{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eiQ3FKJDhBW0"
      },
      "source": [
        "NLP EXERCISE: TEXT-CLASSIFICATION ON A DRUG DATASET.\n",
        "\n",
        "DATA WRANGLING, FINE-TUNING AND PUSHING THE MODEL TO THE HUGGING-FACE HUB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HedrkDuchBW2"
      },
      "outputs": [],
      "source": [
        "#let's install the necessary libraries from Hugging Face\n",
        "!pip install datasets evaluate transformers[sentencepiece] transformers[torch]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "#authenticate into the huggin face\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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          ]
        },
        "id": "Z4IolwVzaQQh",
        "outputId": "c5901a51-7f5f-4ad9-b90d-0cbb1b8c381a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d02e8f7bdeb145d4bae8ebcbceab2e72"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We download the data and unzip the datasets\n",
        "!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": "HyaHqdQRjhN-",
        "outputId": "5d0d6756-215a-4173-a71f-8037b9fc2faf"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-04-30 14:10:46--  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  37.7MB/s    in 1.1s    \n",
            "\n",
            "2024-04-30 14:10:47 (37.7 MB/s) - ‘drugsCom_raw.zip’ saved [42989872]\n",
            "\n",
            "Archive:  drugsCom_raw.zip\n",
            "  inflating: drugsComTest_raw.tsv    \n",
            "  inflating: drugsComTrain_raw.tsv   \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "#save the data into a csv file with both splits\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\")\n",
        "\n",
        "drug_dataset"
      ],
      "metadata": {
        "id": "yifIkdVzj1kS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258,
          "referenced_widgets": [
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          ]
        },
        "outputId": "8857fdda-a578-42bd-ce05-86a80b684fac"
      },
      "execution_count": 4,
      "outputs": [
        {
          "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": "4513ab68f3634e56946c81612a31da0e"
            }
          },
          "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": "76fa80d167b84f1cb3d87486f5bffaad"
            }
          },
          "metadata": {}
        },
        {
          "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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Randomize the order of the rows within each split\n",
        "def randomize_dataset(dataset):\n",
        "    for split in dataset.keys():\n",
        "        shuffled_dataset = dataset[split].shuffle()\n",
        "        dataset[split] = shuffled_dataset\n",
        "    return dataset\n",
        "\n",
        "drug_dataset = randomize_dataset(drug_dataset)"
      ],
      "metadata": {
        "id": "5JkAVnitqs1l"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#rename columns\n",
        "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": "EOOatck3rwmY",
        "outputId": "71de605f-e3ea-4080-b86a-adbf29e2728b"
      },
      "execution_count": 6,
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#remove rows where the condition feature has no data\n",
        "drug_dataset = drug_dataset.filter(lambda x: x[\"condition\"] is not None)"
      ],
      "metadata": {
        "id": "cZzYLJphrw6W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        "outputId": "03a2d67e-c2d4-4226-bf4d-2223526fc617"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/161297 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/53766 [00:00<?, ? examples/s]"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#turn it into lowercase text\n",
        "def lowercase_condition(example):\n",
        "    return {\"condition\": example[\"condition\"].lower()}\n",
        "\n",
        "drug_dataset = drug_dataset.map(lowercase_condition)"
      ],
      "metadata": {
        "id": "MVsier2fsGr8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
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        "outputId": "07875549-92c9-4991-ddbd-2c85dc0aa946"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/160398 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/53471 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to filter out the unwanted string 'users found', because we find that some of the conditions include that weird string sequence\n",
        "def filter_unwanted_string(example):\n",
        "    # Return True if the string is not found in the 'condition' field\n",
        "    return 'users found' not in example['condition']\n",
        "\n",
        "# Apply the filter function to each split\n",
        "drug_dataset = drug_dataset.filter(filter_unwanted_string)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/160398 [00:00<?, ? examples/s]"
            ],
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    },
    {
      "cell_type": "code",
      "source": [
        "#create a feature called 'review length' because we will later remove reviews that are too short\n",
        "def compute_review_length(example):\n",
        "    return {\"review_length\": len(example[\"review\"].split())}\n",
        "\n",
        "drug_dataset = drug_dataset.map(compute_review_length)"
      ],
      "metadata": {
        "id": "1YS2yrZgsGgK",
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      "execution_count": 10,
      "outputs": [
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          "data": {
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              "version_major": 2,
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              "model_id": "e691d67290294b8d9c059fb49a0bb7e5"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#keep only reviews with at least 16 letters\n",
        "drug_dataset = drug_dataset.filter(lambda x: x[\"review_length\"] > 15)\n",
        "print(drug_dataset.num_rows)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99,
          "referenced_widgets": [
            "e9b95795aef84bde907d36085fd3b4ea",
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            "076c64ecd4bf46d7b1eaad6ca2e4b8e5",
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            "ea336bf274e647a89fb0fdf75967c32a",
            "061b9ea1213b4419b4cbd2ac77953565",
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            "0910dafa860c474ca817e69cd833f430",
            "e755039111454de3a6d65f6e66fec03c"
          ]
        },
        "id": "qQk-ReJssefI",
        "outputId": "dacf883d-64b8-4ebc-93f0-e75aab356b5b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/159498 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e9b95795aef84bde907d36085fd3b4ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/53200 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "076c64ecd4bf46d7b1eaad6ca2e4b8e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'train': 150391, 'test': 50146}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DyNIhmXc6GQg",
        "outputId": "8f0af899-c70d-4013-fd07-86da86e5b7bf"
      },
      "execution_count": 12,
      "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: 150391\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 50146\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import html\n",
        "\n",
        "#remove html characters from the review feature\n",
        "new_drug_dataset = drug_dataset.map(\n",
        "    lambda x: {\"review\": [html.unescape(o) for o in x[\"review\"]]}, batched=True)"
      ],
      "metadata": {
        "id": "Hzu-0TCCs7tr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "0f6209647b9448f39db3631bddf16797",
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            "646a0f1583d14d7b9ace31477ffe4f49"
          ]
        },
        "outputId": "49f2d4a8-ccc1-49e3-8ae5-b1792a994ef9"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150391 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f6209647b9448f39db3631bddf16797"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/50146 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b9012d494b044ea2b3e60cc197376846"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create three splits (train, validation and test) with what we have\n",
        "drug_clean = new_drug_dataset[\"train\"].train_test_split(train_size=0.8, seed=42)\n",
        "\n",
        "# Rename the default \"test\" split to \"validation\"\n",
        "drug_clean[\"validation\"] = drug_clean.pop(\"test\")\n",
        "\n",
        "# Add the \"test\" set to our `DatasetDict`\n",
        "drug_clean[\"test\"] = new_drug_dataset[\"test\"]\n",
        "\n",
        "drug_clean"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YpST6_3yBCA",
        "outputId": "6e46ed6a-0aa5-451e-d977-9b0af125de98"
      },
      "execution_count": 14,
      "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: 120312\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 30079\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
              "        num_rows: 50146\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_clean['train'].features"
      ],
      "metadata": {
        "id": "92I1TbyMs7-_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebfbda9b-4ff8-43be-b851-f6d395015ac5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'patient_id': Value(dtype='int64', id=None),\n",
              " 'drugName': Value(dtype='string', id=None),\n",
              " 'condition': Value(dtype='string', id=None),\n",
              " 'review': Value(dtype='string', id=None),\n",
              " 'rating': Value(dtype='float64', id=None),\n",
              " 'date': Value(dtype='string', id=None),\n",
              " 'usefulCount': Value(dtype='int64', id=None),\n",
              " 'review_length': Value(dtype='int64', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "\n",
        "#let's tokenize the review feature, which is the one we'll use as predictive feature\n",
        "raw_datasets = drug_clean\n",
        "checkpoint = \"bert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "def tokenize_function(example):\n",
        "    return tokenizer(example[\"review\"], truncation=True)\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "tokenized_pandas = raw_datasets.map(tokenize_function, batched=True)\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "6z0WrmVrV5Fw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399,
          "referenced_widgets": [
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        },
        "outputId": "c5e9356d-79df-47d1-9ba6-e5385e32b7f1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/50146 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c79287ed57d44622b7086de2b28c3b64"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#let's remove the unwanted columns and change the name of the target feature\n",
        "\n",
        "tokenized_datasets= tokenized_datasets.remove_columns(['patient_id', 'rating', 'drugName', 'date', 'usefulCount', 'review_length', 'review'])\n",
        "tokenized_datasets= tokenized_datasets.rename_column(\n",
        "    original_column_name=\"condition\", new_column_name=\"labels\"\n",
        ")\n",
        "\n",
        "tokenized_pandas= tokenized_pandas.remove_columns(['patient_id', 'rating', 'drugName', 'date', 'usefulCount', 'review_length', 'review'])\n",
        "tokenized_pandas= tokenized_pandas.rename_column(\n",
        "    original_column_name=\"condition\", new_column_name=\"labels\"\n",
        ")"
      ],
      "metadata": {
        "id": "d6El1L0juj9H"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "tokenized_pandas.set_format('pandas')\n",
        "\n",
        "# Calculate label frequencies\n",
        "label_counts_train = tokenized_pandas['train']['labels'].value_counts()\n",
        "\n",
        "# Sort frequencies and select only the 50 most frequent labels\n",
        "sorted_labels = label_counts_train.index[:100]\n",
        "\n",
        "# Subset the dataset\n",
        "for split in tokenized_datasets.keys():\n",
        "    tokenized_datasets[split] = tokenized_datasets[split].filter(lambda example: example['labels'] in sorted_labels)"
      ],
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        {
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              "Filter:   0%|          | 0/120312 [00:00<?, ? examples/s]"
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    {
      "cell_type": "code",
      "source": [
        "#let's change the string labels to integers for the target feature (recode the labels feature)\n",
        "# Identify unique labels\n",
        "unique_labels = set()\n",
        "for split in tokenized_datasets.keys():\n",
        "    unique_labels.update(tokenized_datasets[split]['labels'])\n",
        "\n",
        "# Create a mapping from labels to integers\n",
        "label_to_int = {label: idx for idx, label in enumerate(unique_labels)}\n",
        "\n",
        "# Apply the mapping to convert labels to integers\n",
        "def label_to_int_mapping(example):\n",
        "    example['labels'] = label_to_int[example['labels']]\n",
        "    return example\n",
        "\n",
        "# Update datasets with the new integer labels\n",
        "tokenized_datasets = tokenized_datasets.map(label_to_int_mapping)\n"
      ],
      "metadata": {
        "colab": {
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    },
    {
      "cell_type": "code",
      "source": [
        "# Print each label with its corresponding integer value\n",
        "label_dict = {}\n",
        "for label, integer in label_to_int.items():\n",
        "    label_dict[integer] = label\n",
        "\n",
        "print(label_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFB3Ae0tktn2",
        "outputId": "52bf3133-7a74-48f0-cdfa-02b9f25beb7d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'multiple sclerosis', 1: 'overactive bladde', 2: 'hyperhidrosis', 3: 'ibromyalgia', 4: 'menstrual disorders', 5: 'hypogonadism, male', 6: 'rosacea', 7: 'muscle spasm', 8: 'high blood pressure', 9: 'epilepsy', 10: 'psoriatic arthritis', 11: 'post traumatic stress disorde', 12: 'smoking cessation', 13: 'not listed / othe', 14: 'herpes simplex', 15: 'opiate dependence', 16: 'social anxiety disorde', 17: 'urticaria', 18: 'allergic rhinitis', 19: 'polycystic ovary syndrome', 20: 'obsessive compulsive disorde', 21: 'depression', 22: 'migraine prevention', 23: 'neuropathic pain', 24: 'ankylosing spondylitis', 25: 'skin or soft tissue infection', 26: 'constipation, drug induced', 27: 'obesity', 28: 'vaginal yeast infection', 29: 'osteoarthritis', 30: 'restless legs syndrome', 31: 'plaque psoriasis', 32: 'panic disorde', 33: 'abnormal uterine bleeding', 34: 'adhd', 35: 'high cholesterol', 36: 'diabetes, type 2', 37: 'anxiety and stress', 38: 'asthma, maintenance', 39: 'pneumonia', 40: 'schizophrenia', 41: 'opiate withdrawal', 42: 'osteoporosis', 43: 'influenza', 44: 'weight loss', 45: 'cough and nasal congestion', 46: 'birth control', 47: 'benign prostatic hyperplasia', 48: 'helicobacter pylori infection', 49: 'anxiety', 50: 'bronchitis', 51: 'rheumatoid arthritis', 52: 'narcolepsy', 53: 'generalized anxiety disorde', 54: 'insomnia', 55: 'nasal congestion', 56: 'major depressive disorde', 57: 'schizoaffective disorde', 58: 'psoriasis', 59: 'premenstrual dysphoric disorde', 60: 'bacterial vaginitis', 61: 'motion sickness', 62: 'erectile dysfunction', 63: 'constipation, chronic', 64: 'copd, maintenance', 65: 'back pain', 66: 'alcohol dependence', 67: 'migraine', 68: 'bladder infection', 69: 'underactive thyroid', 70: 'ulcerative colitis', 71: 'chronic pain', 72: 'hiv infection', 73: 'cold sores', 74: 'breast cance', 75: 'bipolar disorde', 76: 'irritable bowel syndrome', 77: 'anesthesia', 78: 'onychomycosis, toenail', 79: 'chlamydia infection', 80: 'gerd', 81: 'endometriosis', 82: 'seizures', 83: 'alcohol withdrawal', 84: 'bowel preparation', 85: 'hot flashes', 86: 'bacterial infection', 87: 'inflammatory conditions', 88: 'constipation', 89: 'headache', 90: 'urinary tract infection', 91: 'sinusitis', 92: 'emergency contraception', 93: 'cough', 94: 'acne', 95: 'atrial fibrillation', 96: 'pain', 97: 'nausea/vomiting', 98: 'hepatitis c', 99: 'postmenopausal symptoms'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from evaluate import load\n",
        "#let's define a comput_metrics function that will return accuracy, precision, recall and f1 for our training\n",
        "def compute_metrics(eval_preds):\n",
        "    logits, labels = eval_preds\n",
        "    preds = np.argmax(logits, axis=-1)\n",
        "\n",
        "    # Load metrics with zero_division parameter set to 1\n",
        "    metrics = {\n",
        "        'accuracy': load('accuracy'),\n",
        "        'precision': load('precision', config={'zero_division': 1}),\n",
        "        'recall': load('recall', config={'zero_division': 1}),\n",
        "        'f1': load('f1', config={'zero_division': 1})\n",
        "    }\n",
        "\n",
        "    # Compute each metric, omitting the 'average' argument for accuracy\n",
        "    results = {}\n",
        "    for metric_name, metric in metrics.items():\n",
        "        if metric_name == 'accuracy':\n",
        "            result = metric.compute(predictions=preds, references=labels)\n",
        "            results[metric_name] = result['accuracy']\n",
        "        else:\n",
        "            result = metric.compute(predictions=preds, references=labels, average='weighted')\n",
        "            results[metric_name] = result[metric_name]\n",
        "\n",
        "    return results"
      ],
      "metadata": {
        "id": "7-nPdlsA2rVv"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, AutoModelForSequenceClassification, Trainer\n",
        "\n",
        "#let's set the training arguments for the model\n",
        "training_args = TrainingArguments(\"test-trainer\")\n",
        "\n",
        "#let's set the training arguments and model, and the trainer arguments below\n",
        "training_args = TrainingArguments(\"bert-drug-review-to-condition\", push_to_hub=True, save_strategy=\"epoch\", evaluation_strategy=\"epoch\")\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=len(label_to_int.items()))\n",
        "\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    training_args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"validation\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
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        "outputId": "28f8196c-de3a-4995-ad7e-2eed4f063e78"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "#let's prepare the metrics, getting first the predictions of the trainer\n",
        "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
        "print(predictions.predictions.shape, predictions.label_ids.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218,
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      "execution_count": 25,
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        {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(26702, 100) (26702,)\n"
          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "#given that we get all the logs from the trainer, we'll select the label with the highest likelihood with an argmax function\n",
        "preds = np.argmax(predictions.predictions, axis=-1)"
      ],
      "metadata": {
        "id": "DgBVUDfQzZSM"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#let's train the model right away\n",
        "trainer.train()"
      ],
      "metadata": {
        "id": "7WZ77bmbG5Zt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "07f6ac79-ae60-4aff-9950-d7d8c167e2f3"
      },
      "execution_count": 27,
      "outputs": [
        {
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