SentenceTransformerTest.ipynb

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            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/86.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/86.0 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m44.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m61.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m35.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
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      "source": [
        "!pip install -q sentence_transformers"
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    },
    {
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      "source": [
        "## Import"
      ],
      "metadata": {
        "id": "X0YkkkzPYFgF"
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      "source": [
        "\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from pprint import pprint"
      ],
      "metadata": {
        "id": "Hep8vx3CX7-5"
      },
      "execution_count": 3,
      "outputs": []
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          "metadata": {}
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        {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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          "metadata": {}
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          "metadata": {}
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              "version_major": 2,
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Some alternative models"
      ],
      "metadata": {
        "id": "ZQLOfAQIJzer"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = SentenceTransformer('all-MiniLM-L6-v2')"
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        "id": "840KS-Zbfd8J",
        "outputId": "7ede70e2-b9c3-4232-9a42-c58a5c3ae625"
      },
      "execution_count": 16,
      "outputs": [
        {
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          "metadata": {}
        },
        {
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          "metadata": {}
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        {
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          "metadata": {}
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          "metadata": {}
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          "metadata": {}
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            ],
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = SentenceTransformer('all-MiniLM-L12-v2')"
      ],
      "metadata": {
        "id": "DKaaki5zfOYE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Use model to generate embeddings and check similarities based on those"
      ],
      "metadata": {
        "id": "ahZqj2_pJ9JY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentences = ['I want to take some risks and make as much money as possible in a short time frame',\n",
        "             'I am rather risk-averse and when it comes to investing I believe in the tortoise beats the hare.',\n",
        "             'One has to balance the risk and return in investing, I am personally tilted towards controlling the former.']\n",
        "\n",
        "sentence_embeddings = model.encode(sentences)\n",
        ""
      ],
      "metadata": {
        "id": "bjoIsg6VdciW"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentence_embeddings.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sIo1CoWjcV10",
        "outputId": "dfc3eb5f-fe41-45f8-9654-aeb3fe7e4372"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 384)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"\"\"Similarity between 1st and 2nd is {cosine_similarity(sentence_embeddings[0].reshape(1, -1),\n",
        "                sentence_embeddings[1].reshape(1, -1))[0][0]:.3}\"\"\")\n",
        "\n",
        "print(f\"\"\"Similarity between 1st and 3rd is {cosine_similarity(sentence_embeddings[0].reshape(1, -1),\n",
        "                sentence_embeddings[2].reshape(1, -1))[0][0]:.3}\"\"\")\n",
        "\n",
        "print(f\"\"\"Similarity between 2nd and 3rd is {cosine_similarity(sentence_embeddings[1].reshape(1, -1),\n",
        "                sentence_embeddings[2].reshape(1, -1))[0][0]:.3}\"\"\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3V2hLYZlYyZE",
        "outputId": "0b25ddeb-da1d-4154-ec5e-f3ae6127774d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Similarity between 1st and 2nd is 0.27\n",
            "Similarity between 1st and 3rd is 0.26\n",
            "Similarity between 2nd and 3rd is 0.565\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K7Itfwqcd7Ut"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}