diff --git "a/Code_for_training.ipynb" "b/Code_for_training.ipynb"
deleted file mode 100644--- "a/Code_for_training.ipynb"
+++ /dev/null
@@ -1,5135 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "te5uCzPlhBWx"
-      },
-      "source": [
-        "# Processing the data (PyTorch)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "eiQ3FKJDhBW0"
-      },
-      "source": [
-        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-      ]
-    },
-    {
-      "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": [
-            "2459586b900d4972893416101cf66518",
-            "bb63af0e6e2d44d39dea0c7fb50f2675",
-            "b84acaf57645444a93e8344e62b02f37",
-            "0adacec9605245e8813c1eb6c375f97c",
-            "3556f7c20dd942d9845a172d2fb3a308",
-            "c9c4f33186b34af5bfe4b23f5a94cd92",
-            "d9fce65fdc0f4f4e99511a95049b0627",
-            "1dfa79c17ca34ccb8a15887162536305",
-            "20b45c6e63d94306a2cd368e6dfc0719",
-            "7853cc389609486e9774a6327cbc6d7f",
-            "0bfa8f38be0c4d5e8b1a8f2a21623e2f",
-            "f363053a53f442379a9be069597c70dc",
-            "1c90fe7a114140a9b32d8107c2af011c",
-            "b45e9bbed2c34078813f4d8be11ba856",
-            "784ba2ebff4d451485ba312e0dd065e7",
-            "74757608b6d945b186ce03761a04a067",
-            "6f292a0a27234aaebaa90c47edbaa6bd",
-            "d35e9d78443840a6af111a3e3245184d",
-            "589080843ecd489488d86c10bc3c3d67",
-            "404e6261b69646be85dd78a0fb1dc3cc",
-            "adafd48f91e54326a4a5b2bb297e3d47",
-            "56a61a4c28de4eb9833e571d4067c4c4",
-            "954f23b0ee9249df81dd30725c9954b0",
-            "ec75914e1c264260a9f573728880474a",
-            "e998f2b2d75d4f3887c59669603793df",
-            "eea3bf1ec05c4ba28cd5f1baa44bcfd8",
-            "1b22a95ee6ff4fdca56a966d35c46ddf",
-            "83c197270ac54969af02fa998fec28f1",
-            "21453473c9f04ef992fe35419f6afff9",
-            "3980e52353a14d538c1dc4b1b45d1396",
-            "1fa7b92f0588403bb146f602942baea6",
-            "20169140dbe04320b1a7d090d738f273"
-          ]
-        },
-        "id": "Z4IolwVzaQQh",
-        "outputId": "7f4c4beb-0cdf-4549-cfb9-6496f50c7118"
-      },
-      "execution_count": 137,
-      "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": "2459586b900d4972893416101cf66518"
-            }
-          },
-          "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": "e7723f9b-c62c-4aca-e53c-dbc6d2c08912"
-      },
-      "execution_count": 107,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--2024-04-28 15:34:17--  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.1’\n",
-            "\n",
-            "drugsCom_raw.zip.1      [     <=>            ]  41.00M  40.1MB/s    in 1.0s    \n",
-            "\n",
-            "2024-04-28 15:34:18 (40.1 MB/s) - ‘drugsCom_raw.zip.1’ saved [42989872]\n",
-            "\n",
-            "Archive:  drugsCom_raw.zip\n",
-            "replace drugsComTest_raw.tsv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
-            "  inflating: drugsComTest_raw.tsv    \n",
-            "replace drugsComTrain_raw.tsv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\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/"
-        },
-        "outputId": "a0b787f7-ed71-4825-d638-378c0b198dac"
-      },
-      "execution_count": 108,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "DatasetDict({\n",
-              "    train: Dataset({\n",
-              "        features: ['Unnamed: 0', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-              "        num_rows: 161297\n",
-              "    })\n",
-              "    test: Dataset({\n",
-              "        features: ['Unnamed: 0', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-              "        num_rows: 53766\n",
-              "    })\n",
-              "})"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 108
-        }
-      ]
-    },
-    {
-      "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": "a2443b2e-db62-45a0-87d5-320d69b08404"
-      },
-      "execution_count": 109,
-      "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": 109
-        }
-      ]
-    },
-    {
-      "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"
-      },
-      "execution_count": 110,
-      "outputs": []
-    },
-    {
-      "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"
-      },
-      "execution_count": 111,
-      "outputs": []
-    },
-    {
-      "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"
-      },
-      "execution_count": 112,
-      "outputs": []
-    },
-    {
-      "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/"
-        },
-        "id": "qQk-ReJssefI",
-        "outputId": "e1587a33-b6e4-4a7b-fcd7-2c9b8f2fb6f4"
-      },
-      "execution_count": 113,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "{'train': 151236, 'test': 50396}\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "drug_dataset"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "DyNIhmXc6GQg",
-        "outputId": "e1807a4e-66c0-419d-c7da-e5eaeab995f5"
-      },
-      "execution_count": 114,
-      "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: 151236\n",
-              "    })\n",
-              "    test: Dataset({\n",
-              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-              "        num_rows: 50396\n",
-              "    })\n",
-              "})"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 114
-        }
-      ]
-    },
-    {
-      "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"
-      },
-      "execution_count": 115,
-      "outputs": []
-    },
-    {
-      "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": "21252016-7685-44b0-8c29-79031b276811"
-      },
-      "execution_count": 116,
-      "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: 120988\n",
-              "    })\n",
-              "    validation: Dataset({\n",
-              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-              "        num_rows: 30248\n",
-              "    })\n",
-              "    test: Dataset({\n",
-              "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-              "        num_rows: 50396\n",
-              "    })\n",
-              "})"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 116
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "drug_clean['train'].features"
-      ],
-      "metadata": {
-        "id": "92I1TbyMs7-_",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "8c7fbcb9-cc0d-4831-af30-5b7e6c18905c"
-      },
-      "execution_count": 117,
-      "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": 117
-        }
-      ]
-    },
-    {
-      "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",
-        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-      ],
-      "metadata": {
-        "id": "6z0WrmVrV5Fw",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 113,
-          "referenced_widgets": [
-            "8815bc4f84334c66a72bef825a716be6",
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-        "outputId": "233d5e71-1fce-4968-f654-b05192d26de2"
-      },
-      "execution_count": 118,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/120988 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "8815bc4f84334c66a72bef825a716be6"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/30248 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "e87b26d89be642348040ae7e03c75fec"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/50396 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "5ba5166459f24d09ae9d2474ea6d6731"
-            }
-          },
-          "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_datasets"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "d6El1L0juj9H",
-        "outputId": "0e44c904-8cb2-46cd-cd1d-226dbceabec1"
-      },
-      "execution_count": 119,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "DatasetDict({\n",
-              "    train: Dataset({\n",
-              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
-              "        num_rows: 120988\n",
-              "    })\n",
-              "    validation: Dataset({\n",
-              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
-              "        num_rows: 30248\n",
-              "    })\n",
-              "    test: Dataset({\n",
-              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
-              "        num_rows: 50396\n",
-              "    })\n",
-              "})"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 119
-        }
-      ]
-    },
-    {
-      "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": [
-            "640ea213b58743db982787a16098a5f6",
-            "7e523c1c758c4fdc851e7de2995663ec",
-            "e03faaed4ed3418f8c5377a4b964ce51",
-            "808dc246ad8a495d9e32ba4160d411d8",
-            "a0c4d900489b4248b83c16f2dd78b69c",
-            "3d56aa845cd64c7ea2bfc9025d6b46c1",
-            "85db6ff81f6f4b30aa4419cfb69965da",
-            "175e7c76cd5e4184b12a242091044270",
-            "f5823fe5ca1149febeb3402e630313e9",
-            "d943f41a1b954a42b6ed5e95eee774da",
-            "1da9eef7812148da84a89f955624f2e8",
-            "4cd88736b2d242e5860524248a40bdac",
-            "4badf69964774d40a1c59ed9d8c856bd",
-            "0a652cb799d14c3fb4cf5e7c99fafcd6",
-            "e27cbc2f733f4196b4e56120d102a672",
-            "7285363ba66e464ea7bd29b90aa96048",
-            "54360d81897a472f9031ddf7f3dbb631",
-            "84b65ad25a59499ebe45dae115bc1eb0",
-            "22c8754e0923449d9a7fd41560dafc64",
-            "10fb15d5acbf4861b197b0f392f27415",
-            "a813a744e3cf4e50a9a0ad49b66dfe22",
-            "cdbff344d21b4b41908a51fdec79eea3",
-            "c38906d5a105433daf02b649a7ad1d3a",
-            "4d0c18659f1f4530a2449039d4a489e0",
-            "8491626754d943f2929078d77fead314",
-            "debee1308f624438b8dadf91199f0815",
-            "8afd7b5689f24a9a92e1f383f6a11c9f",
-            "e2d1d653dcb740b08a258587ae8771e8",
-            "fc91476a348547e791c0e3cfba272553",
-            "324c23ae36c945b690f95c9fb5dfc073",
-            "d3e0d027c2fe4843b2a3aa07ad307037",
-            "41ddd2aa5bab4f0e98ef10630795e144",
-            "29d8cd1bc52941bcb059fa3322c4582f"
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-        },
-        "id": "lbN-QjL1D8cc",
-        "outputId": "d8f903dc-1c9d-47cb-bbd4-648fc38ffd53"
-      },
-      "execution_count": 120,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/120988 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "640ea213b58743db982787a16098a5f6"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/30248 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "4cd88736b2d242e5860524248a40bdac"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/50396 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "c38906d5a105433daf02b649a7ad1d3a"
-            }
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#Given that the original dataset has more than 800 labels and this is only an exercise, let's simplify the training by selecting only the first 21 labels\n",
-        "# Define a filter function\n",
-        "def filter_labels(example):\n",
-        "    # Return True for rows where 'labels' value is between 0 and 20\n",
-        "    return 0 <= example['labels'] <= 20\n",
-        "\n",
-        "# Apply the filter function to each split in the dataset\n",
-        "tokenized_datasets = tokenized_datasets.filter(filter_labels)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 113,
-          "referenced_widgets": [
-            "e1ecbabc81674880b4efad0c872a7640",
-            "45a9324ae5b7425d8a87ec8c0f6d3ec2",
-            "9b3304a2f2df48c3bde983fc50c84fb4",
-            "2bf8f0e112cf4794ab58f7b2dc81ad7c",
-            "10224b3d6ce74e6d9056defa5b08496d",
-            "ad722aa9df84408b9c928536e03e28a3",
-            "f27963080e7649fda9652ffda57fd9be",
-            "3b89f92b77f4454ba771112263704b7a",
-            "d846f47a939040cab160cefd79e4ee07",
-            "0db3d9f23972430e947470426ecde995",
-            "8b8ee6df28e84442a6eb230ed9599203",
-            "328a9a2f72bf455cb2eb257d10acea17",
-            "c762e043ce9e4827b42564cac11d5f3d",
-            "69c5e736c5b04258b38a919afe7e51cc",
-            "e03f0f42fdef4aa3ac2ed5413f44f9e7",
-            "2f87bb5f1f1341df91a985eb5194a06e",
-            "9968cea444e44817b170d0f82ca3f179",
-            "42daad42bfcd4772b9a2898852a204a7",
-            "e26b1cbd5e604a748ef8b3edcb130f75",
-            "adc3d24a3fa74db8820dbcccec76a78e",
-            "d995be46e0b24196ae98a12118432c63",
-            "5bb7a38de241439884681189d6319785",
-            "6c2b86f8a18d489ba55129f6f2156136",
-            "eec9c87d06704080961008082b49f33e",
-            "4dd5e1f200cc4add92c4d324e8831543",
-            "f0473a4ab39248a48c42b637a17fd859",
-            "166f54f054ba4a4aa5900be02d7e8773",
-            "e37b4e2867e1477aba431599f9c4e30e",
-            "c153075c37474941945582005cb8ed23",
-            "d4a306b271f6490ba6d262693a3015e4",
-            "d38411aae9bd4fefa203623fa720318b",
-            "fdb315c8de7d40df8c764d314411ddec",
-            "47d7865fb9cd4251b9fe2d344fa199ab"
-          ]
-        },
-        "id": "h1N6SME9Nev0",
-        "outputId": "4536c675-8fa8-46b8-9caf-cb763383ed22"
-      },
-      "execution_count": 121,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Filter:   0%|          | 0/120988 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "e1ecbabc81674880b4efad0c872a7640"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Filter:   0%|          | 0/30248 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "328a9a2f72bf455cb2eb257d10acea17"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Filter:   0%|          | 0/50396 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "6c2b86f8a18d489ba55129f6f2156136"
-            }
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "tokenized_datasets"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "eR4uLDbrEwFN",
-        "outputId": "358d204b-d48a-48e4-f6ce-828235b69d0f"
-      },
-      "execution_count": 122,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "DatasetDict({\n",
-              "    train: Dataset({\n",
-              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
-              "        num_rows: 904\n",
-              "    })\n",
-              "    validation: Dataset({\n",
-              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
-              "        num_rows: 253\n",
-              "    })\n",
-              "    test: Dataset({\n",
-              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
-              "        num_rows: 369\n",
-              "    })\n",
-              "})"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 122
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from transformers import TrainingArguments\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=21)\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/"
-        },
-        "id": "g2nbHhwJX34P",
-        "outputId": "383851cd-3e7c-44e6-ec62-e5f36a069fb6"
-      },
-      "execution_count": 138,
-      "outputs": [
-        {
-          "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": [
-        "#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": 108
-        },
-        "id": "dbc8lXxOw-FA",
-        "outputId": "5b7df439-f30f-4bc9-dbed-a7ed0cd52303"
-      },
-      "execution_count": 139,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": []
-          },
-          "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: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
-            "  _warn_prf(average, modifier, msg_start, len(result))\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": 140,
-      "outputs": []
-    },
-    {
-      "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": "6x4ETEv1Fr_2"
-      },
-      "execution_count": 141,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#let's train the model right away\n",
-        "trainer.train()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 348
-        },
-        "id": "7WZ77bmbG5Zt",
-        "outputId": "b8100baf-99fc-425c-9a5b-e74ab3562d29"
-      },
-      "execution_count": 142,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='339' max='339' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [339/339 02:18, 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>No log</td>\n",
-              "      <td>1.137511</td>\n",
-              "      <td>0.774704</td>\n",
-              "      <td>0.730148</td>\n",
-              "      <td>0.774704</td>\n",
-              "      <td>0.744985</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2</td>\n",
-              "      <td>No log</td>\n",
-              "      <td>0.559548</td>\n",
-              "      <td>0.885375</td>\n",
-              "      <td>0.867484</td>\n",
-              "      <td>0.885375</td>\n",
-              "      <td>0.872829</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>3</td>\n",
-              "      <td>No log</td>\n",
-              "      <td>0.430789</td>\n",
-              "      <td>0.920949</td>\n",
-              "      <td>0.906086</td>\n",
-              "      <td>0.920949</td>\n",
-              "      <td>0.910576</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",
-            "/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=339, training_loss=1.01418788313514, metrics={'train_runtime': 138.9845, 'train_samples_per_second': 19.513, 'train_steps_per_second': 2.439, 'total_flos': 245512156913712.0, 'train_loss': 1.01418788313514, 'epoch': 3.0})"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 142
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "#it's good. Let's push the model to the hugging face hub\n",
-        "trainer.push_to_hub()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 70
-        },
-        "id": "DxFz2gWSMUI6",
-        "outputId": "9e83f368-3084-4168-c828-1665b934e769"
-      },
-      "execution_count": 143,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "CommitInfo(commit_url='https://huggingface.co/Marcuswas/bert-drug-review-to-condition/commit/d6f99808a6fad08574389e154a44694bbd24ad5d', commit_message='End of training', commit_description='', oid='d6f99808a6fad08574389e154a44694bbd24ad5d', pr_url=None, pr_revision=None, pr_num=None)"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            }
-          },
-          "metadata": {},
-          "execution_count": 143
-        }
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "gpuType": "T4"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "widgets": {
-      "application/vnd.jupyter.widget-state+json": {
-        "2459586b900d4972893416101cf66518": {
-          "model_module": "@jupyter-widgets/controls",
-          "model_name": "VBoxModel",
-          "model_module_version": "1.5.0",
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-            "_model_module": "@jupyter-widgets/controls",
-            "_model_module_version": "1.5.0",
-            "_model_name": "VBoxModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/controls",
-            "_view_module_version": "1.5.0",
-            "_view_name": "VBoxView",
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-              "IPY_MODEL_adafd48f91e54326a4a5b2bb297e3d47",
-              "IPY_MODEL_56a61a4c28de4eb9833e571d4067c4c4",
-              "IPY_MODEL_954f23b0ee9249df81dd30725c9954b0",
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