diff --git "a/FineTuning.ipynb" "b/FineTuning.ipynb"
new file mode 100644--- /dev/null
+++ "b/FineTuning.ipynb"
@@ -0,0 +1,7255 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "ename": "",
+          "evalue": "",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[1;31mRunning cells with 'Python 3.12.3' requires the ipykernel package.\n",
+            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+            "\u001b[1;31mCommand: '/usr/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+          ]
+        }
+      ],
+      "source": [
+        "import spacy\n",
+        "\n",
+        "# Load the English NLP model\n",
+        "nlp = spacy.load(\"en_core_web_sm\")\n",
+        "\n",
+        "def extract_item_name(sentence):\n",
+        "    doc = nlp(sentence)\n",
+        "    possible_items = []\n",
+        "\n",
+        "    for chunk in doc.noun_chunks:\n",
+        "        filtered_tokens = [token.text for token in chunk if not (token.pos_ in [\"DET\", \"NUM\"])]\n",
+        "\n",
+        "        if any(tok.pos_ in [\"NOUN\", \"PROPN\"] for tok in chunk):\n",
+        "            possible_items.append(\" \".join(filtered_tokens))\n",
+        "\n",
+        "    if possible_items:\n",
+        "        return max(possible_items, key=len)\n",
+        "\n",
+        "    return None\n",
+        "\n",
+        "# Example usage\n",
+        "sentence = \"Can I get one Chicken Katsu Curry with extra rice?\"\n",
+        "item_name = extract_item_name(sentence)\n",
+        "print(f\"Extracted item name: {item_name}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PnK_4azs5LBd"
+      },
+      "source": [
+        "# **1. Installation of Required Libraries**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dAq-cgrU5QRJ",
+        "outputId": "58e436b6-734e-4dd5-85d0-71e559ccaedd"
+      },
+      "outputs": [],
+      "source": [
+        "# %pip install transformers datasets torch scikit-learn -q\n",
+        "# %pip install transformers[torch] -q\n",
+        "# %pip install accelerate -U -q"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Gef1it6r5dbW"
+      },
+      "source": [
+        "# **2. Implementation Code**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "q7lTlKG55Wmz"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+            "  from .autonotebook import tqdm as notebook_tqdm\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "from datasets import load_dataset\n",
+        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n",
+        "from sklearn.metrics import accuracy_score, precision_recall_fscore_support"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import torch\n",
+        "from torch import nn\n",
+        "from transformers import AutoTokenizer, AutoModelForSequenceClassification"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dataset = pd.read_excel(\"fine_tune_dataset.xlsx\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Sentence</th>\n",
+              "      <th>Operation</th>\n",
+              "      <th>Item Name</th>\n",
+              "      <th>Quantity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Add 3 bananas to my cart.</td>\n",
+              "      <td>add</td>\n",
+              "      <td>bananas</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Can you put 5 oranges in the cart?</td>\n",
+              "      <td>add</td>\n",
+              "      <td>oranges</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Please remove 1 pack of cookies from the cart.</td>\n",
+              "      <td>remove</td>\n",
+              "      <td>cookies</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>I need to update the cart with 4 bottles of milk.</td>\n",
+              "      <td>update</td>\n",
+              "      <td>milk</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Take 2 bags of chips out of my cart.</td>\n",
+              "      <td>remove</td>\n",
+              "      <td>chips</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                            Sentence Operation Item Name  \\\n",
+              "0                          Add 3 bananas to my cart.       add   bananas   \n",
+              "1                 Can you put 5 oranges in the cart?       add   oranges   \n",
+              "2     Please remove 1 pack of cookies from the cart.    remove   cookies   \n",
+              "3  I need to update the cart with 4 bottles of milk.    update      milk   \n",
+              "4               Take 2 bags of chips out of my cart.    remove     chips   \n",
+              "\n",
+              "  Quantity  \n",
+              "0        3  \n",
+              "1        5  \n",
+              "2        1  \n",
+              "3        4  \n",
+              "4        2  "
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset.head(5)"
+      ]
+    },
+    {
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+          ]
+        },
+        "id": "PGoLzOa15u3b",
+        "outputId": "89654562-f5c9-42f5-86ef-b6db95043edf"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:1617: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be deprecated in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "# dataset = load_dataset(\"imdb\")\n",
+        "model_name = \"distilbert-base-uncased\"\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "AahEP4v2HtaW"
+      },
+      "outputs": [],
+      "source": [
+        "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "device = torch.device(\"mps\" if torch.backends.mps.is_available() else \"cpu\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "device(type='cpu')"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "device"
+      ]
+    },
+    {
+      "cell_type": "code",
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+      "metadata": {
+        "colab": {
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+          "height": 113,
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+        "id": "UZs355r-52fp",
+        "outputId": "3b88332e-0877-4ea5-f834-bcdcd5b42b00"
+      },
+      "outputs": [],
+      "source": [
+        "dataset['tokenized'] = dataset['Sentence'].apply(lambda x: tokenizer(x, padding='max_length', truncation=True, return_tensors=\"pt\"))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
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+              "<style scoped>\n",
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+              "\n",
+              "    .dataframe thead th {\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Sentence</th>\n",
+              "      <th>Operation</th>\n",
+              "      <th>Item Name</th>\n",
+              "      <th>Quantity</th>\n",
+              "      <th>tokenized</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>644</th>\n",
+              "      <td>What is currently in my shopping cart?</td>\n",
+              "      <td>view</td>\n",
+              "      <td>none</td>\n",
+              "      <td>none</td>\n",
+              "      <td>[input_ids, attention_mask]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>645</th>\n",
+              "      <td>Please show my cart.</td>\n",
+              "      <td>view</td>\n",
+              "      <td>none</td>\n",
+              "      <td>none</td>\n",
+              "      <td>[input_ids, attention_mask]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>646</th>\n",
+              "      <td>Can I see the current contents of my cart?</td>\n",
+              "      <td>view</td>\n",
+              "      <td>none</td>\n",
+              "      <td>none</td>\n",
+              "      <td>[input_ids, attention_mask]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>647</th>\n",
+              "      <td>Could you show me my cart status?</td>\n",
+              "      <td>view</td>\n",
+              "      <td>none</td>\n",
+              "      <td>none</td>\n",
+              "      <td>[input_ids, attention_mask]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>648</th>\n",
+              "      <td>What are the items in my cart right now?</td>\n",
+              "      <td>view</td>\n",
+              "      <td>none</td>\n",
+              "      <td>none</td>\n",
+              "      <td>[input_ids, attention_mask]</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                       Sentence Operation Item Name Quantity  \\\n",
+              "644      What is currently in my shopping cart?      view      none     none   \n",
+              "645                        Please show my cart.      view      none     none   \n",
+              "646  Can I see the current contents of my cart?      view      none     none   \n",
+              "647           Could you show me my cart status?      view      none     none   \n",
+              "648    What are the items in my cart right now?      view      none     none   \n",
+              "\n",
+              "                       tokenized  \n",
+              "644  [input_ids, attention_mask]  \n",
+              "645  [input_ids, attention_mask]  \n",
+              "646  [input_ids, attention_mask]  \n",
+              "647  [input_ids, attention_mask]  \n",
+              "648  [input_ids, attention_mask]  "
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset.tail(5)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "operation_mapping = {'add': 0, 'remove': 1, 'update': 2, 'view': 3}\n",
+        "dataset['Operation_Label'] = dataset['Operation'].map(operation_mapping)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "dbWw2dD-58Ky"
+      },
+      "outputs": [],
+      "source": [
+        "train_operation_df, test_operation_df = train_test_split(dataset[['Sentence', 'Operation']], test_size=0.2, random_state=42)\n",
+        "\n",
+        "train_quantity_df, test_quantity_df = train_test_split(dataset[['Sentence', 'Quantity']], test_size=0.2, random_state=42)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "N7caBVWU59i0",
+        "outputId": "e958a3e8-1c8c-45f5-c24e-0808e5a6e9a8"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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",
+            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "DistilBertForSequenceClassification(\n",
+              "  (distilbert): DistilBertModel(\n",
+              "    (embeddings): Embeddings(\n",
+              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
+              "      (position_embeddings): Embedding(512, 768)\n",
+              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "      (dropout): Dropout(p=0.1, inplace=False)\n",
+              "    )\n",
+              "    (transformer): Transformer(\n",
+              "      (layer): ModuleList(\n",
+              "        (0-5): 6 x TransformerBlock(\n",
+              "          (attention): MultiHeadSelfAttention(\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
+              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
+              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
+              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
+              "          )\n",
+              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "          (ffn): FFN(\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
+              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
+              "            (activation): GELUActivation()\n",
+              "          )\n",
+              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "        )\n",
+              "      )\n",
+              "    )\n",
+              "  )\n",
+              "  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
+              "  (classifier): Linear(in_features=768, out_features=20, bias=True)\n",
+              "  (dropout): Dropout(p=0.2, inplace=False)\n",
+              ")"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Operation classification model (3 labels: add, remove, update)\n",
+        "operation_model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(operation_mapping))\n",
+        "operation_model.to(device)\n",
+        "\n",
+        "# Quantity classification model (for simplicity, assuming up to 20 distinct quantities)\n",
+        "quantity_model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=20)\n",
+        "quantity_model.to(device)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "u6FezLZY6IZz",
+        "outputId": "70232730-d898-4d4b-c0b5-0742441541f1"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "from transformers import Trainer, TrainingArguments\n",
+        "\n",
+        "# Training arguments\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=\"./results\",\n",
+        "    evaluation_strategy=\"epoch\",\n",
+        "    save_strategy=\"epoch\",\n",
+        "    learning_rate=2e-5,\n",
+        "    per_device_train_batch_size=16,\n",
+        "    per_device_eval_batch_size=16,\n",
+        "    num_train_epochs=10,\n",
+        "    weight_decay=0.01,\n",
+        "    logging_dir='./logs',\n",
+        "    logging_steps=10,\n",
+        "    load_best_model_at_end=True,\n",
+        "    save_total_limit=2,\n",
+        "    save_steps=500,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import accuracy_score, precision_recall_fscore_support"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "ACVm1fs46JTj"
+      },
+      "outputs": [],
+      "source": [
+        "def compute_metrics(pred):\n",
+        "    labels = pred.label_ids\n",
+        "    preds = pred.predictions.argmax(-1)\n",
+        "    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='weighted')\n",
+        "    acc = accuracy_score(labels, preds)\n",
+        "    return {\n",
+        "        'accuracy': acc,\n",
+        "        'f1': f1,\n",
+        "        'precision': precision,\n",
+        "        'recall': recall\n",
+        "    }"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Define the mapping from operation strings to integer labels\n",
+        "operation_label_mapping = {\n",
+        "    \"add\": 0,\n",
+        "    \"remove\": 1,\n",
+        "    \"update\": 2,\n",
+        "    \"view\": 3\n",
+        "}\n",
+        "\n",
+        "# Apply the mapping to the dataset\n",
+        "train_operation_df[\"Operation_Label\"] = train_operation_df[\"Operation\"].map(operation_label_mapping)\n",
+        "test_operation_df[\"Operation_Label\"] = test_operation_df[\"Operation\"].map(operation_label_mapping)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "class CustomDataset(torch.utils.data.Dataset):\n",
+        "    def __init__(self, df, tokenizer, max_length=128):\n",
+        "        self.df = df\n",
+        "        self.tokenizer = tokenizer\n",
+        "        self.max_length = max_length\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.df)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        # Ensure the idx is within the correct range\n",
+        "        if torch.is_tensor(idx):\n",
+        "            idx = idx.tolist()\n",
+        "\n",
+        "        # Fetch the row by index\n",
+        "        sentence = self.df.iloc[idx][\"Sentence\"]\n",
+        "        operation_label = self.df.iloc[idx][\"Operation_Label\"]  # Use the integer label now\n",
+        "\n",
+        "        # Tokenize the sentence\n",
+        "        encoding = self.tokenizer(\n",
+        "            sentence,\n",
+        "            padding=\"max_length\",\n",
+        "            truncation=True,\n",
+        "            max_length=self.max_length,\n",
+        "            return_tensors=\"pt\"\n",
+        "        )\n",
+        "\n",
+        "        # Create a dictionary with input data and label\n",
+        "        item = {\n",
+        "            \"input_ids\": encoding[\"input_ids\"].squeeze(),\n",
+        "            \"attention_mask\": encoding[\"attention_mask\"].squeeze(),\n",
+        "            \"labels\": torch.tensor(operation_label, dtype=torch.long)  # Use the integer label\n",
+        "        }\n",
+        "\n",
+        "        return item\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "train_dataset = CustomDataset(train_operation_df, tokenizer)\n",
+        "test_dataset = CustomDataset(test_operation_df, tokenizer)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "yT6a4s8f6L5s"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "  3%|▎         | 10/330 [02:19<1:15:04, 14.08s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 1.3225, 'grad_norm': 2.9805526733398438, 'learning_rate': 1.9393939393939395e-05, 'epoch': 0.3}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "  6%|▌         | 20/330 [05:32<1:51:00, 21.49s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 1.0513, 'grad_norm': 4.0864691734313965, 'learning_rate': 1.8787878787878792e-05, 'epoch': 0.61}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "  9%|▉         | 30/330 [09:31<2:03:13, 24.65s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.7933, 'grad_norm': 2.4576473236083984, 'learning_rate': 1.8181818181818182e-05, 'epoch': 0.91}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 10%|█         | 33/330 [10:43<1:54:46, 23.19s/it]/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: 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, f\"{metric.capitalize()} is\", len(result))\n",
+            "                                                  \n",
+            " 10%|█         | 33/330 [12:13<1:54:46, 23.19s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 0.5467941164970398, 'eval_accuracy': 0.9615384615384616, 'eval_f1': 0.9429468916648404, 'eval_precision': 0.9255437426169133, 'eval_recall': 0.9615384615384616, 'eval_runtime': 89.7219, 'eval_samples_per_second': 1.449, 'eval_steps_per_second': 0.1, 'epoch': 1.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 12%|█▏        | 40/330 [15:38<2:12:24, 27.39s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.4953, 'grad_norm': 2.06632924079895, 'learning_rate': 1.7575757575757576e-05, 'epoch': 1.21}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 15%|█▌        | 50/330 [20:07<2:02:16, 26.20s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.3063, 'grad_norm': 2.299341917037964, 'learning_rate': 1.6969696969696972e-05, 'epoch': 1.52}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 18%|█▊        | 60/330 [24:52<2:11:44, 29.28s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.2376, 'grad_norm': 1.2459572553634644, 'learning_rate': 1.6363636363636366e-05, 'epoch': 1.82}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 20%|██        | 66/330 [27:37<1:51:29, 25.34s/it]/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: 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, f\"{metric.capitalize()} is\", len(result))\n",
+            "                                                  \n",
+            " 20%|██        | 66/330 [28:59<1:51:29, 25.34s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 0.12502779066562653, 'eval_accuracy': 0.9615384615384616, 'eval_f1': 0.9430944642212248, 'eval_precision': 0.9260878010878011, 'eval_recall': 0.9615384615384616, 'eval_runtime': 81.6251, 'eval_samples_per_second': 1.593, 'eval_steps_per_second': 0.11, 'epoch': 2.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 21%|██        | 70/330 [31:09<2:48:27, 38.88s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.1322, 'grad_norm': 0.8425148129463196, 'learning_rate': 1.575757575757576e-05, 'epoch': 2.12}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 24%|██▍       | 80/330 [36:58<2:26:20, 35.12s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.1026, 'grad_norm': 0.41122281551361084, 'learning_rate': 1.5151515151515153e-05, 'epoch': 2.42}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 27%|██▋       | 90/330 [43:09<2:28:03, 37.02s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.098, 'grad_norm': 0.6253001093864441, 'learning_rate': 1.4545454545454546e-05, 'epoch': 2.73}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "                                                  \n",
+            " 30%|███       | 99/330 [50:07<2:02:06, 31.72s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 0.043199870735406876, 'eval_accuracy': 1.0, 'eval_f1': 1.0, 'eval_precision': 1.0, 'eval_recall': 1.0, 'eval_runtime': 95.933, 'eval_samples_per_second': 1.355, 'eval_steps_per_second': 0.094, 'epoch': 3.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 30%|███       | 100/330 [50:46<4:00:59, 62.87s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.0445, 'grad_norm': 0.29822495579719543, 'learning_rate': 1.3939393939393942e-05, 'epoch': 3.03}\n"
+          ]
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+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 33%|███▎      | 110/330 [56:38<2:11:19, 35.81s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.052, 'grad_norm': 0.4001183807849884, 'learning_rate': 1.3333333333333333e-05, 'epoch': 3.33}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
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+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.0348, 'grad_norm': 0.4419863224029541, 'learning_rate': 1.2727272727272728e-05, 'epoch': 3.64}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
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+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.0251, 'grad_norm': 0.19218851625919342, 'learning_rate': 1.2121212121212122e-05, 'epoch': 3.94}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "                                                     \n",
+            " 40%|████      | 132/330 [1:10:10<1:10:49, 21.46s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
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+          "text": [
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+            "                                                     \n",
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+            " 73%|███████▎  | 240/330 [1:53:02<35:02, 23.36s/it]"
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+          "name": "stdout",
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+            " 76%|███████▌  | 250/330 [1:57:05<33:26, 25.08s/it]"
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+            " 79%|███████▉  | 260/330 [2:01:03<27:29, 23.56s/it]"
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+            " 82%|████████▏ | 270/330 [2:06:20<29:51, 29.87s/it]"
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+            "                                                   \n",
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+          ]
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+          "name": "stdout",
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+            "{'eval_loss': 0.008067782036960125, 'eval_accuracy': 1.0, 'eval_f1': 1.0, 'eval_precision': 1.0, 'eval_recall': 1.0, 'eval_runtime': 84.4345, 'eval_samples_per_second': 1.54, 'eval_steps_per_second': 0.107, 'epoch': 9.0}\n"
+          ]
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+          "name": "stderr",
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+            " 91%|█████████ | 300/330 [2:20:29<17:05, 34.18s/it]"
+          ]
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+        {
+          "name": "stdout",
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+            "{'loss': 0.0086, 'grad_norm': 0.08502928912639618, 'learning_rate': 1.8181818181818183e-06, 'epoch': 9.09}\n"
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+            " 94%|█████████▍| 310/330 [2:22:41<04:38, 13.92s/it]"
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+          "name": "stdout",
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+            "{'loss': 0.0089, 'grad_norm': 0.0942268893122673, 'learning_rate': 1.2121212121212122e-06, 'epoch': 9.39}\n"
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+        {
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+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.0097, 'grad_norm': 0.08703630417585373, 'learning_rate': 6.060606060606061e-07, 'epoch': 9.7}\n"
+          ]
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+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
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+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.0092, 'grad_norm': 0.07591152936220169, 'learning_rate': 0.0, 'epoch': 10.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "                                                   \n",
+            "100%|██████████| 330/330 [2:26:54<00:00,  8.78s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 0.007898399606347084, 'eval_accuracy': 1.0, 'eval_f1': 1.0, 'eval_precision': 1.0, 'eval_recall': 1.0, 'eval_runtime': 24.8792, 'eval_samples_per_second': 5.225, 'eval_steps_per_second': 0.362, 'epoch': 10.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 330/330 [2:26:57<00:00, 26.72s/it]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'train_runtime': 8817.7112, 'train_samples_per_second': 0.589, 'train_steps_per_second': 0.037, 'train_loss': 0.15002734196005446, 'epoch': 10.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 9/9 [00:21<00:00,  2.40s/it]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Evaluation Results: {'eval_loss': 0.007898399606347084, 'eval_accuracy': 1.0, 'eval_f1': 1.0, 'eval_precision': 1.0, 'eval_recall': 1.0, 'eval_runtime': 24.4138, 'eval_samples_per_second': 5.325, 'eval_steps_per_second': 0.369, 'epoch': 10.0}\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "('./fine-tuned-operation-model/tokenizer_config.json',\n",
+              " './fine-tuned-operation-model/special_tokens_map.json',\n",
+              " './fine-tuned-operation-model/vocab.txt',\n",
+              " './fine-tuned-operation-model/added_tokens.json',\n",
+              " './fine-tuned-operation-model/tokenizer.json')"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "operation_trainer = Trainer(\n",
+        "    model=operation_model,\n",
+        "    args=training_args,\n",
+        "    train_dataset=train_dataset,\n",
+        "    eval_dataset=test_dataset,\n",
+        "    compute_metrics=compute_metrics\n",
+        ")\n",
+        "\n",
+        "# Train the model\n",
+        "operation_trainer.train()\n",
+        "\n",
+        "# Evaluate the model\n",
+        "eval_results = operation_trainer.evaluate()\n",
+        "print(f\"Evaluation Results: {eval_results}\")\n",
+        "\n",
+        "# Save the model\n",
+        "operation_model.save_pretrained(\"./fine-tuned-operation-model\")\n",
+        "tokenizer.save_pretrained(\"./fine-tuned-operation-model\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "from torch.utils.data import Dataset\n",
+        "\n",
+        "class QuantityDataset(Dataset):\n",
+        "    def __init__(self, df, tokenizer, max_length=128):\n",
+        "        self.df = df\n",
+        "        self.tokenizer = tokenizer\n",
+        "        self.max_length = max_length\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.df)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        # Ensure the idx is within the correct range\n",
+        "        if torch.is_tensor(idx):\n",
+        "            idx = idx.tolist()\n",
+        "\n",
+        "        # Fetch the row by index\n",
+        "        sentence = self.df.iloc[idx][\"Sentence\"]\n",
+        "        quantity_label = self.df.iloc[idx][\"Quantity\"]  # Use the integer label now\n",
+        "\n",
+        "        # Tokenize the sentence\n",
+        "        encoding = self.tokenizer(\n",
+        "            sentence,\n",
+        "            padding=\"max_length\",\n",
+        "            truncation=True,\n",
+        "            max_length=self.max_length,\n",
+        "            return_tensors=\"pt\"\n",
+        "        )\n",
+        "\n",
+        "        # Create a dictionary with input data and label\n",
+        "        item = {\n",
+        "            \"input_ids\": encoding[\"input_ids\"].squeeze(),\n",
+        "            \"attention_mask\": encoding[\"attention_mask\"].squeeze(),\n",
+        "            \"labels\": torch.tensor(quantity_label, dtype=torch.long)  # Use the integer label\n",
+        "        }\n",
+        "\n",
+        "        return item\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Replace NaN in 'quantity' column with 0\n",
+        "train_quantity_df['Quantity'] = pd.to_numeric(train_quantity_df['Quantity'], errors='coerce').fillna(0)\n",
+        "test_quantity_df['Quantity'] = pd.to_numeric(test_quantity_df['Quantity'], errors='coerce').fillna(0)\n",
+        "\n",
+        "# train_quantity_df[\"Quantity\"] = train_quantity_df['Quantity'].fillna(0)\n",
+        "# test_quantity_df[\"Quantity\"] = test_quantity_df['Quantity'].fillna(0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "train_quantity_df[\"Quantity\"] = train_quantity_df[\"Quantity\"].astype(int)\n",
+        "test_quantity_df[\"Quantity\"] = test_quantity_df[\"Quantity\"].astype(int)\n",
+        "\n",
+        "# Create a CustomDataset for the quantity model\n",
+        "train_quantity_dataset = QuantityDataset(train_quantity_df, tokenizer)\n",
+        "test_quantity_dataset = QuantityDataset(test_quantity_df, tokenizer)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
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+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 2.9295, 'grad_norm': 3.623546600341797, 'learning_rate': 1.9393939393939395e-05, 'epoch': 0.3}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
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+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 2.7411, 'grad_norm': 3.69571852684021, 'learning_rate': 1.8787878787878792e-05, 'epoch': 0.61}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "  9%|▉         | 30/330 [06:17<1:12:06, 14.42s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 2.6183, 'grad_norm': 3.1961872577667236, 'learning_rate': 1.8181818181818182e-05, 'epoch': 0.91}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 10%|█         | 33/330 [06:43<50:47, 10.26s/it]  /home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: 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, f\"{metric.capitalize()} is\", len(result))\n",
+            "\n",
+            " 10%|█         | 33/330 [07:23<50:47, 10.26s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 2.5352845191955566, 'eval_accuracy': 0.16153846153846155, 'eval_f1': 0.04493122771268466, 'eval_precision': 0.026094674556213022, 'eval_recall': 0.16153846153846155, 'eval_runtime': 39.9116, 'eval_samples_per_second': 3.257, 'eval_steps_per_second': 0.225, 'epoch': 1.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 12%|█▏        | 40/330 [09:24<1:27:07, 18.03s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 2.5101, 'grad_norm': 3.4881746768951416, 'learning_rate': 1.7575757575757576e-05, 'epoch': 1.21}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
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+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 2.4296, 'grad_norm': 3.767667770385742, 'learning_rate': 1.6969696969696972e-05, 'epoch': 1.52}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
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+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 2.3016, 'grad_norm': 3.896134376525879, 'learning_rate': 1.6363636363636366e-05, 'epoch': 1.82}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 20%|██        | 66/330 [15:19<46:31, 10.57s/it]/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: 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, f\"{metric.capitalize()} is\", len(result))\n",
+            "\n",
+            " 20%|██        | 66/330 [15:57<46:31, 10.57s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 2.022275447845459, 'eval_accuracy': 0.7538461538461538, 'eval_f1': 0.6758809376557094, 'eval_precision': 0.6825497412803915, 'eval_recall': 0.7538461538461538, 'eval_runtime': 37.7538, 'eval_samples_per_second': 3.443, 'eval_steps_per_second': 0.238, 'epoch': 2.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 21%|██        | 70/330 [16:47<1:05:01, 15.01s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 2.0848, 'grad_norm': 3.96289324760437, 'learning_rate': 1.575757575757576e-05, 'epoch': 2.12}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 24%|██▍       | 80/330 [18:18<38:32,  9.25s/it]  "
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 1.8483, 'grad_norm': 4.099489212036133, 'learning_rate': 1.5151515151515153e-05, 'epoch': 2.42}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 27%|██▋       | 90/330 [19:47<35:48,  8.95s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 1.6186, 'grad_norm': 3.6174874305725098, 'learning_rate': 1.4545454545454546e-05, 'epoch': 2.73}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 30%|███       | 99/330 [21:03<28:52,  7.50s/it]/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: 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, f\"{metric.capitalize()} is\", len(result))\n",
+            "\n",
+            " 30%|███       | 99/330 [21:27<28:52,  7.50s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 1.1958974599838257, 'eval_accuracy': 0.9, 'eval_f1': 0.8644405594405594, 'eval_precision': 0.864920976459438, 'eval_recall': 0.9, 'eval_runtime': 23.2626, 'eval_samples_per_second': 5.588, 'eval_steps_per_second': 0.387, 'epoch': 3.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
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+            " 30%|███       | 100/330 [21:37<58:28, 15.25s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 1.4045, 'grad_norm': 3.234818696975708, 'learning_rate': 1.3939393939393942e-05, 'epoch': 3.03}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 33%|███▎      | 110/330 [23:25<36:25,  9.94s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 1.1426, 'grad_norm': 3.4251556396484375, 'learning_rate': 1.3333333333333333e-05, 'epoch': 3.33}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 36%|███▋      | 120/330 [24:56<34:09,  9.76s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 1.071, 'grad_norm': 3.120529890060425, 'learning_rate': 1.2727272727272728e-05, 'epoch': 3.64}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 39%|███▉      | 130/330 [27:13<44:59, 13.50s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.9142, 'grad_norm': 2.7460267543792725, 'learning_rate': 1.2121212121212122e-05, 'epoch': 3.94}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 40%|████      | 132/330 [27:32<37:37, 11.40s/it]/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: 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, f\"{metric.capitalize()} is\", len(result))\n",
+            "\n",
+            " 40%|████      | 132/330 [28:12<37:37, 11.40s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 0.724678099155426, 'eval_accuracy': 0.9461538461538461, 'eval_f1': 0.9320176899124267, 'eval_precision': 0.9390220306009779, 'eval_recall': 0.9461538461538461, 'eval_runtime': 40.0706, 'eval_samples_per_second': 3.244, 'eval_steps_per_second': 0.225, 'epoch': 4.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 42%|████▏     | 140/330 [30:17<49:37, 15.67s/it]  "
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.8155, 'grad_norm': 3.3547778129577637, 'learning_rate': 1.1515151515151517e-05, 'epoch': 4.24}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 45%|████▌     | 150/330 [33:00<49:20, 16.45s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.7016, 'grad_norm': 2.3194897174835205, 'learning_rate': 1.0909090909090909e-05, 'epoch': 4.55}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 48%|████▊     | 160/330 [35:39<45:30, 16.06s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.5749, 'grad_norm': 2.30808687210083, 'learning_rate': 1.0303030303030304e-05, 'epoch': 4.85}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 50%|█████     | 165/330 [36:50<36:54, 13.42s/it]/home/ig-420/Igenerate/Huggingface code/order_bot/env/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1531: 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, f\"{metric.capitalize()} is\", len(result))\n",
+            "\n",
+            " 50%|█████     | 165/330 [37:30<36:54, 13.42s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 0.46791934967041016, 'eval_accuracy': 0.9769230769230769, 'eval_f1': 0.9695848595848596, 'eval_precision': 0.9638571954361429, 'eval_recall': 0.9769230769230769, 'eval_runtime': 39.9977, 'eval_samples_per_second': 3.25, 'eval_steps_per_second': 0.225, 'epoch': 5.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 52%|█████▏    | 170/330 [38:45<46:11, 17.32s/it]  "
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.5659, 'grad_norm': 2.5183117389678955, 'learning_rate': 9.696969696969698e-06, 'epoch': 5.15}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 55%|█████▍    | 180/330 [42:07<52:03, 20.83s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.4952, 'grad_norm': 2.0987327098846436, 'learning_rate': 9.090909090909091e-06, 'epoch': 5.45}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 58%|█████▊    | 190/330 [45:08<38:43, 16.60s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.464, 'grad_norm': 1.7576655149459839, 'learning_rate': 8.484848484848486e-06, 'epoch': 5.76}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 60%|██████    | 198/330 [47:34<36:21, 16.52s/it]\n",
+            " 60%|██████    | 198/330 [48:24<36:21, 16.52s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 0.3288252353668213, 'eval_accuracy': 0.9923076923076923, 'eval_f1': 0.9922344322344322, 'eval_precision': 0.993006993006993, 'eval_recall': 0.9923076923076923, 'eval_runtime': 50.5178, 'eval_samples_per_second': 2.573, 'eval_steps_per_second': 0.178, 'epoch': 6.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 61%|██████    | 200/330 [49:10<1:05:58, 30.45s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.4007, 'grad_norm': 1.777916669845581, 'learning_rate': 7.87878787878788e-06, 'epoch': 6.06}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 64%|██████▎   | 210/330 [52:02<34:34, 17.29s/it]  "
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.3738, 'grad_norm': 1.672868251800537, 'learning_rate': 7.272727272727273e-06, 'epoch': 6.36}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 67%|██████▋   | 220/330 [54:57<33:06, 18.06s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.3339, 'grad_norm': 1.8212429285049438, 'learning_rate': 6.666666666666667e-06, 'epoch': 6.67}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 70%|██████▉   | 230/330 [57:32<24:28, 14.69s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.3164, 'grad_norm': 1.6466906070709229, 'learning_rate': 6.060606060606061e-06, 'epoch': 6.97}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 70%|███████   | 231/330 [57:39<20:39, 12.52s/it]\n",
+            " 70%|███████   | 231/330 [58:18<20:39, 12.52s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 0.2524698078632355, 'eval_accuracy': 1.0, 'eval_f1': 1.0, 'eval_precision': 1.0, 'eval_recall': 1.0, 'eval_runtime': 39.1902, 'eval_samples_per_second': 3.317, 'eval_steps_per_second': 0.23, 'epoch': 7.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 73%|███████▎  | 240/330 [1:01:00<28:14, 18.83s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.314, 'grad_norm': 1.2753565311431885, 'learning_rate': 5.4545454545454545e-06, 'epoch': 7.27}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 76%|███████▌  | 250/330 [1:03:48<23:05, 17.32s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.2747, 'grad_norm': 1.2842819690704346, 'learning_rate': 4.848484848484849e-06, 'epoch': 7.58}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 79%|███████▉  | 260/330 [1:06:34<19:31, 16.74s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.2593, 'grad_norm': 1.2315500974655151, 'learning_rate': 4.242424242424243e-06, 'epoch': 7.88}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 80%|████████  | 264/330 [1:07:35<16:07, 14.66s/it]\n",
+            " 80%|████████  | 264/330 [1:08:20<16:07, 14.66s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 0.20570391416549683, 'eval_accuracy': 1.0, 'eval_f1': 1.0, 'eval_precision': 1.0, 'eval_recall': 1.0, 'eval_runtime': 44.7212, 'eval_samples_per_second': 2.907, 'eval_steps_per_second': 0.201, 'epoch': 8.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 82%|████████▏ | 270/330 [1:10:02<18:52, 18.88s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.2295, 'grad_norm': 1.2762770652770996, 'learning_rate': 3.6363636363636366e-06, 'epoch': 8.18}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 85%|████████▍ | 280/330 [1:12:19<09:35, 11.50s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.2501, 'grad_norm': 1.7389901876449585, 'learning_rate': 3.0303030303030305e-06, 'epoch': 8.48}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 88%|████████▊ | 290/330 [1:14:18<07:50, 11.77s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.2288, 'grad_norm': 1.2991100549697876, 'learning_rate': 2.4242424242424244e-06, 'epoch': 8.79}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 90%|█████████ | 297/330 [1:15:50<06:16, 11.42s/it]\n",
+            " 90%|█████████ | 297/330 [1:16:33<06:16, 11.42s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 0.18122541904449463, 'eval_accuracy': 1.0, 'eval_f1': 1.0, 'eval_precision': 1.0, 'eval_recall': 1.0, 'eval_runtime': 43.2859, 'eval_samples_per_second': 3.003, 'eval_steps_per_second': 0.208, 'epoch': 9.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 91%|█████████ | 300/330 [1:17:23<10:40, 21.34s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.2134, 'grad_norm': 1.2514674663543701, 'learning_rate': 1.8181818181818183e-06, 'epoch': 9.09}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 94%|█████████▍| 310/330 [1:20:04<05:09, 15.49s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.2234, 'grad_norm': 1.5625360012054443, 'learning_rate': 1.2121212121212122e-06, 'epoch': 9.39}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 97%|█████████▋| 320/330 [1:22:57<02:54, 17.45s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.2101, 'grad_norm': 1.0155290365219116, 'learning_rate': 6.060606060606061e-07, 'epoch': 9.7}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 330/330 [1:25:26<00:00, 13.85s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'loss': 0.2211, 'grad_norm': 1.2600934505462646, 'learning_rate': 0.0, 'epoch': 10.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "100%|██████████| 330/330 [1:26:13<00:00, 13.85s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'eval_loss': 0.17457057535648346, 'eval_accuracy': 1.0, 'eval_f1': 1.0, 'eval_precision': 1.0, 'eval_recall': 1.0, 'eval_runtime': 43.9486, 'eval_samples_per_second': 2.958, 'eval_steps_per_second': 0.205, 'epoch': 10.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 330/330 [1:26:17<00:00, 15.69s/it]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'train_runtime': 5177.7572, 'train_samples_per_second': 1.002, 'train_steps_per_second': 0.064, 'train_loss': 1.0024475039857808, 'epoch': 10.0}\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 9/9 [00:34<00:00,  3.79s/it]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Evaluation Results for Quantity Model: {'eval_loss': 0.17457057535648346, 'eval_accuracy': 1.0, 'eval_f1': 1.0, 'eval_precision': 1.0, 'eval_recall': 1.0, 'eval_runtime': 38.991, 'eval_samples_per_second': 3.334, 'eval_steps_per_second': 0.231, 'epoch': 10.0}\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "('./fine-tuned-quantity-model/tokenizer_config.json',\n",
+              " './fine-tuned-quantity-model/special_tokens_map.json',\n",
+              " './fine-tuned-quantity-model/vocab.txt',\n",
+              " './fine-tuned-quantity-model/added_tokens.json',\n",
+              " './fine-tuned-quantity-model/tokenizer.json')"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "quantity_trainer = Trainer(\n",
+        "    model=quantity_model,  # The model for quantities (numeric prediction)\n",
+        "    args=training_args,\n",
+        "    train_dataset=train_quantity_dataset,  # Training dataset for quantity\n",
+        "    eval_dataset=test_quantity_dataset,  # Evaluation dataset for quantity\n",
+        "    compute_metrics=compute_metrics\n",
+        ")\n",
+        "\n",
+        "# Train the quantity model\n",
+        "quantity_trainer.train()\n",
+        "\n",
+        "# Evaluate the quantity model on the test dataset\n",
+        "eval_results_quantity = quantity_trainer.evaluate(eval_dataset=test_quantity_dataset)\n",
+        "print(f\"Evaluation Results for Quantity Model: {eval_results_quantity}\")\n",
+        "\n",
+        "# Save the trained quantity model and tokenizer\n",
+        "quantity_model.save_pretrained(\"./fine-tuned-quantity-model\")\n",
+        "tokenizer.save_pretrained(\"./fine-tuned-quantity-model\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m8KqgPo3Dhta"
+      },
+      "source": [
+        "# **3. Using the Model for Prediction**\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import spacy\n",
+        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+        "\n",
+        "nlp = spacy.load(\"en_core_web_sm\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xPJuvRerDyq3",
+        "outputId": "87f5572c-97e4-4b6a-f07d-f43342f90ba7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "DistilBertForSequenceClassification(\n",
+              "  (distilbert): DistilBertModel(\n",
+              "    (embeddings): Embeddings(\n",
+              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
+              "      (position_embeddings): Embedding(512, 768)\n",
+              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "      (dropout): Dropout(p=0.1, inplace=False)\n",
+              "    )\n",
+              "    (transformer): Transformer(\n",
+              "      (layer): ModuleList(\n",
+              "        (0-5): 6 x TransformerBlock(\n",
+              "          (attention): MultiHeadSelfAttention(\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
+              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
+              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
+              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
+              "          )\n",
+              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "          (ffn): FFN(\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
+              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
+              "            (activation): GELUActivation()\n",
+              "          )\n",
+              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+              "        )\n",
+              "      )\n",
+              "    )\n",
+              "  )\n",
+              "  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
+              "  (classifier): Linear(in_features=768, out_features=20, bias=True)\n",
+              "  (dropout): Dropout(p=0.2, inplace=False)\n",
+              ")"
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "operation_model_name = \"./fine-tuned-operation-model\"\n",
+        "quantity_model_name = \"./fine-tuned-quantity-model\"\n",
+        "\n",
+        "operation_tokenizer = AutoTokenizer.from_pretrained(operation_model_name)\n",
+        "quantity_tokenizer = AutoTokenizer.from_pretrained(quantity_model_name)\n",
+        "\n",
+        "operation_model = AutoModelForSequenceClassification.from_pretrained(operation_model_name)\n",
+        "quantity_model = AutoModelForSequenceClassification.from_pretrained(quantity_model_name)\n",
+        "\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "operation_model.to(device)\n",
+        "quantity_model.to(device)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "PvzjdLk0Dm1W"
+      },
+      "outputs": [],
+      "source": [
+        "def predict_operation(sentence):\n",
+        "    inputs = operation_tokenizer(sentence, padding=True, truncation=True, return_tensors=\"pt\").to(device)\n",
+        "    with torch.no_grad():\n",
+        "        outputs = operation_model(**inputs)\n",
+        "    logits = outputs.logits\n",
+        "    predictions = torch.softmax(logits, dim=-1)\n",
+        "    predicted_label = torch.argmax(predictions, dim=-1).item()\n",
+        "    operation = \"Add\" if predicted_label == 0 else \"Remove\"  # Adjust based on your label encoding\n",
+        "    return operation, predictions[0].cpu().numpy()\n",
+        "\n",
+        "def predict_quantity(sentence):\n",
+        "    inputs = quantity_tokenizer(sentence, padding=True, truncation=True, return_tensors=\"pt\").to(device)\n",
+        "    with torch.no_grad():\n",
+        "        outputs = quantity_model(**inputs)\n",
+        "    logits = outputs.logits\n",
+        "    predictions = torch.softmax(logits, dim=-1)\n",
+        "    predicted_quantity = torch.argmax(predictions, dim=-1).item()\n",
+        "    return predicted_quantity, predictions[0].cpu().numpy()\n",
+        "\n",
+        "def extract_item_name(sentence):\n",
+        "    doc = nlp(sentence)\n",
+        "    noun_chunks = [chunk.text for chunk in doc.noun_chunks]\n",
+        "    for chunk in noun_chunks:\n",
+        "        if any(tok.pos_ == \"NOUN\" for tok in nlp(chunk).doc):\n",
+        "            return chunk\n",
+        "\n",
+        "    return None"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BjgV2dU1DsMk",
+        "outputId": "5651b853-9c6a-4ca9-a289-8cc154416d00"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sentence: Add soups to my cart.\n",
+            "Predicted Operation: Add\n",
+            "Operation Probabilities: [0.9962047  0.00111221 0.00120974 0.00147345]\n",
+            "Predicted Quantity: 1\n",
+            "Quantity Probabilities: [0.07971817 0.2707245  0.1223022  0.11824853 0.03780839 0.09126952\n",
+            " 0.06610113 0.05348995 0.03518798 0.02410132 0.02650298 0.00751828\n",
+            " 0.01491447 0.00636614 0.00600904 0.00775851 0.00745718 0.00924029\n",
+            " 0.00832553 0.00695582]\n",
+            "Item Name -----> soups\n"
+          ]
+        }
+      ],
+      "source": [
+        "sentence = \"Add soups to my cart.\"\n",
+        "\n",
+        "# Predict operation and quantity\n",
+        "operation, operation_probs = predict_operation(sentence)\n",
+        "quantity, quantity_probs = predict_quantity(sentence)\n",
+        "\n",
+        "print(f\"Sentence: {sentence}\")\n",
+        "print(f\"Predicted Operation: {operation}\")\n",
+        "print(f\"Operation Probabilities: {operation_probs}\")\n",
+        "\n",
+        "print(f\"Predicted Quantity: {quantity}\")\n",
+        "print(f\"Quantity Probabilities: {quantity_probs}\")\n",
+        "\n",
+        "print(f\"Item Name -----> {extract_item_name(sentence=sentence)}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0vTikcxxEBAt"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
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+    "accelerator": "GPU",
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