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@jamescalam
Created May 2, 2021 10:31
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{
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics.pairwise import cosine_similarity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's calculate cosine similarity for sentence `0`:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.33088905, 0.7219259 , 0.55483633]], dtype=float32)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# convert from PyTorch tensor to numpy array\n",
"mean_pooled = mean_pooled.detach().numpy()\n",
"\n",
"# calculate\n",
"cosine_similarity(\n",
" [mean_pooled[0]],\n",
" mean_pooled[1:]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These similarities translate to:\n",
"\n",
"| Index | Sentence | Similarity |\n",
"| --- | --- | --- |\n",
"| 1 | \"The fish dreamed of escaping the fishbowl and into the toilet where he saw his friend go.\" | 0.3309 |\n",
"| 2 | \"The person box was packed with jelly many dozens of months later.\" | 0.7219 |\n",
"| 3 | \"He found a leprechaun in his walnut shell.\" | 0.5548 |"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "ML",
"language": "python",
"name": "ml"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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