Time_to_slice_and_dice.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Vonewman/Hugginface-course/blob/main/Time_to_slice_and_dice.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Time to slice and dice"
      ],
      "metadata": {
        "id": "j_Zn9AldQuZZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ],
      "metadata": {
        "id": "ZgURI0pfQxdG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cAJPGRMpP2yp",
        "outputId": "c9840539-06f9-45b5-86fb-2ec7e7970593"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.8.0-py3-none-any.whl (452 kB)\n",
            "\u001b[K     |████████████████████████████████| 452 kB 5.2 MB/s \n",
            "\u001b[?25hCollecting evaluate\n",
            "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
            "\u001b[K     |████████████████████████████████| 81 kB 11.0 MB/s \n",
            "\u001b[?25hCollecting transformers[sentencepiece]\n",
            "  Downloading transformers-4.25.1-py3-none-any.whl (5.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.8 MB 73.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from datasets) (1.21.6)\n",
            "Collecting responses<0.19\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5)\n",
            "Requirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.3.6)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (4.64.1)\n",
            "Collecting xxhash\n",
            "  Downloading xxhash-3.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (213 kB)\n",
            "\u001b[K     |████████████████████████████████| 213 kB 50.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.3)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (6.0)\n",
            "Collecting multiprocess\n",
            "  Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n",
            "\u001b[K     |████████████████████████████████| 132 kB 60.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (2.23.0)\n",
            "Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2022.11.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from datasets) (21.3)\n",
            "Collecting huggingface-hub<1.0.0,>=0.2.0\n",
            "  Downloading huggingface_hub-0.11.1-py3-none-any.whl (182 kB)\n",
            "\u001b[K     |████████████████████████████████| 182 kB 62.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (22.1.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (6.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.8.2)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (4.0.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.3)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (2.1.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets) (4.4.0)\n",
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            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->datasets) (3.0.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (3.0.4)\n",
            "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
            "\u001b[K     |████████████████████████████████| 127 kB 62.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.6 MB 49.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (2022.6.2)\n",
            "Collecting sentencepiece!=0.1.92,>=0.1.91\n",
            "  Downloading sentencepiece-0.1.97-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 60.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf<=3.20.2 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (3.19.6)\n",
            "Installing collected packages: urllib3, xxhash, tokenizers, responses, multiprocess, huggingface-hub, transformers, sentencepiece, datasets, evaluate\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "Successfully installed datasets-2.8.0 evaluate-0.4.0 huggingface-hub-0.11.1 multiprocess-0.70.14 responses-0.18.0 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.25.1 urllib3-1.25.11 xxhash-3.2.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\"\n",
        "!unzip drugsCom_raw.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cX8OQdOXQ031",
        "outputId": "657c6a6e-14e7-4a69-bd8d-2beb843eb0dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-03 10:54:02--  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: 42989872 (41M) [application/x-httpd-php]\n",
            "Saving to: ‘drugsCom_raw.zip’\n",
            "\n",
            "drugsCom_raw.zip    100%[===================>]  41.00M  30.8MB/s    in 1.3s    \n",
            "\n",
            "2023-01-03 10:54:04 (30.8 MB/s) - ‘drugsCom_raw.zip’ saved [42989872/42989872]\n",
            "\n",
            "Archive:  drugsCom_raw.zip\n",
            "  inflating: drugsComTest_raw.tsv    \n",
            "  inflating: drugsComTrain_raw.tsv   \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "data_files = {\"train\": \"drugsComTrain_raw.tsv\", \"test\": \"drugsComTest_raw.tsv\"}\n",
        "# \\t is the tab character in Python\n",
        "drug_dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")"
      ],
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      "execution_count": null,
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration default-ec41c87d452ec392\n",
            "WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/csv/default-ec41c87d452ec392/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f0e0b28e37d4bb9ac5350b479cfaf92"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_sample = drug_dataset[\"train\"].shuffle(seed=42).select(range(1000))\n",
        "# Peek at the first few examples\n",
        "drug_sample[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OwyUbFn3S3m6",
        "outputId": "a1d78db9-8174-40d0-fc62-07a62d40bc34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/csv/default-ec41c87d452ec392/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317/cache-e16daf45e9269029.arrow\n"
          ]
        },
        {
          "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": 14
        }
      ]
    },
    {
      "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": "5hiA7usLUrE4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset = drug_dataset.rename_column(\n",
        "    original_column_name=\"Unnamed: 0\", new_column_name=\"patient_id\"\n",
        ")\n",
        "drug_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nsl1dKFlWAjq",
        "outputId": "e90ed5db-bfae-49d1-cfae-8b631f763f64"
      },
      "execution_count": null,
      "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": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def filter_nones(x):\n",
        "    return x[\"condition\"] is not None"
      ],
      "metadata": {
        "id": "GQ7rd5ioXmFd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(lambda x: x * x)(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BI0OJ3cPY-gQ",
        "outputId": "5c1e2bd0-e480-4d83-e043-e76207f8356b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(lambda base, height: 0.5 * base * height)(4, 8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t9ITuD78ZFMA",
        "outputId": "14166df4-6f3d-4fdb-f463-f3360afc2201"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16.0"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset = drug_dataset.filter(lambda x: x[\"condition\"] is not None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        "id": "Tmhfwf-SZN4B",
        "outputId": "2fb8566c-8288-46a4-f184-4db6ade5d69b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/162 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "69a5ddd1755948078068bb3967857aa8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/54 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a8b61cb452a406fa37bc1e4113e2dec"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def lowercase_condition(example):\n",
        "    return {\"condition\": example[\"condition\"].lower()}"
      ],
      "metadata": {
        "id": "bgioqBuwZQ65"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset = drug_dataset.map(lowercase_condition)\n",
        "# Vérification que la mise en minuscule a fonctionné\n",
        "drug_dataset[\"train\"][\"condition\"][:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
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            "4df1ee283d49475a997a64bec0211cb4",
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            "3bf046158e04442286c867b82106e5a8",
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        },
        "id": "Z4wWxSMmZtaj",
        "outputId": "393fb83c-4cc2-48fc-8cc7-42a08bb33cdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/160398 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "621a71dbfdc44902a0d243b478139662"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/53471 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e110fd64d9ca401a86dd02e5ba7617b0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['left ventricular dysfunction', 'adhd', 'birth control']"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_review_length(example):\n",
        "    return {\"review_length\": len(example[\"review\"].split())}"
      ],
      "metadata": {
        "id": "gbwPw9ZXZvof"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset = drug_dataset.map(compute_review_length)\n",
        "# Inspecter le premier exemple d'entraînement\n",
        "drug_dataset[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220,
          "referenced_widgets": [
            "c3e7c893bfa94c63acb05287a81c13ce",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/160398 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c3e7c893bfa94c63acb05287a81c13ce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/53471 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2408aa27b5f40a0b39d62c057cdf68d"
            }
          },
          "metadata": {}
        },
        {
          "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": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset[\"train\"].sort(\"review_length\")[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7u5S91GQa5ip",
        "outputId": "4b1d6280-8c6c-4cd8-cb4a-61e8f6a39c18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'patient_id': [103488, 23627, 20558],\n",
              " 'drugName': ['Loestrin 21 1 / 20', 'Chlorzoxazone', 'Nucynta'],\n",
              " 'condition': ['birth control', 'muscle spasm', 'pain'],\n",
              " 'review': ['\"Excellent.\"', '\"useless\"', '\"ok\"'],\n",
              " 'rating': [10.0, 1.0, 6.0],\n",
              " 'date': ['November 4, 2008', 'March 24, 2017', 'August 20, 2016'],\n",
              " 'usefulCount': [5, 2, 10],\n",
              " 'review_length': [1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset = drug_dataset.filter(lambda x: x[\"review_length\"] > 30)\n",
        "print(drug_dataset.num_rows)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
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        "outputId": "217b3c4a-4791-4f9e-a570-14c386b29f49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/161 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60fbda9044364f7096bc315ed0f3f22d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/54 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "37522d773c5b4dd38d691651113dede3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'train': 138514, 'test': 46108}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import html\n",
        "\n",
        "text = \"I&#039;m a transformer called BERT\"\n",
        "html.unescape(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "DzrAxo3TcZH2",
        "outputId": "cb31dba6-8b17-49a2-a069-ca5c3cade771"
      },
      "execution_count": null,
      "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": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset = drug_dataset.map(lambda x: {\"review\": html.unescape(x[\"review\"])})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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      },
      "execution_count": null,
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              "  0%|          | 0/138514 [00:00<?, ?ex/s]"
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    {
      "cell_type": "code",
      "source": [
        "new_drug_dataset = drug_dataset.map(\n",
        "    lambda x: {\"review\": [html.unescape(o) for o in x[\"review\"]]}, batched=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
        "\n",
        "\n",
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"review\"], truncation=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "935epSpIdQ87",
        "outputId": "731414f9-0fe1-44ac-ed4b-1457beb99350"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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              "Downloading:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "%%time \n",
        "tokenized_dataset = drug_dataset.map(tokenize_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121,
          "referenced_widgets": [
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        "id": "SY-HzToYdd2W",
        "outputId": "41a6079c-aa89-4c25-f175-e2c20028aa44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/139 [00:00<?, ?ba/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-ec41c87d452ec392/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317/cache-4ffc23015eb959d9.arrow\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1min 7s, sys: 810 ms, total: 1min 7s\n",
            "Wall time: 49.1 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "slow_tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\", use_fast=False)\n",
        "\n",
        "\n",
        "def slow_tokenize_function(examples):\n",
        "    return slow_tokenizer(examples[\"review\"], truncation=True)\n",
        "\n",
        "\n",
        "tokenized_dataset = drug_dataset.map(slow_tokenize_function, batched=True, num_proc=8)"
      ],
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        "        max_length=128,\n",
        "        return_overflowing_tokens=True,\n",
        "    )"
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        "[len(inp) for inp in result[\"input_ids\"]]"
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        ")"
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        "    result = tokenizer(\n",
        "        examples[\"review\"],\n",
        "        truncation=True,\n",
        "        max_length=128,\n",
        "        return_overflowing_tokens=True,\n",
        "    )\n",
        "    # Extraire la correspondance entre les nouveaux et les anciens 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"
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        "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)\n",
        "tokenized_dataset"
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              "  0%|          | 0/139 [00:00<?, ?ba/s]"
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            "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",
              "})"
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          "metadata": {},
          "execution_count": 42
        }
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        "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)\n",
        "tokenized_dataset"
      ],
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        "colab": {
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        },
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      },
      "execution_count": null,
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          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-ec41c87d452ec392/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317/cache-0ff6b754bc22e6b1.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-ec41c87d452ec392/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317/cache-9820442ed9f82d8b.arrow\n"
          ]
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        {
          "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": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset.set_format(\"pandas\")"
      ],
      "metadata": {
        "id": "EcnfdmcVnpWd"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset[\"train\"][:3]"
      ],
      "metadata": {
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        "id": "8mtJYxlLzClr",
        "outputId": "bc2c437b-92e4-498c-dd06-d97e2eb0daef"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   patient_id    drugName  ... usefulCount review_length\n",
              "0       95260  Guanfacine  ...         192           141\n",
              "1       92703      Lybrel  ...          17           134\n",
              "2      138000  Ortho Evra  ...          10            89\n",
              "\n",
              "[3 rows x 8 columns]"
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              "      <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",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eaf648c8-07d7-4688-8929-bd7b322dfc63')\"\n",
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              "\n",
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              "          const element = document.querySelector('#df-eaf648c8-07d7-4688-8929-bd7b322dfc63');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = drug_dataset[\"train\"][:]"
      ],
      "metadata": {
        "id": "gvGzcmieQZ-P"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frequencies = (\n",
        "    train_df[\"condition\"]\n",
        "    .value_counts()\n",
        "    .to_frame()\n",
        "    .reset_index()\n",
        "    .rename(columns={\"index\": \"condition\", \"condition\": \"frequency\"})\n",
        ")\n",
        "\n",
        "frequencies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mOX3-Wy58Nl1",
        "outputId": "b77780b2-cf14-4e3c-f43f-ceb8aedcff0c"
      },
      "execution_count": 66,
      "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"
            ],
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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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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>depression</td>\n",
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              "    </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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              "  </tbody>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dc989be9-1246-491e-932a-465e54b9c989');\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n",
        "\n",
        "freq_dataset = Dataset.from_pandas(frequencies)\n",
        "freq_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vWakrV708RCb",
        "outputId": "e26f03ab-f21d-425e-a732-3055ba890476"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['condition', 'frequency'],\n",
              "    num_rows: 819\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset.reset_format()"
      ],
      "metadata": {
        "id": "aSeVxYgoQv-A"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset_clean = drug_dataset[\"train\"].train_test_split(train_size=0.8, seed=42)\n",
        "# Renommer la division par défaut \"test\" en \"validation\"\n",
        "drug_dataset_clean[\"validation\"] = drug_dataset_clean.pop(\"test\")\n",
        "# Ajoutez le jeu \"test\" à notre `DatasetDict`\n",
        "drug_dataset_clean[\"test\"] = drug_dataset[\"test\"]\n",
        "drug_dataset_clean"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y8vv99m-8YUK",
        "outputId": "4d2224da-9d2d-4a70-8fa3-7fa749fe5454"
      },
      "execution_count": 69,
      "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": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset_clean"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vjd583W5C2te",
        "outputId": "2dcf4e42-e9b1-4d94-dbac-8885609dd654"
      },
      "execution_count": 70,
      "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": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset_clean.save_to_disk(\"drug-reviews\")"
      ],
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        {
          "output_type": "display_data",
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            "text/plain": [
              "Flattening the indices:   0%|          | 0/111 [00:00<?, ?ba/s]"
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          "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": "c54262d60cee4f67a4cd1c0d2afdc475"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Flattening the indices:   0%|          | 0/28 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ea04326017f047b59fd5de5a36c4c586"
            }
          },
          "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": "cbc23ecb87564789bb08e4e8b64c1f9f"
            }
          },
          "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": "77a461e2dea14756b84f9e57415840ed"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_from_disk\n",
        "\n",
        "drug_dataset_reloaded = load_from_disk(\"drug-reviews\")\n",
        "drug_dataset_reloaded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "pbKWr6pPNzrz",
        "outputId": "726afcaf-e531-4d8a-d723-82f510633f51"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/datasets/dataset_dict.py:1241: FutureWarning: 'fs' was is deprecated in favor of 'storage_options' in version 2.8.0 and will be removed in 3.0.0.\n",
            "You can remove this warning by passing 'storage_options=fs.storage_options' instead.\n",
            "  warnings.warn(\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: 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": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for split, dataset in drug_dataset_clean.items():\n",
        "    dataset.to_json(f\"drug-reviews-{split}.jsonl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "469199e88d024077b56f829154910626",
            "ce94d5f64928410eb10e69d174d338dd"
          ]
        },
        "id": "Pxv6BO2vRLMN",
        "outputId": "b4ade766-5253-4689-f09a-bf3e1d9171e1"
      },
      "execution_count": 73,
      "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": "398cf77f307c4ddfbdb3395741577743"
            }
          },
          "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": "7206fe15f2b64c93b313171ae67a123d"
            }
          },
          "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": "c4de1fef90b34111afccffeb2b22b96b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!head -n 1 drug-reviews-train.jsonl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E-OFEfKnRT6q",
        "outputId": "4b13f0b7-7643-4b2e-f939-ebf7c7c1dd50"
      },
      "execution_count": 74,
      "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": [
        "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217,
          "referenced_widgets": [
            "683a0c44e4ce4179b8b8a88518786e41",
            "902cb052a34642b5b4143be61d42f5cf",
            "aa2005486b794289abddfda2cf49a9a6",
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          ]
        },
        "id": "xM4ofcyQRWzi",
        "outputId": "43710e16-1942-4c4a-8261-034147820422"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration default-cb3c19e9741290b9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-cb3c19e9741290b9/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "683a0c44e4ce4179b8b8a88518786e41"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "deb8e8af93384bacab4477254b853681"
            }
          },
          "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": "0a04ce798075436aaea72f8f8c9f965a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4f046119c4804af7986a5274e2f785fb"
            }
          },
          "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": "adb1c85121564017ab95096de72c9139"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-cb3c19e9741290b9/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rZZ3nuG3Rd3m"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qBQUHqnsVtbh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}