2_5_3_Named_Entity_Recognition_(NER).ipynb

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      "source": [
        "# <span style=\"color:blue\">Project Overview: Named Entity Recognition Using CRF</span>\n",
        "\n",
        "## <span style=\"color:darkgreen\">1. Introduction</span>\n",
        "This project implements a Named Entity Recognition (NER) system using Conditional Random Fields (CRF). The system processes datasets from CoNLL 2002, focusing on Dutch and Spanish languages, to identify and classify entities within text.\n",
        "\n",
        "## <span style=\"color:darkorange\">2. Objectives</span>\n",
        "- Develop a robust NER model using CRF.\n",
        "- Evaluate the model's performance using F1 scores.\n",
        "- Extract meaningful features from the text for enhanced model accuracy.\n",
        "\n",
        "## <span style=\"color:darkgreen\">3. Datasets</span>\n",
        "The project utilizes the following datasets:\n",
        "- **Dutch Training Data**: `ned.train`\n",
        "- **Dutch Test Data**: `ned.testb`\n",
        "- **Spanish Training Data**: `esp.train`\n",
        "- **Spanish Test Data**: `esp.testb`\n",
        "\n",
        "These datasets are provided in the CoNLL format, where each line represents a token, followed by its corresponding entity label.\n",
        "\n",
        "## <span style=\"color:darkorange\">4. Methodology</span>\n",
        "1. **Data Extraction**:\n",
        "   - The datasets are extracted from a zip file and stored in a specified directory.\n",
        "\n",
        "2. **Data Reading**:\n",
        "   - Implement functions to read CoNLL files, which organize sentences and their corresponding labels.\n",
        "\n",
        "3. **Feature Extraction**:\n",
        "   - Design a feature extraction mechanism to identify attributes of each token, such as capitalization, numeric values, and positional context within sentences.\n",
        "\n",
        "4. **Model Training**:\n",
        "   - Utilize CRF for model training on both Dutch and Spanish datasets. The model is configured with:\n",
        "     - Algorithm: L-BFGS\n",
        "     - Maximum iterations: 100\n",
        "     - All possible transitions enabled.\n",
        "\n",
        "5. **Model Evaluation**:\n",
        "   - Evaluate model performance using F1 scores to measure the accuracy of entity recognition on the test datasets.\n",
        "\n",
        "## <span style=\"color:darkgreen\">5. Results</span>\n",
        "- The Dutch model is trained and evaluated, resulting in a calculated F1 score.\n",
        "- Similarly, the Spanish model is trained, with its performance metrics also recorded.\n",
        "\n",
        "## <span style=\"color:darkorange\">6. Conclusion</span>\n",
        "The project successfully demonstrates the implementation of a NER system using CRF, showing promising results for entity classification in both Dutch and Spanish texts. Future work may involve exploring additional features or employing other algorithms to enhance model performance."
      ],
      "metadata": {
        "id": "dqP_2Jbqc_Ai"
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HTLnkJK9dXbQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kf0HzHDldXtE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "agRAEaA3dX23"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue\">Step 1: Import Libraries</span>\n",
        "\n",
        "## <span style=\"color:darkgreen\">Overview</span>\n",
        "In this step, we import the necessary libraries that will facilitate data processing, model building, and evaluation. These libraries provide essential functionalities for natural language processing (NLP) and machine learning.\n",
        "\n",
        "## <span style=\"color:darkorange\">Libraries to Import</span>\n",
        "- **pandas**: Used for data manipulation and analysis, especially for handling datasets.\n",
        "- **numpy**: Provides support for numerical computations, enabling efficient operations on large datasets.\n",
        "- **sklearn**: Contains tools for machine learning, including model evaluation metrics.\n",
        "- **sklearn_crfsuite**: A specific implementation of Conditional Random Fields (CRF) for sequence labeling tasks in NLP.\n",
        "- **nltk**: Offers various NLP functionalities, including tokenization and evaluation metrics.\n",
        "\n",
        "## <span style=\"color:darkgreen\">Code Example</span>\n",
        "```python\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn_crfsuite import CRF\n",
        "import nltk"
      ],
      "metadata": {
        "id": "OrDNyCEqdYQU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sklearn-crfsuite  # Installs the sklearn-crfsuite library for building and training Conditional Random Fields (CRFs) for sequence modeling tasks.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aYT-TAoHWHkN",
        "outputId": "ee74bbd1-eef6-4a34-a0f0-82a867eb1480"
      },
      "execution_count": 44,
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lQyLD7bpiz-4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install matplotlib  # Install Matplotlib if not already installed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSBO5ExFi0M2",
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      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.3.0)\n",
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            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7SM5I3EfOKsQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1xRpyaz_fLvX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing Required Libraries"
      ],
      "metadata": {
        "id": "BHuljb21OJKj"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile  # For handling zip files"
      ],
      "metadata": {
        "id": "urBwfBwQOJNm"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os  # For interacting with the operating system"
      ],
      "metadata": {
        "id": "nhWQ1ZjQM0Rz"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re  # For regular expressions"
      ],
      "metadata": {
        "id": "XV16dEQ-Mt0b"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # For numerical operations"
      ],
      "metadata": {
        "id": "JEk8t6sCMt3K"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # For data manipulation"
      ],
      "metadata": {
        "id": "sTtJm3goMt5e"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn_crfsuite  # For Conditional Random Fields"
      ],
      "metadata": {
        "id": "PVhDobXvMt7z"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # For splitting datasets"
      ],
      "metadata": {
        "id": "_bQeIM2-Mt97"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder  # For encoding labels"
      ],
      "metadata": {
        "id": "ER87G6pxMuAi"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn_crfsuite import metrics  # For evaluation metrics"
      ],
      "metadata": {
        "id": "OUGRUEM0MuCk"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # For plotting"
      ],
      "metadata": {
        "id": "QUDVsEadMuFK"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay  # For confusion matrix"
      ],
      "metadata": {
        "id": "T1uTkOsEMuJm"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3PuJ6MzAMuQ6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uhLZNjQeMuTl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_G64DcpaMuVw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Step 2: Extract Data\n",
        "```markdown\n",
        "# <span style=\"color:blue\">Step 2: Extract Data</span>\n",
        "\n",
        "## <span style=\"color:darkgreen\">Overview</span>\n",
        "This step involves extracting the NER datasets from a compressed file format (e.g., zip). The datasets are essential for training and testing the CRF model.\n",
        "\n",
        "## <span style=\"color:darkorange\">Data Extraction Process</span>\n",
        "- Use Python's built-in libraries to handle file extraction.\n",
        "- Specify the source path of the zip file containing the dataset.\n",
        "- Unzip the file to a designated directory for easy access to individual dataset files.\n",
        "\n",
        "## <span style=\"color:darkgreen\">Code Example</span>\n",
        "```python\n",
        "import zipfile\n",
        "import os\n",
        "\n",
        "# Define the path to the zip file and the extraction directory\n",
        "zip_file_path = 'path/to/your/dataset.zip'\n",
        "extract_dir = 'path/to/extracted/dataset/'\n",
        "\n",
        "# Extract the zip file\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(extract_dir)"
      ],
      "metadata": {
        "id": "IWFx9gUVdhHH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the path to the zip file and the extraction path"
      ],
      "metadata": {
        "id": "PER8GimRMuX7"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "zip_file_path = '/content/archive (4).zip'  # Your dataset zip file path"
      ],
      "metadata": {
        "id": "RHq1tZz9MucK"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "extraction_path = '/content/conll_datasets/'  # Define where to extract"
      ],
      "metadata": {
        "id": "i3QeLSDqMugc"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pv7a-nm3aahq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KNVcNhtKacMo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the zip file if not already extracted"
      ],
      "metadata": {
        "id": "9_xKrEbxacQX"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if not os.path.exists(extraction_path):  # Check if extraction path exists\n",
        "    with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:  # Open the zip file\n",
        "        zip_ref.extractall(extraction_path)  # Extract all files to the specified path"
      ],
      "metadata": {
        "id": "5E79m5GHacUe"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H5T5yWyLajTJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o0Y9ThqSajWn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# List the directories and their files"
      ],
      "metadata": {
        "id": "IESfgDLfajfP"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"CoNLL Dataset Directories and their Files:\")  # Print header\n",
        "for dataset in ['conll2000', 'conll2002', 'conll2007']:  # Iterate through datasets\n",
        "    dataset_path = os.path.join(extraction_path, dataset)  # Create dataset path\n",
        "    print(f\"\\nFiles in {dataset}:\")  # Print dataset name\n",
        "    for root, dirs, files in os.walk(dataset_path):  # Walk through the dataset directory\n",
        "        for file in files:  # Iterate through files\n",
        "            print(os.path.join(root, file))  # Print file path"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LRQujNq0acY4",
        "outputId": "f10df31a-7058-4faa-a649-0f7192854541"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CoNLL Dataset Directories and their Files:\n",
            "\n",
            "Files in conll2000:\n",
            "/content/conll_datasets/conll2000/test.txt\n",
            "/content/conll_datasets/conll2000/README\n",
            "/content/conll_datasets/conll2000/train.txt\n",
            "/content/conll_datasets/conll2000/conll2000/test.txt\n",
            "/content/conll_datasets/conll2000/conll2000/README\n",
            "/content/conll_datasets/conll2000/conll2000/train.txt\n",
            "\n",
            "Files in conll2002:\n",
            "/content/conll_datasets/conll2002/ned.train\n",
            "/content/conll_datasets/conll2002/README\n",
            "/content/conll_datasets/conll2002/esp.testa\n",
            "/content/conll_datasets/conll2002/ned.testa\n",
            "/content/conll_datasets/conll2002/esp.testb\n",
            "/content/conll_datasets/conll2002/ned.testb\n",
            "/content/conll_datasets/conll2002/esp.train\n",
            "/content/conll_datasets/conll2002/conll2002/ned.train\n",
            "/content/conll_datasets/conll2002/conll2002/README\n",
            "/content/conll_datasets/conll2002/conll2002/esp.testa\n",
            "/content/conll_datasets/conll2002/conll2002/ned.testa\n",
            "/content/conll_datasets/conll2002/conll2002/esp.testb\n",
            "/content/conll_datasets/conll2002/conll2002/ned.testb\n",
            "/content/conll_datasets/conll2002/conll2002/esp.train\n",
            "\n",
            "Files in conll2007:\n",
            "/content/conll_datasets/conll2007/eus.test\n",
            "/content/conll_datasets/conll2007/README\n",
            "/content/conll_datasets/conll2007/esp.test\n",
            "/content/conll_datasets/conll2007/esp.train\n",
            "/content/conll_datasets/conll2007/eus.train\n",
            "/content/conll_datasets/conll2007/conll2007/eus.test\n",
            "/content/conll_datasets/conll2007/conll2007/README\n",
            "/content/conll_datasets/conll2007/conll2007/esp.test\n",
            "/content/conll_datasets/conll2007/conll2007/esp.train\n",
            "/content/conll_datasets/conll2007/conll2007/eus.train\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "55QnetNIaoPQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3tusDS9ZaoTF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sPlX_ILUaoXi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the paths to the CoNLL-2002 training and test files"
      ],
      "metadata": {
        "id": "C5H_aiJ9aoZv"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ned_train_path = '/content/conll_datasets/conll2002/ned.train'  # Path for Dutch training data"
      ],
      "metadata": {
        "id": "9n7z7wVWack_"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "esp_train_path = '/content/conll_datasets/conll2002/esp.train'  # Path for Spanish training data"
      ],
      "metadata": {
        "id": "PFH1IqQ7aakz"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ned_test_path = '/content/conll_datasets/conll2002/ned.testb'  # Path for Dutch test data"
      ],
      "metadata": {
        "id": "45SJ34i5aanO"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "esp_test_path = '/content/conll_datasets/conll2002/esp.testb'  # Path for Spanish test data"
      ],
      "metadata": {
        "id": "2KrZOmQtaapp"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KUgWoEWaa0Jn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kVZ40JD1a0NA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tsOzJGFua0QN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Step 3: Read Data\n",
        "```markdown\n",
        "# <span style=\"color:blue\">Step 3: Read Data</span>\n",
        "\n",
        "## <span style=\"color:darkgreen\">Overview</span>\n",
        "In this step, we read the extracted CoNLL dataset files into a structured format. This process involves organizing sentences and their corresponding entity labels for analysis.\n",
        "\n",
        "## <span style=\"color:darkorange\">Data Reading Process</span>\n",
        "- Define functions to read individual CoNLL files.\n",
        "- Parse each line of the file to separate tokens and labels.\n",
        "- Store sentences and labels in suitable data structures (e.g., lists or dictionaries) for further use.\n",
        "\n",
        "## <span style=\"color:darkgreen\">Code Example</span>\n",
        "```python\n",
        "def read_conll_file(file_path):\n",
        "    sentences = []\n",
        "    labels = []\n",
        "    with open(file_path, 'r', encoding='utf-8') as f:\n",
        "        sentence = []\n",
        "        label = []\n",
        "        for line in f:\n",
        "            if line.strip():\n",
        "                token, tag = line.split()\n",
        "                sentence.append(token)\n",
        "                label.append(tag)\n",
        "            else:\n",
        "                if sentence:\n",
        "                    sentences.append(sentence)\n",
        "                    labels.append(label)\n",
        "                    sentence = []\n",
        "                    label = []\n",
        "    return sentences, labels"
      ],
      "metadata": {
        "id": "ygoR8Z7DdpcP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to read and display the contents of a CoNLL file"
      ],
      "metadata": {
        "id": "i6KaeQIsa0VG"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def read_conll_file(file_path):  # Define function to read CoNLL file\n",
        "    sentences = []  # Initialize list for sentences\n",
        "    sentence = []  # Initialize current sentence\n",
        "    labels = []  # Initialize list for labels\n",
        "\n",
        "    with open(file_path, 'r', encoding='utf-8') as f:  # Open the file\n",
        "        for line in f:  # Iterate through lines\n",
        "            line = line.strip()  # Remove leading/trailing whitespace\n",
        "            if line:  # If line is not empty\n",
        "                parts = line.split()  # Split line into parts\n",
        "                word = parts[0]  # Get the word\n",
        "                label = parts[-1]  # Assuming the label is in the last position\n",
        "                sentence.append(word)  # Add word to current sentence\n",
        "                labels.append(label)  # Add label to current labels\n",
        "            else:  # If line is empty (end of a sentence)\n",
        "                if sentence:  # If there is a current sentence\n",
        "                    sentences.append((sentence, labels))  # Add sentence and labels to list\n",
        "                    sentence = []  # Reset current sentence\n",
        "                    labels = []  # Reset current labels\n",
        "    return sentences  # Return the list of sentences and labels"
      ],
      "metadata": {
        "id": "Fpdfdll1a0YS"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RR7gfnhSa7ma"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ANizUsM6iHOA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Dutch and Spanish training and test data"
      ],
      "metadata": {
        "id": "zW2cMJi6a7vz"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ned_train_sentences = read_conll_file(ned_train_path)  # Read Dutch training data"
      ],
      "metadata": {
        "id": "cKwmBh3ra7yL"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "esp_train_sentences = read_conll_file(esp_train_path)  # Read Spanish training data"
      ],
      "metadata": {
        "id": "fDUaU00Qa71a"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ned_test_sentences = read_conll_file(ned_test_path)  # Read Dutch test data"
      ],
      "metadata": {
        "id": "55BlPqu6a0jn"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "esp_test_sentences = read_conll_file(esp_test_path)  # Read Spanish test data"
      ],
      "metadata": {
        "id": "dxLmvUHnaasN"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5HXuZiR7aawU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0MVrJ__xaayj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tgnPzNSYbJ5Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to print dataset details"
      ],
      "metadata": {
        "id": "MaSHB4z-aa0t"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_dataset_details(sentences, language):  # Define function to print details\n",
        "    print(f\"--- {language} Dataset Details ---\")  # Print dataset details header\n",
        "    print(f\"Total Sentences: {len(sentences)}\")  # Print total number of sentences\n",
        "    print(\"Sample Sentences:\")  # Print header for sample sentences\n",
        "    for i in range(min(3, len(sentences))):  # Print up to 3 sample sentences\n",
        "        print(f\"Sentence {i+1}: {' '.join(sentences[i][0])}\")  # Print the sentence\n",
        "        print(f\"Labels {i+1}: {sentences[i][1]}\")  # Print corresponding labels\n",
        "        print()  # Print a new line for better readability"
      ],
      "metadata": {
        "id": "RSrfSdTAaa22"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3Beb_Re2MulS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wGd9yMBjMupK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print details for each dataset"
      ],
      "metadata": {
        "id": "s0gFpdOLMutt"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print_dataset_details(ned_train_sentences, \"Dutch Training\")  # Print Dutch training details"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i0JxMdsdMuv3",
        "outputId": "177ca57e-ee97-40b7-d636-e57d11795c88"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Dutch Training Dataset Details ---\n",
            "Total Sentences: 15806\n",
            "Sample Sentences:\n",
            "Sentence 1: -DOCSTART- De tekst van het arrest is nog niet schriftelijk beschikbaar maar het bericht werd alvast bekendgemaakt door een communicatiebureau dat Floralux inhuurde .\n",
            "Labels 1: ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'O', 'O']\n",
            "\n",
            "Sentence 2: In '81 regulariseert de toenmalige Vlaamse regering de toestand met een BPA dat het bedrijf op eigen kosten heeft laten opstellen .\n",
            "Labels 2: ['O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']\n",
            "\n",
            "Sentence 3: publicatie\n",
            "Labels 3: ['O']\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TdSRfO7HfkFb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NZqJiImgfkIw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print_dataset_details(esp_train_sentences, \"Spanish Training\")  # Print Spanish training details"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iXjD57AMux9",
        "outputId": "5dc7ed1e-aea6-4d76-d2b9-a69b328ac7c7"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Spanish Training Dataset Details ---\n",
            "Total Sentences: 8323\n",
            "Sample Sentences:\n",
            "Sentence 1: Melbourne ( Australia ) , 25 may ( EFE ) .\n",
            "Labels 1: ['B-LOC', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'O', 'O']\n",
            "\n",
            "Sentence 2: -\n",
            "Labels 2: ['O']\n",
            "\n",
            "Sentence 3: El Abogado General del Estado , Daryl Williams , subrayó hoy la necesidad de tomar medidas para proteger al sistema judicial australiano frente a una página de internet que imposibilita el cumplimiento de los principios básicos de la Ley .\n",
            "Labels 3: ['O', 'B-PER', 'I-PER', 'I-PER', 'I-PER', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O']\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Hk1iH8M3fk_E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ne7qGf0JflFr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print_dataset_details(ned_test_sentences, \"Dutch Test\")  # Print Dutch test details"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ie3oCCxsMu0D",
        "outputId": "847cf372-f9b6-41fb-e96b-2b1e82aec85c"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Dutch Test Dataset Details ---\n",
            "Total Sentences: 5195\n",
            "Sample Sentences:\n",
            "Sentence 1: -DOCSTART- Dat is in Italië , Spanje of Engeland misschien geen probleem , maar volgens ' Der Kaiser ' in Duitsland wel .\n",
            "Labels 1: ['O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'B-LOC', 'O', 'O']\n",
            "\n",
            "Sentence 2: sectie\n",
            "Labels 2: ['O']\n",
            "\n",
            "Sentence 3: Zürich\n",
            "Labels 3: ['B-LOC']\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rDMdib3AfmPM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5RJ_0P_jfmS7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print_dataset_details(esp_test_sentences, \"Spanish Test\")  # Print Spanish test details"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_sSlGM-Mu2W",
        "outputId": "1afc3a10-ae2a-4764-cde8-d32be1d67014"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Spanish Test Dataset Details ---\n",
            "Total Sentences: 1517\n",
            "Sample Sentences:\n",
            "Sentence 1: La Coruña , 23 may ( EFECOM ) .\n",
            "Labels 1: ['B-LOC', 'I-LOC', 'O', 'O', 'O', 'O', 'B-ORG', 'O', 'O']\n",
            "\n",
            "Sentence 2: -\n",
            "Labels 2: ['O']\n",
            "\n",
            "Sentence 3: Las reservas \" on line \" de billetes aéreos a través de Internet aumentaron en España un 300 por ciento en el primer trimestre de este año con respecto al mismo período de 1999 , aseguró hoy Iñigo García Aranda , responsable de comunicación de Savia Amadeus .\n",
            "Labels 3: ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'I-ORG', 'O']\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fDr1Umjsem6m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F-xuCxIdfo5E"
      },
      "execution_count": 199,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NcDB_JzDeqfK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot sentence length distribution for both Dutch and Spanish training data"
      ],
      "metadata": {
        "id": "O7rD2pDybcbW"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_sentence_length_distribution(ned_train_sentences, 'Dutch Training')  # Plot for Dutch training\n",
        "plot_sentence_length_distribution(esp_train_sentences, 'Spanish Training')  # Plot for Spanish training"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "c5bz9rD0eyRZ",
        "outputId": "dd5027f0-a4d9-4aac-8dad-dbb338aa33b1"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6RrPWCoPe1ts"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3TeSQfppe1x0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SNjg-e4ue66-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot label distribution for both Dutch and Spanish training data"
      ],
      "metadata": {
        "id": "YwEB88uhe695"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_label_distribution(ned_train_sentences, 'Dutch Training')  # Plot for Dutch training\n",
        "plot_label_distribution(esp_train_sentences, 'Spanish Training')  # Plot for Spanish training"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VoO3QvyRe7BN",
        "outputId": "e9661c46-661d-4588-84c7-99f1b50d62f7"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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cOQoMDFRAQIBat25tTXO/VOXKlVObNm3Uv39/HT58WDNmzNB1113ncrvFhx9+WB9//LHuuOMO/eMf/9CePXv07rvvulxk7lJz69q1qzp06KCxY8dq//79atq0qb799lt9/vnnio2NzbPukrRz5069++67MsYoNTVVmzZt0sKFC3XmzBlNnz5dd9xxhzW2YcOGuvHGG/X000/rxIkTKleunD744ANlZWXlWW+LFi304Ycfavjw4WrVqpXKli2rrl27qk+fPnrnnXc0fPhw/fzzz2rbtq3S0tL03Xff6dFHH9Xdd99dkuW7qFevnmrVqqUnn3xSv//+u4KCgvTJJ58UyzUOisvIkSP17rvv6vbbb9fjjz9u3cqwevXqOnHiRLHPtgGAax3NAQCAW73xxhv5xvv166d+/fopOTlZb775pr755hs1aNBA7777rhYuXKhVq1bleU6fPn1kt9s1Y8YMHTlyRDfccINmzZrl8q1xgwYNtG7dOk2ePFnz58/X8ePHFRYWpubNm2vChAlFriPnuT4+PipXrpwaN26sGTNmqH///n95Ebu//e1v2r9/v+bOnatjx46pQoUKat++vSZPnqzg4GBJF06FWLBggZ5++mkNGjRIWVlZmjdvXpGbA2PGjFFCQoKmTJmi06dP67bbbtPrr79u3eNeuvDN7LRp0zR9+nTFxsaqZcuWWrx4sUaMGOGyrkvJzW6364svvtCECRP04Ycfat68eYqMjNRLL72UZ70lbdmyZVq2bJnsdruCgoIUFRWlvn37auDAgWrQoEGe8e+9957++c9/6oUXXlBISIgGDBigDh066Pbbb3cZ9+ijj2rjxo2aN2+eXnnlFdWoUUNdu3aVw+HQkiVL9Pzzz+v999/XJ598ovLly1vNK3fy9vbWl19+qSeeeEJTpkyRn5+funfvrscee0xNmzZ1a245IiIitHLlSj3xxBP617/+pYoVK2rIkCEKCAjQE0888acNOQBAXjbj7nlrAAAAQDGJjY3Vm2++qTNnzpTYhTMB4FrANQcAAABwVbr4FpXHjx/X//3f/6lNmzY0BgDgEnFaAQAAAK5K0dHRuuWWW1S/fn0dPnxYb7/9tlJTUzV+/Hh3pwYAVx2aAwAAALgqde7cWR9//LHeeust2Ww2XX/99Xr77bfVrl07d6cGAFcdrjkAAAAAAICH45oDAAAAAAB4OJoDAAAAAAB4OK45UIKcTqeSkpIUGBgom83m7nQAAAAAANc4Y4xOnz6tqlWrym4veH4AzYESlJSUpIiICHenAQAAAADwML/99pvCw8MLXE5zoAQFBgZKurBTgoKC3JwNAAAAAOBal5qaqoiICOvzaEFoDpSgnFMJgoKCaA4AAAAAAErMX53azgUJAQAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcDQHAAAAAADwcF7uTgClU+bkEe5O4arjPXGau1MAAAAAgCJh5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB6O5gAAAAAAAB7Orc2B1atXq2vXrqpatapsNpsWLVrkstxms+X789JLL1ljIiMj8yx/4YUXXNaTkJCgtm3bys/PTxEREZo6dWqeXBYuXKh69erJz89PjRs31pIlS1yWG2M0YcIEValSRf7+/urYsaN27dpVfBsDAAAAAAA3cWtzIC0tTU2bNtXs2bPzXX7o0CGXn7lz58pms+nee+91GffMM8+4jHv88cetZampqerUqZNq1Kih9evX66WXXtKkSZP01ltvWWPWrFmjXr16acCAAYqPj1e3bt3UrVs3bdmyxRozdepUvfrqq5ozZ47Wrl2rgIAAxcTE6Pz588W8VQAAAAAAKFle7nzxO++8U3feeWeByytXruzy+PPPP1eHDh1Us2ZNl3hgYGCesTnee+89ZWRkaO7cufLx8VHDhg21ceNGTZ8+XQMHDpQkzZw5U3fccYeeeuopSdKzzz6rZcuWadasWZozZ46MMZoxY4bGjRunu+++W5L0zjvvqFKlSlq0aJF69uxZ5G0AAAAAAIC7ubU5cCkOHz6sr776SgsWLMiz7IUXXtCzzz6r6tWrq3fv3ho2bJi8vC6UFhcXp3bt2snHx8caHxMToxdffFEnT55UaGio4uLiNHz4cJd1xsTEWKc57Nu3T8nJyerYsaO1PDg4WK1bt1ZcXFyBzYH09HSlp6dbj1NTUyVJWVlZysrKkiTZ7XbZ7XY5nU45nU5rbE48Oztbxpi/jDscDtlsNmu9ueOSlJ2dXai4l5eXjDHKtuWeVGLkMEZOSSbfuE3GZrOiNhnZjZHTZpNRrrgxssso22aTXOJO2aU8cbtxyiZdlMuFuCQ5Cxl3GKdMnnjx12TLte1LbD/littsNjkcjjzHUkHxUnvsURM1URM1URM1URM1URM1UVOx1ZR7zJ+5apoDCxYsUGBgoO655x6X+BNPPKHrr79e5cqV05o1a/T000/r0KFDmj59uiQpOTlZUVFRLs+pVKmStSw0NFTJyclWLPeY5ORka1zu5+U3Jj9TpkzR5MmT88Tj4+MVEBAgSapYsaJq1aqlffv26ejRo9aY8PBwhYeHa+fOnUpJSbHiNWvWVFhYmLZs2aJz585Z8Xr16ikkJETx8fEuB2aTJk3k4+OjdevWueTQsmVLZWRkKCEhwYo5HA61atVKKSkp2hZZ34r7Zaar0cHdOh4YqsQKVa140LkzqpOcqEMhFXQoNMyKVzh9UpHHknSgfBUdCwy14lVOHlG1U0e1p1J1pfqXteI1jiWp4umT2latls57+1rx2smJCj53Rpuq15XT/scH+IYHd8snK1PxuXKUpOb7tynDy1tbw6+zYnanU9cnblOqf1ntqlzjitZ0Otc2Lqn9tH37divu7++vpk2b6tixY9q7d68VDw4OVv369ZWUlKSDBw9a8dJ67FETNVETNVETNVETNVETNVFT8dUUGRmpwrCZwrYRrjCbzabPPvtM3bp1y3d5vXr1dPvtt+u111770/XMnTtX//znP3XmzBn5+vqqU6dOioqK0ptvvmmN+fXXX9WwYUP9+uuvql+/vnx8fLRgwQL16tXLGvP6669r8uTJOnz4sNasWaObb75ZSUlJqlKlijXmH//4h2w2mz788MN8c8lv5kBERISOHz+uoKAgSaW385X+7MhcUWYOFKYmrzF/XAiTDiU1URM1URM1URM1URM1URM1lYaa0tLSFBISopSUFOtzaH6uipkDP/zwg3bs2FHgh/DcWrduraysLO3fv19169ZV5cqVdfjwYZcxOY9zrlNQ0Jjcy3NiuZsDhw8fVrNmzQrMxdfXV76+vnniXl5e1mkPOXJ28MVyDsLCxi9eb1HiNptNDuPME7dLUr5xI+XTY7IbIylv3HHJ8byvealxWwHx4qwpv215pfdTfvGCjqVLjbvr2KMmaqImaiooTk3UJFFTQTleapyaqEmipoJyvNR4aa/JlutLzz/j1rsVFNbbb7+tFi1aqGnTpn85duPGjbLb7QoLuzAlPDo6WqtXr1ZmZqY1ZtmyZapbt65CQ0OtMcuXL3dZz7JlyxQdHS1JioqKUuXKlV3GpKamau3atdYYAAAAAACuVm6dOXDmzBnt3r3berxv3z5t3LhR5cqVU/Xq1SVd+BC+cOFCTZs2Lc/z4+LitHbtWnXo0EGBgYGKi4vTsGHD9MADD1gf/Hv37q3JkydrwIABGjVqlLZs2aKZM2fqlVdesdYzdOhQtW/fXtOmTVOXLl30wQcfaN26ddbtDm02m2JjY/Xcc8+pdu3aioqK0vjx41W1atUCT4MAAAAAAOBq4dbmwLp169ShQwfrcc4dA/r27av58+dLkj744AMZY1yuB5DD19dXH3zwgSZNmqT09HRFRUVp2LBhLnceCA4O1rfffqshQ4aoRYsWqlChgiZMmGDdxlCSbrrpJr3//vsaN26cxowZo9q1a2vRokVq1KiRNWbkyJFKS0vTwIEDderUKbVp00ZLly6Vn59fcW8WAAAAAABKVKm5IKEnSE1NVXBw8F9eCKI0yJw8wt0pXHW8J+ad3QIAAAAA7lTYz6FXxTUHAAAAAADAlUNzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD0dzAAAAAAAAD+fW5sDq1avVtWtXVa1aVTabTYsWLXJZ3q9fP9lsNpefO+64w2XMiRMndP/99ysoKEghISEaMGCAzpw54zImISFBbdu2lZ+fnyIiIjR16tQ8uSxcuFD16tWTn5+fGjdurCVLlrgsN8ZowoQJqlKlivz9/dWxY0ft2rWreDYEAAAAAABu5NbmQFpampo2barZs2cXOOaOO+7QoUOHrJ///ve/Lsvvv/9+bd26VcuWLdPixYu1evVqDRw40FqempqqTp06qUaNGlq/fr1eeuklTZo0SW+99ZY1Zs2aNerVq5cGDBig+Ph4devWTd26ddOWLVusMVOnTtWrr76qOXPmaO3atQoICFBMTIzOnz9fjFsEAAAAAICSZzPGGHcnIUk2m02fffaZunXrZsX69eunU6dO5ZlRkGPbtm1q0KCBfvnlF7Vs2VKStHTpUnXu3FkHDx5U1apV9cYbb2js2LFKTk6Wj4+PJGn06NFatGiRtm/fLknq0aOH0tLStHjxYmvdN954o5o1a6Y5c+bIGKOqVatqxIgRevLJJyVJKSkpqlSpkubPn6+ePXsWqsbU1FQFBwcrJSVFQUFBl7qJSlTm5BHuTuGq4z1xmrtTAAAAAAAXhf0cWuqvObBq1SqFhYWpbt26Gjx4sI4fP24ti4uLU0hIiNUYkKSOHTvKbrdr7dq11ph27dpZjQFJiomJ0Y4dO3Ty5ElrTMeOHV1eNyYmRnFxcZKkffv2KTk52WVMcHCwWrdubY0BAAAAAOBq5eXuBP7MHXfcoXvuuUdRUVHas2ePxowZozvvvFNxcXFyOBxKTk5WWFiYy3O8vLxUrlw5JScnS5KSk5MVFRXlMqZSpUrWstDQUCUnJ1ux3GNyryP38/Ibk5/09HSlp6dbj1NTUyVJWVlZysrKkiTZ7XbZ7XY5nU45nU5rbE48OztbuSd3FBR3OByy2WzWenPHJSk7O7tQcS8vLxljlG3L3TcychgjpySTb9wmY7NZUZuM7MbIabPJKFfcGNlllG2zSS5xp+xSnrjdOGWTLsrlQlySnIWMO4xTJk+8+Guy5dr2JbafcsVtNpscDkeeY6mgeKk99qiJmqiJmqiJmqiJmqiJmqip2Goq7MkCpbo5kHu6fuPGjdWkSRPVqlVLq1at0m233ebGzApnypQpmjx5cp54fHy8AgICJEkVK1ZUrVq1tG/fPh09etQaEx4ervDwcO3cuVMpKSlWvGbNmgoLC9OWLVt07tw5K16vXj2FhIQoPj7e5cBs0qSJfHx8tG7dOpccWrZsqYyMDCUkJFgxh8OhVq1aKSUlRdsi61txv8x0NTq4W8cDQ5VYoaoVDzp3RnWSE3UopIIOhf7RpKlw+qQijyXpQPkqOhYYasWrnDyiaqeOak+l6kr1L2vFaxxLUsXTJ7WtWi2d9/a14rWTExV87ow2Va8rp/2PD/AND+6WT1am4nPlKEnN929Thpe3toZfZ8XsTqeuT9ymVP+y2lW5xhWt6XSubVxS+ynn1BhJ8vf3V9OmTXXs2DHt3bvXigcHB6t+/fpKSkrSwYMHrXhpPfaoiZqoiZqoiZqoiZqoiZqoqfhqioyMVGGU6msO5KdixYp67rnn9M9//lNz587ViBEjrNMDpAvfyvv5+WnhwoXq3r27+vTpo9TUVJfrFqxcuVK33nqrTpw4odDQUFWvXl3Dhw9XbGysNWbixIlatGiRNm3apL1796pWrVqKj49Xs2bNrDHt27dXs2bNNHPmzHxzzW/mQEREhI4fP26d61FaO1/pz47MFWXmQGFq8hrzwh+50KGkJmqiJmqiJmqiJmqiJmqiplJQU1pamkJCQv7ymgOleubAxQ4ePKjjx4+rSpUqkqTo6GidOnVK69evV4sWLSRJK1askNPpVOvWra0xY8eOVWZmpry9vSVJy5YtU926dRUaGmqNWb58uUtzYNmyZYqOjpYkRUVFqXLlylq+fLnVHEhNTdXatWs1ePDgAvP19fWVr69vnriXl5e8vFw3fc4OvljOQVjY+MXrLUrcZrPJYZx54nZJyjdupHx6THZjJOWNOy45nvc1LzVuKyBenDXlty2v9H7KL17QsXSpcXcde9RETdRETQXFqYmaJGoqKMdLjVMTNUnUVFCOlxov7TXZcn3p+WfcekHCM2fOaOPGjdq4caOkCxf+27hxow4cOKAzZ87oqaee0k8//aT9+/dr+fLluvvuu3XdddcpJiZGklS/fn3dcccdeuSRR/Tzzz/rf//7nx577DH17NlTVatemCreu3dv+fj4aMCAAdq6das+/PBDzZw5U8OHD7fyGDp0qJYuXapp06Zp+/btmjRpktatW6fHHntM0oWNGRsbq+eee05ffPGFNm/erD59+qhq1ap/OdMBAAAAAIDSzq0zB9atW6cOHTpYj3M+sPft21dvvPGGEhIStGDBAp06dUpVq1ZVp06d9Oyzz7p8G//ee+/pscce02233Sa73a57771Xr776qrU8ODhY3377rYYMGaIWLVqoQoUKmjBhggYOHGiNuemmm/T+++9r3LhxGjNmjGrXrq1FixapUaNG1piRI0cqLS1NAwcO1KlTp9SmTRstXbpUfn5+V3ITAQAAAABwxZWaaw54gsLeX7I0yJw8wt0pXHW8J05zdwoAAAAA4KKwn0PdeloBAAAAAABwP5oDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OJoDAAAAAAB4OLc2B1avXq2uXbuqatWqstlsWrRokbUsMzNTo0aNUuPGjRUQEKCqVauqT58+SkpKcllHZGSkbDaby88LL7zgMiYhIUFt27aVn5+fIiIiNHXq1Dy5LFy4UPXq1ZOfn58aN26sJUuWuCw3xmjChAmqUqWK/P391bFjR+3atav4NgYAAAAAAG7i1uZAWlqamjZtqtmzZ+dZdvbsWW3YsEHjx4/Xhg0b9Omnn2rHjh3629/+lmfsM888o0OHDlk/jz/+uLUsNTVVnTp1Uo0aNbR+/Xq99NJLmjRpkt566y1rzJo1a9SrVy8NGDBA8fHx6tatm7p166YtW7ZYY6ZOnapXX31Vc+bM0dq1axUQEKCYmBidP3++mLcKAAAAAAAly2aMMe5OQpJsNps+++wzdevWrcAxv/zyi2644QYlJiaqevXqki7MHIiNjVVsbGy+z3njjTc0duxYJScny8fHR5I0evRoLVq0SNu3b5ck9ejRQ2lpaVq8eLH1vBtvvFHNmjXTnDlzZIxR1apVNWLECD355JOSpJSUFFWqVEnz589Xz549C1VjamqqgoODlZKSoqCgoEI9x10yJ49wdwpXHe+J09ydAgAAAAC4KOzn0KvqmgMpKSmy2WwKCQlxib/wwgsqX768mjdvrpdeeklZWVnWsri4OLVr185qDEhSTEyMduzYoZMnT1pjOnbs6LLOmJgYxcXFSZL27dun5ORklzHBwcFq3bq1NQYAAAAAgKuVl7sTKKzz589r1KhR6tWrl0u344knntD111+vcuXKac2aNXr66ad16NAhTZ8+XZKUnJysqKgol3VVqlTJWhYaGqrk5GQrlntMcnKyNS738/Ibk5/09HSlp6dbj1NTUyVJWVlZVgPDbrfLbrfL6XTK6XRaY3Pi2dnZyj25o6C4w+GQzWZzaYzkxCUpOzu7UHEvLy8ZY5Rty903MnIYI6ckk2/cJmOzWVGbjOzGyGmzyShX3BjZZZRts0kucafsUp643Thlky7K5UJckpyFjDuMUyZPvPhrsuXa9iW2n3LFbTabHA5HnmOpoHipPfaoiZqoiZqoiZqoiZqoiZqoqdhqKuzJAldFcyAzM1P/+Mc/ZIzRG2+84bJs+PDh1v83adJEPj4++uc//6kpU6bI19e3pFN1MWXKFE2ePDlPPD4+XgEBAZKkihUrqlatWtq3b5+OHj1qjQkPD1d4eLh27typlJQUK16zZk2FhYVpy5YtOnfunBWvV6+eQkJCFB8f73Jg5myTdevWueTQsmVLZWRkKCEhwYo5HA61atVKKSkp2hZZ34r7Zaar0cHdOh4YqsQKVa140LkzqpOcqEMhFXQoNMyKVzh9UpHHknSgfBUdCwy14lVOHlG1U0e1p1J1pfqXteI1jiWp4umT2latls57/7HPaicnKvjcGW2qXldO+x8f4Bse3C2frEzF58pRkprv36YML29tDb/OitmdTl2fuE2p/mW1q3KNK1rT6VzbuKT2U86pMZLk7++vpk2b6tixY9q7d68VDw4OVv369ZWUlKSDBw9a8dJ67FETNVETNVETNVETNVETNVFT8dUUGRmpwij11xzIaQzs3btXK1asUPny5f90PVu3blWjRo20fft21a1bV3369FFqaqrLnRBWrlypW2+9VSdOnFBoaKiqV6+u4cOHu1y3YOLEiVq0aJE2bdqkvXv3qlatWoqPj1ezZs2sMe3bt1ezZs00c+bMfHPJb+ZARESEjh8/bs1+KK2dr/RnR+aKMnOgMDV5jfnjLhl0KKmJmqiJmqiJmqiJmqiJmqipNNSUlpamkJCQv7zmQKmeOZDTGNi1a5dWrlz5l40BSdq4caPsdrvCwi586xsdHa2xY8cqMzNT3t7ekqRly5apbt26Cg0NtcYsX77cpTmwbNkyRUdHS5KioqJUuXJlLV++3GoOpKamau3atRo8eHCBufj6+uY7e8HLy0teXq6bPmcHXyznICxs/OL1FiVus9nkMM48cbsk5Rs3Uj49JrsxkvLGHZccz/ualxq3FRAvzpry25ZXej/lFy/oWLrUuLuOPWqiJmqipoLi1ERNEjUVlOOlxqmJmiRqKijHS42X9ppsub70/DNubQ6cOXNGu3fvth7v27dPGzduVLly5VSlShXdd9992rBhgxYvXqzs7Gzr/P5y5crJx8dHcXFxWrt2rTp06KDAwEDFxcVp2LBheuCBB6wP/r1799bkyZM1YMAAjRo1Slu2bNHMmTP1yiuvWK87dOhQtW/fXtOmTVOXLl30wQcfaN26ddbtDm02m2JjY/Xcc8+pdu3aioqK0vjx41W1atU/vbsCAAAAAABXA7eeVrBq1Sp16NAhT7xv376aNGlSngsJ5li5cqVuueUWbdiwQY8++qi2b9+u9PR0RUVF6cEHH9Tw4cNdvrFPSEjQkCFD9Msvv6hChQp6/PHHNWrUKJd1Lly4UOPGjdP+/ftVu3ZtTZ06VZ07d7aWG2M0ceJEvfXWWzp16pTatGmj119/XXXq1Cl0vdzK8NrGrQwBAAAAlDaF/Rxaaq454AloDlzbaA4AAAAAKG0K+zk07wkNAAAAAADAo9AcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAw9EcAAAAAADAwxWpObB3797izgMAAAAAALhJkZoD1113nTp06KB3331X58+fL+6cAAAAAABACSpSc2DDhg1q0qSJhg8frsqVK+uf//ynfv755+LODQAAAAAAlIAiNQeaNWummTNnKikpSXPnztWhQ4fUpk0bNWrUSNOnT9fRo0eLO08AAAAAAHCFXNYFCb28vHTPPfdo4cKFevHFF7V79249+eSTioiIUJ8+fXTo0KHiyhMAAAAAAFwhl9UcWLdunR599FFVqVJF06dP15NPPqk9e/Zo2bJlSkpK0t13311ceQIAAAAAgCvEqyhPmj59uubNm6cdO3aoc+fOeuedd9S5c2fZ7Rd6DVFRUZo/f74iIyOLM1cAAAAAAHAFFKk58MYbb+ihhx5Sv379VKVKlXzHhIWF6e23376s5AAAAAAAwJVXpObArl27/nKMj4+P+vbtW5TVAwAAAACAElSkaw7MmzdPCxcuzBNfuHChFixYcNlJAQAAAACAklOk5sCUKVNUoUKFPPGwsDD961//uuykAAAAAABAySlSc+DAgQOKiorKE69Ro4YOHDhw2UkBAAAAAICSU6TmQFhYmBISEvLEN23apPLly192UgAAAAAAoOQUqTnQq1cvPfHEE1q5cqWys7OVnZ2tFStWaOjQoerZs2dx5wgAAAAAAK6gIt2t4Nlnn9X+/ft12223ycvrwiqcTqf69OnDNQcAAAAAALjKFKk54OPjow8//FDPPvusNm3aJH9/fzVu3Fg1atQo7vwAAAAAAMAVVqTmQI46deqoTp06xZULAAAAAABwgyI1B7KzszV//nwtX75cR44ckdPpdFm+YsWKYkkOAAAAAABceUVqDgwdOlTz589Xly5d1KhRI9lstuLOCwAAAAAAlJAiNQc++OADffTRR+rcuXNx5wMAAAAAAEpYkW5l6OPjo+uuu+6yX3z16tXq2rWrqlatKpvNpkWLFrksN8ZowoQJqlKlivz9/dWxY0ft2rXLZcyJEyd0//33KygoSCEhIRowYIDOnDnjMiYhIUFt27aVn5+fIiIiNHXq1Dy5LFy4UPXq1ZOfn58aN26sJUuWXHIuAAAAAABcjYrUHBgxYoRmzpwpY8xlvXhaWpqaNm2q2bNn57t86tSpevXVVzVnzhytXbtWAQEBiomJ0fnz560x999/v7Zu3aply5Zp8eLFWr16tQYOHGgtT01NVadOnVSjRg2tX79eL730kiZNmqS33nrLGrNmzRr16tVLAwYMUHx8vLp166Zu3bppy5Ytl5QLAAAAAABXI5spwif87t27a+XKlSpXrpwaNmwob29vl+WffvrppSdis+mzzz5Tt27dJF34pr5q1aoaMWKEnnzySUlSSkqKKlWqpPnz56tnz57atm2bGjRooF9++UUtW7aUJC1dulSdO3fWwYMHVbVqVb3xxhsaO3askpOT5ePjI0kaPXq0Fi1apO3bt0uSevToobS0NC1evNjK58Ybb1SzZs00Z86cQuVSGKmpqQoODlZKSoqCgoIueRuVpMzJI9ydwlXHe+I0d6cAAAAAAC4K+zm0SDMHQkJC1L17d7Vv314VKlRQcHCwy09x2Ldvn5KTk9WxY0crFhwcrNatWysuLk6SFBcXp5CQEKsxIEkdO3aU3W7X2rVrrTHt2rWzGgOSFBMTox07dujkyZPWmNyvkzMm53UKkwsAAAAAAFerIl2QcN68ecWdRx7JycmSpEqVKrnEK1WqZC1LTk5WWFiYy3IvLy+VK1fOZUxUVFSedeQsCw0NVXJy8l++zl/lkp/09HSlp6dbj1NTUyVJWVlZysrKkiTZ7XbZ7XY5nU6XW0LmxLOzs11O3ygo7nA4ZLPZrPXmjksXbj9ZmLiXl5eMMcq25e4bGTmMkVOSyTduk8l1xwqbjOzGyGmzyShX3BjZZZRts0kucafsUp643Thlky7K5UJckpyFjDuMUyZPvPhrsuXa9iW2n3LFbTabHA5HnmOpoHipPfaoiZqoiZqoiZqoiZqoiZqoqdhqKuzJAkVqDkgXPuCuWrVKe/bsUe/evRUYGKikpCQFBQWpbNmyRV3tNWXKlCmaPHlynnh8fLwCAgIkSRUrVlStWrW0b98+HT161BoTHh6u8PBw7dy5UykpKVa8Zs2aCgsL05YtW3Tu3DkrXq9ePYWEhCg+Pt7lwGzSpIl8fHy0bt06lxxatmypjIwMJSQkWDGHw6FWrVopJSVF2yLrW3G/zHQ1OrhbxwNDlVihqhUPOndGdZITdSikgg6F/tGkqXD6pCKPJelA+So6FhhqxaucPKJqp45qT6XqSvX/4xipcSxJFU+f1LZqtXTe29eK105OVPC5M9pUva6c9j8+wDc8uFs+WZmKz5WjJDXfv00ZXt7aGv7HxTLtTqeuT9ymVP+y2lW5xhWt6XSubVxS+ynn1BhJ8vf3V9OmTXXs2DHt3bvXigcHB6t+/fpKSkrSwYMHrXhpPfaoiZqoiZqoiZqoiZqoiZqoqfhqioyMVGEU6ZoDiYmJuuOOO3TgwAGlp6dr586dqlmzpoYOHar09HTNmTPnUleZ55oDe/fuVa1atRQfH69mzZpZ49q3b69mzZpp5syZmjt3rkaMGGGdHiBdaFr4+flp4cKF6t69u/r06aPU1FSXOyGsXLlSt956q06cOKHQ0FBVr15dw4cPV2xsrDVm4sSJWrRokTZt2lSoXPKT38yBiIgIHT9+3DrXo7R2vtKfHZkrysyBwtTkNeaFP3KhQ0lN1ERN1ERN1ERN1ERN1ERNpaCmtLQ0hYSE/OU1B4o0c2Do0KFq2bKlNm3apPLly1vx7t2765FHHinKKvOIiopS5cqVtXz5cusDeWpqqtauXavBgwdLkqKjo3Xq1CmtX79eLVq0kCStWLFCTqdTrVu3tsaMHTtWmZmZ1oUTly1bprp16yo0NNQas3z5cpfmwLJlyxQdHV3oXPLj6+srX1/fPHEvLy95eblu+pwdfLGcg7Cw8YvXW5S4zWaTwzjzxO2SlG/cSPn0mOzGSMobd1xyPO9rXmrcVkC8OGvKb1te6f2UX7ygY+lS4+469qiJmqiJmgqKUxM1SdRUUI6XGqcmapKoqaAcLzVe2muy5frS888UqTnwww8/aM2aNS4X+ZMuTFf4/fffC72eM2fOaPfu3dbjffv2aePGjSpXrpyqV6+u2NhYPffcc6pdu7aioqI0fvx4Va1a1ZpdUL9+fd1xxx165JFHNGfOHGVmZuqxxx5Tz549VbXqhanivXv31uTJkzVgwACNGjVKW7Zs0cyZM/XKK69Yrzt06FC1b99e06ZNU5cuXfTBBx9o3bp11u0ObTbbX+YCAAAAAMDVqkjNAafTmWc6hSQdPHhQgYGBhV7PunXr1KFDB+vx8OHDJUl9+/bV/PnzNXLkSKWlpWngwIE6deqU2rRpo6VLl8rPz896znvvvafHHntMt912m+x2u+699169+uqr1vLg4GB9++23GjJkiFq0aKEKFSpowoQJGjhwoDXmpptu0vvvv69x48ZpzJgxql27thYtWqRGjRpZYwqTCwAAAAAAV6MiXXOgR48eCg4O1ltvvaXAwEAlJCSoYsWKuvvuu1W9evUSuZvB1aiw95csDTInj3B3Clcd74nT3J0CAAAAALgo7OfQIs0cmDZtmmJiYtSgQQOdP39evXv31q5du1ShQgX997//LXLSAAAAAACg5BWpORAeHq5Nmzbpgw8+UEJCgs6cOaMBAwbo/vvvl7+/f3HnCAAAAAAArqAiNQekC1dAfOCBB4ozFwAAAAAA4AZFag688847f7q8T58+RUoGAAAAAACUvCI1B4YOHeryODMzU2fPnpWPj4/KlClDcwAAAAAAgKuIvShPOnnypMvPmTNntGPHDrV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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SVt5RG2SfAI0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fZUveuE9fAMr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "74q_cEEzeycz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to Load Data"
      ],
      "metadata": {
        "id": "yiPOku1Ybcdn"
      },
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_data(file_path):  # Define function to load data\n",
        "    sentences = []  # Initialize list for sentences\n",
        "    labels = []  # Initialize list for labels\n",
        "    with open(file_path, 'r', encoding='utf-8') as f:  # Open the file\n",
        "        sentence = []  # Initialize current sentence\n",
        "        label = []  # Initialize current label\n",
        "        for line in f:  # Iterate through lines\n",
        "            line = line.strip()  # Remove leading/trailing whitespace\n",
        "            if line:  # If line is not empty\n",
        "                tokens = line.split()  # Split line into tokens\n",
        "                word = tokens[0]  # Get the word\n",
        "                tag = tokens[-1]  # Get the tag (assumed to be the last token)\n",
        "                sentence.append(word)  # Add word to current sentence\n",
        "                label.append(tag)  # Add tag to current labels\n",
        "            else:  # If line is empty (end of a sentence)\n",
        "                if sentence:  # If there is a current sentence\n",
        "                    sentences.append(sentence)  # Add sentence to list\n",
        "                    labels.append(label)  # Add labels to list\n",
        "                    sentence = []  # Reset current sentence\n",
        "                    label = []  # Reset current labels\n",
        "        if sentence:  # Add the last sentence if not followed by an empty line\n",
        "            sentences.append(sentence)  # Add sentence to list\n",
        "            labels.append(label)  # Add labels to list\n",
        "    return sentences, labels  # Return sentences and labels"
      ],
      "metadata": {
        "id": "I_MXkkjZbcgO"
      },
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eNaPeJv-bciQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dznJt_zobcwx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OTXZvuLgdwqM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Step 4: Feature Extraction\n",
        "```markdown\n",
        "# <span style=\"color:blue\">Step 4: Feature Extraction</span>\n",
        "\n",
        "## <span style=\"color:darkgreen\">Overview</span>\n",
        "Feature extraction is a critical step in machine learning, especially for NER tasks. This process involves identifying relevant characteristics of tokens to improve model performance.\n",
        "\n",
        "## <span style=\"color:darkorange\">Feature Extraction Process</span>\n",
        "- Design a feature extraction function that identifies various attributes of each token, such as:\n",
        "  - **Word itself**: The actual text of the token.\n",
        "  - **Capitalization**: Whether the token is capitalized.\n",
        "  - **Digit presence**: If the token contains digits.\n",
        "  - **Position in the sentence**: Relative position of the token within the sentence.\n",
        "- This information helps the CRF model learn patterns associated with different entity types.\n",
        "\n",
        "## <span style=\"color:darkgreen\">Code Example</span>\n",
        "```python\n",
        "def extract_features(sentence):\n",
        "    features = []\n",
        "    for i, token in enumerate(sentence):\n",
        "        word_features = {\n",
        "            'word': token,\n",
        "            'is_capitalized': token[0].isupper(),\n",
        "            'is_digit': token.isdigit(),\n",
        "            'position': i,\n",
        "        }\n",
        "        features.append(word_features)\n",
        "    return features"
      ],
      "metadata": {
        "id": "yZ1zbgNKdxAV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to Extract Features"
      ],
      "metadata": {
        "id": "RtFigM4Ubc1N"
      },
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_features(sentences):  # Define function to extract features\n",
        "    features = []  # Initialize list for features\n",
        "    for sentence in sentences:  # Iterate through sentences\n",
        "        sentence_features = []  # Initialize list for features of the current sentence\n",
        "        for i, word in enumerate(sentence):  # Enumerate words in the sentence\n",
        "            word_features = {  # Create a dictionary of features for the word\n",
        "                'word': word,  # The word itself\n",
        "                'is_capitalized': word[0].upper() == word[0],  # Check if the word is capitalized\n",
        "                'is_numeric': word.isdigit(),  # Check if the word is numeric\n",
        "                'is_alphabetic': word.isalpha(),  # Check if the word is alphabetic\n",
        "                'position': i,  # Position of the word in the sentence\n",
        "                'prev_word': sentence[i - 1] if i > 0 else \"<START>\",  # Previous word or <START>\n",
        "                'next_word': sentence[i + 1] if i + 1 < len(sentence) else \"<END>\",  # Next word or <END>\n",
        "            }\n",
        "            sentence_features.append(word_features)  # Add word features to the sentence features\n",
        "        features.append(sentence_features)  # Add features of the current sentence to the features list\n",
        "    return features  # Return the features"
      ],
      "metadata": {
        "id": "8KNmxkp0bdIA"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mlVeWuv_Mu4U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ukkT_IAqMu8r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to Prepare Data for CRF Model"
      ],
      "metadata": {
        "id": "T9qMXRg0Mu_B"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_data(sentences, labels):  # Define function to prepare data\n",
        "    X = extract_features(sentences)  # Extract features from sentences\n",
        "    y = labels  # Use labels as they are\n",
        "    return X, y  # Return features and labels"
      ],
      "metadata": {
        "id": "ft7Hm6C4MvBS"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DncfOOijMvDx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gEyytOhYMvFr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Datasets"
      ],
      "metadata": {
        "id": "bjvb3FzbMvJk"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ned_train_sentences, ned_train_labels = load_data(ned_train_path)  # Load Dutch training data"
      ],
      "metadata": {
        "id": "peHnLwAIMvL1"
      },
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ned_test_sentences, ned_test_labels = load_data(ned_test_path)  # Load Dutch test data"
      ],
      "metadata": {
        "id": "Gaw_WjX4MvQF"
      },
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "esp_train_sentences, esp_train_labels = load_data(esp_train_path)  # Load Spanish training data"
      ],
      "metadata": {
        "id": "oMpQnHz3MvSe"
      },
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "esp_test_sentences, esp_test_labels = load_data(esp_test_path)  # Load Spanish test data"
      ],
      "metadata": {
        "id": "SkCHRC62MvUl"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g94tGXzNMvWu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FnjkZhwQMvY-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z3P14BFad_r6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Step 5: Model Training\n",
        "```markdown\n",
        "# <span style=\"color:blue\">Step 5: Model Training</span>\n",
        "\n",
        "## <span style=\"color:darkgreen\">Overview</span>\n",
        "This step involves training the CRF model on the prepared dataset, utilizing the features extracted in the previous step. Model training is crucial for learning to classify entities in text.\n",
        "\n",
        "## <span style=\"color:darkorange\">Model Training Process</span>\n",
        "- Initialize the CRF model with appropriate parameters, such as:\n",
        "  - **Algorithm**: L-BFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno)\n",
        "  - **Max iterations**: Number of iterations for optimization.\n",
        "- Train the model using the training dataset and the extracted features.\n",
        "\n",
        "## <span style=\"color:darkgreen\">Code Example</span>\n",
        "```python\n",
        "# Initialize the CRF model\n",
        "crf_model = CRF(algorithm='lbfgs', max_iterations=100)\n",
        "\n",
        "# Prepare the training data\n",
        "X_train = [extract_features(sentence) for sentence in train_sentences]\n",
        "y_train = train_labels\n",
        "\n",
        "# Train the model\n",
        "crf_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "meeGphRjeAIc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare Data"
      ],
      "metadata": {
        "id": "FuH67bisMvbW"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_ned_train, y_ned_train = prepare_data(ned_train_sentences, ned_train_labels)  # Prepare Dutch training data"
      ],
      "metadata": {
        "id": "Z49RKaXYMvdX"
      },
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_ned_test, y_ned_test = prepare_data(ned_test_sentences, ned_test_labels)  # Prepare Dutch test data"
      ],
      "metadata": {
        "id": "CoYk5DGjMvfj"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_esp_train, y_esp_train = prepare_data(esp_train_sentences, esp_train_labels)  # Prepare Spanish training data"
      ],
      "metadata": {
        "id": "MetZYHHUMvkK"
      },
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_esp_test, y_esp_test = prepare_data(esp_test_sentences, esp_test_labels)  # Prepare Spanish test data"
      ],
      "metadata": {
        "id": "Xsm06_WKMvmv"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6sh-N0H_MvrG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3UqIabATvIX6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train CRF Model"
      ],
      "metadata": {
        "id": "UjwClOuZvIa9"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crf_model = sklearn_crfsuite.CRF(\n",
        "    algorithm='lbfgs',  # formme deri\n",
        "    c1=0.1,  # formme deri\n",
        "    c2=0.1,  # formme deri\n",
        "    max_iterations=100,  # formme deri\n",
        "    all_possible_transitions=True,  # formme deri\n",
        ")  # formme deri"
      ],
      "metadata": {
        "id": "-pieb4phvIeN"
      },
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kJkhZ2pzvR2v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_OYkOS24vR6G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train Dutch and Spanish NER Models"
      ],
      "metadata": {
        "id": "yT0H6M79vR8_"
      },
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crf_model.fit(X_ned_train, y_ned_train)  # formme deri"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "GiNHAAO4vInk",
        "outputId": "da7cb930-2b1e-4912-9f72-2f8c3fef0539"
      },
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRF(algorithm='lbfgs', all_possible_transitions=True, c1=0.1, c2=0.1,\n",
              "    max_iterations=100)"
            ],
            "text/html": [
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              "  /* 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",
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              "  --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",
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              "    /* 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",
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              "  padding: 0;\n",
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              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
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              "  padding: 0;\n",
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              "#sk-container-id-5 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",
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              "#sk-container-id-5 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-5 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",
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              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 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-5 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
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              "\n",
              "#sk-container-id-5 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
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              "  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-5 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-5 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-5 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",
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              "  color: var(--sklearn-color-text);\n",
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              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-5 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",
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              "\n",
              "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-5 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CRF(algorithm=&#x27;lbfgs&#x27;, all_possible_transitions=True, c1=0.1, c2=0.1,\n",
              "    max_iterations=100)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;CRF<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>CRF(algorithm=&#x27;lbfgs&#x27;, all_possible_transitions=True, c1=0.1, c2=0.1,\n",
              "    max_iterations=100)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kW64X_ubvIq-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crf_model.fit(X_esp_train, y_esp_train)  # formme deri"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "HxBiS75MvIuA",
        "outputId": "252acd1a-c523-4e08-f900-335068a6a3f8"
      },
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRF(algorithm='lbfgs', all_possible_transitions=True, c1=0.1, c2=0.1,\n",
              "    max_iterations=100)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {\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-6 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-6 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-6 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-6 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-6 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-6 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-6 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-6 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-6 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-6 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-6 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-6 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-6 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-6 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CRF(algorithm=&#x27;lbfgs&#x27;, all_possible_transitions=True, c1=0.1, c2=0.1,\n",
              "    max_iterations=100)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;CRF<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>CRF(algorithm=&#x27;lbfgs&#x27;, all_possible_transitions=True, c1=0.1, c2=0.1,\n",
              "    max_iterations=100)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lb_zfniZvae4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3q1zTf4ZvaiJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z4jrfrxhxSzu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Step 6: Model Evaluation\n",
        "```markdown\n",
        "# <span style=\"color:blue\">Step 6: Model Evaluation</span>\n",
        "\n",
        "## <span style=\"color:darkgreen\">Overview</span>\n",
        "Model evaluation is essential to assess the performance of the trained CRF model. This step measures the accuracy of the entity recognition on test datasets.\n",
        "\n",
        "## <span style=\"color:darkorange\">Evaluation Process</span>\n",
        "- Use the trained CRF model to predict labels for the test dataset.\n",
        "- Calculate evaluation metrics, such as:\n",
        "  - **F1 Score**: Harmonic mean of precision and recall, providing a balance between the two.\n",
        "  - **Classification Report**: Detailed report including precision, recall, and support for each entity class.\n",
        "\n",
        "## <span style=\"color:darkgreen\">Code Example</span>\n",
        "```python\n",
        "# Prepare the test data\n",
        "X_test = [extract_features(sentence) for sentence in test_sentences]\n",
        "y_test = test_labels\n",
        "\n",
        "# Predict labels\n",
        "y_pred = crf_model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "enTN89dixTJp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate Model"
      ],
      "metadata": {
        "id": "spj0G_Myvalc"
      },
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model(model, X_test, y_test):  # formme deri\n",
        "    y_pred = model.predict(X_test)  # formme deri\n",
        "    f1_score = metrics.flat_f1_score(y_test, y_pred, average='weighted', labels=model.classes_)  # formme deri\n",
        "    precision = metrics.flat_precision_score(y_test, y_pred, average='weighted', labels=model.classes_)  # formme deri\n",
        "    recall = metrics.flat_recall_score(y_test, y_pred, average='weighted', labels=model.classes_)  # formme deri\n",
        "    return f1_score, precision, recall  # formme deri"
      ],
      "metadata": {
        "id": "U2_3VrsYvaon"
      },
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XE0HOZ8kvj0n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f2MRU1V-vj3m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s4qfXazXvnbc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate Dutch Model"
      ],
      "metadata": {
        "id": "Uhqc3CsUvney"
      },
      "execution_count": 207,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1_ned, precision_ned, recall_ned = evaluate_model(crf_model, X_ned_test, y_ned_test)  # formme deri"
      ],
      "metadata": {
        "id": "PaX7rcaxvnks"
      },
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Dutch NER Model - F1 Score: {f1_ned}, Precision: {precision_ned}, Recall: {recall_ned}\")  # formme deri"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zPwztPAfvrKT",
        "outputId": "cae28454-d344-4d0b-87e7-19a978082ccc"
      },
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dutch NER Model - F1 Score: 0.9135505382604692, Precision: 0.9121807988157395, Recall: 0.9250514537496014\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-IrOMzu7vrNg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate Spanish Model"
      ],
      "metadata": {
        "id": "iuRUvzxFvrQj"
      },
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1_esp, precision_esp, recall_esp = evaluate_model(crf_model, X_esp_test, y_esp_test)  # formme deri"
      ],
      "metadata": {
        "id": "be4sbhVxvrTq"
      },
      "execution_count": 211,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Spanish NER Model - F1 Score: {f1_esp}, Precision: {precision_esp}, Recall: {recall_esp}\")  # formme deri"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E4tDlsknvrWk",
        "outputId": "3bdd4e07-6961-4b36-8ecc-c65f2665a3fc"
      },
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Spanish NER Model - F1 Score: 0.9646658299991987, Precision: 0.9641358928194558, Recall: 0.9659635573321949\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VedtryDwvrZM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M1RBXWSmv5u4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define function to plot confusion matrix"
      ],
      "metadata": {
        "id": "pxJR9cZNv5yI"
      },
      "execution_count": 213,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(y_test, y_pred, class_labels, model_name):  # formme deri\n",
        "    cm = confusion_matrix(y_test, y_pred, labels=class_labels)  # formme deri\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_labels)  # formme deri\n",
        "    disp.plot(cmap=plt.cm.Blues)  # formme deri\n",
        "    plt.title(f'Confusion Matrix - {model_name}')  # formme deri\n",
        "    plt.show()  # formme deri"
      ],
      "metadata": {
        "id": "XbApv2Mxv51r"
      },
      "execution_count": 214,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VV9ncipkv_xY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WXkfeDI2v54p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you have your class labels defined"
      ],
      "metadata": {
        "id": "hLLCNZXcv571"
      },
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_labels_ned = ['O', 'B-ORG', 'I-ORG', 'B-PER', 'I-PER', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']  # formme deri"
      ],
      "metadata": {
        "id": "arjwVmD_wGF3"
      },
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_labels_esp = ['O', 'B-ORG', 'I-ORG', 'B-PER', 'I-PER', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']  # formme deri"
      ],
      "metadata": {
        "id": "0dtJ5wcPwGJd"
      },
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OFDDOdQcwLW4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1O7dS3q0wLbs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predictions for Dutch model"
      ],
      "metadata": {
        "id": "MrY-Ut4awLfJ"
      },
      "execution_count": 218,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_ned = crf_model.predict(X_ned_test)  # formme deri"
      ],
      "metadata": {
        "id": "PNN2FybHwLi2"
      },
      "execution_count": 219,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_ned_test_flat = [item for sublist in y_ned_test for item in sublist]  # Flatten the list of true labels  # formme deri\n"
      ],
      "metadata": {
        "id": "_5tVej1lwLmE"
      },
      "execution_count": 220,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_ned_flat = [item for sublist in y_pred_ned for item in sublist]  # Flatten the list of predicted labels  # formme deri\n"
      ],
      "metadata": {
        "id": "i3IK9mfZwGMj"
      },
      "execution_count": 221,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XzpouUonwTCQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EuvOLTrLwTFV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predictions for Spanish model"
      ],
      "metadata": {
        "id": "JVP32d3_wTI2"
      },
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_esp = crf_model.predict(X_esp_test)  # formme deri"
      ],
      "metadata": {
        "id": "rh58DG1ywTL_"
      },
      "execution_count": 223,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_esp_test_flat = [item for sublist in y_esp_test for item in sublist]  # Flatten the list of true labels  # formme deri\n"
      ],
      "metadata": {
        "id": "LTxEajVBwTPo"
      },
      "execution_count": 224,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_esp_flat = [item for sublist in y_pred_esp for item in sublist]  # Flatten the list of predicted labels  # formme deri\n"
      ],
      "metadata": {
        "id": "WW2ksWiSwGP6"
      },
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GEUC18edwdAY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "A-nnr7rpwdDe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Lxq_cEmBwdHJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot confusion matrices"
      ],
      "metadata": {
        "id": "AnfzoHrvwdRZ"
      },
      "execution_count": 226,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_ned_test_flat, y_pred_ned_flat, class_labels_ned, \"Dutch NER\")  # formme deri"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "UKpvyrnLwfoB",
        "outputId": "2681a70b-1404-47a2-f0fa-c4b601bdc050"
      },
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-ZOhpudAwg_z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kIwQq9WzwhHS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_esp_test_flat, y_pred_esp_flat, class_labels_esp, \"Spanish NER\")  # formme deri"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "WBF_c-r_whbB",
        "outputId": "dd20a35d-93fc-4d63-90c6-af6b2d5077f1"
      },
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UzGPwvb9wkOJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ms_oql7KwkSH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ACZeLK-jwkVi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue\">Step 7: Conclusion</span>\n",
        "\n",
        "## <span style=\"color:darkgreen\">Overview</span>\n",
        "In this final step, we summarize the results, reflect on the project, and discuss potential improvements for the Named Entity Recognition (NER) model.\n",
        "\n",
        "## <span style=\"color:darkorange\">Conclusion Points</span>\n",
        "- **Model Performance**: The CRF model demonstrated a robust ability to accurately identify named entities within the Dutch and Spanish datasets. Evaluation metrics, including precision, recall, and F1 scores, indicate a strong performance overall. For instance, high recall values suggest that the model successfully identifies a majority of true positive entities, while precision indicates the accuracy of those predictions.\n",
        "\n",
        "- **Challenges Encountered**: Throughout the project, we faced several challenges. One notable challenge was handling the variability in the datasets, particularly the differences in tokenization and labeling standards between Dutch and Spanish texts. Moreover, the presence of ambiguous entities, such as words that can function as different parts of speech, added complexity to the model's learning process.\n",
        "\n",
        "- **Future Improvements**:\n",
        "    - **Feature Engineering**: One of the most promising areas for improvement is feature engineering. While we extracted basic features like capitalization and digit presence, additional linguistic features such as part-of-speech tags or contextual embeddings from models like BERT could enhance the model's understanding of language nuances.\n",
        "    - **Hyperparameter Tuning**: Adjusting the hyperparameters of the CRF model could lead to improved performance. Exploring different optimization algorithms, regularization techniques, and maximum iterations can significantly impact the model's accuracy.\n",
        "    - **Exploring Alternative Models**: While CRFs are effective for sequence labeling tasks, experimenting with deep learning approaches, such as Long Short-Term Memory (LSTM) networks or Transformers, might yield better results. These models can capture more complex patterns in data and provide improved contextual understanding.\n",
        "    - **Cross-Lingual Transfer Learning**: Given the multilingual nature of the datasets, investigating cross-lingual transfer learning could be beneficial. Training on one language and fine-tuning the model on another can leverage the similarities between languages and improve overall performance.\n",
        "\n",
        "## <span style=\"color:darkgreen\">Final Thoughts</span>\n",
        "This project serves as a solid foundation for implementing Named Entity Recognition systems using Conditional Random Fields. It emphasizes the importance of feature extraction, data preprocessing, and thorough model evaluation in achieving accurate results. By identifying entities within unstructured text, we unlock valuable insights that can drive various applications, such as information retrieval, sentiment analysis, and automated customer support.\n",
        "\n",
        "Furthermore, the insights gained from this project underline the necessity for continuous improvement and adaptation in machine learning methodologies. As we refine our models and techniques, we contribute to the evolving landscape of natural language processing, enhancing our capability to understand and process human language effectively.\n",
        "\n",
        "In conclusion, while the current model shows promising results, there is significant potential for further advancements. Continued exploration in this field could lead to breakthroughs that make NER systems even more powerful and versatile in real-world applications."
      ],
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  ]
}