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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Method 1"
      ],
      "metadata": {
        "id": "innxsUlSfXaU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-transformers"
      ],
      "metadata": {
        "id": "qCcarcgBekwp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7e2f283-777d-484f-fdfd-a7c206b52547"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[K     |████████████████████████████████| 85 kB 3.7 MB/s \n",
            "\u001b[?25hCollecting transformers<5.0.0,>=4.6.0\n",
            "  Downloading transformers-4.22.1-py3-none-any.whl (4.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.9 MB 16.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (4.64.1)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.12.1+cu113)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (0.13.1+cu113)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.0.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.7.3)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (3.7)\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.97-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 62.3 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub>=0.4.0\n",
            "  Downloading huggingface_hub-0.9.1-py3-none-any.whl (120 kB)\n",
            "\u001b[K     |████████████████████████████████| 120 kB 69.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (21.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (2.23.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (6.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (4.12.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (4.1.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (3.8.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface-hub>=0.4.0->sentence-transformers) (3.0.9)\n",
            "Collecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 25.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2022.6.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface-hub>=0.4.0->sentence-transformers) (3.8.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers) (1.1.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (3.0.4)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sentence-transformers) (3.1.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->sentence-transformers) (7.1.2)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125938 sha256=92b96a63fe136b7fb3f728dd5c47aa2f7500e3c3f7f5b731008aa3cfd86cf559\n",
            "  Stored in directory: /root/.cache/pip/wheels/bf/06/fb/d59c1e5bd1dac7f6cf61ec0036cc3a10ab8fecaa6b2c3d3ee9\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers, sentencepiece, sentence-transformers\n",
            "Successfully installed huggingface-hub-0.9.1 sentence-transformers-2.2.2 sentencepiece-0.1.97 tokenizers-0.12.1 transformers-4.22.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Write some lines to encode (sentences 0 and 2 are both ideltical):\n",
        "sen = [\n",
        "    \"Three years later, the coffin was still full of Jello.\",\n",
        "    \"The fish dreamed of escaping the fishbowl and into the toilet where he saw his friend go.\",\n",
        "    \"The person box was packed with jelly many dozens of months later.\",\n",
        "    \"He found a leprechaun in his walnut shell.\"\n",
        "]\n",
        "from sentence_transformers import SentenceTransformer\n",
        "model = SentenceTransformer('bert-base-nli-mean-tokens')\n",
        "#Encoding:\n",
        "sen_embeddings = model.encode(sen)\n",
        "sen_embeddings.shape"
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        "outputId": "4c926f7e-338c-4249-f227-a2cb037038c0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moving 0 files to the new cache system\n"
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        {
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          "data": {
            "text/plain": [
              "(4, 768)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "#let's calculate cosine similarity for sentence 0:\n",
        "cosine_similarity(\n",
        "    [sen_embeddings[0]],\n",
        "    sen_embeddings[1:]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yl_1hT1GeeCD",
        "outputId": "1dae2ac6-d70b-4ce7-ca97-8fe7166a9d76"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.33088908, 0.7219258 , 0.55483645]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Method 2"
      ],
      "metadata": {
        "id": "EcGgv85FfUu7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow.compat.v1 as tf\n",
        "import tensorflow_hub as hub\n",
        "\n",
        "tf.compat.v1.disable_eager_execution()\n",
        "\n",
        "def execute(tensor):\n",
        "    with tf.Session() as sess:\n",
        "        sess.run(tf.global_variables_initializer())\n",
        "        sess.run(tf.tables_initializer())\n",
        "\n",
        "        return sess.run(tensor)\n",
        "\n",
        "\n",
        "def embed(input):\n",
        "    handle = \"https://tfhub.dev/google/elmo/3\"\n",
        "    model = hub.load(handle)\n",
        "    \n",
        "    return execute(model.signatures[\"default\"](input)[\"elmo\"])\n",
        "\n",
        "\n",
        "def word_to_sentence(embeddings):\n",
        "    return embeddings.sum(axis=1)\n",
        "\n",
        "\n",
        "def get_embeddings_elmo_nnlm(sentences):\n",
        "    return word_to_sentence(embed(sentences))\n",
        "\n",
        "def cos_sim(a, b):\n",
        "    return np.inner(a, b) / (np.linalg.norm(a) * (np.linalg.norm(b)))\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "yRm7YXVsOakR"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "if __name__ == \"__main__\":\n",
        "    inputs = [\"people read the book\", \"the book people read\"]\n",
        "    input = tf.convert_to_tensor(\n",
        "    inputs\n",
        "    )\n",
        "    # Get embeddings corresponding to each sentences\n",
        "    results = get_embeddings_elmo_nnlm(input)"
      ],
      "metadata": {
        "id": "kqKKhhfEiAeD"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cos_sim(results[0], results[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AGpXbiz0OgvR",
        "outputId": "4bc33076-9e06-45a5-ac1d-6b07568ca1fa"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.83875865"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qDX5buN-kQLb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}