LegalTextAugment1.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shreyasi2002/Legal-Augmentation/blob/main/LegalTextAugment1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ],
      "metadata": {
        "id": "_hqXEWnVQHkt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torch>=1.6.0 transformers>=4.11.3 sentencepiece"
      ],
      "metadata": {
        "id": "Yj5j2NRb0513"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install nltk>=3.4.5"
      ],
      "metadata": {
        "id": "10QoSx__07pL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install numpy requests nlpaug "
      ],
      "metadata": {
        "id": "GUTIsJCv0_Fv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f50fe61f-b4a3-4707-b103-f867eaaf5dd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (1.22.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (2.27.1)\n",
            "Collecting nlpaug\n",
            "  Downloading nlpaug-1.1.11-py3-none-any.whl (410 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m410.5/410.5 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests) (2.0.12)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests) (3.4)\n",
            "Requirement already satisfied: gdown>=4.0.0 in /usr/local/lib/python3.9/dist-packages (from nlpaug) (4.6.6)\n",
            "Requirement already satisfied: pandas>=1.2.0 in /usr/local/lib/python3.9/dist-packages (from nlpaug) (1.5.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from gdown>=4.0.0->nlpaug) (4.65.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.9/dist-packages (from gdown>=4.0.0->nlpaug) (4.11.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from gdown>=4.0.0->nlpaug) (1.16.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from gdown>=4.0.0->nlpaug) (3.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=1.2.0->nlpaug) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=1.2.0->nlpaug) (2022.7.1)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.9/dist-packages (from beautifulsoup4->gdown>=4.0.0->nlpaug) (2.4)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.9/dist-packages (from requests) (1.7.1)\n",
            "Installing collected packages: nlpaug\n",
            "Successfully installed nlpaug-1.1.11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sacremoses"
      ],
      "metadata": {
        "id": "ekULB8bg02MB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93650259-b8b7-43fa-9cd8-484af765c6a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m880.6/880.6 kB\u001b[0m \u001b[31m36.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.9/dist-packages (from sacremoses) (2022.10.31)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from sacremoses) (1.16.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.9/dist-packages (from sacremoses) (8.1.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.9/dist-packages (from sacremoses) (1.2.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from sacremoses) (4.65.0)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895259 sha256=3799ec0fd89ce941a7c0428988ca34990a78c6ee7bd4ff40133900f00590b8aa\n",
            "  Stored in directory: /root/.cache/pip/wheels/12/1c/3d/46cf06718d63a32ff798a89594b61e7f345ab6b36d909ce033\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses\n",
            "Successfully installed sacremoses-0.0.53\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading the Data"
      ],
      "metadata": {
        "id": "8M_iCUsU0nPP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5mbaWD5Z0MrW"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "with open('train.json') as f:\n",
        "    data = json.load(f)\n",
        "    f.close()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(data)"
      ],
      "metadata": {
        "id": "YRcZJMQJ0fT2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data[0]['data']['text'])"
      ],
      "metadata": {
        "id": "SH4LN2J-y4pv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res=data[0]['annotations'][0]['result']\n",
        "print(res)"
      ],
      "metadata": {
        "id": "m4Govml90g2C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Easy Data Augmentation (EDA"
      ],
      "metadata": {
        "id": "yhD5OAufUweL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Synonym Replacement"
      ],
      "metadata": {
        "id": "9seNQ0M8TUFX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load Data"
      ],
      "metadata": {
        "id": "usGdSTfbVpKy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "with open('train.json') as f:\n",
        "    data = json.load(f)\n",
        "    f.close()"
      ],
      "metadata": {
        "id": "CbJp8v5QVqNj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nlpaug.augmenter.word as naw\n",
        "aug = naw.SynonymAug(aug_src='wordnet')"
      ],
      "metadata": {
        "id": "CIxiuF5LVFnI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Synonym Replacement on single Document"
      ],
      "metadata": {
        "id": "ylKmvq6YSTei"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_new = data\n",
        "for i in range(len(res)):\n",
        "  text1 = res[i]['value']['text']\n",
        "  augmented_text = aug.augment(text1)\n",
        "  data_new[0]['annotations'][0]['result'][i]['value']['text'] = augmented_text\n",
        "  # print(augmented_text[0], '\\n')\n",
        "\n",
        "print(data_new[0])"
      ],
      "metadata": {
        "id": "X92yz7wMOBRM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Synonym Replacement on all docmuents"
      ],
      "metadata": {
        "id": "Gk--qSV5V4BZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import secrets\n",
        "import string\n",
        "\n",
        "# Function to generate random string of 32 character for ID\n",
        "def generate_id(N): \n",
        "  res = ''.join(secrets.choice(string.ascii_lowercase + string.digits) for i in range(N))\n",
        "  return res"
      ],
      "metadata": {
        "id": "0YQW5AA04rG8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for j in range(len(data)):\n",
        "  idx=0\n",
        "  data_str = \"\"\n",
        "  res = data[j]['annotations'][0]['result']\n",
        "  \n",
        "  for i in range(len(res)):\n",
        "    text1 = res[i]['value']['text']\n",
        "    augmented_text = aug.augment(text1)\n",
        "    data[j]['annotations'][0]['result'][i]['value']['text'] = augmented_text[0]\n",
        "\n",
        "    data[j]['annotations'][0]['result'][i]['value']['start'] = idx\n",
        "    data[j]['annotations'][0]['result'][i]['value']['end'] = idx + len(augmented_text[0])\n",
        "    idx = idx + len(augmented_text[0])\n",
        "    data[j]['annotations'][0]['result'][i]['id'] = generate_id(32)\n",
        "\n",
        "    if i>0:\n",
        "      data_str = str(data_str) + \" \" + str(augmented_text[0])\n",
        "    else:\n",
        "      data_str = str(data_str) + str(augmented_text[0])\n",
        "    \n",
        "  data[j]['data'] = data_str"
      ],
      "metadata": {
        "id": "4rsnQXfQhsLv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"sample-synonym_replacement.json\", \"w\") as outfile:\n",
        "    json.dump(data, outfile)"
      ],
      "metadata": {
        "id": "mpiA48qvibxt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Random Insertion"
      ],
      "metadata": {
        "id": "VB74L_FKWlaq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Random Deletion"
      ],
      "metadata": {
        "id": "eRImatd2Wo9P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Contextual Word Embedding"
      ],
      "metadata": {
        "id": "dDvBmoiVSdKe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load Data"
      ],
      "metadata": {
        "id": "A2BQOwsOWeGY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "with open('train.json') as f:\n",
        "    data = json.load(f)\n",
        "    f.close()"
      ],
      "metadata": {
        "id": "RqmXJ01VVtCe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nlpaug.augmenter.word as naw\n",
        "aug = naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action=\"substitute\")"
      ],
      "metadata": {
        "id": "16SSDmZqVMFJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Contextual Word Embedding on Single Document"
      ],
      "metadata": {
        "id": "ZYJsiNbVTQsy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_new = data\n",
        "for i in range(len(res)):\n",
        "  text1 = res[i]['value']['text']\n",
        "  augmented_text = aug.augment(text1)\n",
        "  data_new[0]['annotations'][0]['result'][i]['value']['text'] = augmented_text\n",
        "\n",
        "print(data_new[0])\n"
      ],
      "metadata": {
        "id": "BhPs5_LrQFBr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Contextual Embedding on All documents"
      ],
      "metadata": {
        "id": "eh_d1En5VzM-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import secrets\n",
        "import string\n",
        "\n",
        "# Function to generate random string of 32 character for ID\n",
        "def generate_id(N): \n",
        "  res = ''.join(secrets.choice(string.ascii_lowercase + string.digits) for i in range(N))\n",
        "  return res"
      ],
      "metadata": {
        "id": "QB6nGpAYV90L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for j in range(len(data)):\n",
        "  idx=0\n",
        "  data_str = \"\"\n",
        "  res = data[j]['annotations'][0]['result']\n",
        "  \n",
        "  for i in range(len(res)):\n",
        "    text1 = res[i]['value']['text']\n",
        "    augmented_text = aug.augment(text1)\n",
        "    data[j]['annotations'][0]['result'][i]['value']['text'] = augmented_text[0]\n",
        "\n",
        "    data[j]['annotations'][0]['result'][i]['value']['start'] = idx\n",
        "    data[j]['annotations'][0]['result'][i]['value']['end'] = idx + len(augmented_text[0])\n",
        "    idx = idx + len(augmented_text[0])\n",
        "    data[j]['annotations'][0]['result'][i]['id'] = generate_id(32)\n",
        "\n",
        "    if i>0:\n",
        "      data_str = str(data_str) + \" \" + str(augmented_text[0])\n",
        "    else:\n",
        "      data_str = str(data_str) + str(augmented_text[0])\n",
        "    \n",
        "  data[j]['data'] = data_str"
      ],
      "metadata": {
        "id": "dTbHScLBVAer"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"sample-contextual_embedding.json\", \"w\") as outfile:\n",
        "    json.dump(data, outfile)"
      ],
      "metadata": {
        "id": "hNmNJhB4VboK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Backtranslation"
      ],
      "metadata": {
        "id": "FmXyAwdF0x8L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Testing BT on single sentence"
      ],
      "metadata": {
        "id": "r-veZJjAWMwf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nlpaug.augmenter.word as naw\n",
        "\n",
        "text2='this appeal coming on for hearing this day, the court delivered the following : - judgment heard the learned counsel for the appellant and the learned government pleader. 2. the accused is in appeal in the following circumstances : - the appellant was accused of offences punishable under sections 498a and 306 of the indian penal code, 1860 ( hereinafter referred to as the ` ipc \\', for brevity )'\n",
        "back_translation_aug = naw.BackTranslationAug( from_model_name='facebook/wmt19-en-de', to_model_name='facebook/wmt19-de-en')\n",
        "augmented_text1 = back_translation_aug.augment(text2)"
      ],
      "metadata": {
        "id": "lPSd8utc0ka7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Augmented Text:\")\n",
        "print(augmented_text1)"
      ],
      "metadata": {
        "id": "dqlnQN5SGpnC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load Data"
      ],
      "metadata": {
        "id": "EkFpBkeMWZ-Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "with open('train.json') as f:\n",
        "    data = json.load(f)\n",
        "    f.close()\n",
        "\n",
        "res=data[0]['annotations'][0]['result']"
      ],
      "metadata": {
        "id": "JWsCzuH1WZVW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nlpaug.augmenter.word as naw\n",
        "back_translation_aug = naw.BackTranslationAug( from_model_name='facebook/wmt19-en-de', to_model_name='facebook/wmt19-de-en')"
      ],
      "metadata": {
        "id": "_O7NNRlJXPPR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "BT on Single Document"
      ],
      "metadata": {
        "id": "9-OuYcwaXTnw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_new = data\n",
        "for i in range(len(res)):\n",
        "  text1 = res[i]['value']['text']\n",
        "  augmented_text = back_translation_aug.augment(text1)\n",
        "  data_new[0]['annotations'][0]['result'][i]['value']['text'] = augmented_text\n",
        "\n",
        "print(data_new[0])"
      ],
      "metadata": {
        "id": "3fhErCGBXMZt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "BT on all documents"
      ],
      "metadata": {
        "id": "yPvdfK1GXchc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import secrets\n",
        "import string\n",
        "\n",
        "# Function to generate random string of 32 character for ID\n",
        "def generate_id(N): \n",
        "  res = ''.join(secrets.choice(string.ascii_lowercase + string.digits) for i in range(N))\n",
        "  return res"
      ],
      "metadata": {
        "id": "l82NAhrxXZTB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for j in range(len(data)):\n",
        "  idx=0\n",
        "  data_str = \"\"\n",
        "  res = data[j]['annotations'][0]['result']\n",
        "  \n",
        "  for i in range(len(res)):\n",
        "    text1 = res[i]['value']['text']\n",
        "    augmented_text = back_translation_aug.augment(text1)\n",
        "    data[j]['annotations'][0]['result'][i]['value']['text'] = augmented_text[0]\n",
        "\n",
        "    data[j]['annotations'][0]['result'][i]['value']['start'] = idx\n",
        "    data[j]['annotations'][0]['result'][i]['value']['end'] = idx + len(augmented_text[0])\n",
        "    idx = idx + len(augmented_text[0])\n",
        "    data[j]['annotations'][0]['result'][i]['id'] = generate_id(32)\n",
        "\n",
        "    if i>0:\n",
        "      data_str = str(data_str) + \" \" + str(augmented_text[0])\n",
        "    else:\n",
        "      data_str = str(data_str) + str(augmented_text[0])\n",
        " \n",
        "  data[j]['data'] = data_str"
      ],
      "metadata": {
        "id": "5I_07D47XhWO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"sample-backtranslation.json\", \"w\") as outfile:\n",
        "    json.dump(data, outfile)"
      ],
      "metadata": {
        "id": "aRizzLX3XtRN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### SMOTE"
      ],
      "metadata": {
        "id": "AykivlzhIAcZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "owzJChnRKnJb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train_data = []\n",
        "train_data_txt = []\n",
        "train_data_lbl = []\n",
        "\n",
        "for j in range(len(data)):\n",
        "  res = data[j]['annotations'][0]['result']\n",
        "  for i in range(len(res)):\n",
        "    train_data_txt.append(res[i]['value']['text'])\n",
        "    train_data_lbl.append(res[i]['value']['labels'][0])\n",
        "  \n",
        "train_df = pd.DataFrame({'text': train_data_txt, 'label': train_data_lbl})\n",
        "\n",
        "print(train_df)"
      ],
      "metadata": {
        "id": "aaJ6nH38JWVo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "# Convert the text data to numerical features\n",
        "tfidf = TfidfVectorizer()\n",
        "X = tfidf.fit_transform(train_df['text'])\n",
        "\n",
        "# Apply SMOTE to the numerical features\n",
        "smote = SMOTE()\n",
        "X_resampled, y_resampled = smote.fit_resample(X, train_df['label'])\n",
        "\n",
        "# Convert the synthetic samples to text\n",
        "resampled_text = [tfidf.inverse_transform(sample) for sample in X_resampled]\n",
        "\n",
        "# Combine the original and synthetic samples\n",
        "resampled_data = pd.DataFrame({'text': resampled_text, 'label': y_resampled})\n",
        "\n",
        "print(resampled_data)"
      ],
      "metadata": {
        "id": "Be4Fl2P1H-p8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## GPT2"
      ],
      "metadata": {
        "id": "P8EdQxQax6fU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n"
      ],
      "metadata": {
        "id": "KOxRWzvsx6GZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10964625-d0dd-47fa-bf2d-eb6210c42193"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.28.1-py3-none-any.whl (7.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m39.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m57.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.13.4-py3-none-any.whl (200 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m200.1/200.1 kB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.11.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.0.12)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.4 tokenizers-0.13.3 transformers-4.28.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
        "import torch\n",
        "\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2-medium').to(device)\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium')"
      ],
      "metadata": {
        "id": "7KrIt4q7EZl7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "abc60ba36bf54f71b8936553e65547a7",
            "3e7db6bef7b34448bfe6e19b377b9b7f",
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            "6bf6ba893ef249f89193312f06f9b816",
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            "3bcd973320c548519e4b8edf881d77f9",
            "7ef9a6e7867f4b55a9fbc0caf2790465",
            "350b25cf52db42cc95368533b0de7d33",
            "546c2288f2644631924b72e40f2dd106",
            "86d49f88f86346e898a2a387ccfa9057",
            "f8e55d5112c544b2887caaf930a98a12",
            "b0b171497a184286959ccfc37915ad55",
            "e5454742b45d4f41bd8e32c52da9d043",
            "f58238cb6086487c993264ad8c4247f4",
            "834703eb09574630a89e175b1263bd1c",
            "72c9996df35d4a35a9f8b26d5a2a2b53",
            "7a941453c7e74afa850f9c1ba2a9e144",
            "a7716445c33c4e269131b9c29cb96768",
            "4d78773ccbbe4592a3f97b1d7f2e2b90",
            "b6738fbf5fa34371ba0967fc74c78798",
            "7c99bd9ee34e432fb21d21e65696a797",
            "b29923f54641457d9e48ef5a68f5fd77",
            "2ae6b40f5dc947eab0141b73501b2571",
            "86caffa266da4000936db123d26f265b",
            "2a913ce5271b4d64a27628a4856d9bd7",
            "8a8d4ee6c02c40afa7ef3530cfea4ad6",
            "42d207336b0c41f79f68b3e9af744179",
            "20f8d942099c4796ac378b3ca51606d7",
            "856bd9e528eb480f8575e8e15e5f5678",
            "d6ffd70e968a40e29a48873341a0a947",
            "358f0da02290405f917e45ba0c6b5465",
            "61d9ba7e5b6747b1a6cafeed5880fc6e",
            "17d68f82426b465a888dc50e22f63db8",
            "7c49780233d345d79e47ade1191928da",
            "9fbcdbb753a64facb8c7e886492e12b2",
            "03acc50b918949b69d108cba51c184c3",
            "aa34b0f2f08145e196bbf3229c75718c",
            "085365e6b4324550ba35c536e5b658b1",
            "548ab3e169cc4888b86d02dd95ccd8fb",
            "cb28efe4cb534c8f95c9d1be7e2a6a59",
            "375cf1bfbc59479d869e5a059e9a0919",
            "0e14a10bad824f9cba4babc442371f4a",
            "34319bb4ee954937b41526e433dcd0b5",
            "8926d0ed350f423fa1da12be536b71e8",
            "a320b57b082f425fabfab611a614c36c",
            "a28fe4164cab43d397602cac39f271c0",
            "f85c939a3c654d35a95ffca454141f1c",
            "e104ff12445b4b1e8dfaa121fb6e6f73",
            "a30a0ddf9b65453b91152be0ac48a1fb",
            "fc3d3de4068645b8a089289872256712",
            "7ae7b1b8bfcd4c37a50c4fb69a76c044",
            "bb9f6355ecab43019d548208db919d25",
            "f2b4aa83a5784c8dae556dc3eeec178a",
            "9640db8bb65c4d6fbb73d23ded001f70",
            "2f60d0c58ec24db99670608e473307c9"
          ]
        },
        "outputId": "37385030-ee58-4a9b-ab40-f95858807e3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/718 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "abc60ba36bf54f71b8936553e65547a7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.52G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b0b171497a184286959ccfc37915ad55"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ae6b40f5dc947eab0141b73501b2571"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7c49780233d345d79e47ade1191928da"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a320b57b082f425fabfab611a614c36c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "with open('train.json') as f:\n",
        "    data = json.load(f)\n",
        "    f.close()"
      ],
      "metadata": {
        "id": "fYPElkhkE5W5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "541abd8b-a674-42d6-fee1-007aeb3d7df9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-54c640b95dc4>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'train.json'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'train.json'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# res=data[0]['annotations'][0]['result']\n",
        "# input_text = res[0]['value']['text']\n",
        "input_text = 'It is claimed that the accused had looked after Lakshmi well till'\n",
        "print('Original Text: \\n'+input_text)\n",
        "input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)\n",
        "output_ids = model.generate(\n",
        "  input_ids.to(device),\n",
        "  max_length=100,\n",
        "  do_sample=True,\n",
        "  num_beams=5, \n",
        "  no_repeat_ngram_size=3, \n",
        "  early_stopping=True\n",
        ")\n",
        "output_ids = output_ids.to('cpu')\n",
        "output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
        "print('Synthetic Text: \\n'+output_text)"
      ],
      "metadata": {
        "id": "kdB6tCxPJU8p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ead7d831-aa92-4671-f5bf-c8f06d13f69e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Text: \n",
            "It is claimed that the accused had looked after Lakshmi well till\n",
            "Synthetic Text: \n",
            "It is claimed that the accused had looked after Lakshmi well till her death.\n",
            "\n",
            "The police, however, said that Lakshmi's family had filed a complaint against the accused.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "GPT2 Paraphrasing on Single Document"
      ],
      "metadata": {
        "id": "IjKSHkqnFIyq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "res=data[0]['annotations'][0]['result']\n",
        "data_new = data\n",
        "for i in range(len(res)):\n",
        "  input_text = res[i]['value']['text']\n",
        "  input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)\n",
        "  output_ids = model.generate(\n",
        "    input_ids.to(device),\n",
        "    max_length=100,\n",
        "    do_sample=True,\n",
        "    top_k=50,\n",
        "    temperature=0.7\n",
        "  )\n",
        "  output_ids = output_ids.to('cpu')\n",
        "  output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
        "  data_new[0]['annotations'][0]['result'][i]['value']['text'] = output_text\n",
        "\n",
        "print(data_new[0])\n"
      ],
      "metadata": {
        "id": "2qO7og39FIyq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "GPT2 Paraphrasing on All documents"
      ],
      "metadata": {
        "id": "VBMFHMpmFIyq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import secrets\n",
        "import string\n",
        "\n",
        "# Function to generate random string of 32 character for ID\n",
        "def generate_id(N): \n",
        "  res = ''.join(secrets.choice(string.ascii_lowercase + string.digits) for i in range(N))\n",
        "  return res"
      ],
      "metadata": {
        "id": "WwJQWJOZFIyq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for j in range(len(data)):\n",
        "  idx=0\n",
        "  data_str = \"\"\n",
        "  res = data[j]['annotations'][0]['result']\n",
        "  \n",
        "  for i in range(len(res)):\n",
        "    text1 = res[i]['value']['text']\n",
        "    augmented_text = aug.augment(text1)\n",
        "    data[j]['annotations'][0]['result'][i]['value']['text'] = augmented_text[0]\n",
        "\n",
        "    data[j]['annotations'][0]['result'][i]['value']['start'] = idx\n",
        "    data[j]['annotations'][0]['result'][i]['value']['end'] = idx + len(augmented_text[0])\n",
        "    idx = idx + len(augmented_text[0])\n",
        "    data[j]['annotations'][0]['result'][i]['id'] = generate_id(32)\n",
        "\n",
        "    if i>0:\n",
        "      data_str = str(data_str) + \" \" + str(augmented_text[0])\n",
        "    else:\n",
        "      data_str = str(data_str) + str(augmented_text[0])\n",
        "    \n",
        "  data[j]['data'] = data_str"
      ],
      "metadata": {
        "id": "Zwm0i-DrFIyr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"sample-paraphrase.json\", \"w\") as outfile:\n",
        "    json.dump(data, outfile)"
      ],
      "metadata": {
        "id": "EpRugX1rFIyr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Using T5 model"
      ],
      "metadata": {
        "id": "beVIPGThOePA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentencepiece\n",
        "!pip install torch\n",
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQrStowTP9jI",
        "outputId": "7f466f9a-6689-4798-aff5-4e7482eaab9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.9/dist-packages (0.1.98)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.9/dist-packages (2.0.0+cu118)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.9/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from torch) (3.11.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.9/dist-packages (from torch) (1.11.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.9/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch) (16.0.1)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch) (3.25.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->torch) (2.1.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch) (1.3.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.9/dist-packages (4.28.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.4)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.11.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "with open('train.json') as f:\n",
        "    data = json.load(f)\n",
        "    f.close()"
      ],
      "metadata": {
        "id": "TLetfSv3UASB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "05539a9c-8127-428b-c5d1-78bf82ff72ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-54c640b95dc4>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'train.json'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'train.json'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Summarize"
      ],
      "metadata": {
        "id": "1UiZV1vPplpT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import T5ForConditionalGeneration, T5Tokenizer\n",
        "import torch\n",
        "\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "model_name = 't5-base'\n",
        "tokenizer = T5Tokenizer.from_pretrained(model_name)\n",
        "model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)\n",
        "\n",
        "# res=data[0]['annotations'][0]['result']\n",
        "# input_text = res[0]['value']['text']\n",
        "input_text = 'It is claimed that the accused had looked after Lakshmi well till the birth of their first child, which was a girl and he was not happy with the girl child and thereafter started ill-treating Lakshmi, apart from which, he was also demanding dowry and on that pretext, he used to assault her continuously.\\n'\n",
        "print(\"Original Text: \\n\"+input_text)\n",
        "task_prefix = 'Summarize: \\n'\n",
        "inputs = tokenizer(task_prefix+input_text, return_tensors='pt').to(device)\n",
        "output_ids = model.generate(\n",
        "  input_ids=inputs[\"input_ids\"].to(device),\n",
        "  do_sample=True,\n",
        "  max_length=100,\n",
        "  num_beams=5, \n",
        "  no_repeat_ngram_size=3, \n",
        "  early_stopping=True\n",
        ")\n",
        "output_ids = output_ids.to('cpu')\n",
        "output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
        "print(\"Output Text: \\n\"+output_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wHe5SL-mOdzx",
        "outputId": "180fb3c4-ad5c-4dfa-d403-4e15cb428586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Text: \n",
            "It is claimed that the accused had looked after Lakshmi well till the birth of their first child, which was a girl and he was not happy with the girl child and thereafter started ill-treating Lakshmi, apart from which, he was also demanding dowry and on that pretext, he used to assault her continuously.\n",
            "\n",
            "Output Text: \n",
            "Lakshmi's father allegedly started ill-treating her and demanding dowry. on that pretext, he used to assault her continuously.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Back Translation"
      ],
      "metadata": {
        "id": "rt0OXVuNpntq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import T5ForConditionalGeneration, T5Tokenizer\n",
        "import torch\n",
        "\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "model_name = 't5-base'\n",
        "tokenizer = T5Tokenizer.from_pretrained(model_name)\n",
        "model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)\n",
        "\n",
        "# res=data[0]['annotations'][0]['result']\n",
        "# input_text = res[0]['value']['text']\n",
        "input_text = 'It is claimed that the accused had looked after Lakshmi well till the birth of their first child, which was a girl and he was not happy with the girl child and thereafter started ill-treating Lakshmi, apart from which, he was also demanding dowry and on that pretext, he used to assault her continuously.\\n'\n",
        "print(\"Original Text: \\n\"+input_text)\n",
        "\n",
        "# Translating to German\n",
        "task_prefix1 = 'translate English to French: '\n",
        "inputs = tokenizer(task_prefix1+input_text, return_tensors='pt').to(device)\n",
        "output_ids1 = model.generate(\n",
        "  input_ids=inputs[\"input_ids\"].to(device),\n",
        "  do_sample=True,\n",
        "  max_length=100,\n",
        "  num_beams=4, \n",
        "  no_repeat_ngram_size=3, \n",
        "  early_stopping=True\n",
        ")\n",
        "output_ids1 = output_ids1.to('cpu')\n",
        "output_text1 = tokenizer.decode(output_ids1[0], skip_special_tokens=True)\n",
        "print(\"First Translation: \\n\"+output_text1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f341c550-fbb2-43f0-8824-29129c76100e",
        "id": "aX9ZfqLspkOL"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/models/t5/tokenization_t5.py:163: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Text: \n",
            "It is claimed that the accused had looked after Lakshmi well till the birth of their first child, which was a girl and he was not happy with the girl child and thereafter started ill-treating Lakshmi, apart from which, he was also demanding dowry and on that pretext, he used to assault her continuously.\n",
            "\n",
            "First Translation: \n",
            "Il est affirmé que l'accusé s'était bien occupé de Lakshmi jusqu'à la naissance de leur premier enfant, qui était une fille, et qu'il n'était pas satisfait de la fille en question, en commençant par la suite à la maltraiter, hormis cela, il exigeait également la dot; sous ce\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
        "\n",
        "model_name = \"Helsinki-NLP/opus-mt-fr-en\"\n",
        "# model_name = \"facebook/nllb-200-distilled-600M\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)"
      ],
      "metadata": {
        "id": "XBmRg64j00DS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Translating Back to English\n",
        "# task_prefix2 = 'translate French to English: '\n",
        "input_text2 = output_text1\n",
        "inputs2 = tokenizer(input_text2, return_tensors='pt').to(device)\n",
        "output_ids2 = model.generate(\n",
        "  input_ids=inputs2[\"input_ids\"].to(device),\n",
        "  do_sample=True,\n",
        "  max_length=100,\n",
        "  num_beams=4, \n",
        "  no_repeat_ngram_size=3, \n",
        "  early_stopping=True\n",
        ")\n",
        "output_ids2 = output_ids2.to('cpu')\n",
        "output_text2 = tokenizer.decode(output_ids2[0], skip_special_tokens=True)\n",
        "print(\"Output Text: \\n\"+output_text2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOPYPC8b4RnK",
        "outputId": "5993c5be-aa12-4e19-8ef7-360d62b6ba9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Output Text: \n",
            "It is claimed that the accused had properly cared for Lakshmi until the birth of their first child, who was a daughter, and that he was not satisfied with the girl in question, beginning subsequently to mistreat her, except for that, he also demanded dowry; under this\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Random Insertions using BERT"
      ],
      "metadata": {
        "id": "-AcjRrUUXDnA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
        "\n",
        "import torch\n",
        "\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"law-ai/InLegalBERT\")\n",
        "\n",
        "model = AutoModelForMaskedLM.from_pretrained(\"law-ai/InLegalBERT\").to(device)"
      ],
      "metadata": {
        "id": "MapAYI3LOKFG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "with open('train.json') as f:\n",
        "    data = json.load(f)\n",
        "    f.close()"
      ],
      "metadata": {
        "id": "ax6X1asuXJ-_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "def mask_words(text, mask_prob=0.15):\n",
        "  words = text.split()\n",
        "  output_words = []\n",
        "  for word in words:\n",
        "      if random.uniform(0, 1) < mask_prob:\n",
        "          output_words.append('[MASK]')\n",
        "      output_words.append(word)\n",
        "  return ' '.join(output_words)"
      ],
      "metadata": {
        "id": "4oHNctj_bHpU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to paraphrase a sentence using random masks and BERT\n",
        "def paraphrase(text):\n",
        "    masked_text = mask_words(text, 0.2)\n",
        "    print(\"Masked Text: \\n\"+masked_text)\n",
        "    tokens = tokenizer.tokenize(masked_text)\n",
        "    masked_indices = [i for i, x in enumerate(tokens) if x == '[MASK]']\n",
        "    if len(masked_indices) == 0:\n",
        "      return text\n",
        "    input_ids = tokenizer.encode(masked_text, return_tensors='pt').to(device)\n",
        "    with torch.no_grad():\n",
        "      outputs = model(input_ids)  \n",
        "    predicted_tokens = []\n",
        "    for i in masked_indices:\n",
        "      predicted_index = torch.argmax(outputs[0][0][i]).item()\n",
        "      predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]\n",
        "      predicted_tokens.append(predicted_token)\n",
        "    output_tokens = tokens.copy()\n",
        "    for i, index in enumerate(masked_indices):\n",
        "      output_tokens[index] = predicted_tokens[i]\n",
        "    output_text = tokenizer.convert_tokens_to_string(output_tokens)\n",
        "    return output_text"
      ],
      "metadata": {
        "id": "GKnUc4J5XJ_A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "old_text = 'In this regard, it is the complainant\\'s case that Laxmi had repeatedly told her about the ill-treatment.'\n",
        "print(\"Original Text: \\n\"+old_text)\n",
        "new_text = paraphrase(old_text)\n",
        "print(\"Synthetic Text: \\n\"+new_text)"
      ],
      "metadata": {
        "id": "MFz1A4iFYNMv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Paraphrase using humarin/chatgpt_paraphraser_on_T5_base\n"
      ],
      "metadata": {
        "id": "wSdhbTvZT7nT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
        "\n",
        "device = \"cuda\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"humarin/chatgpt_paraphraser_on_T5_base\")\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"humarin/chatgpt_paraphraser_on_T5_base\").to(device)\n",
        "\n",
        "def paraphrase(\n",
        "    question,\n",
        "    num_beams=5,\n",
        "    num_beam_groups=5,\n",
        "    num_return_sequences=5,\n",
        "    repetition_penalty=10.0,\n",
        "    diversity_penalty=2.0,\n",
        "    no_repeat_ngram_size=2,\n",
        "    temperature=0.7,\n",
        "    max_length=128\n",
        "):\n",
        "    input_ids = tokenizer(\n",
        "        f'paraphrase: {question}',\n",
        "        return_tensors=\"pt\", padding=\"longest\",\n",
        "        max_length=max_length,\n",
        "        truncation=True,\n",
        "    ).input_ids\n",
        "    \n",
        "    outputs = model.generate(\n",
        "        input_ids.to(device), temperature=temperature, repetition_penalty=repetition_penalty,\n",
        "        num_return_sequences=num_return_sequences, no_repeat_ngram_size=no_repeat_ngram_size,\n",
        "        num_beams=num_beams, num_beam_groups=num_beam_groups,\n",
        "        max_length=max_length, diversity_penalty=diversity_penalty\n",
        "    )\n",
        "\n",
        "    res = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
        "\n",
        "    return res\n"
      ],
      "metadata": {
        "id": "xQrpAXYuT_eP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "old_text = 'In this regard, it is the complainant\\'s case that Laxmi had repeatedly told her about the ill-treatment.'\n",
        "print(\"Original Text: \\n\"+old_text)\n",
        "new_text = paraphrase(old_text)\n",
        "print(\"Synthetic Text: \\n\"+random.choice(new_text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uefnPvOtU3W9",
        "outputId": "8ffd8583-d0ec-48bb-81bd-aa0f148dffb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Text: \n",
            "In this regard, it is the complainant's case that Laxmi had repeatedly told her about the ill-treatment.\n",
            "Synthetic Text: \n",
            "The complainant claims that Laxmi had disclosed the mistreatment to her on multiple occasions.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "# Define two sentences for comparison\n",
        "sentence1 = old_text\n",
        "sentence2 = new_text[0]\n",
        "\n",
        "# Create CountVectorizer to convert sentences to vectors\n",
        "vectorizer = CountVectorizer()\n",
        "sentences = [sentence1, sentence2]\n",
        "sentence_vectors = vectorizer.fit_transform(sentences)\n",
        "\n",
        "# Calculate cosine similarity between the sentence vectors\n",
        "cosine_sim = cosine_similarity(sentence_vectors[0], sentence_vectors[1])\n",
        "\n",
        "# Print cosine similarity score\n",
        "print(sentence1)\n",
        "print(sentence2)\n",
        "print(\"Cosine Similarity Score: \", cosine_sim[0][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yzQVuItd1_If",
        "outputId": "cb7f7afe-ca23-4a3d-ebc0-96fa467c8791"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In this regard, it is the complainant's case that Laxmi had repeatedly told her about the ill-treatment.\n",
            "The complainant claims that Laxmi had disclosed the mistreatment to her on multiple occasions.\n",
            "Cosine Similarity Score:  0.5031152949374527\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BTv7-DfHlQB6",
        "outputId": "486bb5bc-cf68-43c2-e821-3917af187615"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting openai\n",
            "  Downloading openai-0.27.4-py3-none-any.whl (70 kB)\n",
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            "\u001b[?25hInstalling collected packages: multidict, frozenlist, async-timeout, yarl, aiosignal, aiohttp, openai\n",
            "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 frozenlist-1.3.3 multidict-6.0.4 openai-0.27.4 yarl-1.8.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "openai.api_key = \"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\"\n",
        "\n",
        "def generate_movie_recommendations(genre, rating):\n",
        "    prompt = f\"Generate a list of 5 {genre} movies with a rating of at least {rating}.\"\n",
        "    response = openai.Completion.create(\n",
        "        engine=\"text-davinci-002\",\n",
        "        prompt=prompt,\n",
        "        max_tokens=100,\n",
        "        n=1,\n",
        "        stop=None,\n",
        "        temperature=0.5,\n",
        "    )\n",
        "    recommendation_text = response.choices[0].text.strip()\n",
        "    recommendations = recommendation_text.split(\"\\n\")\n",
        "    return recommendations\n"
      ],
      "metadata": {
        "id": "rN_fZL0kVClv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(generate_movie_recommendations(\"Thriller\",5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XikW9ugglNs-",
        "outputId": "b9e6a4b7-bd11-4f6e-ebbc-61081197ab15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['1. The Silence of the Lambs', '2. The Sixth Sense', '3. The Usual Suspects', '4. Se7en', '5. L.A. Confidential']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
        "\n",
        "# Load pre-trained GPT-2 model and tokenizer\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "import random\n",
        "# Define a template with placeholders\n",
        "template = \"The [ADJECTIVE] [NOUN] [VERB] [ADVERB] in the [ADJECTIVE] [NOUN].\"\n",
        "\n",
        "# Define lists of words for each placeholder\n",
        "adjectives = [\"quick\", \"lazy\", \"happy\", \"sad\"]\n",
        "nouns = [\"cat\", \"dog\", \"sun\", \"moon\"]\n",
        "verbs = [\"jumped\", \"ran\", \"slept\", \"cried\"]\n",
        "adverbs = [\"quickly\", \"slowly\", \"happily\", \"sadly\"]\n",
        "\n",
        "# Function to generate text based on template and word lists\n",
        "def generate_text(template, adjectives, nouns, verbs, adverbs):\n",
        "    # Replace each placeholder with a randomly chosen word from the corresponding word list\n",
        "    generated_text = template\n",
        "    for placeholder in [\"[ADJECTIVE]\", \"[NOUN]\", \"[VERB]\", \"[ADVERB]\"]:\n",
        "        word_list = eval(placeholder[1:-1].lower() + \"s\")  # Get the corresponding word list\n",
        "        generated_text = generated_text.replace(placeholder, random.choice(word_list))  # Replace placeholder with random word\n",
        "\n",
        "    # Encode the generated text\n",
        "    input_ids = tokenizer.encode(generated_text, return_tensors=\"pt\")\n",
        "    input_ids = input_ids[:, :-1]  # Remove the last token as it corresponds to the end-of-text token\n",
        "\n",
        "    # Generate text from the model\n",
        "    output = model.generate(input_ids, max_length=100, do_sample=True, pad_token_id=tokenizer.eos_token_id)\n",
        "    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "    \n",
        "    return generated_text\n",
        "\n",
        "# Generate text based on the template\n",
        "generated_text = generate_text(template, adjectives, nouns, verbs, adverbs)\n",
        "print(generated_text)"
      ],
      "metadata": {
        "id": "2TrU_DnfmIiV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = 'In this regard, it is the complainant\\'s case that Laxmi had repeatedly told her about the ill-treatment.'\n",
        "prompt = f\"Replace Named entities by masks such as [NAME], [ADJECTIVE], [NOUN], [VERB], [ADVERB],[PLACE], and [TIME] : {text} \"\n",
        "response = openai.Completion.create(\n",
        "    engine=\"text-davinci-003\",\n",
        "    prompt=prompt,\n",
        "    max_tokens=100,\n",
        "    n=1,\n",
        "    stop=None,\n",
        "    temperature=0.5,\n",
        ")\n",
        "recommendation_text = response.choices[0].text.strip()\n",
        "# recommendations = recommendation_text.split(\"\\n\")\n",
        "print(recommendation_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M0tu9tmRlbSo",
        "outputId": "fe43d8cc-7578-4df2-d124-a5574c691726"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In this regard, it is the complainant's case that [NAME] had repeatedly told her about the ill-treatment.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8v2hUfXLqOX-",
        "outputId": "39bd8884-9910-4660-e8b6-0d08107a2a72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.28.1-py3-none-any.whl (7.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m52.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.13.4-py3-none-any.whl (200 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m200.1/200.1 kB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m105.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.11.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.4 tokenizers-0.13.3 transformers-4.28.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
        "from transformers import pipeline"
      ],
      "metadata": {
        "id": "n9RNMLgil7Uv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"dslim/bert-base-NER\")\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"dslim/bert-base-NER\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
            "0753ae5ea15b4725876a466e18bd0328",
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            "117736a3293e45c2ad5da83b2f500486",
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            "84f6c037232b413496f5ce0267cb8047",
            "9be628c02fbc4d23836a545a34e74b13",
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            "c1b6753747314d7d89c9232bdf780299",
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            "f83e0ad892c8452980b41b293588f745",
            "a63162b69d6548619c8c4a61bda90e1c",
            "cb6994ce11a4461fbb377f66c42c94e1",
            "53d3110ea3ff4b40ab246c42d31b7240",
            "554bf98b5850436e80a4de2c16f34f4d",
            "79568712351b48b1989d387a5624b088",
            "693beec40ecb402da345ae5e07c7315a",
            "e63e6ea0342b4ec187ddf2a849734fb5",
            "786502e40e4a407ebc9d3e6f93a94e24",
            "7d5a330b98554670bcffffc851226754",
            "7dbc4b7991214f8ea97eb140eef3df9b",
            "50906ea464f44d369e603f007e5355dc",
            "c0f114ca512147abad16498b6cea0740",
            "de813267b0cd4ce1b3657dc2b600d573",
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            "ffbeabb5e1f84372ada8c92737b58ffa",
            "5a351bcea1a64a8191b674aa8a586637",
            "9e9e7ae50eff45e99f1ff498e35bfaad",
            "611ecc60727b4b00939d51cb4b50914b",
            "d0020abc48084c38b44c378f0f64c7f5",
            "5685beba598148548485e6dd6accc66b",
            "8ba62098cc2e45059619784d04b271c6",
            "896956a1622e490f87aa87cfcee928de",
            "ec2f2247ddea4a7b9c9000ac738a29c7",
            "ce10acdcce574174b13f26c97b4b58cb",
            "4fb8cffeb4d04b8284b2af1df94a5950",
            "f2367f52de01412199586fdbed045ddb",
            "3124253c15dc461f9e3c2d0bcf9ea026",
            "d8bffb17d5d34a08ab85e6799c5c2f10",
            "64bf7db48ebb40ceb31c764875c7c372",
            "73851d0c27dc4d5e88186939b5be6e07",
            "8c6409d6ac224d95bf6b8d1e8fa7bf18",
            "a0f216bb59134d72bf9d070553ee6851",
            "869c7bdf564946ce8f423dadbce37a8a",
            "5ab0b712ca8643d0a9f090eb407bb53d",
            "78758253942847168d56bf141990a135",
            "e71384a00eb04b4d9ae0264864c65185",
            "63c1de4ac5294b8987dc17464a729470",
            "5833153baf4a4021a23d46c790d2906e",
            "6f5ca71757a54bf094d18a573bfcceeb",
            "deea35e20fae40b8865a61209f631289",
            "9c0876bb144b429498d6d9b2549e7ab9"
          ]
        },
        "id": "0kYTvF9TqNCk",
        "outputId": "83bd9fbf-0de9-4a9a-cfab-4de758d972fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/59.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "0753ae5ea15b4725876a466e18bd0328"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "84f6c037232b413496f5ce0267cb8047"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "f83e0ad892c8452980b41b293588f745"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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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": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/433M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c6409d6ac224d95bf6b8d1e8fa7bf18"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NER = pipeline(\"ner\", model=model, tokenizer=tokenizer)\n",
        "example = \"My name is yugesh and I live in India\"\n"
      ],
      "metadata": {
        "id": "dUMBZs4oqUVT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = NER(example)\n",
        "results \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_51v7KQEqYRN",
        "outputId": "225f7f4d-3bf5-459e-e5cc-65dd95e7981a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'entity': 'B-PER',\n",
              "  'score': 0.80052364,\n",
              "  'index': 4,\n",
              "  'word': 'y',\n",
              "  'start': 11,\n",
              "  'end': 12},\n",
              " {'entity': 'B-LOC',\n",
              "  'score': 0.9997588,\n",
              "  'index': 11,\n",
              "  'word': 'India',\n",
              "  'start': 32,\n",
              "  'end': 37}]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "!pip install spacy\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iLuJdhw6qbZO",
        "outputId": "ac57e9d1-3746-44a1-c269-ccfaadbec22c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.3)\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: spacy in /usr/local/lib/python3.9/dist-packages (3.5.1)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy) (3.0.8)\n",
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            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from spacy) (3.3.0)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertTokenizer, BertForTokenClassification\n",
        "\n",
        "model_name = 'bert-base-cased'\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
        "model = BertForTokenClassification.from_pretrained(model_name)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252,
          "referenced_widgets": [
            "6062376a0c4e4d9e85bbadfb3ea402fe",
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            "b6adca576db3488f93cdb81ec582a4b0",
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            "c98c808e1b77454cb5fc32fa26641102"
          ]
        },
        "id": "5Hf_lS3UrFgD",
        "outputId": "6d9b05e6-2a8c-4d49-f0ce-a6cba8409654"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6062376a0c4e4d9e85bbadfb3ea402fe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "998d8242b6664d4fa7f824bdb12993f9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7762c17f5d164f6398ec918d0d35644d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f7b2263812f498895552526bad5465f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForTokenClassification: ['cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification 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 BertForTokenClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "\n",
        "nlp = spacy.load('en_core_web_sm')\n",
        "\n",
        "def predict_labels(text):\n",
        "    input_ids = tokenizer.encode(text, add_special_tokens=True)\n",
        "    input_ids = torch.tensor([input_ids])\n",
        "    with torch.no_grad():\n",
        "        output = model(input_ids)\n",
        "        logits = output[0]\n",
        "        predictions = torch.argmax(logits, axis=2)\n",
        "        labels = [tokenizer.decode([prediction.item()]) for prediction in predictions[0]]\n",
        "        return labels\n"
      ],
      "metadata": {
        "id": "RmBzTK12rF1E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_named_entities(text):\n",
        "    labels = predict_labels(text)\n",
        "    doc = nlp(text)\n",
        "    new_text = ''\n",
        "    index = 0\n",
        "    for token in doc:\n",
        "        if token.ent_type_:\n",
        "            entity_length = len(token.text)\n",
        "            entity_label = labels[index]\n",
        "            new_text += f'[{entity_label}]'\n",
        "            index += entity_length\n",
        "        else:\n",
        "            new_text += token.text\n",
        "            index += len(token.text)\n",
        "    return new_text\n"
      ],
      "metadata": {
        "id": "blkqJ_YKrJ6E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"My name is yugesh and I live in India\"\n",
        "new_text = replace_named_entities(text)\n",
        "print(new_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "nDlyt7kMrMUi",
        "outputId": "645876f3-c58e-4f06-d769-6e9665346816"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-37-818588c84f3d>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"My name is yugesh and I live in India\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnew_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreplace_named_entities\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_text\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-36-f031ddd3a552>\u001b[0m in \u001b[0;36mreplace_named_entities\u001b[0;34m(text)\u001b[0m\n\u001b[1;32m      8\u001b[0m             \u001b[0mentity_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m             \u001b[0mentity_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m             \u001b[0mnew_text\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34mf'[{entity_label}]'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m             \u001b[0mindex\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mentity_length\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "odtrCClGrOi4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}