diff --git "a/Code_for_training_.ipynb" "b/Code_for_training_.ipynb"
new file mode 100644--- /dev/null
+++ "b/Code_for_training_.ipynb"
@@ -0,0 +1,13973 @@
+{
+  "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": [
+            "d02e8f7bdeb145d4bae8ebcbceab2e72",
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+            "4e2105e83c4743428555be2aa7f4076e",
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+            "241d70158ae740f0ba73be9f2b633801"
+          ]
+        },
+        "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": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2d17144cc7824d96bd47fb37177c13ea"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Filter:   0%|          | 0/53766 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "cd5133bf1421483caf1154a6df4db62f"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "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,
+          "referenced_widgets": [
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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": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fbd40cee0f35475b924ae3771744d936"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/53471 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1b78f53fe8d04d3fbed814738b4183e5"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "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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+        "outputId": "52b7e0d6-01b1-4566-a6da-7653bffb1108"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Filter:   0%|          | 0/160398 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f17a0454262845998abaef34eda884d3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Filter:   0%|          | 0/53471 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0e7d084e39d14e9b8e86d343fb26d01f"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "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",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
+          "referenced_widgets": [
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+          ]
+        },
+        "outputId": "579e1984-1892-4e21-d5af-4d5ebfd6e7ab"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/159498 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "35a71504e50a4b07824f570ce3c84450"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/53200 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "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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+            "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",
+            "0711a39a4af04145a78c75d6ef7f8d7c",
+            "e6822ccb6c6b475e8a6103d49f3b2878",
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+        },
+        "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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+            "cff775fa659d4a05893c77199a7e186c",
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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,
+              "version_minor": 0,
+              "model_id": "10c04aa7b1794343b2c42d68e3d702dd"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "49e12489c23948b99f2898430235e6b0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9db654d34c984ad299f0f4e38dd250e0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "78fe2e13554d46f2bac03b766b335857"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/120312 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "43712c4f73ba4f8c8eda255b99c82f31"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/30079 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a696cff2aa6a4c7cb6d602677e78d1b9"
+            }
+          },
+          "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": [
+              "Map:   0%|          | 0/120312 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b911c03185de4757ada2d7408b437a39"
+            }
+          },
+          "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)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113,
+          "referenced_widgets": [
+            "90bf6be855674146822c011904cce8d4",
+            "ec41b60a65fb48a2a1b6b3ea2f78c683",
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+          ]
+        },
+        "id": "Jo1qXVM_-Zo4",
+        "outputId": "419836f0-9708-4754-9a48-d0f1505955c0"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Filter:   0%|          | 0/120312 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "90bf6be855674146822c011904cce8d4"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Filter:   0%|          | 0/30079 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "dcba6362608c434aa4ddd57479af2fd3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Filter:   0%|          | 0/50146 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6529a1c6b76c4f829a0c020e1c139f6f"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113,
+          "referenced_widgets": [
+            "de89175b3615402aa80db50823540d13",
+            "5f33b344970a43ef87db4ed2cd04c712",
+            "ad7db2036a384866a623d44084892eb5",
+            "e5389af8b3314e9abe82496720412b49",
+            "72363a2b02f1438fb6ea521e056ef86c",
+            "465fa2b3f91c4827b14ad2924829d565",
+            "eb6b7a1ac942446ab5ae0a74a185338a",
+            "9e642184fd834a82a09de135efa47072",
+            "c18bdec1e42a4ae5832194d1c6d36daa",
+            "77f3690dd72f4597807e45b0956128f8",
+            "5ff62f05a0da49698a9dda59b5c47759",
+            "0f999cf07f8c4f3aba0443e228ab340b",
+            "7653d1938d914e878b06d06a89d18a6b",
+            "7aeac158346748658d114ddf3f6a6181",
+            "36d869cb52e0431987bf929eceeacd58",
+            "7308972c1209457cab80ada0ed311e8a",
+            "ef8df5488882454a90477aa8eba1908b",
+            "a5feacdfc65d49c18bf721e4c51bb309",
+            "05a93cecaea046ba9af16d773a945a2c",
+            "319996044c00490490ef2c288ab3ebe1",
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+          ]
+        },
+        "id": "lbN-QjL1D8cc",
+        "outputId": "14de11f9-9c26-4b5f-95fb-de05ce0f6427"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/107120 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "de89175b3615402aa80db50823540d13"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/26702 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0f999cf07f8c4f3aba0443e228ab340b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/44617 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e6a890b4c6114dbfaeae82ca4b7951a7"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "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,
+          "referenced_widgets": [
+            "4f3d9c25c64e4ef59505543a767ae65d",
+            "2d464919c1fc488e876c7228dc247f8a",
+            "e709e46406654777857a6352d5017c11",
+            "130993c8fd194192bea05d7beed52960",
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+            "a0e588f61529405eba64990a8c25f7e5",
+            "942f5d6e06964751bf2f8be5c9feb1f7",
+            "c023d7953aec432290ffce5290b283a1",
+            "545070a385224098aef2a85b1de073ad",
+            "e4eac8c8766c41fba086b77ff82b02ce",
+            "6acab164d415439cb4a978d9b95c81a2"
+          ]
+        },
+        "id": "g2nbHhwJX34P",
+        "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]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4f3d9c25c64e4ef59505543a767ae65d"
+            }
+          },
+          "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,
+          "referenced_widgets": [
+            "1249c3c612e24c1389def9fd4119ed7b",
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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"
+          ]
+        }
+      ]
+    },
+    {
+      "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": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='40170' max='40170' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [40170/40170 1:12:01, Epoch 3/3]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Epoch</th>\n",
+              "      <th>Training Loss</th>\n",
+              "      <th>Validation Loss</th>\n",
+              "      <th>Accuracy</th>\n",
+              "      <th>Precision</th>\n",
+              "      <th>Recall</th>\n",
+              "      <th>F1</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>1</td>\n",
+              "      <td>0.846900</td>\n",
+              "      <td>0.827508</td>\n",
+              "      <td>0.767283</td>\n",
+              "      <td>0.768594</td>\n",
+              "      <td>0.767283</td>\n",
+              "      <td>0.755066</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>2</td>\n",
+              "      <td>0.631900</td>\n",
+              "      <td>0.689465</td>\n",
+              "      <td>0.809415</td>\n",
+              "      <td>0.808975</td>\n",
+              "      <td>0.809415</td>\n",
+              "      <td>0.797826</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>3</td>\n",
+              "      <td>0.411600</td>\n",
+              "      <td>0.667829</td>\n",
+              "      <td>0.837578</td>\n",
+              "      <td>0.832482</td>\n",
+              "      <td>0.837578</td>\n",
+              "      <td>0.831665</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "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",
+            "/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": "execute_result",
+          "data": {
+            "text/plain": [
+              "TrainOutput(global_step=40170, training_loss=0.7559129410606727, metrics={'train_runtime': 4322.1673, 'train_samples_per_second': 74.352, 'train_steps_per_second': 9.294, 'total_flos': 3.0660538964049024e+16, 'train_loss': 0.7559129410606727, 'epoch': 3.0})"
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