diff --git "a/Assignment_2.ipynb" "b/Assignment_2.ipynb"
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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "N3shQZoZPScM",
+        "outputId": "63642e05-bd32-4fd9-f029-8f50148a1e8a"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U sentence_transformers --q"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rcBH0FzwVOk6",
+        "outputId": "f5b4b762-9b30-4474-d1d0-7ba3ab68a2ef"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "pip install datasets --q"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "y-pDMu97XyVd",
+        "outputId": "737160a3-2c34-4293-a129-bb053cd91117"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting sentence-transformers\n",
+            "  Using cached sentence_transformers-3.4.1-py3-none-any.whl.metadata (10 kB)\n",
+            "Requirement already satisfied: scikit-learn in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (1.4.1.post1)\n",
+            "Requirement already satisfied: pandas in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (2.2.1)\n",
+            "Collecting torch\n",
+            "  Downloading torch-2.6.0-cp312-none-macosx_11_0_arm64.whl.metadata (28 kB)\n",
+            "Collecting transformers<5.0.0,>=4.41.0 (from sentence-transformers)\n",
+            "  Downloading transformers-4.48.3-py3-none-any.whl.metadata (44 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: tqdm in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from sentence-transformers) (4.67.1)\n",
+            "Requirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from sentence-transformers) (1.12.0)\n",
+            "Requirement already satisfied: huggingface-hub>=0.20.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from sentence-transformers) (0.28.1)\n",
+            "Requirement already satisfied: Pillow in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from sentence-transformers) (10.2.0)\n",
+            "Requirement already satisfied: numpy<2.0,>=1.19.5 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from scikit-learn) (1.26.4)\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from scikit-learn) (1.3.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from scikit-learn) (3.3.0)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/markushenriksson/Library/Python/3.12/lib/python/site-packages (from pandas) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from pandas) (2024.1)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from pandas) (2024.1)\n",
+            "Requirement already satisfied: filelock in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from torch) (3.16.0)\n",
+            "Requirement already satisfied: typing-extensions>=4.10.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from torch) (4.10.0)\n",
+            "Collecting networkx (from torch)\n",
+            "  Using cached networkx-3.4.2-py3-none-any.whl.metadata (6.3 kB)\n",
+            "Requirement already satisfied: jinja2 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from torch) (3.1.3)\n",
+            "Requirement already satisfied: fsspec in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from torch) (2024.2.0)\n",
+            "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from torch) (69.1.1)\n",
+            "Collecting sympy==1.13.1 (from torch)\n",
+            "  Using cached sympy-1.13.1-py3-none-any.whl.metadata (12 kB)\n",
+            "Collecting mpmath<1.4,>=1.1.0 (from sympy==1.13.1->torch)\n",
+            "  Using cached mpmath-1.3.0-py3-none-any.whl.metadata (8.6 kB)\n",
+            "Requirement already satisfied: packaging>=20.9 in /Users/markushenriksson/Library/Python/3.12/lib/python/site-packages (from huggingface-hub>=0.20.0->sentence-transformers) (24.0)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from huggingface-hub>=0.20.0->sentence-transformers) (6.0.1)\n",
+            "Requirement already satisfied: requests in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from huggingface-hub>=0.20.0->sentence-transformers) (2.32.3)\n",
+            "Requirement already satisfied: six>=1.5 in /Users/markushenriksson/Library/Python/3.12/lib/python/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
+            "Collecting regex!=2019.12.17 (from transformers<5.0.0,>=4.41.0->sentence-transformers)\n",
+            "  Using cached regex-2024.11.6-cp312-cp312-macosx_11_0_arm64.whl.metadata (40 kB)\n",
+            "Collecting tokenizers<0.22,>=0.21 (from transformers<5.0.0,>=4.41.0->sentence-transformers)\n",
+            "  Downloading tokenizers-0.21.0-cp39-abi3-macosx_11_0_arm64.whl.metadata (6.7 kB)\n",
+            "Collecting safetensors>=0.4.1 (from transformers<5.0.0,>=4.41.0->sentence-transformers)\n",
+            "  Downloading safetensors-0.5.2-cp38-abi3-macosx_11_0_arm64.whl.metadata (3.8 kB)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from jinja2->torch) (2.1.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (2024.2.2)\n",
+            "Using cached sentence_transformers-3.4.1-py3-none-any.whl (275 kB)\n",
+            "Downloading torch-2.6.0-cp312-none-macosx_11_0_arm64.whl (66.5 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.5/66.5 MB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
+            "\u001b[?25hUsing cached sympy-1.13.1-py3-none-any.whl (6.2 MB)\n",
+            "Downloading transformers-4.48.3-py3-none-any.whl (9.7 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.7/9.7 MB\u001b[0m \u001b[31m35.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
+            "\u001b[?25hUsing cached networkx-3.4.2-py3-none-any.whl (1.7 MB)\n",
+            "Using cached mpmath-1.3.0-py3-none-any.whl (536 kB)\n",
+            "Using cached regex-2024.11.6-cp312-cp312-macosx_11_0_arm64.whl (284 kB)\n",
+            "Downloading safetensors-0.5.2-cp38-abi3-macosx_11_0_arm64.whl (408 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m408.9/408.9 kB\u001b[0m \u001b[31m27.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading tokenizers-0.21.0-cp39-abi3-macosx_11_0_arm64.whl (2.6 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: mpmath, sympy, safetensors, regex, networkx, torch, tokenizers, transformers, sentence-transformers\n",
+            "Successfully installed mpmath-1.3.0 networkx-3.4.2 regex-2024.11.6 safetensors-0.5.2 sentence-transformers-3.4.1 sympy-1.13.1 tokenizers-0.21.0 torch-2.6.0 transformers-4.48.3\n",
+            "\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "pip install sentence-transformers scikit-learn pandas torch\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "m-tmgXuldd3C"
+      },
+      "outputs": [],
+      "source": [
+        "import seaborn as sns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "1Z0mgYZEgjC4"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import RandomForestClassifier"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "aBmXLbZ4cc1U"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "LXkXdIWgUcWI"
+      },
+      "outputs": [],
+      "source": [
+        "from datasets import load_dataset, Dataset\n",
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "AFkI23ySgtkV"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import accuracy_score"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ehvh1BJZWa_1",
+        "outputId": "212a5f82-885d-4e61-a73f-94dcf12a3a39"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ab0344420d964f64a16c911f17aae057",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "README.md:   0%|          | 0.00/515 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9f86cbde053f4b9e91cff137a924082f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00000-of-00001.parquet:   0%|          | 0.00/5.89M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2f6d00487331444299405cc97d4b18ea",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split:   0%|          | 0/61199 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "     answer                                      system_prompt  \\\n",
+            "0   neutral  You are a financial sentiment analysis expert....   \n",
+            "1   neutral  You are a financial sentiment analysis expert....   \n",
+            "2  negative  You are a financial sentiment analysis expert....   \n",
+            "3  positive  You are a financial sentiment analysis expert....   \n",
+            "4  positive  You are a financial sentiment analysis expert....   \n",
+            "\n",
+            "                                         user_prompt           task_type  \n",
+            "0  According to Gran , the company has no plans t...  sentiment_analysis  \n",
+            "1  Technopolis plans to develop in stages an area...  sentiment_analysis  \n",
+            "2  The international electronic industry company ...  sentiment_analysis  \n",
+            "3  With the new production plant the company woul...  sentiment_analysis  \n",
+            "4  According to the company 's updated strategy f...  sentiment_analysis  \n"
+          ]
+        }
+      ],
+      "source": [
+        "df = load_dataset(\"NickyNicky/Finance_sentiment_and_topic_classification_En\")\n",
+        "\n",
+        "# Converting 'train' split to a Pandas DataFrame\n",
+        "df = pd.DataFrame(df['train'])\n",
+        "\n",
+        "\n",
+        "print(df.head())\n",
+        "\n",
+        "\n",
+        "df.to_csv(\"train_data.csv\", index=False)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "wS-PmD5WWnYC",
+        "outputId": "36732946-2bb0-4f58-f784-5619d77698b9"
+      },
+      "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>answer</th>\n",
+              "      <th>system_prompt</th>\n",
+              "      <th>user_prompt</th>\n",
+              "      <th>task_type</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>neutral</td>\n",
+              "      <td>You are a financial sentiment analysis expert....</td>\n",
+              "      <td>According to Gran , the company has no plans t...</td>\n",
+              "      <td>sentiment_analysis</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>neutral</td>\n",
+              "      <td>You are a financial sentiment analysis expert....</td>\n",
+              "      <td>Technopolis plans to develop in stages an area...</td>\n",
+              "      <td>sentiment_analysis</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>negative</td>\n",
+              "      <td>You are a financial sentiment analysis expert....</td>\n",
+              "      <td>The international electronic industry company ...</td>\n",
+              "      <td>sentiment_analysis</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>positive</td>\n",
+              "      <td>You are a financial sentiment analysis expert....</td>\n",
+              "      <td>With the new production plant the company woul...</td>\n",
+              "      <td>sentiment_analysis</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>positive</td>\n",
+              "      <td>You are a financial sentiment analysis expert....</td>\n",
+              "      <td>According to the company 's updated strategy f...</td>\n",
+              "      <td>sentiment_analysis</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     answer                                      system_prompt  \\\n",
+              "0   neutral  You are a financial sentiment analysis expert....   \n",
+              "1   neutral  You are a financial sentiment analysis expert....   \n",
+              "2  negative  You are a financial sentiment analysis expert....   \n",
+              "3  positive  You are a financial sentiment analysis expert....   \n",
+              "4  positive  You are a financial sentiment analysis expert....   \n",
+              "\n",
+              "                                         user_prompt           task_type  \n",
+              "0  According to Gran , the company has no plans t...  sentiment_analysis  \n",
+              "1  Technopolis plans to develop in stages an area...  sentiment_analysis  \n",
+              "2  The international electronic industry company ...  sentiment_analysis  \n",
+              "3  With the new production plant the company woul...  sentiment_analysis  \n",
+              "4  According to the company 's updated strategy f...  sentiment_analysis  "
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "cQ3tjGFTW5kE"
+      },
+      "outputs": [],
+      "source": [
+        "df.drop(['system_prompt', 'task_type'], axis=1, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "Va5867ATXAxD",
+        "outputId": "c258f546-d8af-4ba5-8228-3a07f2283baf"
+      },
+      "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>answer</th>\n",
+              "      <th>user_prompt</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>neutral</td>\n",
+              "      <td>According to Gran , the company has no plans t...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>neutral</td>\n",
+              "      <td>Technopolis plans to develop in stages an area...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>negative</td>\n",
+              "      <td>The international electronic industry company ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>positive</td>\n",
+              "      <td>With the new production plant the company woul...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>positive</td>\n",
+              "      <td>According to the company 's updated strategy f...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>61194</th>\n",
+              "      <td>Treasuries | Corporate Debt</td>\n",
+              "      <td>KfW credit line for Uniper could be raised to ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>61195</th>\n",
+              "      <td>Treasuries | Corporate Debt</td>\n",
+              "      <td>KfW credit line for Uniper could be raised to ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>61196</th>\n",
+              "      <td>Treasuries | Corporate Debt</td>\n",
+              "      <td>Russian  https://t.co/R0iPhyo5p7 sells 1 bln r...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>61197</th>\n",
+              "      <td>Treasuries | Corporate Debt</td>\n",
+              "      <td>Global ESG bond issuance posts H1 dip as supra...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>61198</th>\n",
+              "      <td>Treasuries | Corporate Debt</td>\n",
+              "      <td>Brazil's Petrobras says it signed a $1.25 bill...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>61199 rows × 2 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            answer  \\\n",
+              "0                          neutral   \n",
+              "1                          neutral   \n",
+              "2                         negative   \n",
+              "3                         positive   \n",
+              "4                         positive   \n",
+              "...                            ...   \n",
+              "61194  Treasuries | Corporate Debt   \n",
+              "61195  Treasuries | Corporate Debt   \n",
+              "61196  Treasuries | Corporate Debt   \n",
+              "61197  Treasuries | Corporate Debt   \n",
+              "61198  Treasuries | Corporate Debt   \n",
+              "\n",
+              "                                             user_prompt  \n",
+              "0      According to Gran , the company has no plans t...  \n",
+              "1      Technopolis plans to develop in stages an area...  \n",
+              "2      The international electronic industry company ...  \n",
+              "3      With the new production plant the company woul...  \n",
+              "4      According to the company 's updated strategy f...  \n",
+              "...                                                  ...  \n",
+              "61194  KfW credit line for Uniper could be raised to ...  \n",
+              "61195  KfW credit line for Uniper could be raised to ...  \n",
+              "61196  Russian  https://t.co/R0iPhyo5p7 sells 1 bln r...  \n",
+              "61197  Global ESG bond issuance posts H1 dip as supra...  \n",
+              "61198  Brazil's Petrobras says it signed a $1.25 bill...  \n",
+              "\n",
+              "[61199 rows x 2 columns]"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a2PtcHIfeM5t",
+        "outputId": "2214b201-c68d-4112-d224-855bd7103213"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(39641, 2)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# only want to keep rows where 'answer' is 'neutral', 'positive', or 'negative'\n",
+        "df_filtered = df[df[\"answer\"].isin([\"neutral\", \"positive\", \"negative\"])]\n",
+        "\n",
+        "# Showing the shape of the new DataFrame\n",
+        "print(df_filtered.shape)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OzmsDZ-tZuGA",
+        "outputId": "cb743649-521b-45da-8c9a-182fff7584bd"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(5946, 2)\n"
+          ]
+        }
+      ],
+      "source": [
+        "df_sampled = df_filtered.sample(frac=0.15, random_state=42)  # 15% sample\n",
+        "print(df_sampled.shape)  # Checking new size\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "id": "kkaxAfyQdcgZ",
+        "outputId": "1fa73eb8-c4ea-4b0b-8288-7dcb3c517798"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='answer', ylabel='count'>"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.countplot(x=df_sampled[\"answer\"])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "e2DP6ekqfNbe",
+        "outputId": "3aacbc50-8554-40eb-9cdb-c949c30d634e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "answer\n",
+            "negative    1236\n",
+            "neutral     1236\n",
+            "positive    1236\n",
+            "Name: count, dtype: int64\n",
+            "(3708, 2)\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/var/folders/xc/v1l81vkx6fjc9wpqc0tsnl400000gn/T/ipykernel_11468/1830774783.py:5: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+            "  df_balanced = df_sampled.groupby(\"answer\").apply(lambda x: x.sample(min_class_count, random_state=42)).reset_index(drop=True)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Undersampling each class to match the class with the smallest number of samples\n",
+        "min_class_count = df_sampled[\"answer\"].value_counts().min()\n",
+        "\n",
+        "# Sampling an equal number of rows from each class\n",
+        "df_balanced = df_sampled.groupby(\"answer\").apply(lambda x: x.sample(min_class_count, random_state=42)).reset_index(drop=True)\n",
+        "\n",
+        "# Showing the new class distribution\n",
+        "print(df_balanced[\"answer\"].value_counts())\n",
+        "print(df_balanced.shape)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "dJosNJACYDCc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "593c0e8a5f6b4b9495ff422cc2382975",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "850ce1db88c64b81802f2a60f45801d4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8cfd7d7217a24485818919eebbca3cb2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6aa8a1f649ec471ebe07ee374e80de62",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "cb49e7e953654af8af5fd5d22f78ce59",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "12bb5d1510b1422582f091a49fa617a0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b9632a0063044733bd70e443fce6caed",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5d345282cdd6400d8bc229280df8766e",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0473d385d77c406a94075d701d4565e1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "643a5bf87437468e8a168e55687814d5",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4560c43f64884ba9b236962626a1f784",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "1_Pooling%2Fconfig.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Load model\n",
+        "from sentence_transformers import SentenceTransformer # import the SentenceTransformer class\n",
+        "model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
+        "\n",
+        "# Converting text to embeddings\n",
+        "X = model.encode(df_balanced[\"user_prompt\"].tolist(), convert_to_numpy=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mH4_pI6YZa3E",
+        "outputId": "667d2b0f-a60a-4afd-e2aa-270ef9d5b8de"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Label Mapping: {'negative': 0, 'neutral': 1, 'positive': 2}\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "\n",
+        "# Encode labels\n",
+        "label_encoder = LabelEncoder()\n",
+        "y = label_encoder.fit_transform(df_balanced[\"answer\"])\n",
+        "\n",
+        "# Saving the mapping\n",
+        "label_mapping = dict(zip(label_encoder.classes_, label_encoder.transform(label_encoder.classes_)))\n",
+        "print(\"Label Mapping:\", label_mapping)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "I5gpynBmZe1h"
+      },
+      "outputs": [],
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "jCUXUqgRgdeJ",
+        "outputId": "8e27311c-e799-4a89-f319-f7b6f4e07e75"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "RandomForestClassifier(random_state=42)"
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+        "clf.fit(X_train, y_train)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.66      0.52      0.58       277\n",
+            "           1       0.62      0.80      0.70       237\n",
+            "           2       0.55      0.52      0.54       228\n",
+            "\n",
+            "    accuracy                           0.61       742\n",
+            "   macro avg       0.61      0.61      0.61       742\n",
+            "weighted avg       0.61      0.61      0.60       742\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sentence_transformers import SentenceTransformer\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "from sklearn.metrics import classification_report\n",
+        "\n",
+        "# Load model (already done)\n",
+        "model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
+        "\n",
+        "# Converting text to embeddings\n",
+        "X = model.encode(df_balanced[\"user_prompt\"].tolist(), convert_to_numpy=True)\n",
+        "\n",
+        "# Encode labels (already done)\n",
+        "label_encoder = LabelEncoder()\n",
+        "y = label_encoder.fit_transform(df_balanced[\"answer\"])\n",
+        "\n",
+        "# Train-test split (already done)\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "\n",
+        "# Initialize and train RandomForestClassifier\n",
+        "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+        "clf.fit(X_train, y_train)\n",
+        "\n",
+        "# Make predictions on the test set\n",
+        "y_pred = clf.predict(X_test)\n",
+        "\n",
+        "# Print classification report to evaluate performance\n",
+        "print(classification_report(y_test, y_pred))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+            "To disable this warning, you can either:\n",
+            "\t- Avoid using `tokenizers` before the fork if possible\n",
+            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
+            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+            "To disable this warning, you can either:\n",
+            "\t- Avoid using `tokenizers` before the fork if possible\n",
+            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
+            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+            "To disable this warning, you can either:\n",
+            "\t- Avoid using `tokenizers` before the fork if possible\n",
+            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
+            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+            "To disable this warning, you can either:\n",
+            "\t- Avoid using `tokenizers` before the fork if possible\n",
+            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
+            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+            "To disable this warning, you can either:\n",
+            "\t- Avoid using `tokenizers` before the fork if possible\n",
+            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
+            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+            "To disable this warning, you can either:\n",
+            "\t- Avoid using `tokenizers` before the fork if possible\n",
+            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
+            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+            "To disable this warning, you can either:\n",
+            "\t- Avoid using `tokenizers` before the fork if possible\n",
+            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
+            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+            "To disable this warning, you can either:\n",
+            "\t- Avoid using `tokenizers` before the fork if possible\n",
+            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
+            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+            "To disable this warning, you can either:\n",
+            "\t- Avoid using `tokenizers` before the fork if possible\n",
+            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Best Parameters: {'max_depth': 20, 'min_samples_split': 5, 'n_estimators': 200}\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.model_selection import GridSearchCV\n",
+        "\n",
+        "param_grid = {\n",
+        "    'n_estimators': [50, 100, 200],\n",
+        "    'max_depth': [10, 20, 30],\n",
+        "    'min_samples_split': [2, 5, 10]\n",
+        "}\n",
+        "\n",
+        "grid_search = GridSearchCV(estimator=clf, param_grid=param_grid, cv=3, n_jobs=-1)\n",
+        "grid_search.fit(X_train, y_train)\n",
+        "print(\"Best Parameters:\", grid_search.best_params_)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Forest Hyperparameters:\n",
+            "Number of Estimators (n_estimators): 200\n",
+            "Max Depth (max_depth): 20\n",
+            "Min Samples Split (min_samples_split): 5\n",
+            "Min Samples Leaf (min_samples_leaf): 1\n",
+            "Random State (random_state): 42\n",
+            "Max Features (max_features): sqrt\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the hyperparameters of the trained model\n",
+        "print(\"Random Forest Hyperparameters:\")\n",
+        "print(f\"Number of Estimators (n_estimators): {clf.n_estimators}\")\n",
+        "print(f\"Max Depth (max_depth): {clf.max_depth}\")\n",
+        "print(f\"Min Samples Split (min_samples_split): {clf.min_samples_split}\")\n",
+        "print(f\"Min Samples Leaf (min_samples_leaf): {clf.min_samples_leaf}\")\n",
+        "print(f\"Random State (random_state): {clf.random_state}\")\n",
+        "print(f\"Max Features (max_features): {clf.max_features}\")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-4 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-4 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-4 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-4 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-4 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=20, min_samples_split=5, n_estimators=200,\n",
+              "                       random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=20, min_samples_split=5, n_estimators=200,\n",
+              "                       random_state=42)</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "RandomForestClassifier(max_depth=20, min_samples_split=5, n_estimators=200,\n",
+              "                       random_state=42)"
+            ]
+          },
+          "execution_count": 57,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "\n",
+        "# Updated parameters based on your best values\n",
+        "clf = RandomForestClassifier(\n",
+        "    n_estimators=200,          # Set number of trees to 200\n",
+        "    max_depth=20,              # Set max depth to 20\n",
+        "    min_samples_split=5,       # Set min samples split to 5\n",
+        "    random_state=42,           # Keep random state for reproducibility\n",
+        "    max_features='sqrt',       # Default 'sqrt' for Random Forest (best practice)\n",
+        "    min_samples_leaf=1         # Default value is 1, change if needed\n",
+        ")\n",
+        "\n",
+        "# Train the model with the new parameters\n",
+        "clf.fit(X_train, y_train)\n",
+        "\n",
+        "# You can now use clf for prediction and evaluation as usual\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.66      0.52      0.58       277\n",
+            "           1       0.62      0.80      0.70       237\n",
+            "           2       0.55      0.52      0.54       228\n",
+            "\n",
+            "    accuracy                           0.61       742\n",
+            "   macro avg       0.61      0.61      0.61       742\n",
+            "weighted avg       0.61      0.61      0.60       742\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(classification_report(y_test, y_pred))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "\n",
+        "# Generate predictions on the test data\n",
+        "y_pred = clf.predict(X_test)\n",
+        "\n",
+        "# Create the confusion matrix\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "\n",
+        "# Plot the confusion matrix\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n",
+        "plt.title(\"Confusion Matrix for Financial Sentiment Classifier\")\n",
+        "plt.xlabel(\"Predicted Label\")\n",
+        "plt.ylabel(\"True Label\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "v8DE8aAzg4jQ",
+        "outputId": "5ce78149-c53b-45f3-994f-5f6c7d21b819"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Predicted Label: neutral\n"
+          ]
+        }
+      ],
+      "source": [
+        "new_texts = [\"The company is doing OK\"]\n",
+        "new_embeddings = model.encode(new_texts, convert_to_numpy=True)\n",
+        "predicted_label = clf.predict(new_embeddings)\n",
+        "\n",
+        "# Convert back to original label names\n",
+        "decoded_label = label_encoder.inverse_transform(predicted_label)\n",
+        "print(\"Predicted Label:\", decoded_label[0])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['random_forest_classifier.pkl']"
+            ]
+          },
+          "execution_count": 49,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Save SBERT model\n",
+        "\n",
+        "\n",
+        "# Save RandomForest model\n",
+        "import joblib\n",
+        "joblib.dump(clf, \"random_forest_classifier.pkl\")  # Save the trained classifier model\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+            "To disable this warning, you can either:\n",
+            "\t- Avoid using `tokenizers` before the fork if possible\n",
+            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: transformers in /opt/anaconda3/lib/python3.12/site-packages (4.45.2)\n",
+            "Requirement already satisfied: datasets in /opt/anaconda3/lib/python3.12/site-packages (3.1.0)\n",
+            "Requirement already satisfied: huggingface_hub in /opt/anaconda3/lib/python3.12/site-packages (0.26.2)\n",
+            "Requirement already satisfied: filelock in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (3.13.1)\n",
+            "Requirement already satisfied: numpy>=1.17 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (2.0.2)\n",
+            "Requirement already satisfied: packaging>=20.0 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (24.2)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (6.0.1)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (2023.10.3)\n",
+            "Requirement already satisfied: requests in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (2.32.3)\n",
+            "Requirement already satisfied: safetensors>=0.4.1 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (0.4.5)\n",
+            "Requirement already satisfied: tokenizers<0.21,>=0.20 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (0.20.3)\n",
+            "Requirement already satisfied: tqdm>=4.27 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (4.66.4)\n",
+            "Requirement already satisfied: pyarrow>=15.0.0 in /opt/anaconda3/lib/python3.12/site-packages (from datasets) (17.0.0)\n",
+            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /opt/anaconda3/lib/python3.12/site-packages (from datasets) (0.3.8)\n",
+            "Requirement already satisfied: pandas in /opt/anaconda3/lib/python3.12/site-packages (from datasets) (2.2.3)\n",
+            "Requirement already satisfied: xxhash in /opt/anaconda3/lib/python3.12/site-packages (from datasets) (3.5.0)\n",
+            "Requirement already satisfied: multiprocess<0.70.17 in /opt/anaconda3/lib/python3.12/site-packages (from datasets) (0.70.16)\n",
+            "Requirement already satisfied: fsspec<=2024.9.0,>=2023.1.0 in /opt/anaconda3/lib/python3.12/site-packages (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets) (2024.3.1)\n",
+            "Requirement already satisfied: aiohttp in /opt/anaconda3/lib/python3.12/site-packages (from datasets) (3.9.5)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/anaconda3/lib/python3.12/site-packages (from huggingface_hub) (4.12.2)\n",
+            "Requirement already satisfied: aiosignal>=1.1.2 in /opt/anaconda3/lib/python3.12/site-packages (from aiohttp->datasets) (1.2.0)\n",
+            "Requirement already satisfied: attrs>=17.3.0 in /opt/anaconda3/lib/python3.12/site-packages (from aiohttp->datasets) (23.1.0)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /opt/anaconda3/lib/python3.12/site-packages (from aiohttp->datasets) (1.4.0)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/anaconda3/lib/python3.12/site-packages (from aiohttp->datasets) (6.0.4)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/anaconda3/lib/python3.12/site-packages (from aiohttp->datasets) (1.9.3)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/anaconda3/lib/python3.12/site-packages (from requests->transformers) (2.0.4)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /opt/anaconda3/lib/python3.12/site-packages (from requests->transformers) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/anaconda3/lib/python3.12/site-packages (from requests->transformers) (2.2.2)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /opt/anaconda3/lib/python3.12/site-packages (from requests->transformers) (2024.8.30)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/anaconda3/lib/python3.12/site-packages (from pandas->datasets) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/lib/python3.12/site-packages (from pandas->datasets) (2024.1)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/lib/python3.12/site-packages (from pandas->datasets) (2023.3)\n",
+            "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install transformers datasets huggingface_hub\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}