test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ox_onnx.runtime import OnnxModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = OnnxModel.load('sentence-transformers/all-MiniLM-L6-v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = \"\"\"\n",
    "A deep learning architecture is essentially a blueprint of a neural network, outlining how data flows through multiple interconnected layers, extracting features and making decisions. Key components include input, hidden, and output layers, activation functions, weights, biases, and a loss function. Common architectures are CNNs, RNNs, LSTMs, GRUs, Transformers, GANs, and Autoencoders, each tailored for specific tasks like image recognition, natural language processing, and generative models.\n",
    "\"\"\"*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "embeddings = model.generate([data])\n",
    "print(embeddings)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[101,\n",
       " 1037,\n",
       " 2784,\n",
       " 4083,\n",
       " 4294,\n",
       " 2003,\n",
       " 7687,\n",
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       " 2630,\n",
       " 16550,\n",
       " 1997,\n",
       " 1037,\n",
       " 15756,\n",
       " 2897,\n",
       " 1010,\n",
       " 2041,\n",
       " 16992,\n",
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       " 6223,\n",
       " 2083,\n",
       " 3674,\n",
       " 6970,\n",
       " 24230,\n",
       " 9014,\n",
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       " 14817,\n",
       " 2075,\n",
       " 2838,\n",
       " 1998,\n",
       " 2437,\n",
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       " 1012,\n",
       " 3145,\n",
       " 6177,\n",
       " 2421,\n",
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       " 1010,\n",
       " 1998,\n",
       " 6434,\n",
       " 9014,\n",
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       " 13791,\n",
       " 4972,\n",
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       " 15871,\n",
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       " 13827,\n",
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       " 1998,\n",
       " 1037,\n",
       " 3279,\n",
       " 3853,\n",
       " 1012,\n",
       " 2691,\n",
       " 4294,\n",
       " 2015,\n",
       " 2024,\n",
       " 13229,\n",
       " 2015,\n",
       " 1010,\n",
       " 29300,\n",
       " 3619,\n",
       " 1010,\n",
       " 1048,\n",
       " 3367,\n",
       " 5244,\n",
       " 1010,\n",
       " 24665,\n",
       " 2271,\n",
       " 1010,\n",
       " 19081,\n",
       " 1010,\n",
       " 25957,\n",
       " 2015,\n",
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       " 2368,\n",
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       " 2869,\n",
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       " 2005,\n",
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       " 1998,\n",
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       " 1012,\n",
       " 1037,\n",
       " 2784,\n",
       " 4083,\n",
       " 4294,\n",
       " 2003,\n",
       " 7687,\n",
       " 1037,\n",
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       " 16550,\n",
       " 1997,\n",
       " 1037,\n",
       " 15756,\n",
       " 2897,\n",
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       " 2041,\n",
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       " 1998,\n",
       " 1037,\n",
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       " 1012,\n",
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       " 4294,\n",
       " 2015,\n",
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       " 1998,\n",
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       " 2169,\n",
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       " 2005,\n",
       " 3563,\n",
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       " 5038,\n",
       " 1010,\n",
       " 3019,\n",
       " 2653,\n",
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       " 1010,\n",
       " 1998,\n",
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       " 6024,\n",
       " 4275,\n",
       " 1012,\n",
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       " 2784,\n",
       " 4083,\n",
       " 4294,\n",
       " 2003,\n",
       " 7687,\n",
       " 1037,\n",
       " 2630,\n",
       " 16550,\n",
       " 1997,\n",
       " 1037,\n",
       " 15756,\n",
       " 2897,\n",
       " 1010,\n",
       " 2041,\n",
       " 16992,\n",
       " 2129,\n",
       " 2951,\n",
       " 6223,\n",
       " 2083,\n",
       " 3674,\n",
       " 6970,\n",
       " 24230,\n",
       " 9014,\n",
       " 1010,\n",
       " 14817,\n",
       " 2075,\n",
       " 2838,\n",
       " 1998,\n",
       " 2437,\n",
       " 6567,\n",
       " 1012,\n",
       " 3145,\n",
       " 6177,\n",
       " 2421,\n",
       " 7953,\n",
       " 1010,\n",
       " 5023,\n",
       " 1010,\n",
       " 1998,\n",
       " 6434,\n",
       " 9014,\n",
       " 1010,\n",
       " 13791,\n",
       " 4972,\n",
       " 1010,\n",
       " 15871,\n",
       " 1010,\n",
       " 13827,\n",
       " 2229,\n",
       " 102]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_data = model.encode(data)\n",
    "en_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a deep learning architecture is essentially a blueprint of a neural network, outlining how data flows through multiple interconnected layers, extracting features and making decisions. key components include input, hidden, and output layers, activation functions, weights, biases, and a loss function. common architectures are cnns, rnns, lstms, grus, transformers, gans, and autoencoders, each tailored for specific tasks like image recognition, natural language processing, and generative models. a deep learning architecture is essentially a blueprint of a neural network, outlining how data flows through multiple interconnected layers, extracting features and making decisions. key components include input, hidden, and output layers, activation functions, weights, biases, and a loss function. common architectures are cnns, rnns, lstms, grus, transformers, gans, and autoencoders, each tailored for specific tasks like image recognition, natural language processing, and generative models. a deep learning architecture is essentially a blueprint of a neural network, outlining how data flows through multiple interconnected layers, extracting features and making decisions. key components include input, hidden, and output layers, activation functions, weights, biases'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de_data = model.decode(en_data)\n",
    "de_data"
   ]
  }
 ],
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