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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/skander10/lab-extractive-question-answering/blob/main/lab_extractive_question_answering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "split-aluminum",
+      "metadata": {
+        "id": "split-aluminum",
+        "papermill": {
+          "duration": 0.048394,
+          "end_time": "2021-04-15T21:06:39.560571",
+          "exception": false,
+          "start_time": "2021-04-15T21:06:39.512177",
+          "status": "completed"
+        },
+        "tags": []
+      },
+      "source": [
+        "# LAB | Extractive Question Answering"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e3f44179",
+      "metadata": {
+        "id": "e3f44179"
+      },
+      "source": [
+        "<div style=\"border:1px solid #ccc; border-radius:6px; text-align: center;\">\n",
+        "\n",
+        "<br>\n",
+        "\n",
+        "**Run this notebook in [Google Colab](https://colab.research.google.com/) for GPU acceleration.**\n",
+        "\n",
+        "<br>\n",
+        "\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "prospective-turner",
+      "metadata": {
+        "id": "prospective-turner",
+        "papermill": {
+          "duration": 0.045573,
+          "end_time": "2021-04-15T21:06:39.651272",
+          "exception": false,
+          "start_time": "2021-04-15T21:06:39.605699",
+          "status": "completed"
+        },
+        "tags": []
+      },
+      "source": [
+        "This notebook demonstrates how Pinecone helps you build an extractive question-answering application. To build an extractive question-answering system, we need three main components:\n",
+        "\n",
+        "- A vector index to store and run semantic search\n",
+        "- A retriever model for embedding context passages\n",
+        "- A reader model to extract answers\n",
+        "\n",
+        "We will use the SQuAD dataset, which consists of **questions** and **context** paragraphs containing question **answers**. We generate embeddings for the context passages using the retriever, index them in the vector database, and query with semantic search to retrieve the top k most relevant contexts containing potential answers to our question. We then use the reader model to extract the answers from the returned contexts."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "oC3GG-dWkZJ6",
+      "metadata": {
+        "id": "oC3GG-dWkZJ6"
+      },
+      "source": [
+        "Let's get started by installing the packages needed for notebook to run:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "terminal-export",
+      "metadata": {
+        "id": "terminal-export",
+        "papermill": {
+          "duration": 0.044413,
+          "end_time": "2021-04-15T21:06:39.741951",
+          "exception": false,
+          "start_time": "2021-04-15T21:06:39.697538",
+          "status": "completed"
+        },
+        "tags": []
+      },
+      "source": [
+        "# Install Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "expressed-executive",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-04-15T21:06:39.845309Z",
+          "iopub.status.busy": "2021-04-15T21:06:39.842494Z",
+          "iopub.status.idle": "2021-04-15T21:08:22.163939Z",
+          "shell.execute_reply": "2021-04-15T21:08:22.164616Z"
+        },
+        "id": "expressed-executive",
+        "papermill": {
+          "duration": 102.376674,
+          "end_time": "2021-04-15T21:08:22.165052",
+          "exception": false,
+          "start_time": "2021-04-15T21:06:39.788378",
+          "status": "completed"
+        },
+        "tags": [],
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "33b570f2-93e9-418c-f165-2bb0fe4ea1da"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.1/44.1 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m78.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m515.2/515.2 kB\u001b[0m \u001b[31m34.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m427.3/427.3 kB\u001b[0m \u001b[31m35.8 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[31m21.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m447.8/447.8 kB\u001b[0m \u001b[31m33.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.5/87.5 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.1/50.1 MB\u001b[0m \u001b[31m46.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m106.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "diffusers 0.39.0 requires huggingface-hub<2.0,>=0.34.0, but you have huggingface-hub 0.26.5 which is incompatible.\n",
+            "gradio 6.20.0 requires huggingface-hub<2.0,>=1.2.0, but you have huggingface-hub 0.26.5 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q \\\n",
+        "    transformers==4.46.3 \\\n",
+        "    datasets==4.5.0 \\\n",
+        "    pinecone==5.4.2 \\\n",
+        "    sentence-transformers==3.3.1 \\\n",
+        "    huggingface_hub==0.26.5\n",
+        "\n",
+        "!rm -rf ~/.cache/huggingface"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2DCPtl6IhgSz",
+      "metadata": {
+        "id": "2DCPtl6IhgSz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "60420168-8a07-4fc7-9ef4-f5ffb68087d3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "⚠️ WARNING:\n",
+            "This code cell will restart the kernel to load the newly installed packages...\n",
+            "If you used 'Run all', it will stop here — that's expected.\n",
+            "See the note below on how to continue running the rest of the notebook.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Force-kill the kernel process to restart it, ensuring the newly\n",
+        "# installed/uninstalled package versions are loaded in a fresh process\n",
+        "\n",
+        "import os\n",
+        "import sys\n",
+        "\n",
+        "print(\n",
+        "    \"⚠️ WARNING:\\n\"\n",
+        "    \"This code cell will restart the kernel to load the newly installed packages...\\n\"\n",
+        "    \"If you used 'Run all', it will stop here — that's expected.\\n\"\n",
+        "    \"See the note below on how to continue running the rest of the notebook.\",\n",
+        "    flush=True,\n",
+        ")\n",
+        "sys.stdout.flush()\n",
+        "\n",
+        "os.kill(os.getpid(), 9)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d339487b",
+      "metadata": {
+        "id": "d339487b"
+      },
+      "source": [
+        "<div style=\"border:1px solid #ccc; border-radius:6px; padding:12px;\">\n",
+        "\n",
+        "⚠️ **Do not use \"Run all\" for this whole notebook in one go.** The cell above force-restarts the kernel (`os.kill`) so the newly installed packages load cleanly. Colab's \"Run all\" cannot continue past a kernel restart — it will always stop right after that cell, no matter how many times you retry it.\n",
+        "\n",
+        "Instead, run it in two steps:\n",
+        "1. Run only the two cells above (install + restart). Wait for the kernel to finish restarting (a few seconds).\n",
+        "2. Click on the next cell below, then use **Runtime → Run after** (or manually run the remaining cells one by one / select them and press Shift+Enter). Do not click \"Run all\" again, since it would just re-trigger the restart at the same spot.\n",
+        "\n",
+        "</div>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "29ad3840",
+      "metadata": {
+        "id": "29ad3840"
+      },
+      "source": [
+        "# Load Dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "hgIieQukgagu",
+      "metadata": {
+        "id": "hgIieQukgagu"
+      },
+      "source": [
+        "Now let's load the SQUAD dataset from the HuggingFace Model Hub. We load the dataset into a pandas dataframe and filter the title, question, and context columns, and we drop any duplicate context passages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "J250IJeh7NIb",
+      "metadata": {
+        "id": "J250IJeh7NIb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 275,
+          "referenced_widgets": [
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+            "0c1f8f1d8f5f45128e075e42eddb0308",
+            "e1907c0540074910a3acf52e21d1fa44",
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+            "682038cb1c814f72b418959de2e36c0f",
+            "081c1c1757eb435c927f010a93643dc4",
+            "557ff97daaee4d35b9b5c7aa9766b0b0",
+            "c40eecb3487e4c5b85af3a7e9a6d7d84",
+            "2215f9e048d74b1588548da67b206c20",
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+            "574d0541a5bd42ecb400a365eca3292d",
+            "241f2f8595c946ef91e5322a6ca282a9",
+            "892e95f1270247369e9c9f64130ac34b"
+          ]
+        },
+        "outputId": "acda7db3-0264-4880-e767-2519bdb2b331"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "51f33a86ac2a403491404c34f73059c4"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "train-00000-of-00001.parquet:   0%|          | 0.00/14.5M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "66f5e2dde0454cad90bd2e4c10b76216"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "validation-00000-of-00001.parquet:   0%|          | 0.00/1.82M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a086eb7179884b94a37f18ac3a0c54cf"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split:   0%|          | 0/87599 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "694b0731a85e4d429ae72e3d1e548446"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split:   0%|          | 0/10570 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "abd50798375a481e995ef51047324eeb"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "# load the squad dataset into a pandas dataframe\n",
+        "df = load_dataset(\"rajpurkar/squad\", split=\"train\").to_pandas()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "FcmeNO97dHDO",
+      "metadata": {
+        "id": "FcmeNO97dHDO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 411
+        },
+        "outputId": "388eba3b-ac48-4705-9f2e-8a0dd7f53612"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                          title  \\\n",
+              "0      University_of_Notre_Dame   \n",
+              "5      University_of_Notre_Dame   \n",
+              "10     University_of_Notre_Dame   \n",
+              "15     University_of_Notre_Dame   \n",
+              "20     University_of_Notre_Dame   \n",
+              "...                         ...   \n",
+              "87574                 Kathmandu   \n",
+              "87579                 Kathmandu   \n",
+              "87584                 Kathmandu   \n",
+              "87589                 Kathmandu   \n",
+              "87594                 Kathmandu   \n",
+              "\n",
+              "                                                 context  \n",
+              "0      Architecturally, the school has a Catholic cha...  \n",
+              "5      As at most other universities, Notre Dame's st...  \n",
+              "10     The university is the major seat of the Congre...  \n",
+              "15     The College of Engineering was established in ...  \n",
+              "20     All of Notre Dame's undergraduate students are...  \n",
+              "...                                                  ...  \n",
+              "87574  Institute of Medicine, the central college of ...  \n",
+              "87579  Football and Cricket are the most popular spor...  \n",
+              "87584  The total length of roads in Nepal is recorded...  \n",
+              "87589  The main international airport serving Kathman...  \n",
+              "87594  Kathmandu Metropolitan City (KMC), in order to...  \n",
+              "\n",
+              "[18891 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
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+              "      <td>University_of_Notre_Dame</td>\n",
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+              "      <th>15</th>\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
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+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
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+              "\n",
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+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 18891,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 442,\n        \"samples\": [\n          \"Dominican_Order\",\n          \"Brigham_Young_University\",\n          \"Matter\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18891,\n        \"samples\": [\n          \"More commonly, in cases where there are three or more parties, no one party is likely to gain power alone, and parties work with each other to form coalition governments. This has been an emerging trend in the politics of the Republic of Ireland since the 1980s and is almost always the case in Germany on national and state level, and in most constituencies at the communal level. Furthermore, since the forming of the Republic of Iceland there has never been a government not led by a coalition (usually of the Independence Party and one other (often the Social Democratic Alliance). A similar situation exists in the Republic of Ireland; since 1989, no one party has held power on its own. Since then, numerous coalition governments have been formed. These coalitions have been exclusively led by one of either Fianna F\\u00e1il or Fine Gael. Political change is often easier with a coalition government than in one-party or two-party dominant systems.[dubious \\u2013 discuss] If factions in a two-party system are in fundamental disagreement on policy goals, or even principles, they can be slow to make policy changes, which appears to be the case now in the U.S. with power split between Democrats and Republicans. Still coalition governments struggle, sometimes for years, to change policy and often fail altogether, post World War II France and Italy being prime examples. When one party in a two-party system controls all elective branches, however, policy changes can be both swift and significant. Democrats Woodrow Wilson, Franklin Roosevelt and Lyndon Johnson were beneficiaries of such fortuitous circumstances, as were Republicans as far removed in time as Abraham Lincoln and Ronald Reagan. Barack Obama briefly had such an advantage between 2009 and 2011.\",\n          \"There has been some concern over the potential adverse environmental and ecosystem effects caused by the influx of visitors. Some environmentalists and scientists have made a call for stricter regulations for ships and a tourism quota. The primary response by Antarctic Treaty Parties has been to develop, through their Committee for Environmental Protection and in partnership with IAATO, \\\"site use guidelines\\\" setting landing limits and closed or restricted zones on the more frequently visited sites. Antarctic sightseeing flights (which did not land) operated out of Australia and New Zealand until the fatal crash of Air New Zealand Flight 901 in 1979 on Mount Erebus, which killed all 257 aboard. Qantas resumed commercial overflights to Antarctica from Australia in the mid-1990s.\",\n          \"After World War II, the Guam Organic Act of 1950 established Guam as an unincorporated organized territory of the United States, provided for the structure of the island's civilian government, and granted the people U.S. citizenship. The Governor of Guam was federally appointed until 1968, when the Guam Elective Governor Act provided for the office's popular election.:242 Since Guam is not a U.S. state, U.S. citizens residing on Guam are not allowed to vote for president and their congressional representative is a non-voting member.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ],
+      "source": [
+        "# select only title and context column\n",
+        "df = df[['title', 'context']]\n",
+        "# drop rows containing duplicate context passages\n",
+        "df = df.drop_duplicates(subset='context')\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "57bbcb57",
+      "metadata": {
+        "id": "57bbcb57"
+      },
+      "source": [
+        "# Pinecone Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e24d904c",
+      "metadata": {
+        "id": "e24d904c"
+      },
+      "source": [
+        "Pinecone is a managed vector database that lets us store context embeddings and run fast semantic search over them. In this section, we set up the connection to Pinecone using an API key and create the index that will hold our context embeddings."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "472402a1",
+      "metadata": {
+        "id": "472402a1"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import getpass\n",
+        "from pinecone import Pinecone\n",
+        "\n",
+        "#\n",
+        "# For this notebook, you'll need a Pinecone API key,\n",
+        "# to get one, sign up for free at https://app.pinecone.io/,\n",
+        "# then go to \"API Keys\" and copy the default key (or create a new one).\n",
+        "#\n",
+        "# Once you have your Pinecone API key,\n",
+        "# load it from Colab's \"Secrets\" manager\n",
+        "# (the key icon in the left sidebar).\n",
+        "# Add a secret named PINECONE_API_KEY there and enable notebook access.\n",
+        "#\n",
+        "# Note: Colab Secrets rely on a UI popup to grant access, which only works when\n",
+        "# running in the Colab UI itself. If you're running this notebook through\n",
+        "# another editor (e.g. VS Code's Colab extension), that popup isn't available,\n",
+        "# so we fall back to an environment variable or a secure manual prompt instead.\n",
+        "api_key = os.environ.get('PINECONE_API_KEY')\n",
+        "\n",
+        "\n",
+        "if not api_key:\n",
+        "    try:\n",
+        "        from google.colab import userdata\n",
+        "        api_key = userdata.get('PINECONE_API_KEY')\n",
+        "    except Exception:\n",
+        "        api_key = None\n",
+        "\n",
+        "if not api_key:\n",
+        "    api_key = getpass.getpass('Enter your Pinecone API key: ')\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7ca5ef09",
+      "metadata": {
+        "id": "7ca5ef09"
+      },
+      "source": [
+        "<br>\n",
+        "\n",
+        "Once we have the API key, we can initialize the connection:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "092d1e71",
+      "metadata": {
+        "id": "092d1e71"
+      },
+      "outputs": [],
+      "source": [
+        "from pinecone import Pinecone, ServerlessSpec\n",
+        "\n",
+        "spec = ServerlessSpec(\n",
+        "    cloud=\"aws\", region=\"us-east-1\"\n",
+        ")\n",
+        "\n",
+        "# connect to pinecone environment\n",
+        "pc = Pinecone(\n",
+        "    api_key = api_key\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "58028e12",
+      "metadata": {
+        "id": "58028e12"
+      },
+      "source": [
+        "Now we create a new index called \"question-answering\" — we can name the index anything we want. We specify the metric type as \"cosine\" and dimension as 384 because the retriever we use to generate context embeddings is optimized for cosine similarity and outputs 384-dimension vectors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "b3206184",
+      "metadata": {
+        "id": "b3206184"
+      },
+      "outputs": [],
+      "source": [
+        "index_name = \"question-answering\"\n",
+        "\n",
+        "# check if the extractive-question-answering index exists\n",
+        "if index_name not in pc.list_indexes().names():\n",
+        "    # create the index if it does not exist\n",
+        "    pc.create_index(\n",
+        "        name=index_name,\n",
+        "        dimension=384,\n",
+        "        metric=\"cosine\",\n",
+        "        spec=spec\n",
+        "    )\n",
+        "# connect to extractive-question-answering index we created\n",
+        "index = pc.Index(index_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6e84a3e5",
+      "metadata": {
+        "id": "6e84a3e5"
+      },
+      "source": [
+        "# Initialize Retriever"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "oZzhGS1Lpj0g",
+      "metadata": {
+        "id": "oZzhGS1Lpj0g"
+      },
+      "source": [
+        "Next, we need to initialize our retriever. The retriever will mainly do two things:\n",
+        "\n",
+        "- Generate embeddings for all context passages (context vectors/embeddings)\n",
+        "- Generate embeddings for our questions (query vector/embedding)\n",
+        "\n",
+        "The retriever will generate embeddings in a way that the questions and context passages containing answers to our questions are nearby in the vector space. We can use cosine similarity to calculate the similarity between the query and context embeddings to find the context passages that contain potential answers to our question.\n",
+        "\n",
+        "We will use a SentenceTransformer model named ``multi-qa-MiniLM-L6-cos-v1`` designed for semantic search and trained on 215M (question, answer) pairs from diverse sources as our retriever."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "31a85bb3",
+      "metadata": {
+        "id": "31a85bb3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 483,
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+        {
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+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+              "version_minor": 0,
+              "model_id": "85fce8c956b64c35900fc1cabd4493e0"
+            }
+          },
+          "metadata": {}
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+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SentenceTransformer(\n",
+              "  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel \n",
+              "  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})\n",
+              "  (2): Normalize()\n",
+              ")"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "from sentence_transformers import SentenceTransformer\n",
+        "\n",
+        "# set device to GPU if available\n",
+        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+        "# load the retriever model from huggingface model hub\n",
+        "retriever = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1', device=device)\n",
+        "\n",
+        "\n",
+        "retriever"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8aaad0a2",
+      "metadata": {
+        "id": "8aaad0a2"
+      },
+      "source": [
+        "# Generate Embeddings and Upsert"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "Hgy7AagJtO_p",
+      "metadata": {
+        "id": "Hgy7AagJtO_p"
+      },
+      "source": [
+        "Next, we need to generate embeddings for the context passages. We will do this in batches to help us more quickly generate embeddings and upload them to the Pinecone index. When passing the documents to Pinecone, we need an id (a unique value), context embedding, and metadata for each document representing context passages in the dataset. The metadata is a dictionary containing data relevant to our embeddings, such as the article title, context passage, etc."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "a17824ef",
+      "metadata": {
+        "id": "a17824ef",
+        "tags": [],
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 115,
+          "referenced_widgets": [
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+        "outputId": "bf24a101-f978-498a-f5bd-5881ccb0e506"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/296 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1ad760c5c82047bcb9d2c3c6655f24ff"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'dimension': 384,\n",
+              " 'index_fullness': 0.0,\n",
+              " 'namespaces': {'': {'vector_count': 18891}},\n",
+              " 'total_vector_count': 18891}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "from tqdm.auto import tqdm\n",
+        "\n",
+        "# we will use batches of 64\n",
+        "batch_size = 64\n",
+        "\n",
+        "for i in tqdm(range(0, len(df), batch_size)):\n",
+        "    # find end of batch\n",
+        "    i_end = min(i + batch_size, len(df))\n",
+        "\n",
+        "    # extract batch\n",
+        "    batch = df.iloc[i:i_end]\n",
+        "    # generate embeddings for batch\n",
+        "    emb = retriever.encode(batch['context'].tolist()).tolist()\n",
+        "    # get metadata\n",
+        "    meta = batch.to_dict(orient='records')\n",
+        "    # create unique IDs\n",
+        "    ids = [str(x) for x in range(i, i_end)]\n",
+        "    # add all to upsert list\n",
+        "    to_upsert = list(zip(ids, emb, meta))\n",
+        "    # upsert/insert these records to pinecone\n",
+        "    _ = index.upsert(vectors=to_upsert)\n",
+        "\n",
+        "# check that we have all vectors in index\n",
+        "index.describe_index_stats()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "YFyBYafuJ0y0",
+      "metadata": {
+        "id": "YFyBYafuJ0y0"
+      },
+      "source": [
+        "# Initialize Reader"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "HgdiLCz5ynOk",
+      "metadata": {
+        "id": "HgdiLCz5ynOk"
+      },
+      "source": [
+        "We use the `deepset/electra-base-squad2` model from the HuggingFace model hub as our reader model. We load this model into a \"question-answering\" pipeline from HuggingFace transformers and feed it our questions and context passages individually. The model gives a prediction for each context we pass through the pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
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+      "id": "hg9XTDkIJzH_",
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+          "data": {
+            "text/plain": [
+              "vocab.txt: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e2d0a067bd7f4d809082a60cb0697c92"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1a4a21688b0847fd944759f8d8b35d68"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<transformers.pipelines.question_answering.QuestionAnsweringPipeline at 0x7b45c596d370>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
+      "source": [
+        "from transformers import pipeline\n",
+        "\n",
+        "model_name = 'deepset/electra-base-squad2'\n",
+        "# load the reader model into a question-answering pipeline\n",
+        "\n",
+        "device_id = 0 if torch.cuda.is_available() else -1\n",
+        "reader = pipeline(tokenizer=model_name, model=model_name, task='question-answering', device=device_id)\n",
+        "\n",
+        "reader"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e14d89d6",
+      "metadata": {
+        "id": "e14d89d6"
+      },
+      "source": [
+        "Now all the components we need are ready. Let's write some helper functions to execute our queries. The `get_context` function retrieves the context embeddings containing answers to our question from the Pinecone index, and the `extract_answer` function extracts the answers from these context passages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "lyYaY3QEQiHZ",
+      "metadata": {
+        "id": "lyYaY3QEQiHZ"
+      },
+      "outputs": [],
+      "source": [
+        "# gets context passages from the pinecone index\n",
+        "def get_context(question, top_k):\n",
+        "    # generate embeddings for the question\n",
+        "    xq = retriever.encode(question).tolist()\n",
+        "    # search pinecone index for context passage with the answer\n",
+        "    xc = index.query(vector=xq, top_k=top_k, include_metadata=True)\n",
+        "    # extract the context passage from pinecone search result\n",
+        "    c = [match['metadata']['context'] for match in xc['matches']]\n",
+        "    return c\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "Dc9VYOiUQA7B",
+      "metadata": {
+        "id": "Dc9VYOiUQA7B"
+      },
+      "outputs": [],
+      "source": [
+        "from pprint import pprint\n",
+        "\n",
+        "# extracts answer from the context passage\n",
+        "def extract_answer(question, context):\n",
+        "    results = []\n",
+        "    for c in context:\n",
+        "        # feed the reader the question and contexts to extract answers\n",
+        "        answer = reader(question=question, context=c)\n",
+        "        # add the context to answer dict for printing both together\n",
+        "        answer[\"context\"] = c\n",
+        "        results.append(answer)\n",
+        "    # sort the result based on the score from reader model\n",
+        "    sorted_result = pprint(sorted(results, key=lambda x: x['score'], reverse=True))\n",
+        "    return sorted_result"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "5E3a3dkJ5ZQD",
+      "metadata": {
+        "id": "5E3a3dkJ5ZQD",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "73f1d8d8-1770-4674-ddeb-db05e2a4566b"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['Egypt was producing 691,000 bbl/d of oil and 2,141.05 Tcf of natural gas (in 2013), which makes Egypt as the largest oil producer not member of the Organization of the Petroleum Exporting Countries (OPEC) and the second-largest dry natural gas producer in Africa. In 2013, Egypt was the largest consumer of oil and natural gas in Africa, as more than 20% of total oil consumption and more than 40% of total dry natural gas consumption in Africa. Also, Egypt possesses the largest oil refinery capacity in Africa 726,000 bbl/d (in 2012). Egypt is currently planning to build its first nuclear power plant in El Dabaa city, northern Egypt.',\n",
+              " 'Egypt has a developed energy market based on coal, oil, natural gas, and hydro power. Substantial coal deposits in the northeast Sinai are mined at the rate of about 600,000 tonnes (590,000 long tons; 660,000 short tons) per year. Oil and gas are produced in the western desert regions, the Gulf of Suez, and the Nile Delta. Egypt has huge reserves of gas, estimated at 2,180 cubic kilometres (520 cu mi), and LNG up to 2012 exported to many countries. In 2013, the Egyptian General Petroleum Co (EGPC) said the country will cut exports of natural gas and tell major industries to slow output this summer to avoid an energy crisis and stave off political unrest, Reuters has reported. Egypt is counting on top liquid natural gas (LNG) exporter Qatar to obtain additional gas volumes in summer, while encouraging factories to plan their annual maintenance for those months of peak demand, said EGPC chairman, Tarek El Barkatawy. Egypt produces its own energy, but has been a net oil importer since 2008 and is rapidly becoming a net importer of natural gas.',\n",
+              " \"Iran has the second largest proved gas reserves in the world after Russia, with 33.6 trillion cubic metres, and third largest natural gas production in the world after Indonesia, and Russia. It also ranks fourth in oil reserves with an estimated 153,600,000,000 barrels. It is OPEC's 2nd largest oil exporter and is an energy superpower. In 2005, Iran spent US$4 billion on fuel imports, because of contraband and inefficient domestic use. Oil industry output averaged 4 million barrels per day (640,000 m3/d) in 2005, compared with the peak of six million barrels per day reached in 1974. In the early years of the 2000s (decade), industry infrastructure was increasingly inefficient because of technological lags. Few exploratory wells were drilled in 2005.\"]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ],
+      "source": [
+        "question = \"How much oil is Egypt producing in a day?\"\n",
+        "context = get_context(question, top_k = 3)\n",
+        "context"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "heKNVbWQ_LtC",
+      "metadata": {
+        "id": "heKNVbWQ_LtC"
+      },
+      "source": [
+        "As we can see, the retiever is working fine and gets us the context passage that contains the answer to our question. Now let's use the reader to extract the exact answer from the context passage."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "DQ4GWdbMSjPl",
+      "metadata": {
+        "id": "DQ4GWdbMSjPl",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3412bbd4-7ea7-4e83-8cf6-59d67abf37e9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[{'answer': '691,000 bbl/d',\n",
+            "  'context': 'Egypt was producing 691,000 bbl/d of oil and 2,141.05 Tcf of '\n",
+            "             'natural gas (in 2013), which makes Egypt as the largest oil '\n",
+            "             'producer not member of the Organization of the Petroleum '\n",
+            "             'Exporting Countries (OPEC) and the second-largest dry natural '\n",
+            "             'gas producer in Africa. In 2013, Egypt was the largest consumer '\n",
+            "             'of oil and natural gas in Africa, as more than 20% of total oil '\n",
+            "             'consumption and more than 40% of total dry natural gas '\n",
+            "             'consumption in Africa. Also, Egypt possesses the largest oil '\n",
+            "             'refinery capacity in Africa 726,000 bbl/d (in 2012). Egypt is '\n",
+            "             'currently planning to build its first nuclear power plant in El '\n",
+            "             'Dabaa city, northern Egypt.',\n",
+            "  'end': 33,\n",
+            "  'score': 0.9999852180480957,\n",
+            "  'start': 20},\n",
+            " {'answer': '600,000 tonnes',\n",
+            "  'context': 'Egypt has a developed energy market based on coal, oil, natural '\n",
+            "             'gas, and hydro power. Substantial coal deposits in the northeast '\n",
+            "             'Sinai are mined at the rate of about 600,000 tonnes (590,000 '\n",
+            "             'long tons; 660,000 short tons) per year. Oil and gas are '\n",
+            "             'produced in the western desert regions, the Gulf of Suez, and '\n",
+            "             'the Nile Delta. Egypt has huge reserves of gas, estimated at '\n",
+            "             '2,180 cubic kilometres (520 cu mi), and LNG up to 2012 exported '\n",
+            "             'to many countries. In 2013, the Egyptian General Petroleum Co '\n",
+            "             '(EGPC) said the country will cut exports of natural gas and tell '\n",
+            "             'major industries to slow output this summer to avoid an energy '\n",
+            "             'crisis and stave off political unrest, Reuters has reported. '\n",
+            "             'Egypt is counting on top liquid natural gas (LNG) exporter Qatar '\n",
+            "             'to obtain additional gas volumes in summer, while encouraging '\n",
+            "             'factories to plan their annual maintenance for those months of '\n",
+            "             'peak demand, said EGPC chairman, Tarek El Barkatawy. Egypt '\n",
+            "             'produces its own energy, but has been a net oil importer since '\n",
+            "             '2008 and is rapidly becoming a net importer of natural gas.',\n",
+            "  'end': 180,\n",
+            "  'score': 4.702655972965708e-11,\n",
+            "  'start': 166},\n",
+            " {'answer': 'six million barrels per day',\n",
+            "  'context': 'Iran has the second largest proved gas reserves in the world '\n",
+            "             'after Russia, with 33.6 trillion cubic metres, and third largest '\n",
+            "             'natural gas production in the world after Indonesia, and Russia. '\n",
+            "             'It also ranks fourth in oil reserves with an estimated '\n",
+            "             \"153,600,000,000 barrels. It is OPEC's 2nd largest oil exporter \"\n",
+            "             'and is an energy superpower. In 2005, Iran spent US$4 billion on '\n",
+            "             'fuel imports, because of contraband and inefficient domestic '\n",
+            "             'use. Oil industry output averaged 4 million barrels per day '\n",
+            "             '(640,000 m3/d) in 2005, compared with the peak of six million '\n",
+            "             'barrels per day reached in 1974. In the early years of the 2000s '\n",
+            "             '(decade), industry infrastructure was increasingly inefficient '\n",
+            "             'because of technological lags. Few exploratory wells were '\n",
+            "             'drilled in 2005.',\n",
+            "  'end': 572,\n",
+            "  'score': 8.683973679035617e-12,\n",
+            "  'start': 545}]\n"
+          ]
+        }
+      ],
+      "source": [
+        "extract_answer(question, context)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fMD_ABuDAyhN",
+      "metadata": {
+        "id": "fMD_ABuDAyhN"
+      },
+      "source": [
+        "The reader model predicted with 99% accuracy the correct answer *691,000 bbl/d* as seen from the context passage. Let's run few more queries."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "_4NRgV4mGWoj",
+      "metadata": {
+        "id": "_4NRgV4mGWoj"
+      },
+      "outputs": [],
+      "source": [
+        "question = \"What are the first names of the men that invented youtube?\"\n",
+        "context = get_context(question, top_k=1)\n",
+        "extract_answer(question, context)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "juXlctWgJgMF",
+      "metadata": {
+        "id": "juXlctWgJgMF"
+      },
+      "outputs": [],
+      "source": [
+        "question = \"What is Albert Eistein famous for?\"\n",
+        "context = get_context(question, top_k=1)\n",
+        "extract_answer(question, context)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "OhCgeny_BVno",
+      "metadata": {
+        "id": "OhCgeny_BVno"
+      },
+      "source": [
+        "Let's run another question. This time for top 3 context passages from the retriever."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "iXACn71xmett",
+      "metadata": {
+        "id": "iXACn71xmett"
+      },
+      "outputs": [],
+      "source": [
+        "question = \"Who was the first person to step foot on the moon?\"\n",
+        "context = get_context(question, top_k=3)\n",
+        "extract_answer(question, context)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "27fecadc-6976-43b2-90e1-788a8633ecc7",
+      "metadata": {
+        "id": "27fecadc-6976-43b2-90e1-788a8633ecc7"
+      },
+      "source": [
+        "<br>\n",
+        "\n",
+        "### 🚀 Challenge\n",
+        "\n",
+        "Try adding a few more questions of your own in new cells below (reusing `get_context` and `extract_answer`). Experiment with different `top_k` values and observe:\n",
+        "\n",
+        "- Does the retriever always return a context that actually contains the answer?\n",
+        "- How does the reader's confidence score change with harder or more ambiguous questions?\n",
+        "- What happens with questions that have no good answer in the dataset?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "898daa2f",
+      "metadata": {
+        "id": "898daa2f"
+      },
+      "outputs": [],
+      "source": [
+        "# ...\n",
+        "# ...\n",
+        "# ..."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d4ed6384",
+      "metadata": {
+        "id": "d4ed6384"
+      },
+      "source": [
+        "<br>\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### Clean Up\n",
+        "\n",
+        "Now that we're done experimenting, let's delete the Pinecone index we created. This frees up the resources it was using (and, when using a paid plan, avoids being billed for an index we no longer need)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c53493d1",
+      "metadata": {
+        "id": "c53493d1"
+      },
+      "outputs": [],
+      "source": [
+        "# To delete the Pinecone index,\n",
+        "# uncomment the line below + run this code cell\n",
+        "\n",
+        "# pc.delete_index(index_name)"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "machine_shape": "hm",
+      "provenance": [],
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+        "27fecadc-6976-43b2-90e1-788a8633ecc7",
+        "d4ed6384"
+      ],
+      "include_colab_link": true
+    },
+    "environment": {
+      "name": "tf2-gpu.2-3.m65",
+      "type": "gcloud",
+      "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-3:m65"
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+      "display_name": "Python 3",
+      "name": "python3"
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