Text_Classification.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Installing the simpletransformers library"
      ],
      "metadata": {
        "id": "fYKT8sCkXI6r"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
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          "height": 1000
        },
        "id": "nUeY5ZIFWTEU",
        "outputId": "ea349521-3fd2-46a9-835a-9d9e617c186e"
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        {
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          "text": [
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            "Building wheels for collected packages: subprocess32, pathtools, seqeval, blinker\n",
            "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for subprocess32: filename=subprocess32-3.5.4-py3-none-any.whl size=6502 sha256=57d35d0fbde0edd82d3ec8759cd13a9a7f17bb72f279dc43bdbfa2da352aada4\n",
            "  Stored in directory: /root/.cache/pip/wheels/50/ca/fa/8fca8d246e64f19488d07567547ddec8eb084e8c0d7a59226a\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8806 sha256=b091ae34f590176a6cd81a9115d815e36c31e84441ffb79ce06161bd6502e904\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/31/09/fa59cef12cdcfecc627b3d24273699f390e71828921b2cbba2\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16180 sha256=cacbba8515d568e8f10d0a2be340a34c33d6d7c48c34feeb4d0da05333c6a728\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7\n",
            "  Building wheel for blinker (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for blinker: filename=blinker-1.4-py3-none-any.whl size=13478 sha256=f2b95d29fe8703bd872b2bb32fd041e4e8e310fb912215ea9d9f0075bd98f140\n",
            "  Stored in directory: /root/.cache/pip/wheels/22/f5/18/df711b66eb25b21325c132757d4314db9ac5e8dabeaf196eab\n",
            "Successfully built subprocess32 pathtools seqeval blinker\n",
            "Installing collected packages: prompt-toolkit, ipython, ipykernel, multidict, frozenlist, yarl, smmap, asynctest, async-timeout, aiosignal, pyyaml, gitdb, fsspec, aiohttp, yaspin, xxhash, watchdog, validators, tokenizers, subprocess32, shortuuid, sentry-sdk, sacremoses, pympler, pydeck, pathtools, huggingface-hub, GitPython, docker-pycreds, configparser, blinker, base58, wandb, transformers, streamlit, seqeval, sentencepiece, datasets, simpletransformers\n",
            "  Attempting uninstall: prompt-toolkit\n",
            "    Found existing installation: prompt-toolkit 1.0.18\n",
            "    Uninstalling prompt-toolkit-1.0.18:\n",
            "      Successfully uninstalled prompt-toolkit-1.0.18\n",
            "  Attempting uninstall: ipython\n",
            "    Found existing installation: ipython 5.5.0\n",
            "    Uninstalling ipython-5.5.0:\n",
            "      Successfully uninstalled ipython-5.5.0\n",
            "  Attempting uninstall: ipykernel\n",
            "    Found existing installation: ipykernel 4.10.1\n",
            "    Uninstalling ipykernel-4.10.1:\n",
            "      Successfully uninstalled ipykernel-4.10.1\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "jupyter-console 5.2.0 requires prompt-toolkit<2.0.0,>=1.0.0, but you have prompt-toolkit 3.0.24 which is incompatible.\n",
            "google-colab 1.0.0 requires ipykernel~=4.10, but you have ipykernel 6.7.0 which is incompatible.\n",
            "google-colab 1.0.0 requires ipython~=5.5.0, but you have ipython 7.31.1 which is incompatible.\u001b[0m\n",
            "Successfully installed GitPython-3.1.26 aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 base58-2.1.1 blinker-1.4 configparser-5.2.0 datasets-1.18.0 docker-pycreds-0.4.0 frozenlist-1.3.0 fsspec-2022.1.0 gitdb-4.0.9 huggingface-hub-0.4.0 ipykernel-6.7.0 ipython-7.31.1 multidict-6.0.1 pathtools-0.1.2 prompt-toolkit-3.0.24 pydeck-0.7.1 pympler-1.0.1 pyyaml-6.0 sacremoses-0.0.47 sentencepiece-0.1.96 sentry-sdk-1.5.3 seqeval-1.2.2 shortuuid-1.0.8 simpletransformers-0.63.4 smmap-5.0.0 streamlit-1.4.0 subprocess32-3.5.4 tokenizers-0.10.3 transformers-4.15.0 validators-0.18.2 wandb-0.12.9 watchdog-2.1.6 xxhash-2.0.2 yarl-1.7.2 yaspin-2.1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "IPython",
                  "ipykernel",
                  "prompt_toolkit"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "pip install simpletransformers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing neccessary libraries"
      ],
      "metadata": {
        "id": "EDUDUS0QX06z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from simpletransformers.classification import ClassificationModel, ClassificationArgs\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import logging\n",
        "import re\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "\n",
        "logging.basicConfig(level=logging.INFO)\n",
        "transformers_logger = logging.getLogger(\"transformers\")\n",
        "transformers_logger.setLevel(logging.WARNING)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3eIWp1WSX8lG",
        "outputId": "7e934475-8957-4cab-829e-ec83c4027ccb"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing the dataset"
      ],
      "metadata": {
        "id": "DbFGeq6ufCHQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.read_csv('statement1.csv' )\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "N-wRYzxofAhL",
        "outputId": "4dbbe696-dd4c-4cf7-a3d8-0f6918122baf"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c1919b03-77fd-4916-9aab-416a203ad8c1 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-c1919b03-77fd-4916-9aab-416a203ad8c1');\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",
              "  "
            ],
            "text/plain": [
              "                                           statement          class\n",
              "0  This CREDIT AGREEMENT is entered into as of No...       preamble\n",
              "1  This agreement is dated 22 December 2014 and m...       preamble\n",
              "2  This Agreement and shall be governed by and in...  governing law\n",
              "3  The choice of South African law as the governi...  governing law\n",
              "4  This agreement and other loan documents shall ...  governing law"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bar Chart representation to find the unvariant data"
      ],
      "metadata": {
        "id": "WaYODYkbgGUw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig , ax = plt.subplots()\n",
        "fig.suptitle(\"Y\" , fontsize = 12)\n",
        "train_df[\"class\"].reset_index().groupby(\"class\").count().sort_values(by=\"index\").plot(kind = \"barh\",legend=False,ax=ax).grid(axis = 'x')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "e16jwmyMfsdr",
        "outputId": "c2839f57-1210-4574-efa0-0efb73b947ba"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Text augumentation"
      ],
      "metadata": {
        "id": "xacYrnsShDCL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install nlpaug"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "Majty9DGhAjn",
        "outputId": "0ba5b832-a8a2-4a32-879d-1f9a50901a43"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting nlpaug\n",
            "  Downloading nlpaug-1.1.10-py3-none-any.whl (410 kB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy>=1.16.2 in /usr/local/lib/python3.7/dist-packages (from nlpaug) (1.19.5)\n",
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            "Collecting pandas>=1.2.0\n",
            "  Downloading pandas-1.3.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.3 MB)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.22.0->nlpaug) (1.24.3)\n",
            "Installing collected packages: pandas, nlpaug\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.1.5\n",
            "    Uninstalling pandas-1.1.5:\n",
            "      Successfully uninstalled pandas-1.1.5\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires ipykernel~=4.10, but you have ipykernel 6.7.0 which is incompatible.\n",
            "google-colab 1.0.0 requires ipython~=5.5.0, but you have ipython 7.31.1 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas~=1.1.0; python_version >= \"3.0\", but you have pandas 1.3.5 which is incompatible.\u001b[0m\n",
            "Successfully installed nlpaug-1.1.10 pandas-1.3.5\n"
          ]
        },
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    },
    {
      "cell_type": "code",
      "source": [
        "import nlpaug.augmenter.word as naw\n",
        "aug_df = pd.read_csv('statement1.csv')\n",
        "modified_aug_df = aug_df.copy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwvEV-DFhMVO",
        "outputId": "3bd4d749-0c61-4bce-a46c-3241c27e669a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:summarizer.preprocessing.cleaner:'pattern' package not found; tag filters are not available for English\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Using BERT for Text - Augumentation\n",
        "TOPK=20 #default=100\n",
        "ACT = 'insert' #\"substitute\"\n",
        "aug_bert = naw.ContextualWordEmbsAug(\n",
        "    model_path='distilbert-base-uncased', \n",
        "    device='cuda',\n",
        "    action=ACT, top_k=TOPK)\n",
        "# print(\"Original:\")\n",
        "\n",
        "for ind in aug_df.index:\n",
        "    test_sentence = aug_df['statement'][ind]\n",
        "    # print(ind)\n",
        "    # print(test_sentence)\n",
        "    for ii in range(4):\n",
        "        augmented_text = aug_bert.augment(test_sentence)\n",
        "        # temp = {'statement':augmented_text , 'class':aug_df['class'][ind]}\n",
        "        # modified_aug_df.append(temp ,ignore_index = True )\n",
        "        modified_aug_df.loc[len(modified_aug_df.index)] = [augmented_text , aug_df['class'][ind]]\n",
        "        # print(augmented_text)\n",
        "    # print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "execution_count": 8,
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        {
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    {
      "cell_type": "code",
      "source": [
        "modified_aug_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aD1gf6FKhdHC",
        "outputId": "359d2477-1448-4f6b-8ebd-ade00f46df97"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(130, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Text preprocessing"
      ],
      "metadata": {
        "id": "woV7cELThi7z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "    Pre-processing of Text -\n",
        "        1. Cleaning Text\n",
        "        2. Removing Stop Words\n",
        "        3. Applying Lemmatization\n",
        "    \n",
        "    Preprocess a string.\n",
        "        :parameter\n",
        "        :param text: string - name of column containing text\n",
        "        :param lst_stopwords: list - list of stopwords to remove\n",
        "        :param flg_stemm: bool - whether stemming is to be applied\n",
        "        :param flg_lemm: bool - whether lemmitisation is to be applied\n",
        "    :return\n",
        "        cleaned text\n",
        "'''\n",
        "\n",
        "def utils_preprocess_text(text , flg_stemm=False,flg_lemm=True,lst_stopwords = None):\n",
        "\n",
        "    # cleaning - convert to lowercase , removing punctuations and characters.\n",
        "    text = re.sub(r'[^\\w\\s]', '', str(text).lower().strip())\n",
        "\n",
        "    # Tokenize ( convert string to list ).\n",
        "    lst_text = text.split()\n",
        "\n",
        "    # Removing Stop - Words\n",
        "    if lst_stopwords is not None:\n",
        "        lst_text = [word for word in lst_text if word not in lst_stopwords]\n",
        "\n",
        "    # Stemming ( Removing -ing, -ly .. )\n",
        "    if flg_stemm == True:\n",
        "        ps = nltk.stem.porter.PorterStemmer()\n",
        "        lst_text = [ps.stem(word) for word in lst_text]\n",
        "\n",
        "    ## Lemmatisation (convert the word into root word)\n",
        "    if flg_lemm == True:\n",
        "        lem = nltk.stem.wordnet.WordNetLemmatizer()\n",
        "        lst_text = [lem.lemmatize(word) for word in lst_text]\n",
        "        \n",
        "    text = \" \".join(lst_text)\n",
        "    return text"
      ],
      "metadata": {
        "id": "lu70fmNphmEs"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# importing stop words from NLTK.\n",
        "lst_stopwords = nltk.corpus.stopwords.words(\"english\")"
      ],
      "metadata": {
        "id": "FVOitT4PiS_w"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding cleaned text as a column in training DataFrame.\n",
        "train_df = modified_aug_df.copy()\n",
        "train_df[\"text\"] = train_df[\"statement\"].apply(lambda x: \n",
        "          utils_preprocess_text(x, flg_stemm=False, flg_lemm=True, \n",
        "          lst_stopwords=lst_stopwords))\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "ezmeYQJvibkG",
        "outputId": "de33e9dd-1553-4263-e67b-72eaeca4c6db"
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      "execution_count": 12,
      "outputs": [
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              "0  This CREDIT AGREEMENT is entered into as of No...  ...  credit agreement entered november 23 2010 amon...\n",
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      "source": [
        "# replacing target variables with a number\n",
        "possible_class = train_df['class'].unique()\n",
        "label_dict = {}\n",
        "for index, possible_label in enumerate(possible_class):\n",
        "    label_dict[possible_label] = index\n",
        "label_dict"
      ],
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        "colab": {
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          "data": {
            "text/plain": [
              "{'Indemnification': 3,\n",
              " 'Lender Defaulting': 4,\n",
              " 'governing law': 1,\n",
              " 'other': 2,\n",
              " 'preamble': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['labels'] = train_df['class'].replace(label_dict)\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "fPLgmA8fijLB",
        "outputId": "542d1ea1-336f-47a2-8297-168d646cb541"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-68676973-8531-487c-b26a-b4ba9beb44fe\">\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>statement</th>\n",
              "      <th>class</th>\n",
              "      <th>text</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>This CREDIT AGREEMENT is entered into as of No...</td>\n",
              "      <td>preamble</td>\n",
              "      <td>credit agreement entered november 23 2010 amon...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>This agreement is dated 22 December 2014 and m...</td>\n",
              "      <td>preamble</td>\n",
              "      <td>agreement dated 22 december 2014 made party xy...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>This Agreement and shall be governed by and in...</td>\n",
              "      <td>governing law</td>\n",
              "      <td>agreement shall governed interpreted accordanc...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The choice of South African law as the governi...</td>\n",
              "      <td>governing law</td>\n",
              "      <td>choice south african law governing law finance...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>This agreement and other loan documents shall ...</td>\n",
              "      <td>governing law</td>\n",
              "      <td>agreement loan document shall governed adheren...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-68676973-8531-487c-b26a-b4ba9beb44fe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-68676973-8531-487c-b26a-b4ba9beb44fe 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-68676973-8531-487c-b26a-b4ba9beb44fe');\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",
              "  "
            ],
            "text/plain": [
              "                                           statement  ... labels\n",
              "0  This CREDIT AGREEMENT is entered into as of No...  ...      0\n",
              "1  This agreement is dated 22 December 2014 and m...  ...      0\n",
              "2  This Agreement and shall be governed by and in...  ...      1\n",
              "3  The choice of South African law as the governi...  ...      1\n",
              "4  This agreement and other loan documents shall ...  ...      1\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Final Dataframe"
      ],
      "metadata": {
        "id": "mDk4DQo963uo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# randomly shuffling of training data\n",
        "train_df = train_df.sample(frac = 1)"
      ],
      "metadata": {
        "id": "_Q8RTYsd68fL"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "  Univariate Distribution of the Target Variable by showing labels frequency with a Bar - Plot in the Augumented Dataset.\n",
        "'''\n",
        "fig , ax = plt.subplots()\n",
        "fig.suptitle(\"Y\" , fontsize = 12)\n",
        "train_df[\"class\"].reset_index().groupby(\"class\").count().sort_values(by=\"index\").plot(kind = \"barh\",legend=False,ax=ax).grid(axis = 'x')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "aMiYnN0R7Crw",
        "outputId": "147e825a-7ab2-452c-e4ee-a49e79c51ea4"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing OLD columns\n",
        "train_df.drop(['class'] , axis = 1 , inplace = True)\n",
        "train_df.drop(['statement'] , axis = 1 , inplace = True)"
      ],
      "metadata": {
        "id": "fOqZvcSYinB7"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Sloc0F2Cipq2",
        "outputId": "d1f722eb-8fa1-4a57-d3b6-8c4b5bc5dfcf"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <th>116</th>\n",
              "      <td>lender obligation shall deemed either temporar...</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>agreement application dated 22 december 2014 m...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>defaulting debt lender right approve immediate...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>accordingly borrower shall indemnify whatsoeve...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>borrower certain lender agent party willing co...</td>\n",
              "      <td>2</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-470a9357-b392-4b54-b257-0fb64af24ac1')\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-470a9357-b392-4b54-b257-0fb64af24ac1 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-470a9357-b392-4b54-b257-0fb64af24ac1');\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",
              "  "
            ],
            "text/plain": [
              "                                                  text  labels\n",
              "116  lender obligation shall deemed either temporar...       4\n",
              "31   agreement application dated 22 december 2014 m...       0\n",
              "70   defaulting debt lender right approve immediate...       4\n",
              "58   accordingly borrower shall indemnify whatsoeve...       3\n",
              "126  borrower certain lender agent party willing co...       2"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training and Test Dataset Creation"
      ],
      "metadata": {
        "id": "IAifiiFW7RGW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = train_df.drop('labels' , axis= 1)\n",
        "Y = train_df['labels']"
      ],
      "metadata": {
        "id": "0ZPF-5pbiso0"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y = Y.to_frame()\n",
        "#Y.set_index('labels')"
      ],
      "metadata": {
        "id": "atkfyfnTiweF"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# splitting of training and validation data\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train , X_test , Y_train , Y_test = train_test_split(X , Y , test_size=0.33,random_state = 42)"
      ],
      "metadata": {
        "id": "IuygZDCti068"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df_temp = X_train.copy()\n",
        "train_df_temp['lables'] = Y_train\n",
        "train_df_temp.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GafmdCKLi4Lm",
        "outputId": "5f9955e0-3dc0-4245-bd47-3a71f5220eef"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>lables</th>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>choice south african law governing law finance...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>agreement covenant covenant shall shall govern...</td>\n",
              "      <td>1</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93bfa7cf-05aa-4f30-914c-7b4dfd9c8c34')\"\n",
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              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\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",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-93bfa7cf-05aa-4f30-914c-7b4dfd9c8c34 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-93bfa7cf-05aa-4f30-914c-7b4dfd9c8c34');\n",
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              "            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",
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              "                                                  text  lables\n",
              "114  lender shall deemed deemed defaulting income l...       4\n",
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              "2    agreement shall governed interpreted accordanc...       1\n",
              "3    choice south african law governing law finance...       1\n",
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            ]
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    {
      "cell_type": "markdown",
      "source": [
        "## Model Creation"
      ],
      "metadata": {
        "id": "zsY80WzD7h_z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_args = ClassificationArgs(num_train_epochs=5, overwrite_output_dir = True)"
      ],
      "metadata": {
        "id": "TR5qSfqSi709"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a ClassificationModel\n",
        "model = ClassificationModel(\n",
        "    'bert',\n",
        "    'bert-base-cased',\n",
        "    num_labels=5,\n",
        "    args=model_args,\n",
        "    use_cuda=True,\n",
        ") \n",
        "# Train the model\n",
        "model.train_model(train_df_temp)"
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      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/simpletransformers/classification/classification_model.py:586: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  \"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\"\n",
            "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "88e641d9f9cb4e47ae4986f5fa4f6ef0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/87 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:simpletransformers.classification.classification_utils: Saving features into cached file cache_dir/cached_train_bert_128_5_2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "88c5c16431da4a2d9bac3e56c09b250e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Epoch:   0%|          | 0/5 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3ded73faa7aa4b3097088d3b5e291bc7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Epoch 0 of 5:   0%|          | 0/11 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e56ca636bd5a4fb5ac2e87a948a93242",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Epoch 1 of 5:   0%|          | 0/11 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a7c481dbf609492a9418371bda953217",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Epoch 2 of 5:   0%|          | 0/11 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "017437d6bb9f42178969ba8631b5bc1d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Epoch 3 of 5:   0%|          | 0/11 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "43b7033fa33e438983f653bdc6b693c3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Epoch 4 of 5:   0%|          | 0/11 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:simpletransformers.classification.classification_model: Training of bert model complete. Saved to outputs/.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(55, 0.7784738304940138)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions with the model\n",
        "predictions, raw_outputs = model.predict([\"As soon as practicable after any payment of Indemnified Taxes or Other Taxes by an Obligor to a Governmental Authority, the Obligor shall deliver to the Administrative Agent the original or a certified copy of a receipt issued by such Governmental Authority evidencing such payment, a copy of the return reporting such payment or other evidence of such payment reasonably satisfactory to the Administrative Agent.\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "80550ea26326400a89ae2a88257a57be",
            "f4a8911bf7874a38ab0a089f5e2f9d29",
            "57d90902cd504976b0e20ae353a955cd",
            "fd3ecaf5d1a946a1a6a6c8b0fd8af755",
            "2f47a5edd5444f668390854ab77ea533",
            "2ec9327b3c584ee6b2a88619a4f4f6e9",
            "42c7ae89c1ac4d59a223676c225886a0",
            "ab66ea4e1955461fb778700266fd3651",
            "7289f812007d4120ae3b4d9a6f176a5d",
            "ea64a97251c8457393ac59f1398aa778",
            "c14fe99e7e71486bbdb821aba55a3405",
            "ac1d217f04494d7fb57d5d9b877a2b07",
            "bcc3b02414ca40749ef8b4ea1deda5ad",
            "1f618c5829504dbf8eb1914c0e093a74",
            "a93c67a8cecd417f97e69ae2b032d6aa",
            "dddb5f9e70dc436aa39820d67c4bf366",
            "bc22c38446a54837825f213a1b222550",
            "31802567583b492db66c2523a8902812",
            "26778e073a814b6c82b572a79b8aa82a",
            "f9f32d882fa84b4dbc801ba29973e6ec",
            "019fc5755b2348d5b807cfa437dc0a6a",
            "c7f18d0953f44a28a69f122ba1dc19d0"
          ]
        },
        "id": "6OkiM9ppjGMz",
        "outputId": "3263bc9e-87a3-4043-c05d-15fd2b929079"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "80550ea26326400a89ae2a88257a57be",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ac1d217f04494d7fb57d5d9b877a2b07",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_dict = {3 : 'Indemnification', 4 : 'Lender Defaulting',1 : 'governing law',2 : 'other', 0 : 'preamble'}\n",
        "prediction_dict[predictions[0]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "XV9cdyaDjKlc",
        "outputId": "703b0a93-d155-4834-8b24-44ba2000ced6"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Indemnification'"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls = []\n",
        "for ind in X_test.index:    \n",
        "    ls.append(X_test['text'][ind])\n",
        "# ls\n",
        "y_predict , y_predicted_prob = model.predict(ls)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "5e3b158968dc468cac4173e4677a2607",
            "9db6aa3b64404860a5e615be5fc9591c",
            "8d64c68f6cff45b59bd72d65a1487211",
            "4dd3ecadccaa487e9ac4438a049fd562",
            "2df9ab9d75454134b8e891d82b268666",
            "27fb0d2ff9024a8590261d2eb24837fa",
            "087bc659b45b47f89ca598652a5f01ae",
            "f8caf6a350514ea2ba4f31cb2456064a",
            "7d5648fbf9ee4f0e82f3173c3923201e",
            "9e587fd8e76044448d2f334c7851108e",
            "79ec8fce5c9a406c8f5de1bdaaf84ef6",
            "0df8aa01027945e78c0aae91dd97c6df",
            "9f2289da15ba47b6810be0a0bf12df4d",
            "a1fa96a7173647839d02a70bc9c17b64",
            "00ffdea650844254807d8c1734194e5f",
            "e3224b953d1945ce8a60692d7ed8db17",
            "9796fbb66a634d67862201abb190f8bf",
            "6054b45aded74f10a41d75827178ca56",
            "b77c1d127d9541b3af573de334241037",
            "d52b40b41b7c4c4b8f6a9607920a271c",
            "e374d0b6a22848a3abb48bb0e267fea6",
            "7e2d8a3a015a4e39b6b176ae103794db"
          ]
        },
        "id": "uetjVgg1jLVQ",
        "outputId": "2465970c-bb17-4ce5-9c5c-86504f46d07c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5e3b158968dc468cac4173e4677a2607",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/43 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0df8aa01027945e78c0aae91dd97c6df",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/6 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(Y_test, y_predict)\n",
        "cm_df = pd.DataFrame(cm,\n",
        "                     index = ['preamble','governing law','other', 'Indemnification', 'Lender Defaulting'], \n",
        "                     columns = ['preamble','governing law','other', 'Indemnification', 'Lender Defaulting'])"
      ],
      "metadata": {
        "id": "OsOLqcOejW0_"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting the confusion matrix\n",
        "plt.figure(figsize=(5,4))\n",
        "sns.heatmap(cm_df, annot=True)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.ylabel('Actual Values')\n",
        "plt.xlabel('Predicted Values')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "id": "mpfb8K4cjbWF",
        "outputId": "6089e919-8932-47a9-8b94-ab98e56fcb37"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "f1_score(Y_test, y_predict, labels=None, pos_label=1, average='macro', sample_weight=None, zero_division='warn')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qe-7rBCIjdk8",
        "outputId": "954bd056-d0b8-473a-967d-e7f19439e1c1"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score\n",
        "precision_score(Y_test, y_predict, labels=None, pos_label=1, average='macro', sample_weight=None, zero_division='warn')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7sOrYRgVjfla",
        "outputId": "c408c017-20fa-4b2c-e580-c0f43a03ae1d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import recall_score\n",
        "recall_score(Y_test, y_predict, labels=None, pos_label=1, average='macro', sample_weight=None, zero_division='warn')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BY1B4-k6jhaF",
        "outputId": "4050cea3-c865-4e33-f5ff-6df517fa59fe"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y_test , y_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_NGW7H08ZH0",
        "outputId": "1b59a807-0a05-4514-ee5b-41d33585c2d8"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        10\n",
            "           1       1.00      1.00      1.00         8\n",
            "           2       1.00      1.00      1.00         7\n",
            "           3       1.00      1.00      1.00         9\n",
            "           4       1.00      1.00      1.00         9\n",
            "\n",
            "    accuracy                           1.00        43\n",
            "   macro avg       1.00      1.00      1.00        43\n",
            "weighted avg       1.00      1.00      1.00        43\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prediction on Test Dataset"
      ],
      "metadata": {
        "id": "leARpQes8cDX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = pd.read_csv('test_data.csv')\n",
        "test_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "7P3iWj_l8au8",
        "outputId": "93272402-56af-4f6b-e16e-c67b8ec4adc1"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>Amendment No. 1 dated as of February 4, 2016 (...</td>\n",
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              "      <td>This Agreement, and any non-contractual obliga...</td>\n",
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              "      <th>3</th>\n",
              "      <td>Each Lender agrees to indemnify the Administra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Any Lender that (a) has failed to (i) fund all...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>This Assignment and Assumption shall be govern...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>As soon as practicable after any payment of In...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Non-Defaulting Lender means, at any time, each...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Fundco Facility Agreement means the facility a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Notwithstanding any provision of this Agreemen...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f4d4eb19-26d2-4cf4-ba81-25c163cf7ccf');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
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              "  </div>\n",
              "  "
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            "text/plain": [
              "                                           statement\n",
              "0  Amendment No. 1 dated as of February 4, 2016 (...\n",
              "1  Subject to the Legal Reservations, (a) the cho...\n",
              "2  This Agreement, and any non-contractual obliga...\n",
              "3  Each Lender agrees to indemnify the Administra...\n",
              "4  Any Lender that (a) has failed to (i) fund all...\n",
              "5  This Assignment and Assumption shall be govern...\n",
              "6  As soon as practicable after any payment of In...\n",
              "7  Non-Defaulting Lender means, at any time, each...\n",
              "8  Fundco Facility Agreement means the facility a...\n",
              "9  Notwithstanding any provision of this Agreemen..."
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing Test - Dataset\n",
        "test_df[\"text\"] = test_df[\"statement\"].apply(lambda x: \n",
        "          utils_preprocess_text(x, flg_stemm=False, flg_lemm=True, \n",
        "          lst_stopwords=lst_stopwords))\n",
        "test_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "mNVUVbGC8h-Z",
        "outputId": "44060f1d-a12b-455f-866b-b7a16530636a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-04ac8704-8367-4380-a94d-a01f94cffdeb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        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>statement</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Amendment No. 1 dated as of February 4, 2016 (...</td>\n",
              "      <td>amendment 1 dated february 4 2016 amendment 1 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>subject legal reservation choice law specified...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>This Agreement, and any non-contractual obliga...</td>\n",
              "      <td>agreement noncontractual obligation arising co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Each Lender agrees to indemnify the Administra...</td>\n",
              "      <td>lender agrees indemnify administrative agent h...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Any Lender that (a) has failed to (i) fund all...</td>\n",
              "      <td>lender failed fund portion loan within two bus...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04ac8704-8367-4380-a94d-a01f94cffdeb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-04ac8704-8367-4380-a94d-a01f94cffdeb 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-04ac8704-8367-4380-a94d-a01f94cffdeb');\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",
              "  "
            ],
            "text/plain": [
              "                                           statement                                               text\n",
              "0  Amendment No. 1 dated as of February 4, 2016 (...  amendment 1 dated february 4 2016 amendment 1 ...\n",
              "1  Subject to the Legal Reservations, (a) the cho...  subject legal reservation choice law specified...\n",
              "2  This Agreement, and any non-contractual obliga...  agreement noncontractual obligation arising co...\n",
              "3  Each Lender agrees to indemnify the Administra...  lender agrees indemnify administrative agent h...\n",
              "4  Any Lender that (a) has failed to (i) fund all...  lender failed fund portion loan within two bus..."
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.drop(['statement'] , axis = 1 , inplace = True)"
      ],
      "metadata": {
        "id": "I6Ou3tkd8kBR"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KrjWl-hQ8k5r",
        "outputId": "2b09db5f-f66c-4165-97d0-d22b68cb49e9"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5f1db6e8-9d90-492e-974a-b36fd0b965ab\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "      <td>lender failed fund portion loan within two bus...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f1db6e8-9d90-492e-974a-b36fd0b965ab')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5f1db6e8-9d90-492e-974a-b36fd0b965ab 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-5f1db6e8-9d90-492e-974a-b36fd0b965ab');\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",
              "  "
            ],
            "text/plain": [
              "                                                text\n",
              "0  amendment 1 dated february 4 2016 amendment 1 ...\n",
              "1  subject legal reservation choice law specified...\n",
              "2  agreement noncontractual obligation arising co...\n",
              "3  lender agrees indemnify administrative agent h...\n",
              "4  lender failed fund portion loan within two bus..."
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls = []\n",
        "for ind in test_df.index:    \n",
        "    ls.append(test_df['text'][ind])\n",
        "# ls\n",
        "y_predict ,y_confidence_score = model.predict(ls)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "9e2bc1f172d24b398b950ecbae267a22",
            "703574c49de14851a0d055ac2dc3bfef",
            "fbfd7e39fbf84cc1a12708072ebcfbed",
            "7de6102bc24e4ad5a3eea1df2c01cb67",
            "5ddba3f0441444f997866605c4ad9685",
            "885f1fdc7b2c48a6bf5384445918215f",
            "7a8639da86df4ceca345a453d42117aa",
            "8491f2c11eeb4084af4f5173d92f4f71",
            "eed649ebdf324be1897c72572d71fed8",
            "40625c7bc03441e68545d83637ab7bd9",
            "aa681b2bffb149f8982229a0e839f09e",
            "79a53b42ff98429c8efffd10a107da37",
            "0d2b7b8a1daa49ca81ea195feb61610c",
            "2ccf08386a4d41c9ae96b879549949e5",
            "ab9d45cc12594e40ba0b10dcd05d4569",
            "918a5db36921466ca724a561d80b8c66",
            "5a13a72f18784ef08b53fd090cde333f",
            "936e3aeb3c814b3dbdb767be2310a792",
            "53bb459c7b1e4882b3898bcb0322469a",
            "54247036fb424bd481bc5ebea54c6c22",
            "a0aa42e12278401a9abd3b08ea8fff55",
            "a671e37db3f8494c99d8d8046dedd298"
          ]
        },
        "id": "vlzMoila8puS",
        "outputId": "e7ca94cc-711a-46ca-cfb0-2234286c3cd1"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9e2bc1f172d24b398b950ecbae267a22",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "79a53b42ff98429c8efffd10a107da37",
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            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ]
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_confidence_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nch_B2i88qqc",
        "outputId": "bdd7390e-32c3-49d7-ae6e-92f53205db2a"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2.22645402, -0.32360169, -0.53270668, -0.7225706 , -1.07755184],\n",
              "       [-0.63577455,  2.52232456, -0.15546873, -0.0529381 , -1.0011487 ],\n",
              "       [-1.55797982,  2.05524015, -0.46588567,  0.80339295, -0.44466153],\n",
              "       [-1.72330117, -0.34980065, -0.50606197,  2.4367702 ,  0.33445817],\n",
              "       [-0.94321132, -0.21144062, -0.02994967,  1.57679105, -0.11698589],\n",
              "       [-1.50307286,  1.53463018,  0.38915506,  0.39741066, -0.53901011],\n",
              "       [-0.45227477,  1.04343212, -0.33321664,  1.61558068, -1.18029523],\n",
              "       [-1.356552  , -0.9799909 , -0.48953584,  0.31169161,  2.05380058],\n",
              "       [ 1.55072796, -0.18299897,  0.32603315, -0.74807006, -1.24760509],\n",
              "       [-1.38373184, -0.83236641, -0.7444424 ,  0.07583554,  2.31883669]])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_prediction = []\n",
        "for i in range(len(y_predict)):\n",
        "    final_prediction.append(prediction_dict[y_predict[i]])\n",
        "final_prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HS4rEp9p8vMY",
        "outputId": "412c2368-eed9-4131-e555-a522e9b2633e"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['preamble',\n",
              " 'governing law',\n",
              " 'governing law',\n",
              " 'Indemnification',\n",
              " 'Indemnification',\n",
              " 'governing law',\n",
              " 'Indemnification',\n",
              " 'Lender Defaulting',\n",
              " 'preamble',\n",
              " 'Lender Defaulting']"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_dict_ans = {}\n",
        "for i in range(len(final_prediction)):\n",
        "    final_dict_ans[ls[i]] = final_prediction[i]\n",
        "final_dict_ans"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4T8SRWU68v8W",
        "outputId": "acd7bf8e-2fc0-4b67-c415-09df65512036"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'agreement noncontractual obligation arising connection shall governed construed accordance english law': 'governing law',\n",
              " 'amendment 1 dated february 4 2016 amendment 1 amendment restatement agreement dated january 10 2016 existing restatement agreement among ggg holding u inc borrower cgg societe anonyme incorporated law france parent lender time time party thereto suisse ag administrative agent lender capacity administrative agent collateral agent collateral agent lender': 'preamble',\n",
              " 'assignment assumption shall governed construed accordance law governing credit agreement': 'governing law',\n",
              " 'fundco facility agreement mean facility agreement dated date agreement made fundco initial lender defined fundco facility agreement including borrower devco': 'preamble',\n",
              " 'lender agrees indemnify administrative agent hold harmless rateably according applicable percentage loss claim damage liability related expense including fee charge disbursement counsel may incurred asserted administrative agent way relating arising loan document transaction therein contemplated': 'Indemnification',\n",
              " 'lender failed fund portion loan within two business day date loan required funded hereunder b notified borrower administrative agent writing intend comply funding obligation hereunder c failed within three business day written request administrative agent company': 'Indemnification',\n",
              " 'nondefaulting lender mean time lender defaulting lender time': 'Lender Defaulting',\n",
              " 'notwithstanding provision agreement contrary lender becomes defaulting lender fullest extent permitted applicable law following provision shall apply long lender defaulting lender': 'Lender Defaulting',\n",
              " 'soon practicable payment indemnified tax tax obligor governmental authority obligor shall deliver administrative agent original certified copy receipt issued governmental authority evidencing payment copy return reporting payment evidence payment reasonably satisfactory administrative agent': 'Indemnification',\n",
              " 'subject legal reservation choice law specified eachfinance document governing law finance document recognized enforced relevant jurisdiction andb judgment obtained new york jurisdiction governing law finance document relation finance document recognized enforced relevant jurisdiction': 'governing law'}"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "json_object = json.dumps(final_dict_ans, indent = 4)\n",
        "print(json_object)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cp6IONDn8zGp",
        "outputId": "e716e429-c636-4128-84df-7e3085b70e03"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "    \"amendment 1 dated february 4 2016 amendment 1 amendment restatement agreement dated january 10 2016 existing restatement agreement among ggg holding u inc borrower cgg societe anonyme incorporated law france parent lender time time party thereto suisse ag administrative agent lender capacity administrative agent collateral agent collateral agent lender\": \"preamble\",\n",
            "    \"subject legal reservation choice law specified eachfinance document governing law finance document recognized enforced relevant jurisdiction andb judgment obtained new york jurisdiction governing law finance document relation finance document recognized enforced relevant jurisdiction\": \"governing law\",\n",
            "    \"agreement noncontractual obligation arising connection shall governed construed accordance english law\": \"governing law\",\n",
            "    \"lender agrees indemnify administrative agent hold harmless rateably according applicable percentage loss claim damage liability related expense including fee charge disbursement counsel may incurred asserted administrative agent way relating arising loan document transaction therein contemplated\": \"Indemnification\",\n",
            "    \"lender failed fund portion loan within two business day date loan required funded hereunder b notified borrower administrative agent writing intend comply funding obligation hereunder c failed within three business day written request administrative agent company\": \"Indemnification\",\n",
            "    \"assignment assumption shall governed construed accordance law governing credit agreement\": \"governing law\",\n",
            "    \"soon practicable payment indemnified tax tax obligor governmental authority obligor shall deliver administrative agent original certified copy receipt issued governmental authority evidencing payment copy return reporting payment evidence payment reasonably satisfactory administrative agent\": \"Indemnification\",\n",
            "    \"nondefaulting lender mean time lender defaulting lender time\": \"Lender Defaulting\",\n",
            "    \"fundco facility agreement mean facility agreement dated date agreement made fundco initial lender defined fundco facility agreement including borrower devco\": \"preamble\",\n",
            "    \"notwithstanding provision agreement contrary lender becomes defaulting lender fullest extent permitted applicable law following provision shall apply long lender defaulting lender\": \"Lender Defaulting\"\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "jsonFile = open(\"final_output.json\", \"w\")\n",
        "jsonFile.write(json_object)\n",
        "jsonFile.close()"
      ],
      "metadata": {
        "id": "nOAmkjf78z1m"
      },
      "execution_count": 50,
      "outputs": []
    }
  ]
}