EmbedUsingPretrainedModel.ipynb (1951B)
1 { 2 "cells": [ 3 { 4 "cell_type": "code", 5 "execution_count": 5, 6 "metadata": {}, 7 "outputs": [ 8 { 9 "data": { 10 "text/plain": [ 11 "array([[-0.25, 0.28, 0.01, 0.1 , 0.14, 0.16, 0.25, 0.02, 0.07,\n", 12 " 0.13, -0.19, 0.06, -0.04, -0.07, 0. , -0.08, -0.14, -0.16,\n", 13 " 0.02, -0.24, 0.16, -0.16, -0.03, 0.03, -0.14, 0.03, -0.09,\n", 14 " -0.04, -0.14, -0.19, 0.07, 0.15, 0.18, -0.23, -0.07, -0.08,\n", 15 " 0.01, -0.01, 0.09, 0.14, -0.03, 0.03, 0.08, 0.1 , -0.01,\n", 16 " -0.03, -0.07, -0.1 , 0.05, 0.31],\n", 17 " [-0.2 , 0.2 , -0.08, 0.02, 0.19, 0.05, 0.22, -0.09, 0.02,\n", 18 " 0.19, -0.02, -0.14, -0.2 , -0.04, 0.01, -0.07, -0.22, -0.1 ,\n", 19 " 0.16, -0.44, 0.31, -0.1 , 0.23, 0.15, -0.05, 0.15, -0.13,\n", 20 " -0.04, -0.08, -0.16, -0.1 , 0.13, 0.13, -0.18, -0.04, 0.03,\n", 21 " -0.1 , -0.07, 0.07, 0.03, -0.08, 0.02, 0.05, 0.07, -0.14,\n", 22 " -0.1 , -0.18, -0.13, -0.04, 0.15]], dtype=float32)" 23 ] 24 }, 25 "execution_count": 5, 26 "metadata": {}, 27 "output_type": "execute_result" 28 } 29 ], 30 "source": [ 31 "import tensorflow_hub as hub\n", 32 "import tensorflow as tf\n", 33 "\n", 34 "hub_layer = hub.KerasLayer(\"https://tfhub.dev/google/nnlm-en-dim50/2\")\n", 35 "sentence_embeddings = hub_layer(tf.constant([\"To be\", \"Not to be\"]))\n", 36 "sentence_embeddings.numpy().round(2)" 37 ] 38 } 39 ], 40 "metadata": { 41 "kernelspec": { 42 "display_name": ".venv", 43 "language": "python", 44 "name": "python3" 45 }, 46 "language_info": { 47 "codemirror_mode": { 48 "name": "ipython", 49 "version": 3 50 }, 51 "file_extension": ".py", 52 "mimetype": "text/x-python", 53 "name": "python", 54 "nbconvert_exporter": "python", 55 "pygments_lexer": "ipython3", 56 "version": "3.11.2" 57 } 58 }, 59 "nbformat": 4, 60 "nbformat_minor": 2 61 }