2.2 Embeddings.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-transformers\n",
        "from sentence_transformers import SentenceTransformer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cY-S-cQZZ0sv",
        "outputId": "8d823973-2bc8-4d8e-cecd-de189fd6a7a0"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = SentenceTransformer(\"all-MiniLM-L6-v2\")"
      ],
      "metadata": {
        "id": "oEMywojbZ64s"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define the first set of sentences and get its embeddings using **model.encode()** method."
      ],
      "metadata": {
        "id": "Q73W9bvZdAP3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentences1 = [\"I am a boy\", \"I like music\", \"This movie was awesome\"]"
      ],
      "metadata": {
        "id": "zBSPTzjkaAcq"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings1 = model.encode(sentences1, convert_to_tensor=True)"
      ],
      "metadata": {
        "id": "0PvEOvNcaTWe"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXRMd7xLafbm",
        "outputId": "3903e5ae-115c-4ef8-a37c-67b30a4911eb"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.0648,  0.1115, -0.0009,  ...,  0.0043, -0.0082, -0.1335],\n",
              "        [-0.0072, -0.0693,  0.0058,  ...,  0.0597,  0.0734, -0.0376],\n",
              "        [-0.1320,  0.0089, -0.0492,  ...,  0.0444,  0.0057, -0.0286]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The define the second set of sentences and get its embeddings."
      ],
      "metadata": {
        "id": "1mPvkDJadJVG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentences2 = [\"He is a boy\", \"He likes music\", \"That dog is so ugly\"]"
      ],
      "metadata": {
        "id": "9lA8KF2XagPX"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings2 = model.encode(sentences2, convert_to_tensor=True)"
      ],
      "metadata": {
        "id": "_iKV_qETazJ8"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMu3_Icla2Pj",
        "outputId": "01a81af1-027d-4332-c4cf-a81371c7942f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.0743,  0.1551, -0.0569,  ..., -0.0122,  0.0212, -0.0797],\n",
              "        [ 0.0725,  0.0083,  0.0374,  ...,  0.0592,  0.0626, -0.0019],\n",
              "        [-0.0156,  0.0659,  0.0607,  ..., -0.0024,  0.0495,  0.0512]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To compare both the embeddings use the cosine formula, as it calculates the angle between any two vector in space."
      ],
      "metadata": {
        "id": "5QXbPZOidNyt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import util"
      ],
      "metadata": {
        "id": "MkJYi6_Ta4K1"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cosine_scores = util.cos_sim(embeddings1, embeddings2)"
      ],
      "metadata": {
        "id": "-NifVWVMbKBN"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cosine_scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQRa1RWkbR8D",
        "outputId": "c869664a-71c4-4182-f574-ac7681c62f50"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.7027, 0.1997, 0.1049],\n",
              "        [0.1372, 0.6581, 0.0910],\n",
              "        [0.1497, 0.1047, 0.1928]])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(sentences1)):\n",
        "    print(\"{} \\t\\t {} \\t\\t Score: {:.4f}\".format(sentences1[i],\n",
        "                                                 sentences2[i],\n",
        "                                                 cosine_scores[i][i]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TOpHFT-ebS23",
        "outputId": "bc6f05ce-bb7c-4927-b7b3-97887bf24e9e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I am a boy \t\t He is a boy \t\t Score: 0.7027\n",
            "I like music \t\t He likes music \t\t Score: 0.6581\n",
            "This movie was awesome \t\t That dog is so ugly \t\t Score: 0.1928\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first two sentences in both the set are very similar hence high scores (0.70727 and 0.6581), the last sentences on both the sets are not at all similar hence low score (0.1928)"
      ],
      "metadata": {
        "id": "MLS3sdvbdYA9"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Db1nNuzfboz8"
      },
      "execution_count": 54,
      "outputs": []
    }
  ]
}