{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "minus-gibson",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Enabling eager execution\n",
      "INFO:tensorflow:Enabling v2 tensorshape\n",
      "INFO:tensorflow:Enabling resource variables\n",
      "INFO:tensorflow:Enabling tensor equality\n",
      "INFO:tensorflow:Enabling control flow v2\n"
     ]
    }
   ],
   "source": [
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "alternative-flashing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting en-core-web-md==3.1.0\n",
      "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_md-3.1.0/en_core_web_md-3.1.0-py3-none-any.whl (45.4 MB)\n",
      "Requirement already satisfied: spacy<3.2.0,>=3.1.0 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from en-core-web-md==3.1.0) (3.1.1)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (1.0.5)\n",
      "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.7 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (3.0.8)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (2.0.5)\n",
      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.9.0,>=1.7.4 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (1.7.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (20.9)\n",
      "Requirement already satisfied: numpy>=1.15.0 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (1.20.2)\n",
      "Requirement already satisfied: catalogue<2.1.0,>=2.0.4 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (2.0.4)\n",
      "Requirement already satisfied: pathy>=0.3.5 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (0.4.0)\n",
      "Requirement already satisfied: blis<0.8.0,>=0.4.0 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (0.7.4)\n",
      "Requirement already satisfied: setuptools in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (54.1.1)\n",
      "Requirement already satisfied: wasabi<1.1.0,>=0.8.1 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (0.8.2)\n",
      "Requirement already satisfied: thinc<8.1.0,>=8.0.8 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (8.0.8)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-14 14:04:48.662634: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\n",
      "WARNING: You are using pip version 21.1.2; however, version 21.2.4 is available.\n",
      "You should consider upgrading via the 'C:\\Users\\wma22\\AppData\\Local\\Programs\\Python\\Python39\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (4.59.0)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (2.25.1)\n",
      "Requirement already satisfied: srsly<3.0.0,>=2.4.1 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (2.4.1)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (2.11.3)\n",
      "Requirement already satisfied: typer<0.4.0,>=0.3.0 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (0.3.2)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (3.0.5)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from packaging>=20.0->spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (2.4.7)\n",
      "Requirement already satisfied: smart-open<4.0.0,>=2.2.0 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pathy>=0.3.5->spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (3.0.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (1.26.3)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (4.0.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (2020.12.5)\n",
      "Requirement already satisfied: click<7.2.0,>=7.1.1 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from typer<0.4.0,>=0.3.0->spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (7.1.2)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in c:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from jinja2->spacy<3.2.0,>=3.1.0->en-core-web-md==3.1.0) (1.1.1)\n",
      "[+] Download and installation successful\n",
      "You can now load the package via spacy.load('en_core_web_md')\n"
     ]
    }
   ],
   "source": [
    "!python -m spacy download en_core_web_md"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "shaped-detector",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_md\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "third-genome",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open (\"data/wiki_us.txt\", \"r\") as f:\n",
    "    text = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "disturbed-practice",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The United States of America (U.S.A. or USA), commonly known as the United States (U.S. or US) or America, is a country primarily located in North America.\n"
     ]
    }
   ],
   "source": [
    "doc = nlp(text)\n",
    "sentence1 = list(doc.sents)[0]\n",
    "print (sentence1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "creative-robin",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['country', 'COUNTRY', 'NATION', 'nation', 'COUNTIRES', 'nations', 'member-states', 'worLd', 'World', 'world']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "your_word = \"country\"\n",
    "\n",
    "ms = nlp.vocab.vectors.most_similar(\n",
    "    np.asarray([nlp.vocab.vectors[nlp.vocab.strings[your_word]]]), n=10)\n",
    "words = [nlp.vocab.strings[w] for w in ms[0][0]]\n",
    "distances = ms[2]\n",
    "print(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "medical-party",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc1 = nlp(\"I like salty fries and hamburgers.\")\n",
    "doc2 = nlp(\"Fast food tastes very good.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "logical-connectivity",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I like salty fries and hamburgers. <-> Fast food tastes very good. 0.7799485853415737\n"
     ]
    }
   ],
   "source": [
    "print (doc1, \"<->\", doc2, doc1.similarity(doc2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "severe-advocacy",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc3 = nlp(\"The Empire State Building is in New York.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "working-appearance",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I like salty fries and hamburgers. <-> The Empire State Building is in New York. 0.5196037639243649\n"
     ]
    }
   ],
   "source": [
    "print (doc1, \"<->\", doc3, doc1.similarity(doc3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "graphic-start",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc4 = nlp(\"I enjoy oranges.\")\n",
    "doc5 = nlp(\"I enjoy apples.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "pregnant-efficiency",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I enjoy oranges. <-> I enjoy apples. 0.9607558420297302\n"
     ]
    }
   ],
   "source": [
    "print (doc4, \"<->\", doc5, doc4.similarity(doc5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "enclosed-questionnaire",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc6 = nlp(\"I enjoy burgers.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "confused-coral",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I enjoy oranges. <-> I enjoy burgers. 0.8755329986251214\n"
     ]
    }
   ],
   "source": [
    "print (doc4, \"<->\", doc6, doc4.similarity(doc6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "sensitive-concrete",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "salty fries <-> hamburgers 0.7304624\n"
     ]
    }
   ],
   "source": [
    "french_fries = doc1[2:4]\n",
    "burgers = doc1[5]\n",
    "print(french_fries, \"<->\", burgers, french_fries.similarity(burgers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "informational-roberts",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.blank(\"en\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cloudy-qatar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<spacy.pipeline.sentencizer.Sentencizer at 0x27049c89e80>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.add_pipe(\"sentencizer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "practical-carter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'summary': {'sentencizer': {'assigns': ['token.is_sent_start', 'doc.sents'],\n",
       "   'requires': [],\n",
       "   'scores': ['sents_f', 'sents_p', 'sents_r'],\n",
       "   'retokenizes': False}},\n",
       " 'problems': {'sentencizer': []},\n",
       " 'attrs': {'doc.sents': {'assigns': ['sentencizer'], 'requires': []},\n",
       "  'token.is_sent_start': {'assigns': ['sentencizer'], 'requires': []}}}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.analyze_pipes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "received-proposition",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp2 = spacy.load(\"en_core_web_sm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "closed-phenomenon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'summary': {'tok2vec': {'assigns': ['doc.tensor'],\n",
       "   'requires': [],\n",
       "   'scores': [],\n",
       "   'retokenizes': False},\n",
       "  'tagger': {'assigns': ['token.tag'],\n",
       "   'requires': [],\n",
       "   'scores': ['tag_acc'],\n",
       "   'retokenizes': False},\n",
       "  'parser': {'assigns': ['token.dep',\n",
       "    'token.head',\n",
       "    'token.is_sent_start',\n",
       "    'doc.sents'],\n",
       "   'requires': [],\n",
       "   'scores': ['dep_uas',\n",
       "    'dep_las',\n",
       "    'dep_las_per_type',\n",
       "    'sents_p',\n",
       "    'sents_r',\n",
       "    'sents_f'],\n",
       "   'retokenizes': False},\n",
       "  'attribute_ruler': {'assigns': [],\n",
       "   'requires': [],\n",
       "   'scores': [],\n",
       "   'retokenizes': False},\n",
       "  'lemmatizer': {'assigns': ['token.lemma'],\n",
       "   'requires': [],\n",
       "   'scores': ['lemma_acc'],\n",
       "   'retokenizes': False},\n",
       "  'ner': {'assigns': ['doc.ents', 'token.ent_iob', 'token.ent_type'],\n",
       "   'requires': [],\n",
       "   'scores': ['ents_f', 'ents_p', 'ents_r', 'ents_per_type'],\n",
       "   'retokenizes': False}},\n",
       " 'problems': {'tok2vec': [],\n",
       "  'tagger': [],\n",
       "  'parser': [],\n",
       "  'attribute_ruler': [],\n",
       "  'lemmatizer': [],\n",
       "  'ner': []},\n",
       " 'attrs': {'token.ent_type': {'assigns': ['ner'], 'requires': []},\n",
       "  'doc.ents': {'assigns': ['ner'], 'requires': []},\n",
       "  'token.dep': {'assigns': ['parser'], 'requires': []},\n",
       "  'token.head': {'assigns': ['parser'], 'requires': []},\n",
       "  'token.tag': {'assigns': ['tagger'], 'requires': []},\n",
       "  'token.lemma': {'assigns': ['lemmatizer'], 'requires': []},\n",
       "  'token.ent_iob': {'assigns': ['ner'], 'requires': []},\n",
       "  'doc.tensor': {'assigns': ['tok2vec'], 'requires': []},\n",
       "  'doc.sents': {'assigns': ['parser'], 'requires': []},\n",
       "  'token.is_sent_start': {'assigns': ['parser'], 'requires': []}}}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp2.analyze_pipes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "chemical-amendment",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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