{
 "cells": [
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   "cell_type": "code",
   "execution_count": null,
   "id": "1638b7b97e3bd6f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-22T11:40:21.711998Z",
     "start_time": "2025-11-22T11:40:20.129376Z"
    }
   },
   "outputs": [],
   "source": [
    "import spacy\n",
    "nlp = spacy.load(\"en_core_web_lg\")          # Large model\n",
    "nlp_trf =  spacy.load(\"en_core_web_trf\")    # Transformer Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79941bf4553fd6",
   "metadata": {},
   "source": [
    "Test word vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a3c4314a90086fe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-22T11:47:39.286432Z",
     "start_time": "2025-11-22T11:47:39.271377Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat - dog: 0.802\n",
      "cat - feline: 0.699\n",
      "cat - feral: 0.486\n",
      "cat - vehicle: 0.190\n",
      "cat - car: 0.319\n",
      "dog - cat: 0.802\n",
      "dog - feline: 0.566\n",
      "dog - feral: 0.400\n",
      "dog - vehicle: 0.258\n",
      "dog - car: 0.356\n",
      "feline - cat: 0.699\n",
      "feline - dog: 0.566\n",
      "feline - feral: 0.543\n",
      "feline - vehicle: 0.103\n",
      "feline - car: 0.095\n",
      "feral - cat: 0.486\n",
      "feral - dog: 0.400\n",
      "feral - feline: 0.543\n",
      "feral - vehicle: 0.088\n",
      "feral - car: 0.040\n",
      "vehicle - cat: 0.190\n",
      "vehicle - dog: 0.258\n",
      "vehicle - feline: 0.103\n",
      "vehicle - feral: 0.088\n",
      "vehicle - car: 0.767\n",
      "car - cat: 0.319\n",
      "car - dog: 0.356\n",
      "car - feline: 0.095\n",
      "car - feral: 0.040\n",
      "car - vehicle: 0.767\n"
     ]
    }
   ],
   "source": [
    "def test_word_vectors(word):\n",
    "    print(word, nlp.vocab[word].vector.shape)\n",
    "\n",
    "words = [\"cat\", \"dog\", \"feline\", \"feral\", \"vehicle\", \"car\"]\n",
    "# Test work similarities\n",
    "for word1 in words:\n",
    "    for word2 in words:\n",
    "        if word1 != word2:\n",
    "            similarity = nlp.vocab[word1].similarity(nlp.vocab[word2])\n",
    "            print(f\"{word1} - {word2}: {similarity:.3f}\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f32b5695f554268",
   "metadata": {},
   "source": [
    "Simple averaging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68a6757447e4a1c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-18T23:45:03.085563Z",
     "start_time": "2025-11-18T23:45:03.082190Z"
    }
   },
   "outputs": [],
   "source": [
    "def sentence_similarity_avg(sent1, sent2):\n",
    "    doc1 = nlp(sent1)\n",
    "    doc2 = nlp(sent2)\n",
    "\n",
    "    # Vectors for each word, filter out words without vectors (medium model)\n",
    "    vecs1 = [token.vector for token in doc1 if token.has_vector]\n",
    "    vecs2 = [token.vector for token in doc2 if token.has_vector]\n",
    "\n",
    "    if not vecs1 or not vecs2:\n",
    "        return 0.0\n",
    "\n",
    "    # Average vectors\n",
    "    avg1 = sum(vecs1) / len(vecs1)\n",
    "    avg2 = sum(vecs2) / len(vecs2)\n",
    "\n",
    "    #cosine similarity\n",
    "    from sklearn.metrics.pairwise import cosine_similarity\n",
    "    return cosine_similarity([avg1], [avg2])[0][0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c3aa050f5bc0fe",
   "metadata": {},
   "source": [
    "SIF - Smooth Inverse Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c100956f89d9b581",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentence_similarity_sif(sent1, sent2):\n",
    "    doc1 = nlp(sent1)\n",
    "    doc2 = nlp(sent2)"
   ]
  }
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