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        "<a href=\"https://colab.research.google.com/github/NonKhuna/word-correction/blob/main/DeepGI_clean_correction_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Install library\n",
        "- Download and import library \n",
        "- Download necessaryfile \n",
        "  - Keywords file\n",
        "  - Data which are the result of ASR"
      ],
      "metadata": {
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        "# install library\n",
        "! pip --q install pythainlp\n",
        "! pip --q install fuzzywuzzy[speedup]\n",
        "! pip --q install evaluate\n",
        "! pip --q install jiwer\n",
        "! pip --q install marisa_trie\n",
        "! pip --q install simpletransformers==0.63.9 #0.63.9"
      ],
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.1/82.1 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for validators (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# utilize\n",
        "import re\n",
        "import time\n",
        "import json\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "\n",
        "# evaluate result\n",
        "from fuzzywuzzy import fuzz\n",
        "from evaluate import load\n",
        "\n",
        "# process data\n",
        "import marisa_trie\n",
        "from collections import defaultdict\n",
        "from pythainlp import word_tokenize\n",
        "from pythainlp.util import normalize \n",
        "\n",
        "# **close warning of updating transformers\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "warnings.simplefilter(action='ignore', category=UserWarning)\n",
        "\n",
        "# split data\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "Bep02iPGb5pj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load necessary files"
      ],
      "metadata": {
        "id": "hmc8j1-p15wt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1QT4RkeXe8KjS6TbzQVvbVAxnp1FOXWrF # Load file keywords \n",
        "!gdown 1JFoQSW1AQ3f9hWqUhSC2mCps2rT-iTcy # Load data (the result from ASR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cpd_GXx014AU",
        "outputId": "3ac47f1b-eff9-445d-8f0d-66319a1b0daa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1QT4RkeXe8KjS6TbzQVvbVAxnp1FOXWrF\n",
            "To: /content/keywords_deepGI.json\n",
            "100% 1.23k/1.23k [00:00<00:00, 6.86MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1JFoQSW1AQ3f9hWqUhSC2mCps2rT-iTcy\n",
            "To: /content/model2_test.csv\n",
            "100% 700k/700k [00:00<00:00, 161MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data preparation\n",
        "- Load data into a dataframe."
      ],
      "metadata": {
        "id": "910HmFE9Av-u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# read file csv\n",
        "df = pd.read_csv(\"/content/model2_test.csv\", names=['target_text', 'input_text'], header=None)\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59mIhLzgTB4J",
        "outputId": "b95c1d46-f1ce-45d9-9454-d6784324b86c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2688, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split dataset\n",
        "X = df[['input_text']]\n",
        "y = df[['target_text']]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "train = X_train.join(y_train)\n",
        "test = X_test.join(y_test)\n",
        "train.head()"
      ],
      "metadata": {
        "id": "7Z2cjxAJVHUF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "28a73fa3-7553-4986-a49d-b1e19126e36c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             input_text  \\\n",
              "610   Hello DeepGI เล็กกว่า 5 มิลลิเมตร Sessile Canc...   \n",
              "2004  Hello DeepGI เล็กกว่า 5 มิลลิเมตร Sessile Subu...   \n",
              "1406  Hello DeepGI 5.5 มิลลิเมตร Sessile Lipoma Type...   \n",
              "1213  Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Sessile Sub...   \n",
              "57    Hello DeepGI 5.5 มิลลิเมตร Sessile Lipoma Tide...   \n",
              "\n",
              "                                            target_text  \n",
              "610   Hello DeepGI เล็กกว่า 5 มิลลิเมตร Sessile Canc...  \n",
              "2004  Hello DeepGI เล็กกว่า 5 มิลลิเมตร Sessile Subm...  \n",
              "1406  Hello DeepGI 10.5 มิลลิเมตร Sessile Lipoma Typ...  \n",
              "1213  Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Sessile Sub...  \n",
              "57    Hello DeepGI 10.5 มิลลิเมตร Sessile Lipoma Typ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-65f4c2f4-e644-434e-a801-ec7edff3da94\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>610</th>\n",
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              "      <td>Hello DeepGI เล็กกว่า 5 มิลลิเมตร Sessile Canc...</td>\n",
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              "      <th>2004</th>\n",
              "      <td>Hello DeepGI เล็กกว่า 5 มิลลิเมตร Sessile Subu...</td>\n",
              "      <td>Hello DeepGI เล็กกว่า 5 มิลลิเมตร Sessile Subm...</td>\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-65f4c2f4-e644-434e-a801-ec7edff3da94 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-65f4c2f4-e644-434e-a801-ec7edff3da94');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Custom dictionary\n",
        "- Create a new dictionary using the DeepGI's keywords and target_text (lower_case).\n",
        "- It is necessary to set a `threshold` value which determines the base frequency for new keys."
      ],
      "metadata": {
        "id": "wVzeFMRsn5si"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# initiate a dictionary\n",
        "custom_dict = defaultdict(int)\n",
        "threshold = 50\n",
        "\n",
        "# get keywords from the keyword json file. If the token doesn't exist in the dictionary, the dictionary creates a new key with a threshold value.\n",
        "with open('/content/keywords_deepGI.json') as f :\n",
        "  data = json.load(f)\n",
        "  for field in data :\n",
        "    for token in data[field] :\n",
        "        custom_dict[token] = threshold\n",
        "\n",
        "# get from target_text\n",
        "for i in range(len(df)) :\n",
        "  for x in word_tokenize(df['target_text'][i].lower()) :\n",
        "    custom_dict[x] += 1\n",
        "\n",
        "trie = marisa_trie.Trie(custom_dict.keys())\n",
        "vocabs = custom_dict.keys()\n",
        "custom_dict = list(custom_dict.items())\n",
        "custom_dict[:5]"
      ],
      "metadata": {
        "id": "jZuTaVmAn-7t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "128b2841-fda9-4891-808e-b278cdee5149"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('hello', 2738),\n",
              " ('deepgi', 2738),\n",
              " ('terminal', 50),\n",
              " ('ileum', 50),\n",
              " ('cecum', 50)]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Interesting error\n",
        "ids = [548, 623, 450, 239]\n",
        "df.iloc[ids]"
      ],
      "metadata": {
        "id": "Re8EgfUWjzjv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2397fc24-c805-49fa-c1d9-e1f504870383"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           target_text  \\\n",
              "548  Hello DeepGI 1 เซนติเมตร Flat Subepithelial Ty...   \n",
              "623  Hello DeepGI เล็กกว่า 5 มิลลิเมตร Sessile Canc...   \n",
              "450  Hello DeepGI 1 เซนติเมตร Pedunculated Solitary...   \n",
              "239  Hello DeepGI 10.5 มิลลิเมตร Flat Hemorrhoid Ty...   \n",
              "\n",
              "                                            input_text  \n",
              "548  Hello DeepGI 1 เซนติเมตร Flat Submitting ฟัตร ...  \n",
              "623   Hello DeepGI րեկกว่า 5 มิลลิเมตร Sessile Canc...  \n",
              "450  Hello DeepGI 1 เซนติเมตร Pedaculated Solitary ...  \n",
              "239  Hello DeepGI 5.5 มิลลิเมตร Fat Hemorrhoid ถ่าย...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-502a075f-92e3-49bb-8bf4-2a91ebba9ccb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>target_text</th>\n",
              "      <th>input_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>548</th>\n",
              "      <td>Hello DeepGI 1 เซนติเมตร Flat Subepithelial Ty...</td>\n",
              "      <td>Hello DeepGI 1 เซนติเมตร Flat Submitting ฟัตร ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>623</th>\n",
              "      <td>Hello DeepGI เล็กกว่า 5 มิลลิเมตร Sessile Canc...</td>\n",
              "      <td>Hello DeepGI րեկกว่า 5 มิลลิเมตร Sessile Canc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450</th>\n",
              "      <td>Hello DeepGI 1 เซนติเมตร Pedunculated Solitary...</td>\n",
              "      <td>Hello DeepGI 1 เซนติเมตร Pedaculated Solitary ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>Hello DeepGI 10.5 มิลลิเมตร Flat Hemorrhoid Ty...</td>\n",
              "      <td>Hello DeepGI 5.5 มิลลิเมตร Fat Hemorrhoid ถ่าย...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-502a075f-92e3-49bb-8bf4-2a91ebba9ccb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-502a075f-92e3-49bb-8bf4-2a91ebba9ccb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# First evaluate\n",
        "For comparison.\n",
        "- create a evaluate `evaluate_df` function."
      ],
      "metadata": {
        "id": "LzDSdlQ4YKXc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "def get_nums(text) :\n",
        "  regx = r'\\b\\d+(?:\\.\\d+)?\\b|\\.\\d+\\b'\n",
        "  numbers = []\n",
        "  for match in re.findall(regx, text):\n",
        "    numbers.append(match)\n",
        "\n",
        "  return numbers\n",
        "\n",
        "def evaluate_df(df, target_name=\"target_text\", input_name=\"input_text\", lower_case=True):\n",
        "    \"\"\"\n",
        "    Evaluates a DataFrame to check for missing data, error words, and wrong words.\n",
        "    Args:\n",
        "        df (pandas.DataFrame): The DataFrame to evaluate.\n",
        "        target_name (str): The name of the column containing the target text. Defaults to \"target_text\".\n",
        "        input_name (str): The name of the column containing the input text. Defaults to \"input_text\".\n",
        "        lower_case (boolean): if true, The model compare in lower case.\n",
        "    Returns:\n",
        "        None\n",
        "    \"\"\"\n",
        "    def wrong_word(data):\n",
        "        \"\"\"Checks if there are any words in the input text that are not present in the target text.\n",
        "\n",
        "        Args:\n",
        "            data (pandas.Series): A row of data from the DataFrame.\n",
        "\n",
        "        Returns:\n",
        "            bool: True if there is at least one word in the input text that is not present in the target text, False otherwise.\n",
        "        \"\"\"\n",
        "        if word_error(data): return False\n",
        "        target = set(word_tokenize(data[target_name]))\n",
        "        for x in word_tokenize(data[input_name]):\n",
        "          if x not in target:\n",
        "            return True\n",
        "        return False\n",
        "\n",
        "    def word_error(data):\n",
        "        \"\"\"Checks if there are any words in the input text that are not present in a trie data structure and do not match a regular expression for numbers.\n",
        "\n",
        "        Args:\n",
        "            data (pandas.Series): A row of data from the DataFrame.\n",
        "\n",
        "        Returns:\n",
        "            bool: True if there is at least one word that meets these conditions, False otherwise.\n",
        "        \"\"\"\n",
        "        for x in word_tokenize(data[input_name]):\n",
        "          if not x in trie and not re.match(f\"^[\\d\\.]+$\", x):\n",
        "            return True\n",
        "        return False\n",
        "\n",
        "    def missing_data(data):\n",
        "        \"\"\"Compares the number of words in the target text to the number of words in the input text.\n",
        "\n",
        "        Args:\n",
        "            data (pandas.Series): A row of data from the DataFrame.\n",
        "\n",
        "        Returns:\n",
        "            str: \"missing\" if there are more words in the target text than in the input text, \"exceed\" if there are more words in the input text than in the target text, and \"same\" if there are an equal number of words in both.\n",
        "        \"\"\"\n",
        "        target = len(word_tokenize(data[target_name]))\n",
        "        input = len(word_tokenize(data[input_name]))\n",
        "        if target > input:\n",
        "            return \"missing\"\n",
        "        elif target < input:\n",
        "            return \"exceed\"\n",
        "        else:\n",
        "            return \"same\"\n",
        "    \n",
        "    def wrong_number(data) :\n",
        "        c_r = get_nums(data[target_name])\n",
        "        c_i = get_nums(data[input_name])\n",
        "        if c_r != [] and c_i != c_r :\n",
        "          return True\n",
        "        return False\n",
        "    \n",
        "    df_temp = df.copy()\n",
        "    df_temp[input_name] = df_temp[input_name].str.lower().str.strip()\n",
        "    df_temp[target_name] = df_temp[target_name].str.lower().str.strip()\n",
        "    \n",
        "    df_temp['word_error'] = df_temp.apply(word_error, axis=1)\n",
        "    df_temp['missing_data'] = df_temp.apply(missing_data, axis=1)\n",
        "    df_temp['wrong_word'] = df_temp.apply(wrong_word, axis=1)\n",
        "    df_temp['wrong_number'] = df_temp.apply(wrong_number, axis=1)\n",
        "\n",
        "    word_error_rate = sum(df_temp['word_error'])/ len(df_temp)\n",
        "    exceed_data_rate = sum(df_temp['missing_data']==\"exceed\") / len(df_temp)\n",
        "    missing_data_rate = sum(df_temp['missing_data']==\"missing\") / len(df_temp)\n",
        "    wrong_word_rate = sum(df_temp['wrong_word']) / len(df_temp)\n",
        "    wrong_number_rate = sum(df_temp['wrong_number']) / len(df_temp)\n",
        "  \n",
        "    wer = load(\"wer\")\n",
        "    wer_score = wer.compute(predictions=df_temp[input_name], references=df_temp[target_name])\n",
        "\n",
        "  \n",
        "\n",
        "    df_temp[input_name+\"_isSame\"] = df_temp.apply(lambda x: x[target_name] == x[input_name], axis=1)\n",
        "    print(f\"\\\n",
        "    word_error_rate: {word_error_rate}\\n\\\n",
        "    wrong_number_rate: {wrong_number_rate}\\n\\\n",
        "    exceed_data_rate: {exceed_data_rate}\\n\\\n",
        "    missing_data_rate: {missing_data_rate}\\n\\\n",
        "    wrong_word_rate: {wrong_word_rate}\\n\\\n",
        "    wer_score: {wer_score}\\n\\\n",
        "    accuracy: {sum(df_temp[input_name+'_isSame']) / df_temp.shape[0]}\\\n",
        "    \")\n",
        "    \n",
        "    if lower_case  :\n",
        "      df_temp[input_name] = df[input_name]\n",
        "      df_temp[target_name] = df[target_name]\n",
        "    return df_temp"
      ],
      "metadata": {
        "id": "Zzqu528nfj0C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = evaluate_df(df, lower_case=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170,
          "referenced_widgets": [
            "64cdbdce00cc4b448beea3ce558f74f1",
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            "4f18f9e009c44bf89ed0ad09663a3b4f",
            "b9f4db8e71e743509dfa8b495b2115ef",
            "0244a3d9812846b0ab2b0de433cfac48",
            "8f936badbe954a3c96fa0914543985ee",
            "3c475baa738a433da8e52b462de1fadc",
            "01e760c28b584a89b90879e15db074d4",
            "380902a754414ab983280017baeeb02f",
            "7048fa1aecb04ff5b0cfd11203069ec0"
          ]
        },
        "id": "4pb7sUFFYM1B",
        "outputId": "3666011a-120e-47bb-fb33-361b89ffddbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.49k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    word_error_rate: 0.4799107142857143\n",
            "    wrong_number_rate: 0.20461309523809523\n",
            "    exceed_data_rate: 0.08035714285714286\n",
            "    missing_data_rate: 0.06919642857142858\n",
            "    wrong_word_rate: 0.06398809523809523\n",
            "    wer_score: 0.11743273542600897\n",
            "    accuracy: 0.45163690476190477    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "h7i3VyN_HFZn",
        "outputId": "d3f40a59-43f1-4873-93a8-3c71563941fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            target_text  \\\n",
              "0     Hello DeepGI 10.5 มิลลิเมตร Sessile Polyp NBI ...   \n",
              "1     Hello DeepGI 10.5 มิลลิเมตร Sessile Polyp NBI ...   \n",
              "2     Hello DeepGI 10.5 มิลลิเมตร Sessile Polyp NBI ...   \n",
              "3     Hello DeepGI 10.5 มิลลิเมตร Sessile Polyp NBI ...   \n",
              "4     Hello DeepGI 10.5 มิลลิเมตร Sessile Polyp NBI ...   \n",
              "...                                                 ...   \n",
              "2683  Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Flat Circum...   \n",
              "2684  Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Flat Circum...   \n",
              "2685  Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Flat Circum...   \n",
              "2686  Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Flat Circum...   \n",
              "2687  Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Flat Circum...   \n",
              "\n",
              "                                             input_text  word_error  \\\n",
              "0     Hello DeepGI 10.5 มิลลิเมตร Sessile Polyp NBI ...       False   \n",
              "1     Hello DeepGI 10.5 มิลลิเมตร Sessile Polyp NBI ...       False   \n",
              "2     Hello DeepGI 5.5 มิลลิเมตร Sessile Polyp NBI B...       False   \n",
              "3     Hello DeepGI 5.5 มิลลิเมตร Sessile Polyp NBI B...       False   \n",
              "4     Hello DeepGI 5.5 มิลลิเมตร Sessile Polyp NBI R...       False   \n",
              "...                                                 ...         ...   \n",
              "2683  Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Fat Circumf...        True   \n",
              "2684  Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Fat Circumf...        True   \n",
              "2685  Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Flat Circum...        True   \n",
              "2686  Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Fat Circumf...        True   \n",
              "2687  Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Flat Circum...        True   \n",
              "\n",
              "     missing_data  wrong_word  wrong_number  input_text_isSame  \n",
              "0            same       False         False               True  \n",
              "1            same       False         False               True  \n",
              "2            same        True          True              False  \n",
              "3            same        True          True              False  \n",
              "4            same        True          True              False  \n",
              "...           ...         ...           ...                ...  \n",
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              "2685         same       False         False              False  \n",
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              "      <th>2</th>\n",
              "      <td>Hello DeepGI 10.5 มิลลิเมตร Sessile Polyp NBI ...</td>\n",
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              "      <td>Hello DeepGI 10.5 มิลลิเมตร Sessile Polyp NBI ...</td>\n",
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              "      <td>Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Fat Circumf...</td>\n",
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              "      <th>2687</th>\n",
              "      <td>Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Flat Circum...</td>\n",
              "      <td>Hello DeepGI ใหญ่กว่า 10 มิลลิเมตร Flat Circum...</td>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Norvig's approch\n",
        "Correct the word which has 1-2 distance errors."
      ],
      "metadata": {
        "id": "IiSOLpFy_8Tl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Custom Norvig\n"
      ],
      "metadata": {
        "id": "e5_mNJXnXkfj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "from string import digits\n",
        "from typing import Callable, List, Set, Tuple\n",
        "\n",
        "from pythainlp import thai_digits, thai_letters\n",
        "from pythainlp.corpus import tnc\n",
        "from pythainlp.util import isthaichar\n",
        "\n",
        "eng_letters = []\n",
        "# a-z\n",
        "for ch in range(65, 91):\n",
        "  eng_letters.append(chr(ch))\n",
        "# A-Z\n",
        "for ch in range(97, 123) :\n",
        "  eng_letters.append(chr(ch))\n",
        "\n",
        "\n",
        "def _no_filter(word: str) -> bool:\n",
        "    return True\n",
        "\n",
        "def _is_thai_and_not_num(word: str) -> bool:\n",
        "    for ch in word:\n",
        "        if ch != \".\" and not isthaichar(ch):\n",
        "            return False\n",
        "        if ch in digits or ch in thai_digits:\n",
        "            return False\n",
        "    return True\n",
        "\n",
        "def _keep(\n",
        "    word_freq: int,\n",
        "    min_freq: int,\n",
        "    min_len: int,\n",
        "    max_len: int,\n",
        "    dict_filter: Callable[[str], bool],\n",
        ") -> Callable[[str], bool]:\n",
        "    \"\"\"\n",
        "    Keep only Thai words with at least min_freq frequency\n",
        "    and has length between min_len and max_len characters\n",
        "    \"\"\"\n",
        "    if not word_freq or word_freq[1] < min_freq:\n",
        "        return False\n",
        "\n",
        "    word = word_freq[0]\n",
        "    if not word or len(word) < min_len or len(word) > max_len or word[0] == \".\":\n",
        "        return False\n",
        "\n",
        "    return dict_filter(word)\n",
        "\n",
        "\n",
        "def _edits1(word: str) -> Set[str]:\n",
        "    \"\"\"\n",
        "    Return a set of words with edit distance of 1 from the input word\n",
        "    \"\"\"\n",
        "    splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]\n",
        "    deletes = [L + R[1:] for L, R in splits if R]\n",
        "    transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R) > 1]\n",
        "    replaces = [L + c + R[1:] for L, R in splits if R for c in eng_letters]\n",
        "    inserts = [L + c + R for L, R in splits for c in eng_letters]\n",
        "\n",
        "    return set(deletes + transposes + replaces + inserts)\n",
        "\n",
        "\n",
        "def _edits2(word: str) -> Set[str]:\n",
        "    \"\"\"\n",
        "    Return a set of words with edit distance of 2 from the input word\n",
        "    \"\"\"\n",
        "    return set(e2 for e1 in _edits1(word) for e2 in _edits1(e1))\n",
        "\n",
        "\n",
        "class CustomNorvigSpellChecker:\n",
        "      def __init__(\n",
        "          self,\n",
        "          custom_dict: List[Tuple[str, int]] = None,\n",
        "          min_freq: int = 2,\n",
        "          min_len: int = 2,\n",
        "          max_len: int = 20,\n",
        "          dict_filter: Callable[[str], bool] = _is_thai_and_not_num,\n",
        "      ):\n",
        "        \"\"\"\n",
        "        Initialize Peter Norvig's spell checker object\n",
        "\n",
        "        :param str custom_dict: A list of tuple (word, frequency) to create a spelling dictionary. Default is from Thai National Corpus (around 40,000 words).\n",
        "        :param int min_freq: Minimum frequency of a word to keep (default = 2)\n",
        "        :param int min_len: Minimum length (in characters) of a word to keep (default = 2)\n",
        "        :param int max_len: Maximum length (in characters) of a word to keep (default = 40)\n",
        "        :param func dict_filter: A function to filter the dictionary. Default filter removes any word with number or non-Thai characters. If no filter is required, use None.\n",
        "        \"\"\"\n",
        "        if not custom_dict:  # default, use Thai National Corpus\n",
        "            custom_dict = tnc.word_freqs()\n",
        "\n",
        "        if not dict_filter:\n",
        "            dict_filter = _no_filter\n",
        "\n",
        "        # filter word list\n",
        "        custom_dict = [\n",
        "            word_freq\n",
        "            for word_freq in custom_dict\n",
        "            if _keep(word_freq, min_freq, min_len, max_len, dict_filter)\n",
        "        ]\n",
        "\n",
        "        self.__WORDS = Counter(dict(custom_dict))\n",
        "        self.__WORDS_TOTAL = sum(self.__WORDS.values())\n",
        "        if self.__WORDS_TOTAL < 1:\n",
        "            self.__WORDS_TOTAL = 0\n",
        "        \n",
        "        self.max_len = max_len\n",
        "\n",
        "      def dictionary(self) -> List[Tuple[str, int]]:\n",
        "          \"\"\"\n",
        "          Return the spelling dictionary currently used by this spell checker\n",
        "          \"\"\"\n",
        "          return self.__WORDS.items()\n",
        "\n",
        "\n",
        "      def known(self, words: List[str]) -> List[str]:\n",
        "          \"\"\"\n",
        "          Return a list of given words that found in the spelling dictionary\n",
        "\n",
        "          :param str words: A list of words to check if they are in the spelling dictionary\n",
        "          \"\"\"\n",
        "          return list(w for w in words if w in self.__WORDS)\n",
        "\n",
        "\n",
        "      def prob(self, word: str) -> float:\n",
        "          \"\"\"\n",
        "          Return probability of an input word, according to the spelling dictionary\n",
        "\n",
        "          :param str word: A word to check its probability of occurrence\n",
        "          \"\"\"\n",
        "          return self.__WORDS[word] / self.__WORDS_TOTAL\n",
        "\n",
        "\n",
        "      def freq(self, word: str) -> int:\n",
        "          \"\"\"\n",
        "          Return frequency of an input word, according to the spelling dictionary\n",
        "\n",
        "          :param str word: A word to check its frequency\n",
        "          \"\"\"\n",
        "          return self.__WORDS[word]\n",
        "\n",
        "\n",
        "      def spell(self, word: str) -> List[str]:\n",
        "          \"\"\"\n",
        "          Return a list of possible words, according to edit distance of 1 and 2,\n",
        "          sorted by frequency of word occurrance in the spelling dictionary\n",
        "\n",
        "          :param str word: A word to check its spelling\n",
        "          \"\"\"\n",
        "          if len(word) > self.max_len :\n",
        "            return word\n",
        "\n",
        "          if not word:\n",
        "              return \"\"\n",
        "\n",
        "          candidates = (\n",
        "              self.known([word])\n",
        "              or self.known(_edits1(word))\n",
        "              or self.known(_edits2(word))\n",
        "              or [word]\n",
        "          )\n",
        "          candidates.sort(key=self.freq, reverse=True)\n",
        "\n",
        "          return candidates\n",
        "\n",
        "\n",
        "      def correct(self, word: str) -> str:\n",
        "          \"\"\"\n",
        "          Return the most possible word, using the probability from the spelling dictionary\n",
        "          :param str word: A word to correct its spelling\n",
        "          \"\"\"\n",
        "          if not word:\n",
        "              return \"\"\n",
        "\n",
        "          return self.spell(word)[0]"
      ],
      "metadata": {
        "id": "Dw6799oyQRiO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## `Class DeepGICorrector()`"
      ],
      "metadata": {
        "id": "r_Z-WCnNXo-6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class DeepGICorrector():\n",
        "  def __init__(self, custom_dict, max_sentence_length=70, lower_case=True) :\n",
        "    self.corrector = CustomNorvigSpellChecker(custom_dict, dict_filter=self.fillter_num)\n",
        "    self.max_sentence_length = max_sentence_length\n",
        "    self.lower_case = lower_case\n",
        "  \n",
        "  def fillter_num(self, word) :\n",
        "    for ch in word:\n",
        "      if ch in digits or ch in thai_digits:\n",
        "        return False\n",
        "    return True\n",
        "\n",
        "  def correct(self, words) :\n",
        "    if self.lower_case :\n",
        "      words = words.lower()\n",
        "    words = normalize(words)\n",
        "    tokens = word_tokenize(words)\n",
        "    results = []\n",
        "    for i,token in enumerate(tokens) :\n",
        "      if i > self.max_sentence_length :\n",
        "        results.append(token)\n",
        "        continue\n",
        "      if self.contain_num(token) or (token in trie) or token == \" \":\n",
        "        results.append(token)\n",
        "      else :\n",
        "        token_r = self.corrector.correct(token)\n",
        "        results.append(token_r)\n",
        "    results = \"\".join(results)\n",
        "    return results\n",
        "  \n",
        "  def contain_num(self, word) :\n",
        "    for ch in word:\n",
        "      if ch in digits or ch in thai_digits:\n",
        "        return True\n",
        "    return False"
      ],
      "metadata": {
        "id": "HmXYvT9OACLI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Demo"
      ],
      "metadata": {
        "id": "wTJqcV6iTYlT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentence = df['input_text'][42] + \"บริเวณ anus polpb\"\n",
        "corrector = DeepGICorrector(custom_dict, lower_case=True)\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "print(\"Before:\", sentence)\n",
        "print(\"After:\",corrector.correct(sentence))\n",
        "end = time.time()\n",
        "\n",
        "print(\"Time:\",end - start)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G-_j8A41TXmg",
        "outputId": "44364041-018e-485b-df60-bf51953271cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before: Hello DeepGI 10.5 มิลลิเมตร Sessile Cancer Thai 3 Pseudopolyp Anus EMR บริเวณ anus polpb\n",
            "After: hello deepgi 10.5 มิลลิเมตร sessile cancer thai 3 pseudopolyp anus emr บริเวณ anus polyp\n",
            "Time: 0.9365456104278564\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perform correction"
      ],
      "metadata": {
        "id": "NarV8mZvZ7pZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corrector = DeepGICorrector(custom_dict)\n",
        "corrected_texts = []\n",
        "\n",
        "for i in tqdm(range(len(df))) :\n",
        "  corrected_texts.append(corrector.correct(df['input_text'][i]))\n",
        "df['clean_1_input'] = corrected_texts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Owddi8HYc8n",
        "outputId": "3bde3752-7896-4c69-b604-7c579fc753c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2688/2688 [10:39<00:00,  4.20it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation"
      ],
      "metadata": {
        "id": "SZamAnPKsKQd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = evaluate_df(df, \"target_text\", \"clean_1_input\", lower_case=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xjuOm82UsM_d",
        "outputId": "c0ae200a-a39c-4667-815e-ec8b1f6a55f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    word_error_rate: 0.3005952380952381\n",
            "    wrong_number_rate: 0.20461309523809523\n",
            "    exceed_data_rate: 0.07961309523809523\n",
            "    missing_data_rate: 0.06994047619047619\n",
            "    wrong_word_rate: 0.10416666666666667\n",
            "    wer_score: 0.09786995515695067\n",
            "    accuracy: 0.5863095238095238    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model 1\n",
        "Pre-trained BART with Simple-transformers"
      ],
      "metadata": {
        "id": "otLRCOVdWCi0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare dataset (train/test)"
      ],
      "metadata": {
        "id": "jRZXZpndsZl5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split dataset\n",
        "X = df[['input_text']]\n",
        "y = df[['target_text']]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "train = X_train.join(y_train)\n",
        "test = X_test.join(y_test)\n",
        "train.head()"
      ],
      "metadata": {
        "id": "e_TV1Su4sdVB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## `Class DeepGIBartCorrection()`"
      ],
      "metadata": {
        "id": "y5WGFgE4Xjgq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "import logging\n",
        "from simpletransformers.seq2seq import (\n",
        "    Seq2SeqModel,\n",
        "    Seq2SeqArgs,\n",
        ")\n",
        "\n",
        "\n",
        "\n",
        "class DeepGIBartCorrection() :\n",
        "  def __init__(self, epochs=3, max_length=60, batch_size=4, model_name=\"facebook/m2m100_418M\", src_lang=\"th_TH\", tgt_leng=\"th_TH\", lower_case=True, mask_num=True):\n",
        "    \n",
        "    logging.basicConfig(level=logging.INFO)\n",
        "    transformers_logger = logging.getLogger(\"transformers\")\n",
        "    transformers_logger.setLevel(logging.WARNING)\n",
        "\n",
        "    # set arguments\n",
        "    model_args = Seq2SeqArgs()\n",
        "    model_args.num_train_epochs = epochs\n",
        "    model_args.no_save = True\n",
        "    model_args.evaluate_generated_text = True\n",
        "    model_args.evaluate_during_training = True\n",
        "    model_args.eval_batch_size = batch_size\n",
        "    model_args.train_batch_size= batch_size\n",
        "    model_args.overwrite_output_dir = True\n",
        "    model_args.max_length = max_length\n",
        "    model_args.length_penalty = 2\n",
        "\n",
        "    # Initialize model\n",
        "    self.model = Seq2SeqModel(\n",
        "        encoder_decoder_type=\"mbart\",\n",
        "        encoder_decoder_name=model_name,\n",
        "        args=model_args,\n",
        "        use_cuda= torch.cuda.is_available(),\n",
        "        src_lang=src_lang, tgt_lang=tgt_leng\n",
        "    )\n",
        "\n",
        "    self.lower_case = lower_case\n",
        "    self.mask_num = mask_num\n",
        "\n",
        "  def count_matches(self, labels, preds):\n",
        "      return sum([1 if label == pred else 0 for label, pred in zip(labels, preds)])\n",
        "  \n",
        "  def fit(self, train_set, validation_set):\n",
        "      train_pre = train_set\n",
        "      eval_pre = validation_set\n",
        "\n",
        "      train_pre['input_text'] = train_set['input_text'].map(lambda x: self.preprocess_text(x)[0])\n",
        "      train_pre['target_text'] = train_set['target_text'].map(lambda x: self.preprocess_text(x)[0])\n",
        "      eval_pre['input_text'] = eval_pre['input_text'].map(lambda x: self.preprocess_text(x)[0])\n",
        "      eval_pre['target_text'] = eval_pre['target_text'].map(lambda x: self.preprocess_text(x)[0])\n",
        "\n",
        "      if self.lower_case :\n",
        "        train_pre['input_text'] = train_pre['input_text'].str.lower()\n",
        "        train_pre['target_text'] = train_pre['target_text'].str.lower()\n",
        "        eval_pre['input_text'] = eval_pre['input_text'].str.lower()\n",
        "        eval_pre['target_text'] = eval_pre['target_text'].str.lower()\n",
        "\n",
        "      # Train the model\n",
        "      self.model.train_model(train_pre, eval_data=eval_pre, matches=self.count_matches)\n",
        "      return None\n",
        "\n",
        "  def evaluate(self, data):\n",
        "      # Evaluate the model\n",
        "      data_copy = data\n",
        "      if self.lower_case :\n",
        "        data_copy['input_text'] = data_copy['input_text'].str.lower()\n",
        "        data_copy['target_text'] = data_copy['target_text'].str.lower()\n",
        "      return self.model.eval_model(data_copy)\n",
        "  \n",
        "  def preprocess_text(self, input) :\n",
        "      \"\"\"Preprocesses the input text by replacing all numbers with a special token `#`\n",
        "\n",
        "        Args: \n",
        "          input (Union[str, List[str]]): The input text to preprocess\n",
        "\n",
        "        Returns:\n",
        "          Union[Tuple[str, List[str]], Tuple[List[str], List[List[str]]]]:\n",
        "            If the input is a single string, return a tuple containing the preprocessed text \n",
        "              and a list of the original numbers\n",
        "            If the input is a list of strings, return a tuple containing a list of preprocessed texts\n",
        "            and a list of lists of the original numbers.\n",
        "\n",
        "      \"\"\"\n",
        "      if not self.mask_num :\n",
        "        return input, []\n",
        "      regx = r'\\b\\d+(?:\\.\\d+)?\\b|\\.\\d+\\b'\n",
        "      if type(input) == str :\n",
        "        text = input\n",
        "        numbers = []\n",
        "        for match in re.findall(regx, text):\n",
        "          numbers.append(match)\n",
        "\n",
        "        # Replace all numbers with a special token\n",
        "        text = re.sub(regx, '#', text)\n",
        "        return text, numbers\n",
        "      elif type(input) == list :\n",
        "        list_numbers = []\n",
        "        new_texts =  []\n",
        "        for text in input :\n",
        "          numbers = []\n",
        "          for match in re.findall(regx, text):\n",
        "            numbers.append(match)\n",
        "\n",
        "          # Replace all numbers with a special token\n",
        "          text = re.sub(regx, '#', text)\n",
        "          list_numbers.append(numbers)\n",
        "          new_texts.append(text)\n",
        "        return new_texts, list_numbers\n",
        "\n",
        "  def postprocess_text(self, input_text, input_numbers):\n",
        "      \"\"\"Postprocesses the input text by inserting the original numbers back into the output text at the same positions.\n",
        "\n",
        "        Args:\n",
        "            input_text (Union[str, List[str]]): The input text to postprocess. Can be a single string or a list of strings.\n",
        "            input_numbers (Union[List[str], List[List[str]]]): The original numbers to insert back into the output text. Can be a single list of strings or a list of lists of strings.\n",
        "\n",
        "        Returns:\n",
        "            Union[str, List[str]]: If the input is a single string, returns the postprocessed text. If the input is a list of strings, returns a list of postprocessed texts.\n",
        "      \"\"\"\n",
        "      if not self.mask_num :\n",
        "        return input_text\n",
        "\n",
        "      if type(input_text) == str :\n",
        "        text = input_text \n",
        "        numbers = input_numbers\n",
        "        it = 0\n",
        "        stack = 0\n",
        "        for match in re.finditer(r'#', text):\n",
        "            if (it == len(numbers)) :\n",
        "              return \"false postprocess\"\n",
        "            text = text[:match.start()+stack] + numbers[it] +  text[match.end()+stack:]\n",
        "            stack += len(numbers[it]) - 1\n",
        "            it+=1\n",
        "        return text\n",
        "      elif type(input_text) == list :\n",
        "        out_text = []\n",
        "        for i, text in enumerate(input_text) :\n",
        "          numbers = input_numbers[i]\n",
        "          it = 0\n",
        "          stack = 0\n",
        "          for match in re.finditer(r'#', text):\n",
        "              if (it == len(numbers)) :\n",
        "                continue\n",
        "              text = text[:match.start()+stack] + numbers[it] +  text[match.end()+stack:]\n",
        "              stack += len(numbers[it]) - 1\n",
        "              it+=1\n",
        "          out_text.append(text)\n",
        "        return out_text\n",
        "\n",
        "  def predict(self, data):\n",
        "\n",
        "      data_c = data\n",
        "      if self.lower_case :\n",
        "        if type(data_c) == str :\n",
        "          data_c = data_c.lower()\n",
        "        else :\n",
        "          data_c = [x.lower() for x in data_c]\n",
        "\n",
        "      texts, nums = self.preprocess_text(data_c)\n",
        "      results = self.model.predict(texts)\n",
        "      results = self.postprocess_text(results, nums)\n",
        "\n",
        "      # remove ro_RO\n",
        "      if type(results) == str:\n",
        "        results = re.sub('ro_RO', \"\", results).strip()\n",
        "      elif type(results) == list:\n",
        "        results = [re.sub('ro_RO', \"\", x).strip() for x in results]\n",
        "      return results"
      ],
      "metadata": {
        "id": "tnENAHoRUC4F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Demo"
      ],
      "metadata": {
        "id": "gBUCszSLf-G8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# initial model\n",
        "corrector = DeepGIBartCorrection(epochs=3, max_length=60, batch_size=8, mask_num=True)\n",
        "\n",
        "# Train model\n",
        "corrector.fit(train, test)\n",
        "\n",
        "\n",
        "# model prediction\n",
        "samples = [\n",
        "        \"hello deepgi 10.5 มิลลิเมตร sessile cancer ถ่าย 3 มิลลิเมตร pseudopolyp anus biosp\".lower(),\n",
        "        \"hello deepgi 10.5 มิลลิเมตร sessile cancer thai 3 มิลลิเมตร pseudopolyp anus biosssp\".lower(),\n",
        "         \"Hello DeepGI Rectu Solitary rectal ulcer synซัome เล\".lower(),\n",
        "         \"Hello DeepGI Rectu Soldffitary rectal ulcer syndrome \".lower(),\n",
        "         'hello deepgi 5.5 มิลลิเมตร sessile polyp ถ่าย 3 เสือ pseudopolyp anus biospy'.lower(),\n",
        "         'hello deepgi 1 เซนติเมตร flat submitting ฟัตร submitting ฮิลลิว nbi hyperplastic polyp ascending esd'.lower(),\n",
        "         'Rectu solitary'.lower(),\n",
        "         'pour about preparation with solid stone throughout the right column'.lower(),\n",
        "         'hot snare polypectomy'.lower(),\n",
        "         'flat polp 3 มิลลิเมตร ทิตรันสเมตร'.lower()\n",
        "        ]\n",
        "\n",
        "predicts = corrector.predict(samples)\n",
        "for i,s in enumerate(samples) :\n",
        "  print(f\"input: {s} \\nresult: {predicts[i]}\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 871,
          "referenced_widgets": [
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            "955a035cb31b4312838d4bcdcf50011d",
            "47504c1c63404b6182f1423f5ad345a7",
            "af68833bf777465c94d2be39fc69bfb0",
            "adbf428b825a46cb900b623a7a2c2802",
            "75b718e343344ba0815988bf1001fd06",
            "a631b520fb824ebcb0522b6b6c6e2a9a",
            "83e6e9f2cb9e4be78478d828d949591c",
            "d525fa26a36e4d4da47ca9f421625e84",
            "9bd8da2405154a71911c651cdb9621d9",
            "9b19f6e5939645ee978482d031555a65",
            "8749731e3b5c4cc4ae8d7d4709332ad2",
            "bc1700d5c9c84b1bb1c2d007dc5b2ac9",
            "f22ac5c96e5042b8aa435888f77e629d",
            "64dda04064044a358758f7b67fb4b896"
          ]
        },
        "id": "nic2sHi5f6fL",
        "outputId": "789ad458-3af6-4513-ece2-ac8d52880d63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using a model of type m2m_100 to instantiate a model of type mbart. This is not supported for all configurations of models and can yield errors.\n",
            "Some weights of MBartForConditionalGeneration were not initialized from the model checkpoint at facebook/m2m100_418M and are newly initialized: ['model.decoder.layernorm_embedding.bias', 'model.encoder.embed_positions.weight', 'model.encoder.layernorm_embedding.weight', 'model.decoder.embed_positions.weight', 'model.encoder.layernorm_embedding.bias', 'model.decoder.layernorm_embedding.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'M2M100Tokenizer'. \n",
            "The class this function is called from is 'MBartTokenizer'.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2150 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8084f87ba1c7495b93c29c91ea74210b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6899b92faadf4a218632e8a436c122ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 1:   0%|          | 0/269 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f49fbd9b89814987b171536db0e44721"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/538 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "33dd1f46dfe34a919837b09aa5f747a9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating outputs:   0%|          | 0/68 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ce638a4430a4f3d9553c7d8612b5cf3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating outputs:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "adbf428b825a46cb900b623a7a2c2802"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input: hello deepgi 10.5 มิลลิเมตร sessile cancer ถ่าย 3 มิลลิเมตร pseudopolyp anus biosp \n",
            "result: hello deepgi 10.5 มิลลิเมตร sessile cancer type 3 pseudopolyp anus biopsy\n",
            "\n",
            "input: hello deepgi 10.5 มิลลิเมตร sessile cancer thai 3 มิลลิเมตร pseudopolyp anus biosssp \n",
            "result: hello deepgi 10.5 มิลลิเมตร sessile cancer type 3 pseudopolyp anus biopsy\n",
            "\n",
            "input: hello deepgi rectu solitary rectal ulcer synซัome เล \n",
            "result: hello deepgi เล็กกว่า # มิลลิเมตร flat circumferential solitary rectal ulcer syndrome type # lipoma rectum clipping\n",
            "\n",
            "input: hello deepgi rectu soldffitary rectal ulcer syndrome  \n",
            "result: hello deepgi # มิลลิเมตร flat circumferential solitary rectal ulcer syndrome type # solitary rectal ulcer syndrome rectum clipping\n",
            "\n",
            "input: hello deepgi 5.5 มิลลิเมตร sessile polyp ถ่าย 3 เสือ pseudopolyp anus biospy \n",
            "result: hello deepgi 5.5 มิลลิเมตร sessile polyp type 3 pseudopolyp anus biopsy\n",
            "\n",
            "input: hello deepgi 1 เซนติเมตร flat submitting ฟัตร submitting ฮิลลิว nbi hyperplastic polyp ascending esd \n",
            "result: hello deepgi 1 เซนติเมตร flat subepithelial nbi hyperplastic polyp ascending esd\n",
            "\n",
            "input: rectu solitary \n",
            "result: hello deepgi # มิลลิเมตร flat circumitary rectalignant non invasive polyp anus biopsy\n",
            "\n",
            "input: pour about preparation with solid stone throughout the right column \n",
            "result: hello deepgi # มิลลิเมตร flat colitis type # radiation proctitis anus biopsy\n",
            "\n",
            "input: hot snare polypectomy \n",
            "result: hello deepgi # มิลลิเมตร flat circum polyp type #are\n",
            "\n",
            "input: flat polp 3 มิลลิเมตร ทิตรันสเมตร \n",
            "result: hello deepgi 3 มิลลิเมตร flat circumferential mass type # flat polyp sigmoid polypectomy\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test number encode and decode\n",
        "d,nums = corrector.preprocess_text(\"The 456 is a time d-2 and .265, 035.25\")\n",
        "print(\"example\")\n",
        "print(\"After encode:\", d)\n",
        "print(\"After Decode:\",corrector.postprocess_text(d, nums))"
      ],
      "metadata": {
        "id": "NpY1dWtIXWmr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03f56242-d6db-4dc1-b766-d16b66e285e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "example\n",
            "After encode: The # is a time d-# and #, #\n",
            "After Decode: The 456 is a time d-2 and .265, 035.25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate"
      ],
      "metadata": {
        "id": "dlQwDUxvCxIj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['correct_lm'] = corrector.predict(list(df['input_text']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "12861ac012e04e53929d4bada0ced111",
            "b4b713aea6d14e82a5d83d5c61b25e45",
            "7dde2b0e3418439ebd683c8b01007b9f",
            "f4e6e5ed66774e7aaca87ab43eb3e954",
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            "0fcd7d77a28f4253bbf171de5ede76c1",
            "7b59e1e92a2249c5968f97dd348122a8"
          ]
        },
        "id": "j0pumGAICzOr",
        "outputId": "10710713-8a1e-4ef7-a439-c560cade8b21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating outputs:   0%|          | 0/336 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "12861ac012e04e53929d4bada0ced111"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = evaluate_df(df, \"target_text\", \"correct_lm\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JqGB44B6DCCS",
        "outputId": "36c51552-ee90-4919-c427-f78626a22137"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    word_error_rate: 0.07738095238095238\n",
            "    wrong_number_rate: 0.20461309523809523\n",
            "    exceed_data_rate: 0.024553571428571428\n",
            "    missing_data_rate: 0.004464285714285714\n",
            "    wrong_word_rate: 0.13467261904761904\n",
            "    wer_score: 0.027466367713004484\n",
            "    accuracy: 0.7872023809523809    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model 2 Bert Model (*not used)"
      ],
      "metadata": {
        "id": "3nZMdkRr_Xk3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import logging\n",
        "\n",
        "import pandas as pd\n",
        "from simpletransformers.seq2seq import Seq2SeqModel\n",
        "\n",
        "logging.basicConfig(level=logging.INFO)\n",
        "transformers_logger = logging.getLogger(\"transformers\")\n",
        "transformers_logger.setLevel(logging.WARNING)\n",
        "\n",
        "\n",
        "train_data = [\n",
        "    [\"one\", \"1\"],\n",
        "    [\"two\", \"2\"],\n",
        "]\n",
        "\n",
        "train_df = pd.DataFrame(train_data, columns=[\"input_text\", \"target_text\"])\n",
        "\n",
        "eval_data = [\n",
        "    [\"three\", \"3\"],\n",
        "    [\"four\", \"4\"],\n",
        "]\n",
        "\n",
        "eval_df = pd.DataFrame(eval_data, columns=[\"input_text\", \"target_text\"])\n",
        "\n",
        "model_args = {\n",
        "    \"reprocess_input_data\": True,\n",
        "    \"overwrite_output_dir\": True,\n",
        "    \"max_seq_length\": 10,\n",
        "    \"train_batch_size\": 2,\n",
        "    \"num_train_epochs\": 10,\n",
        "    \"save_eval_checkpoints\": False,\n",
        "    \"save_model_every_epoch\": False,\n",
        "    \"evaluate_generated_text\": True,\n",
        "    \"evaluate_during_training_verbose\": True,\n",
        "    \"use_multiprocessing\": False,\n",
        "    \"max_length\": 15,\n",
        "    \"manual_seed\": 4,\n",
        "}\n",
        "\n",
        "encoder_type = \"roberta\"\n",
        "\n",
        "model = Seq2SeqModel(\n",
        "    encoder_type,\n",
        "    \"roberta-base\",\n",
        "    \"bert-base-cased\",\n",
        "    args=model_args,\n",
        "    use_cuda=True,\n",
        ")\n",
        "\n",
        "model.train_model(train_df)\n",
        "\n",
        "results = model.eval_model(eval_df)\n",
        "\n",
        "print(model.predict([\"five\"]))\n",
        "\n",
        "\n",
        "model1 = Seq2SeqModel(\n",
        "    encoder_type,\n",
        "    encoder_decoder_name=\"outputs\",\n",
        "    args=model_args,\n",
        "    use_cuda=True,\n",
        ")\n",
        "print(model1.predict([\"five\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755,
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        },
        "id": "6u636g_bJjyF",
        "outputId": "439736f5-1801-49f5-f296-bd3d8bf26cf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaModel: ['lm_head.dense.bias', 'lm_head.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.bias']\n",
            "- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertLMHeadModel: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertLMHeadModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertLMHeadModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertLMHeadModel were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['bert.encoder.layer.4.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.0.crossattention.self.key.weight', 'bert.encoder.layer.0.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.5.crossattention.output.dense.bias', 'bert.encoder.layer.5.crossattention.output.dense.weight', 'bert.encoder.layer.6.crossattention.self.key.bias', 'bert.encoder.layer.3.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.7.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.4.crossattention.output.dense.weight', 'bert.encoder.layer.0.crossattention.self.query.bias', 'bert.encoder.layer.10.crossattention.self.query.weight', 'bert.encoder.layer.3.crossattention.output.dense.weight', 'bert.encoder.layer.7.crossattention.self.value.weight', 'bert.encoder.layer.8.crossattention.self.value.weight', 'bert.encoder.layer.9.crossattention.output.dense.weight', 'bert.encoder.layer.6.crossattention.self.query.bias', 'bert.encoder.layer.5.crossattention.self.query.bias', 'bert.encoder.layer.7.crossattention.self.query.bias', 'bert.encoder.layer.4.crossattention.self.query.weight', 'bert.encoder.layer.9.crossattention.self.query.bias', 'bert.encoder.layer.5.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.2.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.output.dense.bias', 'bert.encoder.layer.4.crossattention.self.query.bias', 'bert.encoder.layer.11.crossattention.output.dense.weight', 'bert.encoder.layer.11.crossattention.self.query.bias', 'bert.encoder.layer.6.crossattention.self.key.weight', 'bert.encoder.layer.9.crossattention.output.dense.bias', 'bert.encoder.layer.9.crossattention.self.key.bias', 'bert.encoder.layer.11.crossattention.self.key.bias', 'bert.encoder.layer.8.crossattention.output.dense.weight', 'bert.encoder.layer.4.crossattention.self.value.weight', 'bert.encoder.layer.8.crossattention.self.value.bias', 'bert.encoder.layer.10.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.self.query.bias', 'bert.encoder.layer.7.crossattention.output.dense.weight', 'bert.encoder.layer.6.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.4.crossattention.self.key.bias', 'bert.encoder.layer.10.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.2.crossattention.self.key.bias', 'bert.encoder.layer.5.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.self.key.weight', 'bert.encoder.layer.9.crossattention.self.value.weight', 'bert.encoder.layer.2.crossattention.self.query.weight', 'bert.encoder.layer.3.crossattention.self.query.weight', 'bert.encoder.layer.8.crossattention.self.key.bias', 'bert.encoder.layer.9.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.11.crossattention.self.value.weight', 'bert.encoder.layer.8.crossattention.self.query.weight', 'bert.encoder.layer.5.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.11.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.10.crossattention.output.dense.weight', 'bert.encoder.layer.10.crossattention.self.value.bias', 'bert.encoder.layer.5.crossattention.self.key.weight', 'bert.encoder.layer.11.crossattention.output.dense.bias', 'bert.encoder.layer.7.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.self.query.weight', 'bert.encoder.layer.9.crossattention.self.key.weight', 'bert.encoder.layer.1.crossattention.self.value.weight', 'bert.encoder.layer.11.crossattention.self.query.weight', 'bert.encoder.layer.2.crossattention.output.dense.bias', 'bert.encoder.layer.7.crossattention.self.value.bias', 'bert.encoder.layer.3.crossattention.self.query.bias', 'bert.encoder.layer.3.crossattention.self.key.weight', 'bert.encoder.layer.4.crossattention.output.dense.bias', 'bert.encoder.layer.4.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.8.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.9.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.8.crossattention.self.query.bias', 'bert.encoder.layer.2.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.7.crossattention.output.dense.bias', 'bert.encoder.layer.0.crossattention.output.dense.bias', 'bert.encoder.layer.11.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.7.crossattention.self.query.weight', 'bert.encoder.layer.5.crossattention.self.query.weight', 'bert.encoder.layer.3.crossattention.self.key.bias', 'bert.encoder.layer.5.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.9.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.7.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.6.crossattention.self.value.bias', 'bert.encoder.layer.2.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.6.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.10.crossattention.self.value.weight', 'bert.encoder.layer.10.crossattention.output.dense.bias', 'bert.encoder.layer.5.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.self.query.weight', 'bert.encoder.layer.4.crossattention.self.key.weight', 'bert.encoder.layer.2.crossattention.self.query.bias', 'bert.encoder.layer.11.crossattention.self.value.bias', 'bert.encoder.layer.6.crossattention.output.dense.bias', 'bert.encoder.layer.2.crossattention.self.value.bias', 'bert.encoder.layer.2.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.8.crossattention.output.dense.bias', 'bert.encoder.layer.7.crossattention.self.key.bias', 'bert.encoder.layer.10.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.1.crossattention.self.key.bias', 'bert.encoder.layer.0.crossattention.output.dense.weight', 'bert.encoder.layer.1.crossattention.output.dense.weight', 'bert.encoder.layer.4.crossattention.self.value.bias', 'bert.encoder.layer.8.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.6.crossattention.self.query.weight', 'bert.encoder.layer.2.crossattention.output.dense.weight', 'bert.encoder.layer.3.crossattention.self.value.weight', 'bert.encoder.layer.3.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.6.crossattention.output.dense.weight', 'bert.encoder.layer.8.crossattention.self.key.weight', 'bert.encoder.layer.6.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.output.dense.bias', 'bert.encoder.layer.0.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.self.value.bias', 'bert.encoder.layer.11.crossattention.self.key.weight', 'bert.encoder.layer.9.crossattention.self.query.weight', 'bert.encoder.layer.10.crossattention.self.key.weight', 'bert.encoder.layer.10.crossattention.self.query.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3265252a01b6488fb1db0c054b208a2d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 10:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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              "Running Epoch 1 of 10:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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              "Running Epoch 2 of 10:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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            "text/plain": [
              "Running Epoch 3 of 10:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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              "Running Epoch 4 of 10:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 5 of 10:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "Running Epoch 6 of 10:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "Running Epoch 9 of 10:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
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          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ffb35be4a0c942e58388c4354d240fbe"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[',,,,,,, and and,,, and and']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[',,,,,,, and and,,, and and']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jmr-q42CPvy_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}