watch?v=Y8Tko2YC5hA\" \n",
        "unique_id = link.split(\"=\")[-1]\n",
        "sub = YouTubeTranscriptApi.get_transcript(unique_id)  \n",
        "subtitle = \" \".join([x['text'] for x in sub])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "N1B8krVeE5fu"
      },
      "outputs": [],
      "source": [
        "subtitle = subtitle.replace(\"n\",\"\")\n",
        "sentences = sent_tokenize(subtitle)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "OTG7-NMJE8t8",
        "outputId": "2251e95f-82aa-42f5-e8e2-8d6c13179f2f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'I this video, I\\'m goig to aswer the top 3 questios my studets ask me about Pytho. What is Pytho? What  ca you do with it? Ad why is it so popular? I other words, what does it do that other programmig laguages do\\'t? Pytho is the  world\\'s fastest growig ad most popular programmig laguage, ot just  amogst software egieers, but also amogst mathematicias,  data aalysts, scietists, accoutats, etworkig egieers, ad eve kids! Because it\\'s a very begier friedly programmig  laguage. So people from differet disciplies use Pytho for a variety of differet tasks, such as data aalysis ad visualizatio,  artificial itelligece ad machie learig, automatio  i fact this is oe of the big uses of Pytho amogst people who are ot software developers. If you costatly have to do borig, repetitive  tasks, such as copyig files ad folders aroud, reamig them,  uploadig them to a server, you ca easily write a Pytho script to automate all that ad save your time. Ad that\\'s just oe example, if you cotiuously have to work with excel spreadsheets, PDF\\'s, CS View files, dowload websites ad parse them, you ca automate all that stuff with Pytho. So you do\\'t have to be a software developer to use Pytho. You could be a accoutat, a mathematicia, or a scietist, ad use Pytho  to make your life easier. You ca also use Pytho to build  web, mobile ad desktop applicatios as well as software  testig or eve hackig. So Pytho is a multi purpose laguage.  Now if you have some programmig experiece you may say, \"But Mosh we ca do all this stuff with other programmig laguages, so what\\'s the big deal  about Pytho?\" Here are a few reasos. With Pytho you ca  solve complex problems i less time with fewer lies of code.  Here\\'s a example. Let\\'s say we wat to extract the first three  letters of the text Hello World. This is the code we have to write  i C# this is how we do it i JavaScript ad here\\'s how we  do it i Pytho. See how short ad clea the laguage is? Ad that\\'s just the begiig. Pytho makes a lot of trivial thigs really easy with a simple yet powerful sytax. Here are a few other reasos Pytho is so popular. It\\'s a high level laguage so you do\\'t have to worry about complex tasks such as memory maagemet,  like you do i C++. It\\'s cross platform which meas  you ca build ad ru Pytho applicatios o Widows, Mac,  ad Liux. It has a huge commuity so wheever you get  stuck, there is someoe out there to help. It has a large ecosystem  of libraries, frameworks ad tools which meas whatever you waa do it is likely that someoe else has doe it before because Pytho has bee aroud  for over 20 years. So i a utshell, Pytho is a multi-purpose laguage with a simple, clea, ad begier-friedly  sytax. All of that meas Pytho is awesome. Techically everythig you do with Pytho you ca do with other programmig laguages,  but Pytho\\'s simplicity ad elegace has made it grow way  more tha other programmig laguages. That\\'s why it\\'s the umber oe laguage employers are lookig for. So whether you\\'re a programmer or  a absolute begier, learig Pytho opes up lots of job opportuities  to you. I fact, the average Pytho developer ears a whoppig 116,000 dollars a year. If you foud this video helpful, please support my hard work by likig ad sharig it with others.  Also, be sure to subscribe to my chael, because I have a couple of awesome Pytho tutorials for you, you\\'re goig to see them o the scree ow.  Here\\'s my Pytho tutorial for begiers, it\\'s a great startig poit if you  have limited or o programmig experiece. O the other had, if you  do have some programmig experiece ad wat to quickly get up to speed with Pytho,  I have aother tutorial just for you. I\\'m ot goig to waste your time  tellig you what a variable or a fuctio is. I will talk to you like a programmer. There\\'s ever bee a better time to master Pytho programmig, so click o the tutorial that is right for you ad get started. Thak you for watchig!'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "subtitle"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Installing Transformers**"
      ],
      "metadata": {
        "id": "fZ_-6I-nkIOT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "iJvlXod3Qh63"
      },
      "outputs": [],
      "source": [
        "# Importing dependencies from transformers\n",
        "import torch\n",
        "from transformers import PegasusForConditionalGeneration, PegasusTokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "yLZZB7EXQh64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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        "outputId": "e4f06126-bd8a-45cc-8e19-350b643f658d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.91M [00:00<?, ?B/s]"
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        {
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              "Downloading:   0%|          | 0.00/65.0 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
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              "Downloading:   0%|          | 0.00/87.0 [00:00<?, ?B/s]"
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              "version_minor": 0,
              "model_id": "769d842e920c4e1c8ddcd17042d62ad8"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.39k [00:00<?, ?B/s]"
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              "version_minor": 0,
              "model_id": "2f4b9574be13479c9fad2ebf9f02cb45"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import PegasusForConditionalGeneration, PegasusTokenizer\n",
        "# Load tokenizer \n",
        "tokenizer = PegasusTokenizer.from_pretrained(\"google/pegasus-xsum\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d5ae6a8b52244ae4a6cb4fa47c9a296e",
            "13ac9176e2334321875b987cbc608ff8",
            "060fabd3c33b426b89c9e95b48b8734c",
            "4927926523eb4116abed06b8e0231d45",
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            "dd7da176384346ad9ef697d04e9c0b1a",
            "ff63a89fdb1c4997a2a97cbb80b6712a",
            "2d31da005e464d8d8b41908862d0b73c",
            "36b577219f1b49b7b76243ec9eab5b90",
            "30fb914d086c476e8e669fb15d0759e9"
          ]
        },
        "id": "xcbY9Zn1Qh65",
        "outputId": "f1b63b27-3904-441e-baa9-d38b9822870a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.28G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5ae6a8b52244ae4a6cb4fa47c9a296e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Load model \n",
        "model = PegasusForConditionalGeneration.from_pretrained(\"google/pegasus-xsum\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A6djzmu1Qh65"
      },
      "source": [
        "# **3. Perform Abstractive Summarization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "5tk7q2ijQh68"
      },
      "outputs": [],
      "source": [
        "# Create tokens - number representation of our text\n",
        "tokens = tokenizer(subtitle, truncation=True, padding=\"max_length\", return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4scRHbjeQh69",
        "outputId": "b5048851-c24e-4c46-d134-6396a924a048"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "           145,   171,   126,   532,  6830, 26144,  4259,   264,   131,   116,\n",
              "           199,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Input tokens\n",
        "tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "oTtWNHPeQh6-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "971d412a-2017-4f61-8902-5c829d68e789"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py:1364: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 64 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  UserWarning,\n"
          ]
        }
      ],
      "source": [
        "# Summarize \n",
        "summary = model.generate(**tokens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WwNjTHcZQh6_",
        "outputId": "fff10ef8-ee07-4f81-cd8c-ca2567b27f8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([    0, 20147, 32713,   117,   109,   278,   131,   116,  5266,  1248,\n",
              "         8016,  4259,   205,   785,   431,   208,  8016,  3528,   107,     1])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# Output summary tokens\n",
        "summary[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "eTfoS2JhQh6_",
        "outputId": "9fadce18-55e0-4a8b-89cf-accc330c5a7f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Pytho is the world's fastest growig ad most popular programmigage.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# Decode summary\n",
        "tokenizer.decode(summary[0])"
      ]
    }
  ],
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