notebooks/80_getting_started_with_embeddings.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPg2HdoH8o2p"
      },
      "source": [
        "# Getting Started With Embeddings: Notebook Companion\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0N6a-RPS9jLg"
      },
      "source": [
        "![](/../assets/80_getting_started_with_embeddings/thumbnail.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "70e0sP4t-Cvx"
      },
      "source": [
        "## 1. Embedding a dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "d_McQCASzpT_"
      },
      "outputs": [],
      "source": [
        "model_id = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
        "hf_token = \"get your token in http://hf.co/settings/tokens\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23fNFAm1h24M"
      },
      "source": [
        "The first time you generate the embeddings it may take a while (approximately 20 seconds) for the API to return them. We use the `retry` decorator (install with `pip install retry`) so that if on the first try `output = query(dict(inputs = texts))` doesn't work, wait 10 seconds and try again three times. The reason this happens is because on the first request, the model needs to be downloaded and installed in the server, but subsequent calls are much faster."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "uvEXtWmsgK2B"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install retry"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Kg0HaYGSz2GC"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "from retry import retry\n",
        "\n",
        "api_url = f\"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}\"\n",
        "headers = {\"Authorization\": f\"Bearer {hf_token}\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "DStL7NC_h1J0"
      },
      "outputs": [],
      "source": [
        "@retry(tries=3, delay=10)\n",
        "def query(texts):\n",
        "    response = requests.post(api_url, headers=headers, json={\"inputs\": texts})\n",
        "    result = response.json()\n",
        "    if isinstance(result, list):\n",
        "      return result\n",
        "    elif list(result.keys())[0] == \"error\":\n",
        "      raise RuntimeError(\n",
        "          \"The model is currently loading, please re-run the query.\"\n",
        "          )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "u7iHNzsq05k7"
      },
      "outputs": [],
      "source": [
        "texts = [\"How do I get a replacement Medicare card?\",\n",
        "        \"What is the monthly premium for Medicare Part B?\",\n",
        "        \"How do I terminate my Medicare Part B (medical insurance)?\",\n",
        "        \"How do I sign up for Medicare?\",\n",
        "        \"Can I sign up for Medicare Part B if I am working and have health insurance through an employer?\",\n",
        "        \"How do I sign up for Medicare Part B if I already have Part A?\",\n",
        "        \"What are Medicare late enrollment penalties?\",\n",
        "        \"What is Medicare and who can get it?\",\n",
        "        \"How can I get help with my Medicare Part A and Part B premiums?\",\n",
        "        \"What are the different parts of Medicare?\",\n",
        "        \"Will my Medicare premiums be higher because of my higher income?\",\n",
        "        \"What is TRICARE ?\",\n",
        "        \"Should I sign up for Medicare Part B if I have Veterans’ Benefits?\"]\n",
        "\n",
        "output = query(texts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "PVDIE076NIZB"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "embeddings = pd.DataFrame(output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ElAxRHyzjrd4",
        "outputId": "d349d4b2-c4df-4e93-939b-ff673ac4088a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "         0         1         2         3         4         5         6    \\\n",
            "0  -0.023889  0.055259 -0.011655 -0.033414 -0.012261 -0.024873 -0.012663   \n",
            "1  -0.012688  0.046874 -0.010502 -0.020384 -0.013361  0.042322  0.016628   \n",
            "2   0.000494  0.119412  0.005229 -0.092734  0.007773 -0.005325  0.034506   \n",
            "3  -0.029711  0.023298 -0.057041 -0.012183 -0.013710  0.029796  0.063739   \n",
            "4  -0.025628  0.070389 -0.017380 -0.056567  0.028576  0.052823  0.067062   \n",
            "5  -0.022656  0.021160  0.005105 -0.046494  0.009074  0.041495  0.054268   \n",
            "6  -0.002911  0.060791 -0.009176 -0.006133  0.040492  0.036594  0.002054   \n",
            "7  -0.080526  0.059888 -0.048847 -0.040176 -0.063342  0.041848  0.119045   \n",
            "8  -0.034388  0.072501  0.014440 -0.036695  0.014019  0.063070  0.034683   \n",
            "9  -0.005964  0.025044 -0.003182 -0.025243 -0.039823 -0.012772  0.044713   \n",
            "10 -0.039008 -0.010610 -0.007383 -0.050190 -0.002518 -0.041641  0.026969   \n",
            "11 -0.095983 -0.063012 -0.116906 -0.059075 -0.051323 -0.003439  0.018687   \n",
            "12 -0.011629  0.059619  0.016509 -0.094747 -0.008346  0.070966  0.042429   \n",
            "\n",
            "         7         8         9    ...       374       375       376       377  \\\n",
            "0   0.025346  0.018509 -0.083508  ... -0.161688 -0.046426  0.006004  0.005281   \n",
            "1  -0.004099 -0.002607 -0.010188  ... -0.061594 -0.020717 -0.009082 -0.029260   \n",
            "2  -0.051981 -0.006265 -0.006111  ... -0.108326 -0.049646 -0.073399 -0.029898   \n",
            "3   0.001101 -0.045124 -0.040747  ... -0.117682  0.031924  0.000854  0.020200   \n",
            "4  -0.052618 -0.054702 -0.116230  ... -0.118145  0.013343 -0.055188 -0.032723   \n",
            "5  -0.024185 -0.013483 -0.075966  ... -0.100110  0.010750 -0.031469 -0.004822   \n",
            "6  -0.031345  0.031806 -0.023495  ... -0.028763 -0.060458 -0.018598 -0.040189   \n",
            "7   0.010652 -0.030095 -0.004561  ... -0.144566  0.020404  0.023088  0.005077   \n",
            "8  -0.014531 -0.059862 -0.045383  ... -0.114763 -0.035894 -0.019877 -0.033375   \n",
            "9   0.014535 -0.038213 -0.041149  ... -0.057621  0.021594  0.048983 -0.044541   \n",
            "10 -0.014801 -0.014127 -0.061637  ... -0.098168 -0.031693 -0.052128  0.014774   \n",
            "11  0.006544 -0.049057 -0.031649  ... -0.041085 -0.008593 -0.021544 -0.021112   \n",
            "12 -0.041212 -0.038502 -0.099356  ... -0.135191  0.011535 -0.050499 -0.007376   \n",
            "\n",
            "         378       379       380       381       382       383  \n",
            "0  -0.003342  0.027754  0.020411  0.005778  0.034098 -0.006889  \n",
            "1  -0.066253  0.065257  0.013229 -0.023103 -0.002785  0.010474  \n",
            "2  -0.102734  0.062121  0.034606  0.016877 -0.023861  0.005264  \n",
            "3  -0.020666 -0.005167  0.038370  0.003617  0.033993 -0.010255  \n",
            "4   0.008436  0.019169  0.048212 -0.040412  0.083346  0.026855  \n",
            "5   0.039657  0.026384  0.045514  0.059089 -0.017509  0.007166  \n",
            "6  -0.031486 -0.018299  0.002286 -0.073420  0.016235 -0.000244  \n",
            "7  -0.055645 -0.007675  0.050791 -0.005989  0.134562  0.034817  \n",
            "8  -0.030168  0.039412  0.044993  0.000578 -0.025124  0.034191  \n",
            "9  -0.030137  0.006779  0.054854  0.029937  0.070214  0.041565  \n",
            "10 -0.091150  0.001324  0.053866 -0.083904  0.037684  0.002314  \n",
            "11 -0.019502  0.050039 -0.029175  0.005498  0.152892  0.024720  \n",
            "12  0.084258 -0.008294  0.034186 -0.028212 -0.001166  0.001067  \n",
            "\n",
            "[13 rows x 384 columns]\n"
          ]
        }
      ],
      "source": [
        "print(embeddings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKJJzhYX-OEA"
      },
      "source": [
        "## 2. Host embeddings for free on the Hugging Face Hub\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UGQ0g9DM2wn_"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install huggingface-hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pk9el6S52qZl",
        "outputId": "6410ce0e-a77c-4a69-e0a2-28d2169a1d5a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "        To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/tokens .\n",
            "        (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n",
            "        \n",
            "Token: \n",
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z06NVe-83wf1",
        "outputId": "6549e164-4f49-4211-825c-152be383d8e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[90mgit version 2.17.1\u001b[0m\n",
            "Error: unknown flag: --version\n",
            "\n",
            "\u001b[90mSorry, no usage text found for \"git-lfs\"\u001b[0m\n",
            "\n",
            "You are about to create \u001b[1mdatasets/ITESM/embedded_faqs_medicare\u001b[0m\n",
            "Proceed? [Y/n] y\n",
            "\n",
            "Your repo now lives at:\n",
            "  \u001b[1mhttps://huggingface.co/datasets/ITESM/embedded_faqs_medicare\u001b[0m\n",
            "\n",
            "You can clone it locally with the command below, and commit/push as usual.\n",
            "\n",
            "  git clone https://huggingface.co/datasets/ITESM/embedded_faqs_medicare\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli repo create embedded_faqs_medicare --type dataset --organization ITESM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "beO4nEln40-N",
        "outputId": "53c968da-12c7-4e71-b73b-926bab2ae4bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Error: Failed to call git rev-parse --git-dir --show-toplevel: \"fatal: not a git repository (or any of the parent directories): .git\\n\"\n",
            "Git LFS initialized.\n"
          ]
        }
      ],
      "source": [
        "# This is code required to install git-lfs however it already is installed in Colab instances.\n",
        "#!git lfs install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2O0b5awa4uTd",
        "outputId": "bbe60f52-7057-4fa3-c6a6-91b66dad063b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'embedded_faqs_medicare'...\n",
            "remote: Enumerating objects: 3, done.\u001b[K\n",
            "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (2/2), done.\u001b[K\n",
            "remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (3/3), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://{your HF user here}:{your token here}@huggingface.co/datasets/ITESM/embedded_faqs_medicare"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pSVebbDq6n4g"
      },
      "outputs": [],
      "source": [
        "embeddings.to_csv(\"embedded_faqs_medicare/embeddings.csv\", index=False)\n",
        "print(embeddings.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iezsa2P38rie"
      },
      "source": [
        "Changing directory to our repo `embedded_faqs_medicare`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VU2t0vL18gJw",
        "outputId": "f9b9593f-fd5f-45ba-a352-f9bfdbc2285c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/embedded_faqs_medicare\n"
          ]
        }
      ],
      "source": [
        "%cd embedded_faqs_medicare/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZFTmNB_7BJu",
        "outputId": "6809aa89-f318-43fe-f7bb-5ba359a3bc54"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tracking \"embeddings.csv\"\n"
          ]
        }
      ],
      "source": [
        "!git lfs track *.csv\n",
        "!git add .gitattributes\n",
        "!git add embeddings.csv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-kDcX1lb9xWP"
      },
      "outputs": [],
      "source": [
        "!git config --global user.email \"your email here\"\n",
        "!git config --global user.name \"your git user here\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMsNtanX8yOj",
        "outputId": "89087c65-ca1f-44db-cef6-f047ba91cc06"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[main fc6fd98] First version of the embedded_faqs_medicare dataset\n",
            " 2 files changed, 15 insertions(+)\n",
            " create mode 100644 embeddings.csv\n",
            "Counting objects: 4, done.\n",
            "Delta compression using up to 2 threads.\n",
            "Compressing objects: 100% (4/4), done.\n",
            "Writing objects: 100% (4/4), 47.28 KiB | 4.73 MiB/s, done.\n",
            "Total 4 (delta 1), reused 0 (delta 0)\n",
            "To https://huggingface.co/datasets/ITESM/embedded_faqs_medicare\n",
            "   68bbeb7..fc6fd98  main -> main\n"
          ]
        }
      ],
      "source": [
        "!git commit -m \"First version of the embedded_faqs_medicare dataset\"\n",
        "!git push"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "azsWjRb9-gZY"
      },
      "source": [
        "## 3. Get the most similar Frequently Asked Questions to a query\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "miTP1yro5Xnq"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "c_kc0dbl5H9d",
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      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using custom data configuration ITESM--embedded_faqs_medicare-cb978a6449c82d29\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset csv/ITESM--embedded_faqs_medicare to /root/.cache/huggingface/datasets/ITESM___csv/ITESM--embedded_faqs_medicare-cb978a6449c82d29/0.0.0/51cce309a08df9c4d82ffd9363bbe090bf173197fc01a71b034e8594995a1a58...\n"
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            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
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          },
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              "Downloading data:   0%|          | 0.00/106k [00:00<?, ?B/s]"
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              "0 tables [00:00, ? tables/s]"
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          "output_type": "stream",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/ITESM___csv/ITESM--embedded_faqs_medicare-cb978a6449c82d29/0.0.0/51cce309a08df9c4d82ffd9363bbe090bf173197fc01a71b034e8594995a1a58. Subsequent calls will reuse this data.\n"
          ]
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      ],
      "source": [
        "import torch\n",
        "from datasets import load_dataset\n",
        "\n",
        "faqs_embeddings = load_dataset('ITESM/embedded_faqs_medicare')\n",
        "dataset_embeddings = torch.from_numpy(faqs_embeddings[\"train\"].to_pandas().to_numpy()).to(torch.float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Tb5yJd7ABUBG"
      },
      "outputs": [],
      "source": [
        "question = [\"How can Medicare help me?\"]\n",
        "output = query(question)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3TBXYhi3sof",
        "outputId": "36a24d44-82e6-4150-fc56-7dea0e1a50fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The size of our embedded dataset is torch.Size([13, 384]) and of our embedded query is torch.Size([1, 384]).\n"
          ]
        }
      ],
      "source": [
        "query_embeddings = torch.FloatTensor(output)\n",
        "print(f\"The size of our embedded dataset is {dataset_embeddings.shape} and of our embedded query is {query_embeddings.shape}.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "gwVWBV8i4ldh"
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      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install -U sentence-transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHlOjB-17zKk",
        "outputId": "aca22f34-11c3-4217-a554-3155aa44f723"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/snapshot_download.py:11: FutureWarning: snapshot_download.py has been made private and will no longer be available from version 0.11. Please use `from huggingface_hub import snapshot_download` to import the only public function in this module. Other members of the file may be changed without a deprecation notice.\n",
            "  FutureWarning,\n"
          ]
        }
      ],
      "source": [
        "from sentence_transformers.util import semantic_search\n",
        "\n",
        "hits = semantic_search(query_embeddings, dataset_embeddings, top_k=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
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        },
        "id": "3P2poeGiecM6",
        "outputId": "92013a42-de15-461d-a881-46e71548da49"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['How can I get help with my Medicare Part A and Part B premiums?',\n",
              " 'What is Medicare and who can get it?',\n",
              " 'How do I sign up for Medicare?',\n",
              " 'What are the different parts of Medicare?',\n",
              " 'Will my Medicare premiums be higher because of my higher income?']"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "[texts[hits[0][i]['corpus_id']] for i in range(len(hits[0]))]"
      ]
    }
  ],
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