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Manikanta-Munnangi/Word_Vector_Similarity.ipynb

Created November 18, 2019 16:23
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Word_Vector_Similarity.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# spacy import convention\n",
"import spacy\n",
"\n",
"# load the english model into nlp object.\n",
"nlp=spacy.load('en_core_web_md') "
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" -7.5604e-02, 1.5922e-02, 1.5616e-01, -3.8949e-01, -5.8165e-02,\n",
" -4.3763e-01, 2.4587e-01, -2.3169e-01, -1.4508e-01, 3.5845e-01,\n",
" 1.2437e-01, 2.2588e-01, -1.8963e-02, 7.9287e-02, 1.6775e-01,\n",
" -1.2729e-01, -3.2950e-01, 3.1048e-01, -1.6959e-01, 5.7082e-02,\n",
" -9.8536e-02, -1.1715e-02, 3.9690e-01, 1.0493e-01, 1.9083e-01,\n",
" 1.3871e-01, -1.8307e-02, -7.8323e-02, -4.5149e-02, 6.6471e-02,\n",
" 1.7835e-01, -4.3998e-02, -1.9136e-01, -8.8387e-02, 4.2414e-01,\n",
" 1.1562e-01, 8.0458e-02, -1.0350e-01, -1.8200e-01, -2.0045e-01,\n",
" 1.9755e-01, 3.8457e-02, -1.1081e-01, 2.2978e-01, 3.5781e-01,\n",
" -1.6376e-01, -2.3062e-01, -2.4412e-01, -7.3929e-02, -1.2747e-01,\n",
" 1.4730e-01, 2.5954e-01, 1.8571e-01, 2.7923e-01, 1.8186e-01,\n",
" -1.4550e-01, -2.5523e-01, -2.3418e-01, -2.3684e-01, 5.7909e-02,\n",
" 1.3913e-01, -1.4280e-01, 9.8092e-02, -2.4884e-01, -2.2587e-01,\n",
" 2.2812e-01, 2.3718e-01, -6.6049e-02, 6.3126e-02, -3.4434e-03,\n",
" 2.6542e-01, -4.3094e-02, 9.1002e-02, -2.9563e-02, 1.3626e-01,\n",
" -2.2368e-01, 1.4869e-01, 1.7428e-02, 2.6551e-01, -2.0984e-01,\n",
" -1.6786e-01, 2.1192e-01, 1.2735e-01, 1.6441e-01, 3.3131e-01,\n",
" 1.0661e-01, -2.1155e-01, 2.8474e-02, -9.9419e-02, 3.4635e-01,\n",
" -4.0166e-01, -1.9083e-01, -2.8156e-01, -8.1996e-02, 2.4322e-01,\n",
" 3.0341e-01, -1.4984e-01, -2.9952e-01, -2.8089e-01, -8.2551e-02,\n",
" -3.5457e-01, 8.3108e-02, 7.3193e-02, 5.8555e-02, 4.7347e-02,\n",
" 3.3200e-01, 1.5465e-01, -6.5075e-02, 6.3738e-03, 2.6690e-01,\n",
" -3.3819e-01, -2.1204e-01, 2.2368e-01, 6.2783e-01, 7.0440e-01,\n",
" -2.2196e-01, -1.0377e-01, 6.9900e-02, -1.3201e-01, -2.6255e-01,\n",
" -1.9671e-02, -1.1906e-01, 3.2839e-02, -3.1207e-02, 2.5083e-01,\n",
" -1.4702e-01, 4.4411e-01, -2.1465e-01, 4.5018e-02, -1.4012e-01,\n",
" 4.6586e-02, 2.4790e-01, -1.3205e-01, 1.4456e-01, -1.8638e-01,\n",
" -8.6773e-02, 1.3312e-01, 1.8741e-03, 4.4091e-02, 2.8882e-01,\n",
" -9.0016e-02, -1.8108e-01, 3.3178e-01, 3.1545e-01, 3.7972e-01],\n",
" dtype=float32)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# can access vector from of as word\n",
"nlp.vocab[\"how\"].vector"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
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" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check for other word that don't have vector form \n",
"nlp.vocab[\"jupyter\"].vector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- `so before preprocessing you need to make sure the word has vector form.`"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(True, False)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check with existence of vector with .vector_form attribute\n",
"nlp.vocab[\"how\"].has_vector, nlp.vocab[\"jupyter\"].has_vector"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"# Two Sample sentences\n",
"text=\"how are you\"\n",
"text1=\"how you doing\"\n",
"\n",
"# returns doc containers \n",
"doc=nlp(text)\n",
"doc1=nlp(text1)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9106663802758767"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# similarity between sentences\n",
"doc.similarity(doc1)"
]
}
],
"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": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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