Working_with_Text_using_spaCy.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzaHS_vjxTUq"
      },
      "source": [
        "# Introduction to [spaCy](https://spacy.io/)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**--[Manoj Prabhakar Kannan Ravi](https://www.linkedin.com/in/manojprabhakar-kr/), Doctoral Candidate @ HPI**\n",
        "\n"
      ],
      "metadata": {
        "id": "TdYFfg6zuZ6D"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3hdj765xTUv"
      },
      "source": [
        "### How to install spaCy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install spacy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MtRPqdtr-K_V",
        "outputId": "24a41f6a-a656-43d8-a08c-1767c760b05c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: spacy in /usr/local/lib/python3.7/dist-packages (2.2.4)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.19.5)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.1.3)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.23.0)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (7.4.0)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.0)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (3.0.6)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.4.1)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.8.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy) (57.4.0)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.6)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (4.62.3)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.0.6)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.5)\n",
            "Requirement already satisfied: importlib-metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy) (4.8.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy) (3.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy) (3.10.0.2)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (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<3.0.0,>=2.13.0->spacy) (1.24.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy download en_core_web_sm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dtYLlNxn-QwV",
        "outputId": "4cda9a6e-28e1-4e58-a13c-e77a383b4b40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting en_core_web_sm==2.2.5\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz (12.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 12.0 MB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from en_core_web_sm==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.6)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.8.2)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.6)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.19.5)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (4.62.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (57.4.0)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.0.6)\n",
            "Requirement already satisfied: importlib-metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (4.8.2)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.10.0.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.6.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.4)\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<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2021.10.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.10)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_sm')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krzmYbfHxTUx"
      },
      "source": [
        "### Load the NLP pipeline:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iKUiO-x6xTUw"
      },
      "outputs": [],
      "source": [
        "import spacy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EKTb2JU0xTUy"
      },
      "outputs": [],
      "source": [
        "nlp = spacy.load(\"en_core_web_sm\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Processing Pipeline"
      ],
      "metadata": {
        "id": "S2IQ-I9X4kHI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![spacy-pipeline.PNG](data:image/png;base64,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)\n",
        "\n",
        "Source : https://spacy.io/usage/processing-pipelines"
      ],
      "metadata": {
        "id": "C9_uXyHj3iS6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Apple is looking at buying U.K. startup for $1 billion!\""
      ],
      "metadata": {
        "id": "eMKGFRVD6KKv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc = nlp(text)"
      ],
      "metadata": {
        "id": "K8t6R1Yy_bz1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nlp.pipe_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZimfPaE3fp42",
        "outputId": "ff27484a-a83d-4c40-ff10-41b3e9a30581"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['tagger', 'parser', 'ner']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQKOuXVcWt1h"
      },
      "source": [
        "### Tokenization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l0sx9XMGWt1h",
        "outputId": "713d0e68-dc9f-49cc-c600-5e67c2a2a4a9"
      },
      "source": [
        "print(\"-- Using split(' ')\")\n",
        "print(text.split(' '))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-- Using split(' ')\n",
            "['Apple', 'is', 'looking', 'at', 'buying', 'U.K.', 'startup', 'for', '$1', 'billion!']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rm6N6lohWt1i",
        "outputId": "f8136797-b896-4930-c7ee-5f5f064908bf"
      },
      "source": [
        "tokens = []\n",
        "for token in doc:\n",
        "    tokens.append(token)\n",
        "\n",
        "print(tokens)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Apple, is, looking, at, buying, U.K., startup, for, $, 1, billion, !]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDodwG_IWt1k"
      },
      "source": [
        "### Part-of-speech (POS) "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for token in doc:\n",
        "    print(\"\\t\".join( (token.text, \"-->\", token.lemma_, token.pos_, token.tag_,\n",
        "          token.shape_, str(token.is_alpha), str(token.is_stop))))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o3ByWf5vE5AL",
        "outputId": "114bed34-b785-4e99-ec51-dc1e4e9a259e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apple\t-->\tApple\tPROPN\tNNP\tXxxxx\tTrue\tFalse\n",
            "is\t-->\tbe\tAUX\tVBZ\txx\tTrue\tTrue\n",
            "looking\t-->\tlook\tVERB\tVBG\txxxx\tTrue\tFalse\n",
            "at\t-->\tat\tADP\tIN\txx\tTrue\tTrue\n",
            "buying\t-->\tbuy\tVERB\tVBG\txxxx\tTrue\tFalse\n",
            "U.K.\t-->\tU.K.\tPROPN\tNNP\tX.X.\tFalse\tFalse\n",
            "startup\t-->\tstartup\tNOUN\tNN\txxxx\tTrue\tFalse\n",
            "for\t-->\tfor\tADP\tIN\txxx\tTrue\tTrue\n",
            "$\t-->\t$\tSYM\t$\t$\tFalse\tFalse\n",
            "1\t-->\t1\tNUM\tCD\td\tFalse\tFalse\n",
            "billion\t-->\tbillion\tNUM\tCD\txxxx\tTrue\tFalse\n",
            "!\t-->\t!\tPUNCT\t.\t!\tFalse\tFalse\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spacy.explain(\"NNP\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "CCow1cujgUp3",
        "outputId": "876c604d-f08c-4b50-b1ca-4afb281d3d39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'noun, proper singular'"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TssYpexgxTU0"
      },
      "source": [
        "### Dependency Parsing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vO0dVM7JxTU1",
        "outputId": "088c23a3-a2a7-4a03-e098-52490c7ade4d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apple --> nsubj looking VERB []\n",
            "is --> aux looking VERB []\n",
            "looking --> ROOT looking VERB [Apple, is, at, !]\n",
            "at --> prep looking VERB [buying]\n",
            "buying --> pcomp at ADP [startup, for]\n",
            "U.K. --> compound startup NOUN []\n",
            "startup --> dobj buying VERB [U.K.]\n",
            "for --> prep buying VERB [billion]\n",
            "$ --> quantmod billion NUM []\n",
            "1 --> compound billion NUM []\n",
            "billion --> pobj for ADP [$, 1]\n",
            "! --> punct looking VERB []\n"
          ]
        }
      ],
      "source": [
        "for token in doc:\n",
        "    print(token.text, \"-->\", token.dep_, token.head.text, token.head.pos_,\n",
        "            [child for child in token.children])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spacy.explain(\"nsubj\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "gtZC9o9ggitC",
        "outputId": "c3d81a2f-4170-4015-c36f-eb9ac666ada3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'nominal subject'"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jZlydOayWt1i"
      },
      "source": [
        "### Named Entity Recognition (NER)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FyPItbFPWt1j",
        "outputId": "addf6657-678f-4cbb-bb23-018bb37bc903"
      },
      "source": [
        "for ent in doc.ents:\n",
        "    print(ent.text, ent.start_char, ent.end_char, ent.label_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apple 0 5 ORG\n",
            "U.K. 27 31 GPE\n",
            "$1 billion 44 54 MONEY\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spacy.explain(\"GPE\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "aHOxeuT-gr71",
        "outputId": "2786aa4d-7da5-4728-fe06-5a411c141b5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Countries, cities, states'"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmPz56acxTU_"
      },
      "source": [
        "## Using the Visualizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HnkszrsNxTVA"
      },
      "outputs": [],
      "source": [
        "from spacy import displacy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p3M5hO9ixTVA"
      },
      "source": [
        "The Dependency Parse tree can be visualized by serve and can be seen using the URL [http://localhost:5000/](http://localhost:5000/)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "ZXS7bO4axTVA"
      },
      "outputs": [],
      "source": [
        "#displacy.serve(doc, style='dep')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QHNciP-xTVA"
      },
      "source": [
        "We can use render to display the visualization within the same Jupyter Notebook:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "tHcDJ-HhxTVB",
        "outputId": "c25c11c1-41c6-4f5e-849f-e27b9151f7f3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xml:lang=\"en\" id=\"bfc0d82dd57a458bbe02c98458921e14-0\" class=\"displacy\" width=\"1700\" height=\"362.0\" direction=\"ltr\" style=\"max-width: none; height: 362.0px; color: #000000; background: #ffffff; font-family: Arial; direction: ltr\">\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"272.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">Apple</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">PROPN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"272.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"200\">is</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"200\">AUX</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"272.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"350\">looking</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"350\">VERB</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"272.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"500\">at</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"500\">ADP</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"272.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"650\">buying</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"650\">VERB</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"272.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"800\">U.K.</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"800\">PROPN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"272.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"950\">startup</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"950\">NOUN</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"272.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1100\">for</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1100\">ADP</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"272.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1250\">$</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1250\">SYM</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"272.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1400\">1</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1400\">NUM</tspan>\n",
              "</text>\n",
              "\n",
              "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"272.0\">\n",
              "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1550\">billion!</tspan>\n",
              "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1550\">NUM</tspan>\n",
              "</text>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-bfc0d82dd57a458bbe02c98458921e14-0-0\" stroke-width=\"2px\" d=\"M70,227.0 C70,77.0 345.0,77.0 345.0,227.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-bfc0d82dd57a458bbe02c98458921e14-0-0\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">nsubj</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M70,229.0 L62,217.0 78,217.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-bfc0d82dd57a458bbe02c98458921e14-0-1\" stroke-width=\"2px\" d=\"M220,227.0 C220,152.0 340.0,152.0 340.0,227.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-bfc0d82dd57a458bbe02c98458921e14-0-1\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">aux</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M220,229.0 L212,217.0 228,217.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-bfc0d82dd57a458bbe02c98458921e14-0-2\" stroke-width=\"2px\" d=\"M370,227.0 C370,152.0 490.0,152.0 490.0,227.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-bfc0d82dd57a458bbe02c98458921e14-0-2\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">prep</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M490.0,229.0 L498.0,217.0 482.0,217.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-bfc0d82dd57a458bbe02c98458921e14-0-3\" stroke-width=\"2px\" d=\"M520,227.0 C520,152.0 640.0,152.0 640.0,227.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-bfc0d82dd57a458bbe02c98458921e14-0-3\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">pcomp</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M640.0,229.0 L648.0,217.0 632.0,217.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-bfc0d82dd57a458bbe02c98458921e14-0-4\" stroke-width=\"2px\" d=\"M820,227.0 C820,152.0 940.0,152.0 940.0,227.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-bfc0d82dd57a458bbe02c98458921e14-0-4\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">compound</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M820,229.0 L812,217.0 828,217.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-bfc0d82dd57a458bbe02c98458921e14-0-5\" stroke-width=\"2px\" d=\"M670,227.0 C670,77.0 945.0,77.0 945.0,227.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-bfc0d82dd57a458bbe02c98458921e14-0-5\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">dobj</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M945.0,229.0 L953.0,217.0 937.0,217.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-bfc0d82dd57a458bbe02c98458921e14-0-6\" stroke-width=\"2px\" d=\"M670,227.0 C670,2.0 1100.0,2.0 1100.0,227.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-bfc0d82dd57a458bbe02c98458921e14-0-6\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">prep</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M1100.0,229.0 L1108.0,217.0 1092.0,217.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-bfc0d82dd57a458bbe02c98458921e14-0-7\" stroke-width=\"2px\" d=\"M1270,227.0 C1270,77.0 1545.0,77.0 1545.0,227.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-bfc0d82dd57a458bbe02c98458921e14-0-7\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">quantmod</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M1270,229.0 L1262,217.0 1278,217.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-bfc0d82dd57a458bbe02c98458921e14-0-8\" stroke-width=\"2px\" d=\"M1420,227.0 C1420,152.0 1540.0,152.0 1540.0,227.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-bfc0d82dd57a458bbe02c98458921e14-0-8\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">compound</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M1420,229.0 L1412,217.0 1428,217.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "\n",
              "<g class=\"displacy-arrow\">\n",
              "    <path class=\"displacy-arc\" id=\"arrow-bfc0d82dd57a458bbe02c98458921e14-0-9\" stroke-width=\"2px\" d=\"M1120,227.0 C1120,2.0 1550.0,2.0 1550.0,227.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
              "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
              "        <textPath xlink:href=\"#arrow-bfc0d82dd57a458bbe02c98458921e14-0-9\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">pobj</textPath>\n",
              "    </text>\n",
              "    <path class=\"displacy-arrowhead\" d=\"M1550.0,229.0 L1558.0,217.0 1542.0,217.0\" fill=\"currentColor\"/>\n",
              "</g>\n",
              "</svg></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "displacy.render(doc, style='dep', jupyter=True, options={\"distance\": 150})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZZ4pnkTxTVB"
      },
      "source": [
        "We can also visualize the named entity annotations."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "displacy.render(doc, style='ent', jupyter=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "Y69x_pqTNuDT",
        "outputId": "a64a07c9-4b8c-4b17-ef05-5d6f46142f77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
              "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    Apple\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
              "</mark>\n",
              " is looking at buying \n",
              "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    U.K.\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
              "</mark>\n",
              " startup for \n",
              "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    $1 billion\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
              "</mark>\n",
              "!</div></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## spaCy Matcher"
      ],
      "metadata": {
        "id": "nCWD6-H_jFfD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Source : [blog](https://www.analyticsvidhya.com/blog/2020/03/spacy-tutorial-learn-natural-language-processing/)"
      ],
      "metadata": {
        "id": "qbE_kfFdFsK8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import spaCy Matcher\n",
        "from spacy.matcher import Matcher\n",
        "\n",
        "# Initialize the matcher with the spaCy vocabulary\n",
        "matcher = Matcher(nlp.vocab)\n",
        "\n",
        "doc = nlp(\"Some people start their day with lemon water.\")\n",
        "\n",
        "# Define rule\n",
        "pattern = [{'TEXT': 'lemon'}, {'TEXT': 'water'}]\n",
        "\n",
        "# Add rule\n",
        "matcher.add('rule_1', [pattern])\n",
        "\n",
        "matches = matcher(doc)\n",
        "matches"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PaEnrDSDlInw",
        "outputId": "78bbde23-8bb9-49e0-f399-21653ed66f11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(7604275899133490726, 6, 8)]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract matched text\n",
        "for match_id, start, end in matches:\n",
        "    # Get the matched span\n",
        "    matched_span = doc[start:end]\n",
        "    print(matched_span.text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z9QvPQCDlNBY",
        "outputId": "94251d3d-abcc-405c-c9af-ee4bbed754a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "lemon water\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc1 = nlp(\"Did you read this book?\")\n",
        "doc2 = nlp(\"I will book my tickets\")\n",
        "\n",
        "pattern = [{'TEXT': 'book', 'POS': 'NOUN'}]\n",
        "\n",
        "# Initialize the matcher with the shared vocab\n",
        "matcher = Matcher(nlp.vocab)\n",
        "matcher.add('rule_2', [pattern])"
      ],
      "metadata": {
        "id": "Ki9-XiU8lWSx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matches = matcher(doc1)\n",
        "matches"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4lwrhtqlXca",
        "outputId": "b946aa5c-22fa-4c23-adc7-942cf3519c96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(375134486054924901, 4, 5)]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matches = matcher(doc2)\n",
        "matches"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fb8sXcBBlcnp",
        "outputId": "9437bf80-2345-4fd4-e22e-471957fa9a68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VVPuXENMxTVB"
      },
      "source": [
        "## Semantic similarity using word vectors"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzDzSl8DxTVC"
      },
      "source": [
        "Refer to [spaCy vectors page](https://spacy.io/usage/vectors-similarity) to use other models or custom word vectors.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!python -m spacy download en_core_web_md"
      ],
      "metadata": {
        "id": "5K3riCygUn3U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "-- Example modified from [link](https://spacy.pythonhumanities.com/01_03_word_vectors.html)"
      ],
      "metadata": {
        "id": "utxn2Z4dGgBE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "#nlp = spacy.load(\"en_core_web_md\")  \n",
        "doc1 = nlp(\"I like salty fries and hamburgers.\")\n",
        "doc2 = nlp(\"Fast food tastes very good.\")\n",
        "doc3 = nlp(\"Fast shipments are very good.\")\n",
        "\n",
        "# Similarity of these documents\n",
        "print(doc1, \"<->\", doc2, doc1.similarity(doc2))\n",
        "print(doc1, \"<->\", doc3, doc1.similarity(doc3))\n",
        "print(doc2, \"<->\", doc3, doc2.similarity(doc3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IXAddHTkVSSH",
        "outputId": "3adf3f9e-5307-4a8f-e35a-88cda7511b64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I like salty fries and hamburgers. <-> Fast food tastes very good. 0.3679284418194866\n",
            "I like salty fries and hamburgers. <-> Fast shipments are very good. 0.24717503829951157\n",
            "Fast food tastes very good. <-> Fast shipments are very good. 0.7334099402646285\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sj1LDdXoxTVD",
        "outputId": "7de6046d-851e-4088-cb8d-4343c49b661d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dog True 19.378368 True\n",
            "cat True 19.319294 True\n",
            "banana True 17.693539 True\n",
            "wheel True 19.233456 True\n"
          ]
        }
      ],
      "source": [
        "tokens = nlp(u'dog cat banana wheel')\n",
        "\n",
        "for token in tokens:\n",
        "    print(token.text, token.has_vector, token.vector_norm, token.is_oov)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ARHgWamMxTVE",
        "outputId": "238eb652-e680-4051-dd0f-6205d1eb728c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "banana 96 [ 1.5871911   0.8064396  -0.9054165  -0.8100374   0.95870805  1.0146952\n",
            " -1.1023825   1.8020909   2.5353732   2.7387426  -0.43189812 -0.12808043\n",
            "  0.75751686 -0.9152087  -0.15977871 -0.9103936  -2.1560912   1.32443\n",
            " -0.1405558  -0.6373711   4.32754    -0.88028044  0.03564376 -2.9327438\n",
            " -1.0217257   0.731537    2.4963975   1.0742254   1.4460571  -2.6484275\n",
            "  0.5366294  -1.4870604  -0.83379704 -0.4151709  -1.9360238  -1.7089003\n",
            "  3.7156317   0.58594716  0.29604545 -0.7416147   4.061759    2.0177944\n",
            "  0.8102745  -4.117684    0.5899048   0.5998761  -3.1075888  -0.70453393\n",
            " -0.10131472  1.0950371  -1.2231823  -2.9008093   0.37960178  0.2982519\n",
            " -2.3102129   0.20481265  1.7023772   2.2459512  -1.5036099   2.4872742\n",
            "  0.46261945 -1.469563   -1.963779    4.2778063  -0.8498221   0.89605945\n",
            "  0.6714468  -2.0104113   0.18463123  2.7223854  -2.8811326   0.90999866\n",
            " -1.9458306   2.824358   -1.5410196   0.2469359   1.3095249  -1.3957369\n",
            " -0.6296301  -0.50245476  0.8588661  -1.7943428  -4.1018476  -0.74174714\n",
            " -1.5341759   1.6868057   0.59892476 -2.6711385  -1.703895   -0.92804223\n",
            " -0.150774    0.92698467  2.5210652   3.0843406   2.2907717   0.4413057 ]\n"
          ]
        }
      ],
      "source": [
        "n = 2\n",
        "print(tokens[n].text, len(tokens[n].vector), tokens[n].vector)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Source : [Github repository](https://github.com/dcavar/python-tutorial-notebooks)"
      ],
      "metadata": {
        "id": "Rp8XAoNjDEdI"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5B36R_4hxTVE"
      },
      "source": [
        "Another similarity example for some popular words."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DX9XrJ3fxTVE",
        "outputId": "3545b0d5-3943-433a-b977-1488ad1c0a79"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "queen queen 1.0\n",
            "queen king 0.57581687\n",
            "queen chef 0.3309688\n",
            "king queen 0.57581687\n",
            "king king 1.0\n",
            "king chef 0.44007176\n",
            "chef queen 0.3309688\n",
            "chef king 0.44007176\n",
            "chef chef 1.0\n"
          ]
        }
      ],
      "source": [
        "tokens = nlp(u'queen king chef')\n",
        "\n",
        "for token1 in tokens:\n",
        "    for token2 in tokens:\n",
        "        print(token1, token2, token1.similarity(token2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-SL2oI_TxTVE"
      },
      "source": [
        "### Similarities in Context"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9hBicsdxTVE",
        "outputId": "f9697fd6-aa62-44e5-f34f-cc172d71d770"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "labrador True 19.982306 True\n"
          ]
        }
      ],
      "source": [
        "tokens = nlp(u'labrador')\n",
        "\n",
        "for token in tokens:\n",
        "    print(token.text, token.has_vector, token.vector_norm, token.is_oov)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-TcH63-xTVE",
        "outputId": "70a09210-1c26-4314-e9a0-7f7f8499d130"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1: 0.17863217662087344\n",
            "2: 0.2774142819743359\n",
            "3: 0.1853336600408966\n"
          ]
        }
      ],
      "source": [
        "doc1 = nlp(u\"The labrador barked.\")\n",
        "doc2 = nlp(u\"The labrador swam.\")\n",
        "doc3 = nlp(u\"The people on Labrador are Canadians.\")\n",
        "\n",
        "dog = nlp(u\"dog\")\n",
        "\n",
        "count = 0\n",
        "for doc in [doc1, doc2, doc3]:\n",
        "    lab = doc\n",
        "    count += 1\n",
        "    print(str(count) + \":\", lab.similarity(dog))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRJiwiD4xTVF",
        "outputId": "8d54700f-f9d4-4550-9799-694c32706007"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\"dog bites man\" \"dog bites man\" 1.0\n",
            "\"dog bites man\" \"man bites dog\" 0.9454270050051115\n",
            "\"dog bites man\" \"man dog bites\" 0.8925287164710086\n",
            "\"dog bites man\" \"cat eats mouse\" 0.6138338938504083\n",
            "\"man bites dog\" \"dog bites man\" 0.9454270050051115\n",
            "\"man bites dog\" \"man bites dog\" 1.0\n",
            "\"man bites dog\" \"man dog bites\" 0.8896486160263385\n",
            "\"man bites dog\" \"cat eats mouse\" 0.673638000133504\n",
            "\"man dog bites\" \"dog bites man\" 0.8925287164710086\n",
            "\"man dog bites\" \"man bites dog\" 0.8896486160263385\n",
            "\"man dog bites\" \"man dog bites\" 1.0\n",
            "\"man dog bites\" \"cat eats mouse\" 0.551124818246088\n",
            "\"cat eats mouse\" \"dog bites man\" 0.6138338938504083\n",
            "\"cat eats mouse\" \"man bites dog\" 0.673638000133504\n",
            "\"cat eats mouse\" \"man dog bites\" 0.551124818246088\n",
            "\"cat eats mouse\" \"cat eats mouse\" 1.0\n"
          ]
        }
      ],
      "source": [
        "docs = [nlp(u\"dog bites man\"), nlp(u\"man bites dog\"),\n",
        "        nlp(u\"man dog bites\"), nlp(u\"cat eats mouse\")]\n",
        "\n",
        "for doc in docs:\n",
        "    for other_doc in docs:\n",
        "        print('\"' + doc.text + '\"', '\"' + other_doc.text + '\"', doc.similarity(other_doc))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRCZchglxTVF"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "901_2baMxTVG"
      },
      "source": [
        "-- Modified from [blog](https://www.machinelearningplus.com/nlp/training-custom-ner-model-in-spacy/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_GZF5ENxTVJ"
      },
      "source": [
        "An example code for training an NER model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OwACbb3kxTVG"
      },
      "outputs": [],
      "source": [
        "#nlp = spacy.load('en_core_web_md')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g5nAU3qExTVJ"
      },
      "outputs": [],
      "source": [
        "from spacy.util import minibatch, compounding\n",
        "from pathlib import Path\n",
        "import random"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKPneHuWBWC-"
      },
      "source": [
        "# Load pre-existing spacy model\n",
        "nlp=spacy.load('en_core_web_sm')\n",
        "\n",
        "# Getting the pipeline component\n",
        "ner=nlp.get_pipe(\"ner\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fBUt1YEhxTVJ"
      },
      "source": [
        "The training data format!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EldDy4zBc_i"
      },
      "source": [
        "# training data\n",
        "TRAIN_DATA = [\n",
        "              (\"Walmart is a leading e-commerce company\", {\"entities\": [(0, 7, \"ORG\")]}),\n",
        "              (\"I reached Chennai yesterday.\", {\"entities\": [(19, 28, \"GPE\")]}),\n",
        "              (\"I recently ordered a book from Amazon\", {\"entities\": [(24,32, \"ORG\")]}),\n",
        "              (\"I was driving a BMW\", {\"entities\": [(16,19, \"PRODUCT\")]}),\n",
        "              (\"I ordered this from ShopClues\", {\"entities\": [(20,29, \"ORG\")]}),\n",
        "              (\"Fridge can be ordered in Amazon \", {\"entities\": [(0,6, \"PRODUCT\")]}),\n",
        "              (\"I bought a new Washer\", {\"entities\": [(16,22, \"PRODUCT\")]}),\n",
        "              (\"I bought a old table\", {\"entities\": [(16,21, \"PRODUCT\")]}),\n",
        "              (\"I bought a fancy dress\", {\"entities\": [(18,23, \"PRODUCT\")]}),\n",
        "              (\"I rented a camera\", {\"entities\": [(12,18, \"PRODUCT\")]}),\n",
        "              (\"I rented a tent for our trip\", {\"entities\": [(12,16, \"PRODUCT\")]}),\n",
        "              (\"I rented a screwdriver from our neighbour\", {\"entities\": [(12,22, \"PRODUCT\")]}),\n",
        "              (\"I repaired my computer\", {\"entities\": [(15,23, \"PRODUCT\")]}),\n",
        "              (\"I got my clock fixed\", {\"entities\": [(16,21, \"PRODUCT\")]}),\n",
        "              (\"I got my truck fixed\", {\"entities\": [(16,21, \"PRODUCT\")]}),\n",
        "              (\"Flipkart started it's journey from zero\", {\"entities\": [(0,8, \"ORG\")]}),\n",
        "              (\"I recently ordered from Max\", {\"entities\": [(24,27, \"ORG\")]}),\n",
        "              (\"Flipkart is recognized as leader in market\",{\"entities\": [(0,8, \"ORG\")]}),\n",
        "              (\"I recently ordered from Swiggy\", {\"entities\": [(24,30, \"ORG\")]})\n",
        "              ]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuOAETRTBfRL"
      },
      "source": [
        "# Adding labels to the `ner`\n",
        "for _, annotations in TRAIN_DATA:\n",
        "  for ent in annotations.get(\"entities\"):\n",
        "    ner.add_label(ent[2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BxUqWc7fBgsL"
      },
      "source": [
        "# Disable pipeline components you dont need to change\n",
        "pipe_exceptions = [\"ner\", \"trf_wordpiecer\", \"trf_tok2vec\"]\n",
        "unaffected_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]\n",
        "other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with nlp.disable_pipes(*other_pipes):  # only train NER\n",
        "    for itn in range(30):\n",
        "        random.shuffle(TRAIN_DATA)\n",
        "        losses = {}\n",
        "        # batch up the examples using spaCy's minibatch\n",
        "        batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))\n",
        "        for batch in batches:\n",
        "            texts, annotations = zip(*batch)\n",
        "            nlp.update(\n",
        "                texts,  # batch of texts\n",
        "                annotations,  # batch of annotations\n",
        "                drop=0.5,  # dropout - make it harder to memorise data\n",
        "                losses=losses,\n",
        "            )\n",
        "        print(\"Losses\", losses)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00zvgU1jRerq",
        "outputId": "26215f08-880e-4608-e3f3-4ccac899effb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Losses {'ner': 22.663299785570416}\n",
            "Losses {'ner': 14.440529714026898}\n",
            "Losses {'ner': 9.59392978363121}\n",
            "Losses {'ner': 11.639361066964739}\n",
            "Losses {'ner': 9.861955335195717}\n",
            "Losses {'ner': 14.151953438622172}\n",
            "Losses {'ner': 9.477473568335427}\n",
            "Losses {'ner': 4.9097686585745866}\n",
            "Losses {'ner': 7.46763817722092}\n",
            "Losses {'ner': 5.5857929075102675}\n",
            "Losses {'ner': 12.023708899357182}\n",
            "Losses {'ner': 5.246545431490119}\n",
            "Losses {'ner': 7.607580455684229}\n",
            "Losses {'ner': 5.9596031884685665}\n",
            "Losses {'ner': 4.6183865842090785}\n",
            "Losses {'ner': 5.320895846318014}\n",
            "Losses {'ner': 6.269039736386078}\n",
            "Losses {'ner': 6.555036921551676}\n",
            "Losses {'ner': 3.8680488195167264}\n",
            "Losses {'ner': 3.049194141960027}\n",
            "Losses {'ner': 0.8021311926923247}\n",
            "Losses {'ner': 4.838221342640789}\n",
            "Losses {'ner': 0.8188255526324171}\n",
            "Losses {'ner': 2.6246900092201635}\n",
            "Losses {'ner': 0.19894797566828917}\n",
            "Losses {'ner': 3.477537053434245}\n",
            "Losses {'ner': 0.23884249474980895}\n",
            "Losses {'ner': 3.7102385171306205}\n",
            "Losses {'ner': 0.3729898597010768}\n",
            "Losses {'ner': 1.916065907197498}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qr5Qby39DvyL"
      },
      "source": [
        "output_dir = Path(\"./models/\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWd8p7ohDyUE"
      },
      "source": [
        "if not output_dir.exists():\n",
        "    output_dir.mkdir()\n",
        "nlp.to_disk(output_dir)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfBYeb0UDgbC"
      },
      "source": [
        "nlp2 = spacy.load(output_dir)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RsYiavzbDjXI",
        "outputId": "648e77e2-f007-4386-802e-99cb1f52123c"
      },
      "source": [
        "# Testing the model\n",
        "doc = nlp2(\"I was driving a Alto\")\n",
        "print(\"Entities\", [(ent.text, ent.label_) for ent in doc.ents])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Entities [('Alto', 'PRODUCT')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = \"Apple is looking at buying U.K. startup for $1 billion!\"\n",
        "doc = nlp2(test_data)\n",
        "print(\"Entities\", [(ent.text, ent.label_) for ent in doc.ents])\n",
        "print(\"Tokens\", [(t.text, t.ent_type_, t.ent_iob_) for t in doc])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8Ra0X8t-3WZ",
        "outputId": "b3c208ee-ba5b-4840-88a1-2290f5e3645a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Entities [('Apple', 'ORG'), ('U.K.', 'ORG'), ('$1 billion', 'MONEY')]\n",
            "Tokens [('Apple', 'ORG', 'B'), ('is', '', 'O'), ('looking', '', 'O'), ('at', '', 'O'), ('buying', '', 'O'), ('U.K.', 'ORG', 'B'), ('startup', '', 'O'), ('for', '', 'O'), ('$', 'MONEY', 'B'), ('1', 'MONEY', 'I'), ('billion', 'MONEY', 'I'), ('!', '', 'O')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## spaCyTextBlob"
      ],
      "metadata": {
        "id": "QC-8J8jllDqG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install spacytextblob"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f8ca132-bb60-469a-e953-ddd9e0f95f29",
        "id": "8QPwvNmVlDqH"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: spacytextblob in /usr/local/lib/python3.7/dist-packages (3.0.1)\n",
            "Requirement already satisfied: textblob<0.16.0,>=0.15.3 in /usr/local/lib/python3.7/dist-packages (from spacytextblob) (0.15.3)\n",
            "Requirement already satisfied: spacy<4.0,>=3.0 in /usr/local/lib/python3.7/dist-packages (from spacytextblob) (3.2.1)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.1 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (2.4.2)\n",
            "Requirement already satisfied: typer<0.5.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (0.4.0)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (3.3.0)\n",
            "Requirement already satisfied: thinc<8.1.0,>=8.0.12 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (8.0.13)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (0.8.2)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (4.62.3)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (3.0.6)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (21.3)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (2.0.6)\n",
            "Requirement already satisfied: pathy>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (0.6.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (2.11.3)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.9.0,>=1.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (1.8.2)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (1.0.6)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (2.0.6)\n",
            "Requirement already satisfied: typing-extensions<4.0.0.0,>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (3.10.0.2)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (1.0.1)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.8 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (3.0.8)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (1.19.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (57.4.0)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (0.4.1)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4.0,>=3.0->spacytextblob) (2.23.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from catalogue<2.1.0,>=2.0.6->spacy<4.0,>=3.0->spacytextblob) (3.6.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy<4.0,>=3.0->spacytextblob) (3.0.6)\n",
            "Requirement already satisfied: smart-open<6.0.0,>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from pathy>=0.3.5->spacy<4.0,>=3.0->spacytextblob) (5.2.1)\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<3.0.0,>=2.13.0->spacy<4.0,>=3.0->spacytextblob) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<4.0,>=3.0->spacytextblob) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<4.0,>=3.0->spacytextblob) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<4.0,>=3.0->spacytextblob) (3.0.4)\n",
            "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.7/dist-packages (from textblob<0.16.0,>=0.15.3->spacytextblob) (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk>=3.1->textblob<0.16.0,>=0.15.3->spacytextblob) (1.15.0)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.5.0,>=0.3.0->spacy<4.0,>=3.0->spacytextblob) (7.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy<4.0,>=3.0->spacytextblob) (2.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "from spacytextblob.spacytextblob import SpacyTextBlob"
      ],
      "metadata": {
        "id": "0nvsu1-Gjr7A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy download en_core_web_sm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-kKHsjTjvIC",
        "outputId": "2a526f7a-b8db-429d-ed82-a3d882c1609e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting en-core-web-sm==3.2.0\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.2.0/en_core_web_sm-3.2.0-py3-none-any.whl (13.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 13.9 MB 1.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy<3.3.0,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from en-core-web-sm==3.2.0) (3.2.1)\n",
            "Requirement already satisfied: typing-extensions<4.0.0.0,>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.10.0.2)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.0.6)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.8 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.0.8)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (57.4.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (1.19.5)\n",
            "Requirement already satisfied: pathy>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (0.6.1)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.23.0)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.3.0)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (0.4.1)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.1 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.4.2)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.9.0,>=1.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (1.8.2)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.0.6)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (21.3)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (1.0.6)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.0.6)\n",
            "Requirement already satisfied: typer<0.5.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (0.4.0)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (0.8.2)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (4.62.3)\n",
            "Requirement already satisfied: thinc<8.1.0,>=8.0.12 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (8.0.13)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.11.3)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (1.0.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from catalogue<2.1.0,>=2.0.6->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.6.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.0.6)\n",
            "Requirement already satisfied: smart-open<6.0.0,>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from pathy>=0.3.5->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (5.2.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2021.10.8)\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<3.0.0,>=2.13.0->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (1.24.3)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.5.0,>=0.3.0->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (7.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.0.1)\n",
            "Installing collected packages: en-core-web-sm\n",
            "  Attempting uninstall: en-core-web-sm\n",
            "    Found existing installation: en-core-web-sm 2.2.5\n",
            "    Uninstalling en-core-web-sm-2.2.5:\n",
            "      Successfully uninstalled en-core-web-sm-2.2.5\n",
            "Successfully installed en-core-web-sm-3.2.0\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_sm')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Source : [link](https://spacy.io/universe/project/spacy-textblob)"
      ],
      "metadata": {
        "id": "mEGnkpIeHzR5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nlp = spacy.load('en_core_web_sm')\n",
        "nlp.add_pipe('spacytextblob')\n",
        "text = 'I had a really horrible day. It was the worst day ever! But every now and then I have a really good day that makes me happy.'\n",
        "doc = nlp(text)\n",
        "print('Polarity:', doc._.polarity) \n",
        "print('Subjectivity:', doc._.subjectivity) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b40c2dd1-425b-42c7-fff9-681cf25ef98a",
        "id": "5nZuFKeslDqI"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polarity: -0.125\n",
            "Subjectivity: 0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transcript_text_b = [\"OK, it's – it’s a problem we have to work on. \",\n",
        "                     \"[Mumbled sound of assent]. Can you read it?\",\n",
        "                     \"The product in front of you.\",                   \n",
        "                     \"Right.\"\n",
        "                     \"It seems like it.\",\n",
        "                     \"And maybe you could – Maybe there’s like a little optical thingy here, and you sort of run it along an edge?\",\n",
        "                     \"Or the whole thing is a scanner thing.\"]"
      ],
      "metadata": {
        "id": "yv0jmcMilDqJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import wordcloud\n",
        "import re\n",
        "from wordcloud import WordCloud\n",
        "wc = WordCloud(width=800,height=400).generate(re.sub(r\"\\s+\",\" \", transcript_text_b[4]))\n",
        "wc.to_image()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "ce66ecf4-96e7-4298-d70b-8ed853013b42",
        "id": "ClL8ZiKGlDqJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=800x400 at 0x7FC7CFEEDF50>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc_transcript_b = list(nlp.pipe(transcript_text_b))\n",
        "doc_transcript_b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2631a5a-0b84-438f-8ade-7faace15e251",
        "id": "DchjslaklDqK"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[OK, it's – it’s a problem we have to work on. ,\n",
              " [Mumbled sound of assent]. Can you read it?,\n",
              " The product in front of you.,\n",
              " Right.It seems like it.,\n",
              " And maybe you could – Maybe there’s like a little optical thingy here, and you sort of run it along an edge?,\n",
              " Or the whole thing is a scanner thing.]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for doc in doc_transcript_b:\n",
        "  print('=' * 64)\n",
        "  print(doc.text)\n",
        "  print('Polarity:', doc._.polarity)\n",
        "  print('Sujectivity:', doc._.subjectivity)\n",
        "  print('Assessments:', doc._.assessments)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aac0f397-ebc1-49f9-924e-5b08b4e94848",
        "id": "jYRfgk-8lDqK"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================\n",
            "OK, it's – it’s a problem we have to work on. \n",
            "Polarity: 0.5\n",
            "Sujectivity: 0.5\n",
            "Assessments: [(['ok'], 0.5, 0.5, None)]\n",
            "================================================================\n",
            "[Mumbled sound of assent]. Can you read it?\n",
            "Polarity: 0.4\n",
            "Sujectivity: 0.4\n",
            "Assessments: [(['sound'], 0.4, 0.4, None)]\n",
            "================================================================\n",
            "The product in front of you.\n",
            "Polarity: 0.0\n",
            "Sujectivity: 0.0\n",
            "Assessments: []\n",
            "================================================================\n",
            "Right.It seems like it.\n",
            "Polarity: 0.0\n",
            "Sujectivity: 0.0\n",
            "Assessments: []\n",
            "================================================================\n",
            "And maybe you could – Maybe there’s like a little optical thingy here, and you sort of run it along an edge?\n",
            "Polarity: -0.1875\n",
            "Sujectivity: 0.5\n",
            "Assessments: [(['little'], -0.1875, 0.5, None)]\n",
            "================================================================\n",
            "Or the whole thing is a scanner thing.\n",
            "Polarity: 0.2\n",
            "Sujectivity: 0.4\n",
            "Assessments: [(['whole'], 0.2, 0.4, None)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xA2iHbzkxTUu"
      },
      "source": [
        "## References \n",
        "\n",
        "* https://spacy.io/\n",
        "* [GitHub repo](https://github.com/dcavar/python-tutorial-for-ipython).\n",
        "*   https://spacy.pythonhumanities.com/intro.html\n",
        "*   https://www.analyticsvidhya.com/blog/2020/03/spacy-tutorial-learn-natural-language-processing/\n",
        "*   https://www.machinelearningplus.com/nlp/training-custom-ner-model-in-spacy/\n",
        "*   https://spacy.io/universe/project/spacy-textblob\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "l_8zHt0HQQqq"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "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": "3.9.1"
    },
    "latex_envs": {
      "LaTeX_envs_menu_present": true,
      "autoclose": false,
      "autocomplete": true,
      "bibliofile": "biblio.bib",
      "cite_by": "apalike",
      "current_citInitial": 1,
      "eqLabelWithNumbers": true,
      "eqNumInitial": 1,
      "hotkeys": {
        "equation": "Ctrl-E",
        "itemize": "Ctrl-I"
      },
      "labels_anchors": false,
      "latex_user_defs": false,
      "report_style_numbering": false,
      "user_envs_cfg": false
    },
    "toc": {
      "base_numbering": 1,
      "nav_menu": {},
      "number_sections": false,
      "sideBar": false,
      "skip_h1_title": false,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {},
      "toc_section_display": false,
      "toc_window_display": false
    },
    "colab": {
      "name": "Working with Text using spaCy.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}