operations_with_datasets.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/loganathanspr/nlp_course/blob/main/operations_with_datasets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b2IewOed9vdz"
      },
      "source": [
        "# Time to slice and dice"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6eJHcI59vd2"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "VoAP62059vd2",
        "outputId": "6a3ca6f0-5509-4661-f2be-7f67538b9417",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.14.5-py3-none-any.whl (519 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/519.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/519.6 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m512.0/519.6 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.6/519.6 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting evaluate\n",
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            "\u001b[?25hCollecting transformers[sentencepiece]\n",
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            "Collecting responses<0.19 (from evaluate)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.12.2)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers[sentencepiece])\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers[sentencepiece])\n",
            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hCollecting sentencepiece!=0.1.92,>=0.1.91 (from transformers[sentencepiece])\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.5.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.17.3 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.3 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.33.3 xxhash-3.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "-aqLTl6w9vd3",
        "outputId": "8a268f30-0350-4ad9-e5a1-66639ae70adb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-09-29 10:27:14--  https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified\n",
            "Saving to: ‘drugsCom_raw.zip’\n",
            "\n",
            "drugsCom_raw.zip        [      <=>           ]  41.00M  32.7MB/s    in 1.3s    \n",
            "\n",
            "2023-09-29 10:27:15 (32.7 MB/s) - ‘drugsCom_raw.zip’ saved [42989872]\n",
            "\n",
            "Archive:  drugsCom_raw.zip\n",
            "  inflating: drugsComTest_raw.tsv    \n",
            "  inflating: drugsComTrain_raw.tsv   \n"
          ]
        }
      ],
      "source": [
        "!wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\"\n",
        "!unzip drugsCom_raw.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "656QtVnC9vd3",
        "outputId": "bd473c40-0ae7-4c78-b511-402f78d85f5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "f194b9a4e442454396c1d08b7f4f16b1",
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            "33427e3c2b094fc2ac43f0adcadcb36d",
            "8a232d3d480f47eab9ccf643c1d44008",
            "32b10b9c88ca4e789a83607be9d22ec5",
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            "10b3019efad944d2a36347feef654be0",
            "6e65db21bd274dcea6677ec0efd3ea64",
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            "19eed4669b7542dc80d37fd90fd706f3",
            "950de2fb60c742d1a2ce0b183fbf074d"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f194b9a4e442454396c1d08b7f4f16b1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "631c64071a294d769aaf39837a5c0ae8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b4ca48158ce4f2f9814e047ddb221cf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b2206d03d9e4c63b9892fc046198190"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "data_files = {\"train\": \"drugsComTrain_raw.tsv\", \"test\": \"drugsComTest_raw.tsv\"}\n",
        "drug_dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "QgN8_AvM9vd4",
        "outputId": "1bdbf9c5-67ca-4d3b-9477-dbf3569089de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Unnamed: 0': [87571, 178045, 80482],\n",
              " 'drugName': ['Naproxen', 'Duloxetine', 'Mobic'],\n",
              " 'condition': ['Gout, Acute', 'ibromyalgia', 'Inflammatory Conditions'],\n",
              " 'review': ['\"like the previous person mention, I&#039;m a strong believer of aleve, it works faster for my gout than the prescription meds I take. No more going to the doctor for refills.....Aleve works!\"',\n",
              "  '\"I have taken Cymbalta for about a year and a half for fibromyalgia pain. It is great\\r\\nas a pain reducer and an anti-depressant, however, the side effects outweighed \\r\\nany benefit I got from it. I had trouble with restlessness, being tired constantly,\\r\\ndizziness, dry mouth, numbness and tingling in my feet, and horrible sweating. I am\\r\\nbeing weaned off of it now. Went from 60 mg to 30mg and now to 15 mg. I will be\\r\\noff completely in about a week. The fibro pain is coming back, but I would rather deal with it than the side effects.\"',\n",
              "  '\"I have been taking Mobic for over a year with no side effects other than an elevated blood pressure.  I had severe knee and ankle pain which completely went away after taking Mobic.  I attempted to stop the medication however pain returned after a few days.\"'],\n",
              " 'rating': [9.0, 3.0, 10.0],\n",
              " 'date': ['September 2, 2015', 'November 7, 2011', 'June 5, 2013'],\n",
              " 'usefulCount': [36, 13, 128]}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Randomly look at some samples\n",
        "drug_sample = drug_dataset[\"train\"].shuffle(seed=42).select(range(1000))\n",
        "drug_sample[:3]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(drug_sample)"
      ],
      "metadata": {
        "id": "the_Dn9VC3b_",
        "outputId": "b3327a98-6aa7-461d-8541-7990ec7bab97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datasets.arrow_dataset.Dataset"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(drug_dataset[\"train\"]), type(drug_dataset)"
      ],
      "metadata": {
        "id": "gC1yyvJcC-X1",
        "outputId": "85f4e502-6685-4b2d-f5a9-c8bbbf419067",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(datasets.arrow_dataset.Dataset, datasets.dataset_dict.DatasetDict)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for split in drug_dataset.keys():\n",
        "    assert len(drug_dataset[split]) == len(drug_dataset[split].unique(\"Unnamed: 0\"))"
      ],
      "metadata": {
        "id": "LYadxaj2DDdX"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset"
      ],
      "metadata": {
        "id": "vBx8UY9OP9ak",
        "outputId": "cdec8755-dca1-4f90-904e-0c4634b2e955",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Unnamed: 0', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
              "        num_rows: 161297\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Unnamed: 0', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
              "        num_rows: 53766\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "kC5hFykT9vd5",
        "outputId": "1279f0d1-e39c-4dad-f325-01b18d5b47b4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
              "        num_rows: 161297\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
              "        num_rows: 53766\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "drug_dataset = drug_dataset.rename_column(original_column_name=\"Unnamed: 0\", new_column_name=\"patient_id\")\n",
        "drug_dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test\n",
        "\n",
        "1.   Find number of unique drugs\n",
        "2.   Find number of unique conditions\n",
        "\n"
      ],
      "metadata": {
        "id": "y1LsHFB_QX1s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset"
      ],
      "metadata": {
        "id": "dJ3M1FlCRNKw",
        "outputId": "d438a04b-1b89-4713-add1-8ee779ab5524",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
              "        num_rows: 161297\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
              "        num_rows: 53766\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Size of training data: {drug_dataset['train'].shape}\")"
      ],
      "metadata": {
        "id": "cjTP9qV6Q7RP",
        "outputId": "e55f0439-db61-4288-84c1-3d7ddc0bf2f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of training data: (161297, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Unique drugs in train split: {len(drug_dataset['train'].unique('drugName'))}\")"
      ],
      "metadata": {
        "id": "sBcarInZQgVv",
        "outputId": "130e9c16-60a7-46d2-adc4-e5114b789827",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique drugs in train split: 3436\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Unique conditions in train split: {len(drug_dataset['train'].unique('condition'))}\")"
      ],
      "metadata": {
        "id": "ZdbmuVF6RQw-",
        "outputId": "4b18b103-5440-4cb5-eefd-1fc08fc071df",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique conditions in train split: 885\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using Map"
      ],
      "metadata": {
        "id": "U7QSeGMySrGa"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "dP7xblPv9vd5",
        "outputId": "8d9668d5-fec7-4f92-ef17-8b1af2f204b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371,
          "referenced_widgets": [
            "8e6204d9cbe24fc989e1b5cb41aa3374",
            "d4a09c43563b401bbe1d1c7cf69b5a90",
            "7a7f46de1c3d49cfb090b09103f7a71e",
            "881dbb5dbc664dd288db5b4db03318cd",
            "659b01062c964b21a92837f86a76bbd3",
            "75e6615efe134bc38ae3922626ceeda1",
            "c740466f5cd84cdf87b7ccdedbb3ff81",
            "beaa66e5126f4b57bb8cc94591f3502c",
            "99576fe833284a80b4948c28b6392a42",
            "ac168e48986b45cc99b665dfa2351e59",
            "86c5ac4277364ac9b2adaa3e2160be3a"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/161297 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e6204d9cbe24fc989e1b5cb41aa3374"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-b89e20de16e5>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"condition\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mexample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"condition\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdrug_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlowercase_condition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\u001b[0m\n\u001b[1;32m    851\u001b[0m             \u001b[0mcache_file_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m         return DatasetDict(\n\u001b[0;32m--> 853\u001b[0;31m             {\n\u001b[0m\u001b[1;32m    854\u001b[0m                 k: dataset.map(\n\u001b[1;32m    855\u001b[0m                     \u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    852\u001b[0m         return DatasetDict(\n\u001b[1;32m    853\u001b[0m             {\n\u001b[0;32m--> 854\u001b[0;31m                 k: dataset.map(\n\u001b[0m\u001b[1;32m    855\u001b[0m                     \u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    856\u001b[0m                     \u001b[0mwith_indices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwith_indices\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    590\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"Dataset\"\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"self\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 592\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    593\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    555\u001b[0m         }\n\u001b[1;32m    556\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m         \u001b[0;31m# re-apply format to the output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m   3095\u001b[0m                     \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdesc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"Map\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3096\u001b[0m                 ) as pbar:\n\u001b[0;32m-> 3097\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_single\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdataset_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3098\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3099\u001b[0m                             \u001b[0mshards_done\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36m_map_single\u001b[0;34m(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset)\u001b[0m\n\u001b[1;32m   3448\u001b[0m                     \u001b[0m_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3449\u001b[0m                     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexample\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mshard_iterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3450\u001b[0;31m                         \u001b[0mexample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mapply_function_on_filtered_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3451\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mupdate_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3452\u001b[0m                             \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mapply_function_on_filtered_inputs\u001b[0;34m(pa_inputs, indices, check_same_num_examples, offset)\u001b[0m\n\u001b[1;32m   3351\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mwith_rank\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3352\u001b[0m                 \u001b[0madditional_args\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3353\u001b[0;31m             \u001b[0mprocessed_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfn_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0madditional_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfn_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3354\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocessed_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLazyDict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3355\u001b[0m                 processed_inputs = {\n",
            "\u001b[0;32m<ipython-input-17-b89e20de16e5>\u001b[0m in \u001b[0;36mlowercase_condition\u001b[0;34m(example)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mlowercase_condition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"condition\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mexample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"condition\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdrug_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlowercase_condition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'lower'"
          ]
        }
      ],
      "source": [
        "def lowercase_condition(example):\n",
        "    return {\"condition\": example[\"condition\"].lower()}\n",
        "\n",
        "drug_dataset.map(lowercase_condition)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def lowercase_condition(example):\n",
        "    return {\"condition\": example[\"condition\"].lower()}"
      ],
      "metadata": {
        "id": "r-5HloAhcHAV"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def filter_empty_conditions(example):\n",
        "    return example[\"condition\"] is not None\n",
        "\n",
        "drug_dataset.filter(filter_empty_conditions)"
      ],
      "metadata": {
        "id": "IwmgTiAJXWKY",
        "outputId": "ef251989-b753-49f1-c15a-63cc762cfe87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255,
          "referenced_widgets": [
            "0782423464c34560ac40d0520f3a789e",
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            "63b8ac51912e47ce81b7a3fe78eaeaa7",
            "4407cc9567b3416a8f0bb8ec6ca5446b",
            "35c19ea1ddb141b3922a93d7987fa7ec",
            "dc0574bcfe7b40878ac12011da0062b3",
            "f4cf67e1932942f79725234006d76af5",
            "af4c8e10dc2d46d1943f667f250fb0be",
            "3def881b708147ada07105cd95d7433f",
            "c7d4bc209dc947e19a7f24557b562845",
            "b797b12d79654afc84cbf478c46eb216",
            "c9720ebe9cd54eb99e60ac0027ec4dce",
            "6926e64d742b4c5ab00fe29205d11826",
            "56194349f0ac46d781c9f0114b3a8c86",
            "4b6212dd720347c6bb960a59b670d399",
            "a9c8c8175d9343f0bbc36e7629d43340",
            "e1f00d87bbae495494e0a255617270a0",
            "b032cc5369bc484289e5b2234260bc59",
            "6375cccee8df4fff938356289200f636",
            "da2acda54e96487bb0603862968955c5",
            "cff68c0d8810450e9a440bcf82837645",
            "e3e8253a87ae45dca22ca89dda5d00d0"
          ]
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/161297 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0782423464c34560ac40d0520f3a789e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/53766 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9720ebe9cd54eb99e60ac0027ec4dce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
              "        num_rows: 160398\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
              "        num_rows: 53471\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "DFrwYw1A9vd6",
        "outputId": "44360b68-2867-488f-df4c-cb1dd6f7b0aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "(lambda x: x * x)(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "42snTRuT9vd6",
        "outputId": "3f50c1d3-002c-44d4-cf8f-45a579faf9c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16.0"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "(lambda base, height: 0.5 * base * height)(4, 8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "lPc7MkXw9vd6",
        "outputId": "eee7ea1e-1e36-457e-f31f-d3bd77f166e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "625722d32c2e4e0b89164f5bc0f17670",
            "cdd7a073681349198d3c14dd5905783c",
            "ec3e2f15158b46ffafa14c8efd0f8af6",
            "de565863280f41f2be39fb76ddf6c93e",
            "07b0bed6bea84c859e91e70765313094",
            "1b8579f3b75f4754ba3a31383c995f02",
            "7848be6b8fa5418a985623344b85ee17",
            "068e448a11704735b71592f6ee30429e",
            "9f05426853274d6fa596a79555434aee",
            "66e38fca1c4f404fb8fadfcb3cafe092",
            "d6b54fd171c44d81a5d8e4c32595795e",
            "4a85cf6201a848e6a9d66ef075617d6a",
            "bda27359ccf34549a90e96e901f038b7",
            "55787cd92884416897eb043c0514519a",
            "69fd9e567d1b4f05bd247e1d9ace6a1d",
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            "bbda87ba47ce46ee88aa382aa38e0a90",
            "d41ef16b579b4fb6819e12c30bf2ea49",
            "1d354dec65c64b7f93f35e6e1c871dab",
            "ea7e20ef7a51448bbb0aa5b8cce01a5a",
            "8812497c1ad047e588b0c333393655d8",
            "cf2a325294654d829e834b8b7624b0e7"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/161297 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "625722d32c2e4e0b89164f5bc0f17670"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/53766 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a85cf6201a848e6a9d66ef075617d6a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "drug_dataset = drug_dataset.filter(lambda x: x[\"condition\"] is not None)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset"
      ],
      "metadata": {
        "id": "DpFNDTh-bRtY",
        "outputId": "f91b8042-cee5-4ce5-fcdd-afd22cd2152e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
              "        num_rows: 160398\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
              "        num_rows: 53471\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "Gp8iiF-l9vd6",
        "outputId": "1fe76685-f25e-4697-953e-08e4f9c5c7ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
            "5d071b2aae2d4c019766b89e56b6f5f7",
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            "5cea018db80a466bb69d324be87d7521",
            "0b333e2412bd4f0d8d9181fb8ad45502"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/160398 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d071b2aae2d4c019766b89e56b6f5f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/53471 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a04360e0689f499bbf0b2e156fb4aa1f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['left ventricular dysfunction', 'adhd', 'birth control']"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "drug_dataset = drug_dataset.map(lowercase_condition)\n",
        "drug_dataset[\"train\"][\"condition\"][:3]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_review_length(example):\n",
        "    return {\"review_length\": len(example[\"review\"].split())}"
      ],
      "metadata": {
        "id": "g49L5aXDdwuZ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "O5b5FQc19vd7",
        "outputId": "b7218f49-c849-4d27-95be-c376608da570",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
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            "41ab0e9754974f638eba78bc25393ea7",
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            "ed5915ab9c014d7bbe6ae163c33adead",
            "7a636286903c4f6080d58c15f7131301"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/160398 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90c5faf642014e3b994bd016157677f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/53471 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e9d1fa5bcdd74d2e97d88fc8da8248b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[17, 141, 134, 89, 124]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "drug_dataset = drug_dataset.map(compute_review_length)\n",
        "drug_dataset[\"train\"][\"review_length\"][:5]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset[\"train\"][0]"
      ],
      "metadata": {
        "id": "zH6Dmut3gLGO",
        "outputId": "46ab981b-b827-4d94-e6d3-3691f677d3cf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'patient_id': 206461,\n",
              " 'drugName': 'Valsartan',\n",
              " 'condition': 'left ventricular dysfunction',\n",
              " 'review': '\"It has no side effect, I take it in combination of Bystolic 5 Mg and Fish Oil\"',\n",
              " 'rating': 9.0,\n",
              " 'date': 'May 20, 2012',\n",
              " 'usefulCount': 27,\n",
              " 'review_length': 17}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "TRL-sorJ9vd7",
        "outputId": "28bddbff-6b33-42ed-f6fe-013527c8c743",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'patient_id': [111469, 13653, 53602],\n",
              " 'drugName': ['Ledipasvir / sofosbuvir',\n",
              "  'Amphetamine / dextroamphetamine',\n",
              "  'Alesse'],\n",
              " 'condition': ['hepatitis c', 'adhd', 'birth control'],\n",
              " 'review': ['\"Headache\"', '\"Great\"', '\"Awesome\"'],\n",
              " 'rating': [10.0, 10.0, 10.0],\n",
              " 'date': ['February 3, 2015', 'October 20, 2009', 'November 23, 2015'],\n",
              " 'usefulCount': [41, 3, 0],\n",
              " 'review_length': [1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "drug_dataset[\"train\"].sort(\"review_length\")[:3]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def review_length_less_than_30(example):\n",
        "    return example[\"review_length\"] > 30"
      ],
      "metadata": {
        "id": "XsE-uPs8g9yV"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset.filter(review_length_less_than_30)"
      ],
      "metadata": {
        "id": "jUJR8UQ-hg4V",
        "outputId": "19b47910-d679-41ec-ef12-146df1b5c47f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255,
          "referenced_widgets": [
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            "8d5fc6e90cec485aa1df37da303d5d22",
            "93ee2d805b0345f999fa70ad492142ee",
            "49f47ad3afd24f1dae3b97761fda838f",
            "0db8a7d0ac8648ec82eb9172495386eb",
            "821e92b044cd4dadbf07d68376112b4e",
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            "e25abe6f14cc4173a9e8aed629b95f1f",
            "aec6a80ea6394c16ab69d57ee457ed4f"
          ]
        }
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/160398 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a24318287f4a4c24888d6574cd2cea9a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/53471 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "830175b36b264b8ab01fe3d3316aff0d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 138514\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 46108\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset"
      ],
      "metadata": {
        "id": "bsOC2q7Ghmz9",
        "outputId": "0ba4004f-497d-47da-cdb1-f83958bfd7d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 160398\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 53471\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The returned dataset from map, filter functions need to be assigned to a variable"
      ],
      "metadata": {
        "id": "8WjEHEvkhvAm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset = drug_dataset.filter(review_length_less_than_30)\n",
        "drug_dataset"
      ],
      "metadata": {
        "id": "kYgfo8KXh8vp",
        "outputId": "d6e1a741-ed68-4ee2-ce6d-ca73d68f134c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 138514\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 46108\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "8yDF7suN9vd7",
        "outputId": "6cc199c2-b4cf-4888-acc1-4a98e5fe46b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
            "07b4ca3e9c714ebbbc51df2c6802f7fb",
            "eae8b870b9f74b2a81445d65f9d13871",
            "be679c77ec6f4c2a917ffa331a252f75",
            "59658c37752641b3b647607c1eb17b82",
            "c6b01c6f1c3e4137baf8e7b198504a07",
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            "fda73093ae96471194b322007cc68779",
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            "8d32c3ec9e16490db6ee31546e832219",
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/138514 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "07b4ca3e9c714ebbbc51df2c6802f7fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/46108 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d32c3ec9e16490db6ee31546e832219"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'train': 138514, 'test': 46108}\n"
          ]
        }
      ],
      "source": [
        "# Using lambdas\n",
        "drug_dataset = drug_dataset.filter(lambda x: x[\"review_length\"] > 30)\n",
        "print(drug_dataset.num_rows)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset"
      ],
      "metadata": {
        "id": "RuDJ3-_jihWq",
        "outputId": "30a3d509-fe37-40b8-e8ec-faf0e9b9e7d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 138514\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 46108\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "8KdKEBq29vd7",
        "outputId": "f3f6f3bb-fc4e-452c-dec6-799f6d90f681",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"I'm a transformer called BERT\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "import html\n",
        "\n",
        "text = \"I&#039;m a transformer called BERT\"\n",
        "html.unescape(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "6aMnMIS89vd7",
        "outputId": "06de01fb-dae3-4d53-ae09-070acad259be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "e3da464368db4d9d8911f0b013494ca3",
            "1fdb0c819c2a4685b1723c81be1bb710",
            "48b9a5800afa48acbed82bdbbd9e1d3a",
            "025dc5f4fa1248be857619bcd5aad230",
            "a495b52345e14d66a1e7236777a735e1",
            "2b516d4d084142199aa6649979381137",
            "063b6763f8fd4e928daaf3aeac1dc870",
            "8deb6c0da4d2418daa0742fbac1d0a58",
            "84252cbe575b4d2d9d5d87d252a20ff7",
            "5ec950ce9cfd46b59473f5bc2bcdff10",
            "42bc864e26fc49f3ad7ca07e83842a67",
            "3e28a0828c484804a30bc0f338598c3f",
            "2ee6c49481524e0ba8f6a4f3166ca311",
            "4fc91c068e154d4aafb8c5e9c418791e",
            "78751ac21f3d4f6685dd74518f3e2175",
            "8ceb70203d15424e8a884a4becc280ab",
            "2cd044d0368845dfb149c90e01c4c7cf",
            "deb1dec23c8144b4880167006f5c7f39",
            "b98f42f6c2584e7d9bcc0f46167003ff",
            "a8d372767d63471987a60e2ab4dfd186",
            "1204452eff1a4836b95725158fae3834",
            "4a15f919a5c743b0ba147a73657e5b2d"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/138514 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e3da464368db4d9d8911f0b013494ca3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/46108 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e28a0828c484804a30bc0f338598c3f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "drug_dataset = drug_dataset.map(lambda x: {\"review\": html.unescape(x[\"review\"])})"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Use batched=True to speed up the processing of datasets"
      ],
      "metadata": {
        "id": "2y2YlFaNlH7v"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "ybTmfKRf9vd7",
        "outputId": "2a4d2492-ff14-43fc-9a93-a44985cec9a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "ed3b8ecb9ed549a39fbdf8bb259ef000",
            "db959c20e6774def832a15a1c25db06f"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/138514 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2308ea4816648a0895ef1894935d5c5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/46108 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2cf004998de444b2b99800d288d6c09c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "new_drug_dataset = drug_dataset.map(lambda x: {\"review\": [html.unescape(o) for o in x[\"review\"]]}, batched=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fast tokenizer"
      ],
      "metadata": {
        "id": "zzZjh_1cmPm3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "3UH4gdqD9vd7",
        "outputId": "37c26c9b-0f6c-41fb-e1f7-414e50e5d846",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "def05fd97fcc4612a17a27d0f449e997",
            "f1a2d57e926a4b85b321862bbc4f61a3"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_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": "e28e082504ac4890bd5e3b50e5fde9e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8eb7ed9e9b8c4ad2b1591456ba8140e1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "67cc3c00c1fe452a8662cdeee140a5c5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d68dbe1485c54155a2223bc2b2c1dbc2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
        "\n",
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"review\"], truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "WUwIygfO9vd7",
        "outputId": "5f768ce7-38e5-4fe5-c24f-7bf83e4bd59b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257,
          "referenced_widgets": [
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            "ab836c423df5450790eb165936549cd1",
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            "0ed671b13460476f8c302527d64093d0",
            "3c609b9816ed425bb0b0e3f28c528cc2"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/138514 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04d668f4f5464a8b96650626b37c21d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1min 29s, sys: 834 ms, total: 1min 29s\n",
            "Wall time: 1min 18s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 138514\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 46108\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "# Fast tokenizer with batched=True\n",
        "%time drug_dataset.map(tokenize_function, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fast tokenizer with batched=False\n",
        "%time drug_dataset.map(tokenize_function, batched=False)"
      ],
      "metadata": {
        "id": "HNs553aNno_O",
        "outputId": "1b176ceb-fde1-48be-f77b-3b2787ea530d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289,
          "referenced_widgets": [
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            "f834825e3bbb40e09032df69d92ec738"
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        }
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/138514 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "33800fd2b48c464f99d5cbb49961c609"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/46108 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "02169ad3093e42ef970da42bdc2f1909"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2min 50s, sys: 1.87 s, total: 2min 52s\n",
            "Wall time: 3min 42s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 138514\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 46108\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Slow tokenizer"
      ],
      "metadata": {
        "id": "I8QVS8DUn3Eq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "slow_tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\", use_fast=False)\n",
        "\n",
        "def slow_tokenize_function(examples):\n",
        "    slow_tokenizer(examples[\"review\"], truncation=True)"
      ],
      "metadata": {
        "id": "NSXPdzaxn1tj"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Slow tokenizer with batched=True\n",
        "%time drug_dataset.map(slow_tokenize_function, batched=True)"
      ],
      "metadata": {
        "id": "7rXwamQ7qNZu",
        "outputId": "c6a0d5fc-b59a-41eb-b78c-c98fee85caf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289,
          "referenced_widgets": [
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          ]
        }
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/138514 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7e6c34f741644467b8b7f78989df3864"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/46108 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a83e849597d248e1a963c8dddba84516"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 6min 49s, sys: 1.32 s, total: 6min 51s\n",
            "Wall time: 7min 41s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 138514\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 46108\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Slow tokenizer with batched=False\n",
        "%time drug_dataset.map(slow_tokenize_function, batched=False)"
      ],
      "metadata": {
        "id": "cBC9EibaqeyK",
        "outputId": "098a80f3-11d1-4f14-9073-3167f845a689",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289,
          "referenced_widgets": [
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            "c58d2ac4011c40d68ccdadc377556882"
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        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/138514 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "788145770c9d4bb3b68fe8d1e4e3d718"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/46108 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6a466dd651a4e5daed8eeb75bb0fc60"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 7min 29s, sys: 3.06 s, total: 7min 33s\n",
            "Wall time: 7min 47s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 138514\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 46108\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fast tokenizer with number of processes"
      ],
      "metadata": {
        "id": "T5-HSt8MqsOg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%time drug_dataset.map(tokenize_function, batched=True, num_proc=8)"
      ],
      "metadata": {
        "id": "AiEdD2d4q0y6",
        "outputId": "af2ba00a-7262-419d-d51f-bd32af873be3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289,
          "referenced_widgets": [
            "395786ba67854e0593b29d65f0734743",
            "076b9cd6655f44ad81f5e89927b4d529",
            "73058e509da74c5095dab839e34c3037",
            "f4127e9a8c3d4a4eac082213701d0043",
            "1e5a47a884df4833a8401dd52d316ae3",
            "d676eeafc32c4ca9bc3e212fffaa8b77",
            "e39a3ed935974f55818ece1049ae9dc9",
            "bff82981b1744ac2a435d08e147e680f",
            "cbdd82c3af75492ea87d9e2fac12c9ee",
            "cc4b897f1fdc4d399430cf73ea183a6b",
            "04d7e3475a6f437fba21da65a27f7a7c",
            "1809b08453d746b29e27f4bb394e6b5a",
            "cd86ea6bf9bd45adadbfa3cf97d339e9",
            "9427556776734593b7e57d9f822e9618",
            "932b766bc9614b76a97fd4807e73a9cb",
            "73e0049e87e848e0a2ea49488934f42c",
            "f9f701e8fe5943e48277ca3f887681bc",
            "c8ee5936eaef49eeb3c31d8044aad11a",
            "d479c72b5669483096f02860aca095ae",
            "95aa1bd71574407fb26ae1a382d7c847",
            "35bf1de5e7034a389491b9bcbd9c1e6c",
            "83b0e548bfa84d4cb53a007fed207ab1"
          ]
        }
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=8):   0%|          | 0/138514 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "395786ba67854e0593b29d65f0734743"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=8):   0%|          | 0/46108 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1809b08453d746b29e27f4bb394e6b5a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2.1 s, sys: 468 ms, total: 2.57 s\n",
            "Wall time: 1min 31s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 138514\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 46108\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split one example into multiple examples"
      ],
      "metadata": {
        "id": "0u_HAe_Ky-sH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "bcLNZIQv9vd7"
      },
      "outputs": [],
      "source": [
        "def tokenize_and_split(examples):\n",
        "    return tokenizer(examples[\"review\"],\n",
        "                     truncation=True,\n",
        "                     max_length=128,\n",
        "                     return_overflowing_tokens=True\n",
        "                     )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "N55pqY599vd7",
        "outputId": "c6b0ce31-c082-4a9e-8c7e-bbe7d05a84c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[128, 49]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "result = tokenize_and_split(drug_dataset[\"train\"][0])\n",
        "[len(inp) for inp in result[\"input_ids\"]]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1 example passed to the function is split into 2\n",
        "len(result[\"input_ids\"])"
      ],
      "metadata": {
        "id": "ro2hlZ4iytV5",
        "outputId": "cb5185f6-0fd3-4a04-9f01-6ca321ca9b92",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset"
      ],
      "metadata": {
        "id": "iU0FUi6SzUo5",
        "outputId": "bd3d22aa-7e8a-444e-d65a-f7db7ec2543d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 138514\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 46108\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "IqEUomeV9vd8",
        "outputId": "a994562a-d8ea-4470-f822-8aedbb993911",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284,
          "referenced_widgets": [
            "bbfec47899ce400ca185b1d5118514e3",
            "2cea3d9281c04698a86456564455d158",
            "6a1a18b0c3d540a78fb4aadbb0c462ef",
            "3fce9e47214648df89a390238d26bfe5",
            "37029dc0426e451aa9187a3465d62ae8",
            "26f63428ba414db19dd27b3f44953efa",
            "c1025be929a34393bda3d4d524be42af",
            "901125bf894c4ac49420584c2294603f",
            "7acee8fc37404ce6915a38245c00569f",
            "43992f7bacfc4f97bfb745a08b1dbe37",
            "3d3fec308c9345b586b29e5ac4a53c2c"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/138514 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bbfec47899ce400ca185b1d5118514e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "ArrowInvalid",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mArrowInvalid\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-56-cf598b00f914>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# apply split example to the entire dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtokenized_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdrug_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenize_and_split\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatched\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\u001b[0m\n\u001b[1;32m    851\u001b[0m             \u001b[0mcache_file_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m         return DatasetDict(\n\u001b[0;32m--> 853\u001b[0;31m             {\n\u001b[0m\u001b[1;32m    854\u001b[0m                 k: dataset.map(\n\u001b[1;32m    855\u001b[0m                     \u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    852\u001b[0m         return DatasetDict(\n\u001b[1;32m    853\u001b[0m             {\n\u001b[0;32m--> 854\u001b[0;31m                 k: dataset.map(\n\u001b[0m\u001b[1;32m    855\u001b[0m                     \u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    856\u001b[0m                     \u001b[0mwith_indices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwith_indices\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    590\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"Dataset\"\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"self\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 592\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    593\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    555\u001b[0m         }\n\u001b[1;32m    556\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m         \u001b[0;31m# re-apply format to the output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m   3095\u001b[0m                     \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdesc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"Map\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3096\u001b[0m                 ) as pbar:\n\u001b[0;32m-> 3097\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_single\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdataset_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3098\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3099\u001b[0m                             \u001b[0mshards_done\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36m_map_single\u001b[0;34m(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset)\u001b[0m\n\u001b[1;32m   3491\u001b[0m                                 \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pandas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3492\u001b[0m                             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3493\u001b[0;31m                                 \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3494\u001b[0m                         \u001b[0mnum_examples_progress_update\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mnum_examples_in_batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3495\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_time\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPBAR_REFRESH_TIME_INTERVAL\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py\u001b[0m in \u001b[0;36mwrite_batch\u001b[0;34m(self, batch_examples, writer_batch_size)\u001b[0m\n\u001b[1;32m    556\u001b[0m                 \u001b[0minferred_features\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtyped_sequence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_inferred_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m         \u001b[0mschema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minferred_features\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrow_schema\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpa_writer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 558\u001b[0;31m         \u001b[0mpa_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschema\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    559\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpa_table\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwriter_batch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pyarrow/table.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.Table.from_arrays\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pyarrow/table.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.Table.validate\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pyarrow/error.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.check_status\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mArrowInvalid\u001b[0m: Column 8 named input_ids expected length 1000 but got length 1463"
          ]
        }
      ],
      "source": [
        "# apply split example to the entire dataset\n",
        "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "s5PFBJo69vd8",
        "outputId": "8c89f122-0856-42b6-9e20-8f4be5f9abf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "ce634845b9e24899989a01fedd06b886",
            "b6f1e64bbb154b22a468b115930f8cbe"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/138514 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6efb982fbd7941eab18cd27b44a0191b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/46108 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c23aa47a024424ea984aa6f3de146e3"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# To avoid the mismatch of number of samples in the dataset due to split fucntionality\n",
        "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True, remove_columns=drug_dataset[\"train\"].column_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bYsCuLpI9vd8",
        "outputId": "de5fb7a0-f422-41b2-cc77-faad0d52adb2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(206772, 138514)"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(tokenized_dataset[\"train\"]), len(drug_dataset[\"train\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "YM6adG1K9vd8"
      },
      "outputs": [],
      "source": [
        "def tokenize_and_split(examples):\n",
        "    result = tokenizer(\n",
        "        examples[\"review\"],\n",
        "        truncation=True,\n",
        "        max_length=128,\n",
        "        return_overflowing_tokens=True,\n",
        "    )\n",
        "    # Extract mapping between new and old indices\n",
        "    sample_map = result.pop(\"overflow_to_sample_mapping\")\n",
        "    for key, values in examples.items():\n",
        "        result[key] = [values[i] for i in sample_map]\n",
        "    return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "ZjLH3ubI9vd8",
        "outputId": "5c5c8573-5511-4867-d498-525a0e3dffb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255,
          "referenced_widgets": [
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            "bd4c68a5699a447799135e5e7f1918e9"
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        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/138514 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec67db1b1a71431093709fc53e2401b8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/46108 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "829d19d28f6c443bbdd36dadfbc3390a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 206772\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 68876\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)\n",
        "tokenized_dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Converting to other formats\n"
      ],
      "metadata": {
        "id": "63WtkVxzHaxx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset.set_format(\"pandas\")"
      ],
      "metadata": {
        "id": "EZF20SniHZ-8"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "eIx_V03q9vd8",
        "outputId": "177de5e1-e376-4444-d23e-f9fc20fb05c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   patient_id    drugName      condition  \\\n",
              "0       95260  Guanfacine           adhd   \n",
              "1       92703      Lybrel  birth control   \n",
              "2      138000  Ortho Evra  birth control   \n",
              "\n",
              "                                              review  rating  \\\n",
              "0  \"My son is halfway through his fourth week of ...     8.0   \n",
              "1  \"I used to take another oral contraceptive, wh...     5.0   \n",
              "2  \"This is my first time using any form of birth...     8.0   \n",
              "\n",
              "                date  usefulCount  review_length  \n",
              "0     April 27, 2010          192            141  \n",
              "1  December 14, 2009           17            134  \n",
              "2   November 3, 2015           10             89  "
            ],
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>patient_id</th>\n",
              "      <th>drugName</th>\n",
              "      <th>condition</th>\n",
              "      <th>review</th>\n",
              "      <th>rating</th>\n",
              "      <th>date</th>\n",
              "      <th>usefulCount</th>\n",
              "      <th>review_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>95260</td>\n",
              "      <td>Guanfacine</td>\n",
              "      <td>adhd</td>\n",
              "      <td>\"My son is halfway through his fourth week of ...</td>\n",
              "      <td>8.0</td>\n",
              "      <td>April 27, 2010</td>\n",
              "      <td>192</td>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>92703</td>\n",
              "      <td>Lybrel</td>\n",
              "      <td>birth control</td>\n",
              "      <td>\"I used to take another oral contraceptive, wh...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>December 14, 2009</td>\n",
              "      <td>17</td>\n",
              "      <td>134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>138000</td>\n",
              "      <td>Ortho Evra</td>\n",
              "      <td>birth control</td>\n",
              "      <td>\"This is my first time using any form of birth...</td>\n",
              "      <td>8.0</td>\n",
              "      <td>November 3, 2015</td>\n",
              "      <td>10</td>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-302d82fa-192b-44bb-9c7d-421b5fa43d98')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-302d82fa-192b-44bb-9c7d-421b5fa43d98 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-302d82fa-192b-44bb-9c7d-421b5fa43d98');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [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",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-112cc03a-1700-483f-9820-daaed43f70f8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-112cc03a-1700-483f-9820-daaed43f70f8')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-112cc03a-1700-483f-9820-daaed43f70f8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "drug_dataset[\"train\"][:3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "4KFls5L99vd8"
      },
      "outputs": [],
      "source": [
        "train_df = drug_dataset[\"train\"][:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "zYsDRuxN9vd8",
        "outputId": "176d5be0-6231-4157-9ba2-a7c7a2a3a0d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       condition  frequency\n",
              "0  birth control      27655\n",
              "1     depression       8023\n",
              "2           acne       5209\n",
              "3        anxiety       4991\n",
              "4           pain       4744"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ea5bf75-6d20-4efa-9314-da436508b1d7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>condition</th>\n",
              "      <th>frequency</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>birth control</td>\n",
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              "      <th>1</th>\n",
              "      <td>depression</td>\n",
              "      <td>8023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>acne</td>\n",
              "      <td>5209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>anxiety</td>\n",
              "      <td>4991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>pain</td>\n",
              "      <td>4744</td>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4ea5bf75-6d20-4efa-9314-da436508b1d7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4ea5bf75-6d20-4efa-9314-da436508b1d7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [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",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-64eea4fb-f0f1-4145-aaac-aae3bed679ae button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "frequencies = (train_df[\"condition\"]\n",
        "               .value_counts()\n",
        "               .to_frame()\n",
        "               .reset_index()\n",
        "               .rename(columns={\"index\": \"condition\", \"condition\": \"frequency\"})\n",
        "               )\n",
        "frequencies.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "VPw_qR7d9vd8",
        "outputId": "147acf3c-5349-4794-9581-628d3c7931ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['condition', 'frequency'],\n",
              "    num_rows: 819\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "from datasets import Dataset\n",
        "freq_dataset = Dataset.from_pandas(frequencies)\n",
        "freq_dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = drug_dataset[\"train\"][:]\n",
        "train_df[[\"drugName\", \"rating\"]]"
      ],
      "metadata": {
        "id": "FaXrJua1N5YM",
        "outputId": "8e301720-88c9-49a6-c07b-29cd9a7bcd14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        drugName  rating\n",
              "0                     Guanfacine     8.0\n",
              "1                         Lybrel     5.0\n",
              "2                     Ortho Evra     8.0\n",
              "3       Buprenorphine / naloxone     9.0\n",
              "4                         Cialis     2.0\n",
              "...                          ...     ...\n",
              "138509            Junel 1.5 / 30     6.0\n",
              "138510                   Campral    10.0\n",
              "138511            Metoclopramide     1.0\n",
              "138512        Thyroid desiccated    10.0\n",
              "138513              Lubiprostone     9.0\n",
              "\n",
              "[138514 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dd0be32d-051a-49b8-9c06-ec28093e8950\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>drugName</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Guanfacine</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Lybrel</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ortho Evra</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Buprenorphine / naloxone</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cialis</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138509</th>\n",
              "      <td>Junel 1.5 / 30</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138510</th>\n",
              "      <td>Campral</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138511</th>\n",
              "      <td>Metoclopramide</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138512</th>\n",
              "      <td>Thyroid desiccated</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138513</th>\n",
              "      <td>Lubiprostone</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>138514 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd0be32d-051a-49b8-9c06-ec28093e8950')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dd0be32d-051a-49b8-9c06-ec28093e8950 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dd0be32d-051a-49b8-9c06-ec28093e8950');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [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",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9dea8f5a-391e-4ef4-8d6a-21e57f7d9ed6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9dea8f5a-391e-4ef4-8d6a-21e57f7d9ed6')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9dea8f5a-391e-4ef4-8d6a-21e57f7d9ed6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_df = train_df[[\"drugName\", \"rating\"]].groupby(\"drugName\").mean()\n",
        "mean_dataset = Dataset.from_pandas(mean_df)\n",
        "mean_dataset"
      ],
      "metadata": {
        "id": "7oMVCYbrRCdF",
        "outputId": "f361ee77-6482-412f-ee87-d70a0a7f71fa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['rating', 'drugName'],\n",
              "    num_rows: 3052\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "eV1sA9Pn9vd8"
      },
      "outputs": [],
      "source": [
        "drug_dataset.reset_format()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset"
      ],
      "metadata": {
        "id": "GvKZaHbeWLiw",
        "outputId": "55da27c4-53c7-417a-de77-4bf1656fddfd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 138514\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 46108\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "XG-HMBjx9vd8",
        "outputId": "127de470-7866-491e-bd52-3630f1716a78",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 110811\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 27703\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 46108\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ],
      "source": [
        "drug_dataset_clean = drug_dataset[\"train\"].train_test_split(train_size=0.8, seed=42)\n",
        "# Rename the defautlt \"test\" split to \"validation\"\n",
        "drug_dataset_clean[\"validation\"] = drug_dataset_clean.pop(\"test\")\n",
        "# add back the original test set\n",
        "drug_dataset_clean[\"test\"] = drug_dataset[\"test\"]\n",
        "drug_dataset_clean"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving Datasets"
      ],
      "metadata": {
        "id": "WahoVMMqXycO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "KqC2OPLs9vd8",
        "outputId": "7d0f0038-f379-4f23-80fe-d8c2caeb0a94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "45662634bb3b483289120d1138fdef63",
            "76f4782686dc4acaa580122dde77f7b0",
            "f0bd9c21ed96479ebaea4daa06d26e91",
            "9af2c34538dc41f19609e95fc3ac1ec5",
            "09b9fd8551c5488083e8fec17ac27149",
            "00af860f2c01433491aded8cdf1814f9",
            "5dcff7b75a8d4f7d8ea5a584e83e2098"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/110811 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c74054049814bc9b72ab3df7a7a2f09"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/27703 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef2b5c6fe2984854af3962a63c92c05a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/46108 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7db84631fcae4169a7118f20ba34627c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# to disk\n",
        "drug_dataset_clean.save_to_disk(\"drug-reviews-local\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls drug-reviews-local/"
      ],
      "metadata": {
        "id": "jdzufzpKYBsY",
        "outputId": "4eb6cc45-7c25-418f-b378-b3ee7003aad7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dataset_dict.json  test  train\tvalidation\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cat drug-reviews-local/dataset_dict.json"
      ],
      "metadata": {
        "id": "nUDB1mihYNgc",
        "outputId": "763713f6-4405-4446-a91e-ca4dfce1708c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\"splits\": [\"train\", \"validation\", \"test\"]}"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls drug-reviews-local/train/"
      ],
      "metadata": {
        "id": "FV1mG3sjYS39",
        "outputId": "27bc5ce0-2367-453b-9284-d8df93dd1d1c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data-00000-of-00001.arrow  dataset_info.json  state.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls drug-reviews-local/test/"
      ],
      "metadata": {
        "id": "QmcAnJroYprl",
        "outputId": "3ed2b73c-4b3f-4d4c-cab3-b18a035ad0fe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data-00000-of-00001.arrow  dataset_info.json  state.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cat drug-reviews-local/test/dataset_info.json"
      ],
      "metadata": {
        "id": "QUCHLZf3Yw-S",
        "outputId": "e4974592-e261-4a2c-8571-d66ac3f4e018",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"builder_name\": \"csv\",\n",
            "  \"citation\": \"\",\n",
            "  \"config_name\": \"default\",\n",
            "  \"dataset_name\": \"csv\",\n",
            "  \"dataset_size\": 116144739,\n",
            "  \"description\": \"\",\n",
            "  \"download_checksums\": {\n",
            "    \"/content/drugsComTrain_raw.tsv\": {\n",
            "      \"num_bytes\": 84289175,\n",
            "      \"checksum\": null\n",
            "    },\n",
            "    \"/content/drugsComTest_raw.tsv\": {\n",
            "      \"num_bytes\": 28071166,\n",
            "      \"checksum\": null\n",
            "    }\n",
            "  },\n",
            "  \"download_size\": 112360341,\n",
            "  \"features\": {\n",
            "    \"patient_id\": {\n",
            "      \"dtype\": \"int64\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"drugName\": {\n",
            "      \"dtype\": \"string\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"condition\": {\n",
            "      \"dtype\": \"string\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"review\": {\n",
            "      \"dtype\": \"string\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"rating\": {\n",
            "      \"dtype\": \"float64\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"date\": {\n",
            "      \"dtype\": \"string\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"usefulCount\": {\n",
            "      \"dtype\": \"int64\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"review_length\": {\n",
            "      \"dtype\": \"int64\",\n",
            "      \"_type\": \"Value\"\n",
            "    }\n",
            "  },\n",
            "  \"homepage\": \"\",\n",
            "  \"license\": \"\",\n",
            "  \"size_in_bytes\": 228505080,\n",
            "  \"splits\": {\n",
            "    \"train\": {\n",
            "      \"name\": \"train\",\n",
            "      \"num_bytes\": 87127744,\n",
            "      \"num_examples\": 161297,\n",
            "      \"dataset_name\": \"csv\"\n",
            "    },\n",
            "    \"test\": {\n",
            "      \"name\": \"test\",\n",
            "      \"num_bytes\": 29016995,\n",
            "      \"num_examples\": 53766,\n",
            "      \"dataset_name\": \"csv\"\n",
            "    }\n",
            "  },\n",
            "  \"version\": {\n",
            "    \"version_str\": \"0.0.0\",\n",
            "    \"major\": 0,\n",
            "    \"minor\": 0,\n",
            "    \"patch\": 0\n",
            "  }\n",
            "}"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cat drug-reviews-local/test/dataset_info.json"
      ],
      "metadata": {
        "id": "hDpoKEQmY9tD",
        "outputId": "7d72ccd8-4bac-42db-e5ff-6459d52a31c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"builder_name\": \"csv\",\n",
            "  \"citation\": \"\",\n",
            "  \"config_name\": \"default\",\n",
            "  \"dataset_name\": \"csv\",\n",
            "  \"dataset_size\": 116144739,\n",
            "  \"description\": \"\",\n",
            "  \"download_checksums\": {\n",
            "    \"/content/drugsComTrain_raw.tsv\": {\n",
            "      \"num_bytes\": 84289175,\n",
            "      \"checksum\": null\n",
            "    },\n",
            "    \"/content/drugsComTest_raw.tsv\": {\n",
            "      \"num_bytes\": 28071166,\n",
            "      \"checksum\": null\n",
            "    }\n",
            "  },\n",
            "  \"download_size\": 112360341,\n",
            "  \"features\": {\n",
            "    \"patient_id\": {\n",
            "      \"dtype\": \"int64\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"drugName\": {\n",
            "      \"dtype\": \"string\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"condition\": {\n",
            "      \"dtype\": \"string\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"review\": {\n",
            "      \"dtype\": \"string\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"rating\": {\n",
            "      \"dtype\": \"float64\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"date\": {\n",
            "      \"dtype\": \"string\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"usefulCount\": {\n",
            "      \"dtype\": \"int64\",\n",
            "      \"_type\": \"Value\"\n",
            "    },\n",
            "    \"review_length\": {\n",
            "      \"dtype\": \"int64\",\n",
            "      \"_type\": \"Value\"\n",
            "    }\n",
            "  },\n",
            "  \"homepage\": \"\",\n",
            "  \"license\": \"\",\n",
            "  \"size_in_bytes\": 228505080,\n",
            "  \"splits\": {\n",
            "    \"train\": {\n",
            "      \"name\": \"train\",\n",
            "      \"num_bytes\": 87127744,\n",
            "      \"num_examples\": 161297,\n",
            "      \"dataset_name\": \"csv\"\n",
            "    },\n",
            "    \"test\": {\n",
            "      \"name\": \"test\",\n",
            "      \"num_bytes\": 29016995,\n",
            "      \"num_examples\": 53766,\n",
            "      \"dataset_name\": \"csv\"\n",
            "    }\n",
            "  },\n",
            "  \"version\": {\n",
            "    \"version_str\": \"0.0.0\",\n",
            "    \"major\": 0,\n",
            "    \"minor\": 0,\n",
            "    \"patch\": 0\n",
            "  }\n",
            "}"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "GE2-ArVe9vd9",
        "outputId": "e29d7900-c56b-4508-814c-aaad706a3a67",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 110811\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 27703\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 46108\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ],
      "source": [
        "# loading from disk\n",
        "from datasets import load_from_disk\n",
        "\n",
        "drug_dataset_reloaded = load_from_disk(\"drug-reviews-local\")\n",
        "drug_dataset_reloaded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "qsOZRoLt9vd9",
        "outputId": "6de839dd-26ea-4dfe-a326-2c43bcebd031",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "d5ba6fa0674349dfbec9f37ba404eb75",
            "7e24b93e06104cd5877b09b05e00dd20",
            "8d23bc9b9136493f8d658e6153dc3a2b",
            "3d99eccf657841f6bfda905bc5b28d72",
            "eee8a63cb03546798d2c6afce335bb15",
            "436f3eaa4e6a4f06b536004c612f5c70",
            "9fd4321faebf4ef9851d92eb08a7d8e5",
            "c88c5cb5818840bda192e22deee567fd",
            "f0cf6b8a9bbe40fd8ac21df0c8a8ac82",
            "848d34f94cdf40c583ae93ca5a1bb8b5",
            "40cf0fc452e44c9488510018eda11294",
            "0991da9f063d4734a5bfc01a1270c107",
            "5044bad8b9c24f6f824815130e5a5a9c",
            "a711e7c834144206bf8848e9050bc397",
            "b5c537211abe4d95991c4910314f361b",
            "52990dc311254934b9d29ccf1453e324",
            "117ff6b7907d4c9281ad972623b8c322",
            "8c7bbc08a49a4e0dbf33265fe809330b",
            "6cdb9fef711940ef81597f27adcce159",
            "358a5c2398ec4870b601df03f372f46b",
            "a70fea9a295c431097fa497b3ba935df",
            "9caa259908514b37a1a5abcc8c8287a7",
            "8f93f69d560b428f84a77470223e5647",
            "b1f6bf90df1e4877a6b8209bed5c40f3",
            "43ee23a528d346e4ae87c6a038493132",
            "f03ec09ed0d24e2b9ca58df81ad66a04",
            "0e41d32349d64c5ca6c5e5e7920bd350",
            "2db82b712d414dad9bd8fc1edbb0ec8d",
            "95b33195582b489db90a23e58715d19e",
            "de5aba27b9b9423abf61e8cadbf5aeda",
            "38f8008e087e4878939f0ab0e0fd30bc",
            "a538db639c924082a5cd3fda49ae85dc",
            "63278e9aec6146c8ad5b4237bd6db566"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating json from Arrow format:   0%|          | 0/111 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5ba6fa0674349dfbec9f37ba404eb75"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating json from Arrow format:   0%|          | 0/28 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0991da9f063d4734a5bfc01a1270c107"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating json from Arrow format:   0%|          | 0/47 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f93f69d560b428f84a77470223e5647"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "for split, dataset in drug_dataset_clean.items():\n",
        "    dataset.to_json(f\"drug-reviews-{split}.jsonl\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!head -n 1 drug-reviews-train.jsonl"
      ],
      "metadata": {
        "id": "8famu2j9bWGQ",
        "outputId": "4e1b847d-ba7c-4544-a3fe-ea64e2643cab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\"patient_id\":89879,\"drugName\":\"Cyclosporine\",\"condition\":\"keratoconjunctivitis sicca\",\"review\":\"\\\"I have used Restasis for about a year now and have seen almost no progress.  For most of my life I've had red and bothersome eyes. After trying various eye drops, my doctor recommended Restasis.  He said it typically takes 3 to 6 months for it to really kick in but it never did kick in.  When I put the drops in it burns my eyes for the first 30 - 40 minutes.  I've talked with my doctor about this and he said it is normal but should go away after some time, but it hasn't. Every year around spring time my eyes get terrible irritated  and this year has been the same (maybe even worse than other years) even though I've been using Restasis for a year now. The only difference I notice was for the first couple weeks, but now I'm ready to move on.\\\"\",\"rating\":2.0,\"date\":\"April 20, 2013\",\"usefulCount\":69,\"review_length\":147}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "id": "9BXKx-Efbgth",
        "outputId": "8720898f-9d13-4f3c-d2af-2c411401be41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "drug-reviews\t\t drug-reviews-train.jsonl\tdrugsComTest_raw.tsv\n",
            "drug-reviews-local\t drug-reviews-validation.jsonl\tdrugsComTrain_raw.tsv\n",
            "drug-reviews-test.jsonl  drugsCom_raw.zip\t\tsample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load jsonl files from the disk\n",
        "data_files = {\n",
        "    \"train\": \"drug-reviews-train.jsonl\",\n",
        "    \"validation\": \"drug-reviews-validation.jsonl\",\n",
        "    \"test\": \"drug-reviews-test.jsonl\"\n",
        "}\n",
        "drug_dataset_reloaded = load_dataset(\"json\", data_files=data_files)"
      ],
      "metadata": {
        "id": "Mz0iCpH4cV_f",
        "outputId": "453b55d6-b0fb-4531-92fe-58d23d91ff12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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            "14d7d5cec28444c5986e3c1653c5b384",
            "26214a16a29d488fb94948a4613c92f3",
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            "408ea1af16814252a56089fe76dedefc",
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