11.3: Sentence_Transformers_Sent_Embd_Semantic_Simil_Clustering.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/NLP_transformers/blob/main/11%3A%20Sentence_Transformers_Sent_Embd_Semantic_Simil_Clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h2>Sentence Transformers</h2>"
      ],
      "metadata": {
        "id": "SjNAmtyUT33o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Resources:**\n",
        "\n",
        "*   https://www.sbert.net/index.html\n",
        "*   https://www.sbert.net/docs/pretrained_models.html\n",
        "\n"
      ],
      "metadata": {
        "id": "PC08IHg2jNe1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Use cases:**\n",
        "\n",
        "\n",
        "\n",
        "*   Sentence Embedding\n",
        "*   Sentence Similarity\n",
        "*   Semantic Search\n",
        "*   Clustering\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "aJ9U2MTSQCGe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U sentence-transformers"
      ],
      "metadata": {
        "id": "iBxOfXwbeurS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Generate Embeding**"
      ],
      "metadata": {
        "id": "8Fe3mt_jCTL0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer,util\n",
        "model = SentenceTransformer('all-MiniLM-L6-v2')"
      ],
      "metadata": {
        "id": "QXuwDrEsTl-7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "03fm2wdZejPc"
      },
      "outputs": [],
      "source": [
        "sentences = ['This framework generates embeddings for each input sentence',\n",
        "    'Sentences are passed as a list of string.']\n",
        "\n",
        "embeddings = model.encode(sentences)\n",
        "\n",
        "for sentence, embedding in zip(sentences, embeddings):\n",
        "    print(\"Sentence:\", sentence)\n",
        "    print(\"Embedding:\", embedding)\n",
        "    print(\" \")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cosine-Similarity**"
      ],
      "metadata": {
        "id": "YDc8_VCrCQGt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "emb1 = model.encode(\"I am eating Apple\")\n",
        "emb2 = model.encode(\"I like fruits\")\n",
        "cos_sim = util.cos_sim(emb1, emb2)\n",
        "print(\"Cosine-Similarity:\", cos_sim)"
      ],
      "metadata": {
        "id": "ZGz3bDLhewrZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c3ff2af-b62d-452e-f44a-0e8e97ab708f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cosine-Similarity: tensor([[0.5398]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Compute cosine similarity between all pairs**"
      ],
      "metadata": {
        "id": "Ml5sIVHOCY6u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute cosine similarity between all pairs\n",
        "sentences = ['A man is eating food.',\n",
        "          'A man is eating a piece of bread.',\n",
        "          'The girl is carrying a baby.',\n",
        "          'A man is riding a horse.',\n",
        "          'A woman is playing violin.',\n",
        "          'Two men pushed carts through the woods.',\n",
        "          'A man is riding a white horse on an enclosed ground.',\n",
        "          'A monkey is playing drums.',\n",
        "          'Someone in a gorilla costume is playing a set of drums.'\n",
        "          ]\n",
        "\n",
        "#Encode all sentences\n",
        "embeddings = model.encode(sentences)\n",
        "\n",
        "#Compute cosine similarity between all pairs\n",
        "cos_sim = util.cos_sim(embeddings, embeddings)\n",
        "cos_sim"
      ],
      "metadata": {
        "id": "9lyr6Tjge9UY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8885f9a-7810-4472-90cf-e505965d19d2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 1.0000,  0.7553, -0.1050,  0.2474, -0.0704, -0.0333,  0.1707,  0.0476,\n",
              "          0.0630],\n",
              "        [ 0.7553,  1.0000, -0.0610,  0.1442, -0.0809, -0.0216,  0.1157,  0.0362,\n",
              "          0.0216],\n",
              "        [-0.1050, -0.0610,  1.0000, -0.1088,  0.0217, -0.0413, -0.0928,  0.0231,\n",
              "          0.0247],\n",
              "        [ 0.2474,  0.1442, -0.1088,  1.0000, -0.0348,  0.0362,  0.7369,  0.0821,\n",
              "          0.1389],\n",
              "        [-0.0704, -0.0809,  0.0217, -0.0348,  1.0000, -0.1654, -0.0592,  0.1961,\n",
              "          0.2564],\n",
              "        [-0.0333, -0.0216, -0.0413,  0.0362, -0.1654,  1.0000,  0.0769, -0.0380,\n",
              "         -0.0895],\n",
              "        [ 0.1707,  0.1157, -0.0928,  0.7369, -0.0592,  0.0769,  1.0000,  0.0495,\n",
              "          0.1191],\n",
              "        [ 0.0476,  0.0362,  0.0231,  0.0821,  0.1961, -0.0380,  0.0495,  1.0000,\n",
              "          0.6433],\n",
              "        [ 0.0630,  0.0216,  0.0247,  0.1389,  0.2564, -0.0895,  0.1191,  0.6433,\n",
              "          1.0000]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Add all pairs to a list with their cosine similarity score\n",
        "all_sentence_combinations = []\n",
        "for i in range(len(cos_sim)-1):\n",
        "    for j in range(i+1, len(cos_sim)):\n",
        "        all_sentence_combinations.append((cos_sim[i][j], i, j))\n",
        "all_sentence_combinations        "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "06H7O06b91_q",
        "outputId": "fb54c76b-14fc-4ea9-e9cf-2e31ecb6769d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(0.7553), 0, 1),\n",
              " (tensor(-0.1050), 0, 2),\n",
              " (tensor(0.2474), 0, 3),\n",
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              " (tensor(0.0362), 3, 5),\n",
              " (tensor(0.7369), 3, 6),\n",
              " (tensor(0.0821), 3, 7),\n",
              " (tensor(0.1389), 3, 8),\n",
              " (tensor(-0.1654), 4, 5),\n",
              " (tensor(-0.0592), 4, 6),\n",
              " (tensor(0.1961), 4, 7),\n",
              " (tensor(0.2564), 4, 8),\n",
              " (tensor(0.0769), 5, 6),\n",
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              " (tensor(-0.0895), 5, 8),\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Sort list by the highest cosine similarity score\n",
        "all_sentence_combinations = sorted(all_sentence_combinations, key=lambda x: x[0], reverse=True)\n",
        "\n",
        "print(\"Top-5 most similar pairs:\")\n",
        "for score, i, j in all_sentence_combinations[0:5]:\n",
        "    print(\"{} \\t {} \\t {:.4f}\".format(sentences[i], sentences[j], cos_sim[i][j]))"
      ],
      "metadata": {
        "id": "qhggYpNbfK4J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca8ab28f-cd12-493a-b1c0-88685a7034ec"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top-5 most similar pairs:\n",
            "A man is eating food. \t A man is eating a piece of bread. \t 0.7553\n",
            "A man is riding a horse. \t A man is riding a white horse on an enclosed ground. \t 0.7369\n",
            "A monkey is playing drums. \t Someone in a gorilla costume is playing a set of drums. \t 0.6433\n",
            "A woman is playing violin. \t Someone in a gorilla costume is playing a set of drums. \t 0.2564\n",
            "A man is eating food. \t A man is riding a horse. \t 0.2474\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Semantic search**"
      ],
      "metadata": {
        "id": "E0-VolFWK-Rq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer, util\n",
        "model = SentenceTransformer('clips/mfaq')"
      ],
      "metadata": {
        "id": "wpX-L96mTthR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "question = \"<Q>How many models can I host on HuggingFace?\"\n",
        "answer_1 = \"<A>All plans come with unlimited private models and datasets.\"\n",
        "answer_2 = \"<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem.\"\n",
        "answer_3 = \"<A>Based on how much training data and model variants are created, we send you a compute cost and payment link - as low as $10 per job.\"\n",
        "\n",
        "query_embedding = model.encode(question)\n",
        "corpus_embeddings = model.encode([answer_1, answer_2, answer_3])\n",
        "\n",
        "print(util.semantic_search(query_embedding, corpus_embeddings))"
      ],
      "metadata": {
        "id": "z1gio-Pci9RF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6eef3d30-7c99-4dfe-dfd2-ad1693ecfb7f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[{'corpus_id': 0, 'score': 0.5646325945854187}, {'corpus_id': 2, 'score': 0.5142340660095215}, {'corpus_id': 1, 'score': 0.4730038046836853}]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline"
      ],
      "metadata": {
        "id": "6FG1WXOudgYq"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "qa_model = pipeline(\"question-answering\")\n",
        "question = \"How many models can I host on HuggingFace?\"\n",
        "context = \"All plans come with unlimited private models and datasets.\"\n",
        "qa_model(question = question, context = context)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229,
          "referenced_widgets": [
            "19ef2555b47a40ff8d081afb6c83a092",
            "f7b2f999daa14d17a8f21f26f84522df",
            "70f41ce83f7443d0bb0f5814f51ea6f8",
            "82e9f87151f748b189ffc970100ff5f5",
            "a9d0890ead5c4a359c542eaf1374bf89",
            "d092e8ce12ae4eef8e755b5ebd1bc2a1",
            "7693b34ccb62454da0f88b3c3e68b4f3",
            "b8f2c88d46fc48b9931d263bc9d9b2fc",
            "4d2258e2c8b349adbe8e76cfa62a5575",
            "6fee76f9e2954498a5609574daade980",
            "fcdd95e4f3cd41fe89a357a2372a6de1",
            "36e2ef57041f402fb1c6f87067324339",
            "72f57b60eed744ed8df2cc9e18d98762",
            "ecb2b8df8b8345bdbe8b380144782b74",
            "bf020d04fb804649963395e6eeb2edbf",
            "ad87e9b8e6d54758b6e5337a92b7b034",
            "87e068f1b1f4404ab8f5439eab41f3f9",
            "18c703d18bf94cc19f99d589b2ecb92d",
            "3d3c832f3592431b8b052fb13bf6ae58",
            "7636244a02f04c9297539766e2e7f305",
            "b95ae4d14508412eaeed1d0e46a0c64d",
            "b16ee6316e244e56b2f58defaf97656e",
            "1c56fda6c13845c0a8dacb0648278191",
            "16bad22911da4cf7850a692a655b0f8d",
            "c2ba0220f0634caabd3ad345f84b7db9",
            "450dec53c55541dba716fd45c817a62c",
            "3071254ab7cf4c018cafcb47a54e5527",
            "0805410118b2436c84df879fdf6dfb8b",
            "e9cff726891849df830ed9cd6b2a1db9",
            "7398bcad64d54d869a15b0bdc9a2fdcc",
            "34832003cb484efb9a125d9d60244e7b",
            "f66be071ce0e4f908898993192da4cfe",
            "d482d6e385ba492ea0e815d820fee815",
            "d069a278b32e4dd4a5c18ae7e5f383e5",
            "00b8a08d52b2421c826868f6ffecb071",
            "bc7fa2a0f9c74380ad1ac1ea664d4d15",
            "5d11564c53c14b2094ca9b626e7c5e2e",
            "2941254a7f114a589defaa1d0119f456",
            "672b82f0918646178cea4bfc8dbbcbef",
            "bc449d6eaa3a4e6fb96c475692ddc58f",
            "123174e807464959a865631e15ac5ad7",
            "deed49dd753f4253bc4bd139b99ec0dd",
            "1ff902c8969346029376937a15a8b121",
            "bf53643506754fb2b438a0d933e3e511",
            "b267b10962ea493abbac877363682e15",
            "abb7713f978e43a599e8027ca62d4cb5",
            "d9ffbca795c446f08093fc4b65cffb07",
            "28ce350c221d4c4d8b7d6b4482100ee8",
            "9c99373bbea1475798b63793f1ebd9ae",
            "2cca93ed594b47068f551d3489c0d409",
            "484de15c03894cb6be1323cb485d5d18",
            "27e817a2eb6f438793d8f8105073a53c",
            "02b9d5e543e141a59e01c6a83c88a20e",
            "310e0898d8e84c26b414300d09084cac",
            "0a869c0318854baeb59de99ffb1c6c38"
          ]
        },
        "id": "1Qa18QF-ddMa",
        "outputId": "873dffb8-ba70-42fa-bf17-679cbee0c9c8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-cased-distilled-squad and revision 626af31 (https://huggingface.co/distilbert-base-cased-distilled-squad).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/473 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19ef2555b47a40ff8d081afb6c83a092"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/249M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "36e2ef57041f402fb1c6f87067324339"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c56fda6c13845c0a8dacb0648278191"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading vocab.txt:   0%|          | 0.00/208k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d069a278b32e4dd4a5c18ae7e5f383e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.json:   0%|          | 0.00/426k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b267b10962ea493abbac877363682e15"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'answer': 'unlimited', 'end': 29, 'score': 0.701717734336853, 'start': 20}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(util.semantic_search(query_embedding, corpus_embeddings))"
      ],
      "metadata": {
        "id": "_J6IY-4oC4hF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d40d05d2-30dd-4cda-bddc-3c9b2dada6b5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[{'corpus_id': 0, 'score': 0.5646325945854187}, {'corpus_id': 2, 'score': 0.5142340660095215}, {'corpus_id': 1, 'score': 0.4730038046836853}]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Clustering**"
      ],
      "metadata": {
        "id": "G9tF0hu6K0TD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "import numpy as np\n",
        "\n",
        "embedder = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n",
        "# Corpus with example sentences\n",
        "corpus = ['A man is eating food.',\n",
        "          'A man is eating a piece of bread.',\n",
        "          'Horse is eating grass.',\n",
        "          'A man is eating pasta.',\n",
        "          'A Woman is eating Biryani.',\n",
        "          'The girl is carrying a baby.',\n",
        "          'The baby is carried by the woman',\n",
        "          'A man is riding a horse.',\n",
        "          'A man is riding a white horse on an enclosed ground.',\n",
        "          'A monkey is playing drums.',\n",
        "          'Someone in a gorilla costume is playing a set of drums.',\n",
        "          'A cheetah is running behind its prey.',\n",
        "          'A cheetah chases prey on across a field.',\n",
        "          'The cheetah is chasing a man who is riding the horse.',\n",
        "          'man and women with their baby are watching cheetah in zoo'\n",
        "          ]\n",
        "corpus_embeddings = embedder.encode(corpus)\n",
        "\n",
        "# Normalize the embeddings to unit length\n",
        "corpus_embeddings = corpus_embeddings /  np.linalg.norm(corpus_embeddings, axis=1, keepdims=True)"
      ],
      "metadata": {
        "id": "BC9xCE63G0UF"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corpus_embeddings[0]"
      ],
      "metadata": {
        "id": "ri3xLpcXerJW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c341a6a-6740-4919-b4f4-67ca215fc46d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3.32415439e-02,  4.40606195e-03, -6.27695629e-03,  4.83787209e-02,\n",
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              "        3.74036543e-02,  4.54420522e-02,  3.41177657e-02,  1.79315489e-02,\n",
              "       -8.33183806e-03, -6.03108294e-02,  4.96554822e-02, -5.72078563e-02,\n",
              "       -1.30771575e-02,  1.71759576e-02,  4.65734713e-02, -2.16879174e-02,\n",
              "       -5.93092805e-03, -6.68296888e-02,  5.09953573e-02,  1.10223949e-01,\n",
              "       -8.14205185e-02,  8.81507173e-02, -2.48110481e-03, -4.10473719e-02,\n",
              "       -6.12074845e-02,  1.14096729e-02,  6.72479719e-02,  3.73032019e-02,\n",
              "        8.42115376e-03,  2.33193468e-02,  5.45049272e-02, -3.50759849e-02,\n",
              "        3.10199857e-02, -7.03157252e-03, -3.49309966e-02, -2.34680045e-02,\n",
              "        6.28780052e-02,  1.04743335e-03,  1.42257093e-02,  2.57083122e-03,\n",
              "       -2.43061688e-02,  2.54761018e-02,  2.33831517e-02, -2.72506904e-02,\n",
              "       -1.94816687e-03, -4.63263579e-02,  1.80354025e-02, -7.81259267e-04,\n",
              "       -5.35300076e-02,  2.68155988e-03, -4.89933938e-02, -7.58567452e-03,\n",
              "        1.11466534e-02,  5.76151162e-02, -1.22422157e-02, -7.96029940e-02,\n",
              "        3.67450751e-02,  4.82221916e-02,  8.61350819e-02, -1.69084213e-08,\n",
              "        6.80829510e-02, -1.11274220e-01, -7.76559934e-02,  1.86822228e-02,\n",
              "        4.58801799e-02, -3.83767895e-02, -6.15786724e-02, -1.11348405e-01,\n",
              "        1.09334841e-01,  2.34655365e-02, -9.15681124e-02,  3.59425768e-02,\n",
              "       -1.44541403e-02, -2.52894685e-02, -3.63745750e-03, -7.85412416e-02,\n",
              "        3.37139107e-02,  5.26648248e-04, -3.08579337e-02,  1.23676658e-02,\n",
              "       -3.43577983e-03, -7.16426829e-03, -3.29638571e-02, -3.50171551e-02,\n",
              "        9.90643427e-02,  1.65034495e-02, -3.68755497e-02,  6.94360584e-02,\n",
              "        5.29176258e-02,  5.10925986e-03,  5.38597368e-02,  5.78274280e-02,\n",
              "       -1.45294676e-02,  4.38301712e-02,  8.42988417e-02, -4.70050937e-03,\n",
              "       -3.00964732e-02,  1.93790328e-02,  2.56121457e-02, -9.00143608e-02,\n",
              "       -7.12275458e-03, -1.48720471e-02,  3.97881120e-02, -3.10355611e-03,\n",
              "       -8.67705494e-02,  1.05223835e-01,  1.66949220e-02,  5.55669293e-02,\n",
              "        1.99542288e-02,  1.00048957e-03, -3.83120701e-02,  5.81094511e-02,\n",
              "        3.65598034e-03, -4.72678281e-02,  2.45520249e-02, -2.65945047e-02,\n",
              "        5.68771474e-02,  4.76565491e-03,  5.03440015e-02, -5.63884303e-02,\n",
              "        2.13037468e-02,  6.91730082e-02, -2.45848224e-02, -3.75643745e-02],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# source: https://stackoverflow.com/questions/55619176/how-to-cluster-similar-sentences-using-bert\n",
        "\n",
        "clustering_model = KMeans(n_clusters=4)\n",
        "clustering_model.fit(corpus_embeddings)\n",
        "cluster_assignment = clustering_model.labels_\n",
        "print(cluster_assignment)"
      ],
      "metadata": {
        "id": "f-pCHO7EkDKj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed5e3311-76aa-4a19-d782-2e9da0a55c20"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3 3 1 3 3 0 0 1 1 2 2 1 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clustered_sentences = {}\n",
        "for sentence_id, cluster_id in enumerate(cluster_assignment):\n",
        "    if cluster_id not in clustered_sentences:\n",
        "        clustered_sentences[cluster_id] = []\n",
        "\n",
        "    clustered_sentences[cluster_id].append(corpus[sentence_id])\n",
        "clustered_sentences"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ih-1L7bJJq0k",
        "outputId": "40898aca-b525-4dbb-e2b7-a0cc46d3e564"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: ['The girl is carrying a baby.', 'The baby is carried by the woman'],\n",
              " 1: ['Horse is eating grass.',\n",
              "  'A man is riding a horse.',\n",
              "  'A man is riding a white horse on an enclosed ground.',\n",
              "  'A cheetah is running behind its prey.',\n",
              "  'A cheetah chases prey on across a field.',\n",
              "  'The cheetah is chasing a man who is riding the horse.',\n",
              "  'man and women with their baby are watching cheetah in zoo'],\n",
              " 2: ['A monkey is playing drums.',\n",
              "  'Someone in a gorilla costume is playing a set of drums.'],\n",
              " 3: ['A man is eating food.',\n",
              "  'A man is eating a piece of bread.',\n",
              "  'A man is eating pasta.',\n",
              "  'A Woman is eating Biryani.']}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clustered_sentences = {}\n",
        "for sentence_id, cluster_id in enumerate(cluster_assignment):\n",
        "    if cluster_id not in clustered_sentences:\n",
        "        clustered_sentences[cluster_id] = []\n",
        "\n",
        "    clustered_sentences[cluster_id].append(corpus[sentence_id])\n",
        "clustered_sentences"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O2eINiS3Sivc",
        "outputId": "4de9110b-fb7c-4b2f-ea71-12b0dcb410e6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: ['The girl is carrying a baby.', 'The baby is carried by the woman'],\n",
              " 1: ['Horse is eating grass.',\n",
              "  'A man is riding a horse.',\n",
              "  'A man is riding a white horse on an enclosed ground.',\n",
              "  'A cheetah is running behind its prey.',\n",
              "  'A cheetah chases prey on across a field.',\n",
              "  'The cheetah is chasing a man who is riding the horse.',\n",
              "  'man and women with their baby are watching cheetah in zoo'],\n",
              " 2: ['A monkey is playing drums.',\n",
              "  'Someone in a gorilla costume is playing a set of drums.'],\n",
              " 3: ['A man is eating food.',\n",
              "  'A man is eating a piece of bread.',\n",
              "  'A man is eating pasta.',\n",
              "  'A Woman is eating Biryani.']}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    }
  ]
}