senttok.ipynb

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     "text": [
      "Writing senttok.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile senttok.py\n",
    "import spacy\n",
    "import json\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "\n",
    "def sent_tokenization(text):\n",
    "    text = ''.join(c for c in text if not c.isnumeric())\n",
    "    text = ''.join([x.replace('●','') for x in text])\n",
    "    text = ''.join([x.replace('•','') for x in text])\n",
    "    text = ''.join([x.replace('_','') for x in text])\n",
    "    doc = nlp(text)\n",
    "    sentences = [sent.text for sent in doc.sents]\n",
    "    sentences = [x.replace('\\n','') for x in sentences]\n",
    "    sentences = [x.replace('\\n\\n','') for x in sentences]\n",
    "    return sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "informed-amount",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing compare.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile compare.py\n",
    "from sentence_transformers import SentenceTransformer, util\n",
    "import numpy as np\n",
    "\n",
    "model = SentenceTransformer('stsb-roberta-large')\n",
    "\n",
    "# encode list of sentences to get their embeddings\n",
    "embedding1 = model.encode(sentences1, convert_to_tensor=True)\n",
    "embedding2 = model.encode(sentences2, convert_to_tensor=True)\n",
    "\n",
    "# compute similarity scores of two embeddings\n",
    "cosine_scores = util.pytorch_cos_sim(embedding1, embedding2)\n",
    "score=[]\n",
    "for i in range(len(sentences1)):\n",
    "    for j in range(len(sentences2)):\n",
    "        if cosine_scores[i][j] > 0.5:\n",
    "            score.append(cosine_scores[i][j].item())\n",
    "            print(\"Sentence 1:\", sentences1[i])\n",
    "            print(\"Sentence 2:\", sentences2[j])\n",
    "            print(\"Similarity Score:\", cosine_scores[i][j].item())\n",
    "            print()"
   ]
  },
  {
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   "execution_count": null,
   "id": "stupid-sequence",
   "metadata": {},
   "outputs": [],
   "source": []
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