Similar_Domain_Same_Task.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/jyaacoub/Cross-Domain-Attacks-NLP/blob/main/Similar_Domain_Same_Task.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7EL69jNmfsva",
    "outputId": "725bd3dd-7e42-46b6-a6cb-be6ca19720cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K     |████████████████████████████████| 5.5 MB 7.8 MB/s \n",
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      "\u001b[K     |████████████████████████████████| 41 kB 702 kB/s \n",
      "\u001b[?25h  Building wheel for pinyin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for mpld3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for overrides (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for sqlitedict (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for pptree (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for pycld2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for wikipedia-api (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for word2number (setup.py) ... \u001b[?25l\u001b[?25hdone\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",
      "markdown 3.4.1 requires importlib-metadata>=4.4; python_version < \"3.10\", but you have importlib-metadata 3.10.1 which is incompatible.\n",
      "gym 0.25.2 requires importlib-metadata>=4.8.0; python_version < \"3.10\", but you have importlib-metadata 3.10.1 which is incompatible.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "! pip install transformers torchinfo textattack -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LRYloFjLgUh9",
    "outputId": "6ff089c5-fb73-45ae-9267-ef8c959f9963"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "textattack: Updating TextAttack package dependencies.\n",
      "textattack: Downloading NLTK required packages.\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /root/nltk_data...\n",
      "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
      "[nltk_data] Downloading package omw to /root/nltk_data...\n",
      "[nltk_data] Downloading package universal_tagset to /root/nltk_data...\n",
      "[nltk_data]   Unzipping taggers/universal_tagset.zip.\n",
      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from IPython.core.display import HTML, display\n",
    "\n",
    "import torch\n",
    "from torchinfo import summary\n",
    "\n",
    "import transformers\n",
    "from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline\n",
    "\n",
    "import textattack\n",
    "from textattack import Attack, Attacker, AttackArgs\n",
    "from textattack.datasets import HuggingFaceDataset\n",
    "from textattack.loggers import CSVLogger\n",
    "\n",
    "from textattack.attack_recipes import (\n",
    "    A2TYoo2021,\n",
    "    BAEGarg2019,\n",
    "    BERTAttackLi2020,\n",
    "    CLARE2020,\n",
    "    CheckList2020,\n",
    "    DeepWordBugGao2018,\n",
    "    FasterGeneticAlgorithmJia2019,\n",
    "    GeneticAlgorithmAlzantot2018,\n",
    "    HotFlipEbrahimi2017,\n",
    "    IGAWang2019,\n",
    "    InputReductionFeng2018,\n",
    "    Kuleshov2017,\n",
    "    MorpheusTan2020,\n",
    "    PSOZang2020,\n",
    "    PWWSRen2019,\n",
    "    Pruthi2019,\n",
    "    Seq2SickCheng2018BlackBox,\n",
    "    TextBuggerLi2018,\n",
    "    TextFoolerJin2019\n",
    " )\n",
    "\n",
    "transformers.logging.set_verbosity_error()\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eMpKyFyQhF7l"
   },
   "source": [
    "# Attacks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b8ZKDuvWhINn"
   },
   "source": [
    "## TextFooler\n",
    "Attack from [Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment.](https://arxiv.org/abs/1907.11932)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GQjE7sKPj5Il"
   },
   "source": [
    "## Substitute model"
   ]
  },
  {
   "cell_type": "code",
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    },
    "id": "OM_LuevRg7M1",
    "outputId": "e0e17e0e-9e97-4e50-9098-73cbd2cd250d"
   },
   "outputs": [
    {
     "data": {
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       "model_id": "21386ec0468a47e3a95db47dcf78ac6a",
       "version_major": 2,
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      "text/plain": [
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
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    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "Downloading:   0%|          | 0.00/501M [00:00<?, ?B/s]"
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    {
     "data": {
      "text/plain": [
       "==============================================================================================================\n",
       "Layer (type:depth-idx)                                       Output Shape              Param #\n",
       "==============================================================================================================\n",
       "RobertaForSequenceClassification                             [1, 2]                    --\n",
       "├─RobertaModel: 1-1                                          [1, 9, 768]               --\n",
       "│    └─RobertaEmbeddings: 2-1                                [1, 9, 768]               --\n",
       "│    │    └─Embedding: 3-1                                   [1, 9, 768]               38,603,520\n",
       "│    │    └─Embedding: 3-2                                   [1, 9, 768]               768\n",
       "│    │    └─Embedding: 3-3                                   [1, 9, 768]               394,752\n",
       "│    │    └─LayerNorm: 3-4                                   [1, 9, 768]               1,536\n",
       "│    │    └─Dropout: 3-5                                     [1, 9, 768]               --\n",
       "│    └─RobertaEncoder: 2-2                                   [1, 9, 768]               --\n",
       "│    │    └─ModuleList: 3-6                                  --                        85,054,464\n",
       "├─RobertaClassificationHead: 1-2                             [1, 2]                    --\n",
       "│    └─Dropout: 2-3                                          [1, 768]                  --\n",
       "│    └─Linear: 2-4                                           [1, 768]                  590,592\n",
       "│    └─Dropout: 2-5                                          [1, 768]                  --\n",
       "│    └─Linear: 2-6                                           [1, 2]                    1,538\n",
       "==============================================================================================================\n",
       "Total params: 124,647,170\n",
       "Trainable params: 124,647,170\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (M): 124.65\n",
       "==============================================================================================================\n",
       "Input size (MB): 0.00\n",
       "Forward/backward pass size (MB): 7.53\n",
       "Params size (MB): 498.59\n",
       "Estimated Total Size (MB): 506.12\n",
       "=============================================================================================================="
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SUBSTITUTE_MODEL = \"textattack/roberta-base-imdb\"\n",
    "\n",
    "substitute_tokenizer = AutoTokenizer.from_pretrained(SUBSTITUTE_MODEL)\n",
    "substitute_model = AutoModelForSequenceClassification.from_pretrained(SUBSTITUTE_MODEL)\n",
    "# Move it to GPU\n",
    "substitute_model = substitute_model.cuda()\n",
    "\n",
    "# Wrap it for TextAttack\n",
    "model_wrapper = textattack.models.wrappers.HuggingFaceModelWrapper(\n",
    "    model=substitute_model, \n",
    "    tokenizer=substitute_tokenizer\n",
    ")\n",
    "\n",
    "# Test the model\n",
    "encoded_input = substitute_tokenizer(['I like you. I love you'], return_tensors='pt')\n",
    "summary(substitute_model, input_data=encoded_input.input_ids)"
   ]
  },
  {
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      "21d49c5e3bc44b629cb4eaef443b0739",
      "c497cb0bfc9b4140a88c76007f41baf4"
     ]
    },
    "id": "WggqerLWkb-p",
    "outputId": "d0b49348-bcd8-4b63-9963-09bd928f7838"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bbab7c2121ee4a309a435beb1ff62033",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/1.89k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c55c2c1a5080442ca271688fbfaed487",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading metadata:   0%|          | 0.00/921 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.builder:Using custom data configuration default\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset rotten_tomatoes/default (download: 476.34 KiB, generated: 1.28 MiB, post-processed: Unknown size, total: 1.75 MiB) to /root/.cache/huggingface/datasets/rotten_tomatoes/default/1.0.0/40d411e45a6ce3484deed7cc15b82a53dad9a72aafd9f86f8f227134bec5ca46...\n"
     ]
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     "data": {
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       "model_id": "49ecd05ae65b4700a5c2930f2257e985",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/488k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99ff01014d9a410d88432c25551adf21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/8530 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bebe71495446483da652d8027b68b632",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/1066 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/1066 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset rotten_tomatoes downloaded and prepared to /root/.cache/huggingface/datasets/rotten_tomatoes/default/1.0.0/40d411e45a6ce3484deed7cc15b82a53dad9a72aafd9f86f8f227134bec5ca46. Subsequent calls will reuse this data.\n"
     ]
    },
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "textattack: Loading \u001b[94mdatasets\u001b[0m dataset \u001b[94mrotten_tomatoes\u001b[0m, split \u001b[94mtest\u001b[0m.\n"
     ]
    }
   ],
   "source": [
    "# Define the dataset we are going to pass to the target\n",
    "dataset = HuggingFaceDataset(\n",
    "    name_or_dataset=\"rotten_tomatoes\", \n",
    "    subset=None, \n",
    "    split=\"test\", \n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Cp8ravfKlMVs",
    "outputId": "8421f66a-29a8-43d5-addc-a39fff1e6fa9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "textattack: Unknown if model of class <class 'transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification'> compatible with goal function <class 'textattack.goal_functions.classification.untargeted_classification.UntargetedClassification'>.\n",
      "textattack: Logging to CSV at path log.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attack(\n",
      "  (search_method): GreedyWordSwapWIR(\n",
      "    (wir_method):  delete\n",
      "  )\n",
      "  (goal_function):  UntargetedClassification\n",
      "  (transformation):  WordSwapEmbedding(\n",
      "    (max_candidates):  50\n",
      "    (embedding):  WordEmbedding\n",
      "  )\n",
      "  (constraints): \n",
      "    (0): WordEmbeddingDistance(\n",
      "        (embedding):  WordEmbedding\n",
      "        (min_cos_sim):  0.5\n",
      "        (cased):  False\n",
      "        (include_unknown_words):  True\n",
      "        (compare_against_original):  True\n",
      "      )\n",
      "    (1): PartOfSpeech(\n",
      "        (tagger_type):  nltk\n",
      "        (tagset):  universal\n",
      "        (allow_verb_noun_swap):  True\n",
      "        (compare_against_original):  True\n",
      "      )\n",
      "    (2): UniversalSentenceEncoder(\n",
      "        (metric):  angular\n",
      "        (threshold):  0.840845057\n",
      "        (window_size):  15\n",
      "        (skip_text_shorter_than_window):  True\n",
      "        (compare_against_original):  False\n",
      "      )\n",
      "    (3): RepeatModification\n",
      "    (4): StopwordModification\n",
      "    (5): InputColumnModification(\n",
      "        (matching_column_labels):  ['premise', 'hypothesis']\n",
      "        (columns_to_ignore):  {'premise'}\n",
      "      )\n",
      "  (is_black_box):  True\n",
      ") \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Succeeded / Failed / Skipped / Total] 43 / 0 / 7 / 50: 100%|██████████| 50/50 [02:18<00:00,  2.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "+-------------------------------+--------+\n",
      "| Attack Results                |        |\n",
      "+-------------------------------+--------+\n",
      "| Number of successful attacks: | 43     |\n",
      "| Number of failed attacks:     | 0      |\n",
      "| Number of skipped attacks:    | 7      |\n",
      "| Original accuracy:            | 86.0%  |\n",
      "| Accuracy under attack:        | 0.0%   |\n",
      "| Attack success rate:          | 100.0% |\n",
      "| Average perturbed word %:     | 17.34% |\n",
      "| Average num. words per input: | 18.96  |\n",
      "| Avg num queries:              | 94.44  |\n",
      "+-------------------------------+--------+\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "attack = TextFoolerJin2019.build(model_wrapper)\n",
    "attack_args = textattack.AttackArgs(\n",
    "    num_examples=50,\n",
    "    log_to_csv=\"log.csv\",\n",
    "    disable_stdout=True,\n",
    "    silent=False\n",
    ")\n",
    "\n",
    "attacker = Attacker(attack, dataset, attack_args)\n",
    "attack_results = attacker.attack_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6E_UbrFOj73i"
   },
   "source": [
    "## Target model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1NhpwP2Ij8-q",
    "outputId": "8a978dd0-31fb-4d2f-ae99-3e8175003b95"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "==============================================================================================================\n",
       "Layer (type:depth-idx)                                       Output Shape              Param #\n",
       "==============================================================================================================\n",
       "RobertaForSequenceClassification                             [1, 2]                    --\n",
       "├─RobertaModel: 1-1                                          [1, 9, 768]               --\n",
       "│    └─RobertaEmbeddings: 2-1                                [1, 9, 768]               --\n",
       "│    │    └─Embedding: 3-1                                   [1, 9, 768]               38,603,520\n",
       "│    │    └─Embedding: 3-2                                   [1, 9, 768]               768\n",
       "│    │    └─Embedding: 3-3                                   [1, 9, 768]               394,752\n",
       "│    │    └─LayerNorm: 3-4                                   [1, 9, 768]               1,536\n",
       "│    │    └─Dropout: 3-5                                     [1, 9, 768]               --\n",
       "│    └─RobertaEncoder: 2-2                                   [1, 9, 768]               --\n",
       "│    │    └─ModuleList: 3-6                                  --                        85,054,464\n",
       "├─RobertaClassificationHead: 1-2                             [1, 2]                    --\n",
       "│    └─Dropout: 2-3                                          [1, 768]                  --\n",
       "│    └─Linear: 2-4                                           [1, 768]                  590,592\n",
       "│    └─Dropout: 2-5                                          [1, 768]                  --\n",
       "│    └─Linear: 2-6                                           [1, 2]                    1,538\n",
       "==============================================================================================================\n",
       "Total params: 124,647,170\n",
       "Trainable params: 124,647,170\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (M): 124.65\n",
       "==============================================================================================================\n",
       "Input size (MB): 0.00\n",
       "Forward/backward pass size (MB): 7.53\n",
       "Params size (MB): 498.59\n",
       "Estimated Total Size (MB): 506.12\n",
       "=============================================================================================================="
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TARGET_MODEL = \"textattack/roberta-base-rotten-tomatoes\"\n",
    "\n",
    "target_tokenizer = AutoTokenizer.from_pretrained(TARGET_MODEL)\n",
    "target_model = AutoModelForSequenceClassification.from_pretrained(TARGET_MODEL)\n",
    "# Move it to GPU\n",
    "# target_model = target_model.cuda()\n",
    "\n",
    "# Test the model\n",
    "encoded_input = target_tokenizer(['I like you. I love you'], return_tensors='pt')\n",
    "summary(target_model, input_data=encoded_input.input_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "RtUVYXz_rRXq"
   },
   "outputs": [],
   "source": [
    "def make_prediction(tokenizer: AutoTokenizer, model: AutoModelForSequenceClassification, text: str) -> int:\n",
    "    \"\"\"\n",
    "\n",
    "    \"\"\"\n",
    "    encoded_input = tokenizer(text, return_tensors='pt')\n",
    "    for (k, tensor) in encoded_input.items():\n",
    "        encoded_input[k] = tensor.to(device)\n",
    "\n",
    "    prediction = model(**encoded_input).logits.softmax(dim=1).argmax().item()\n",
    "\n",
    "    return prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M-1jpwi6pgRc",
    "outputId": "e3f7a3c1-87d4-4ad5-afd4-61a4f15181a0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:01<00:00, 32.41it/s]\n"
     ]
    }
   ],
   "source": [
    "target_results = pd.DataFrame(\n",
    "    columns=[\n",
    "        'original_text',\n",
    "        'perturbed_text',\n",
    "        'ground_truth_output',\n",
    "        'original_output',\n",
    "        'perturbed_output',\n",
    "    ]\n",
    ")\n",
    "\n",
    "for attack_result in tqdm(attack_results):\n",
    "    ground_truth_output = attack_result.original_result.ground_truth_output\n",
    "    original_text = attack_result.original_text()\n",
    "    perturbed_text = attack_result.perturbed_text()\n",
    "\n",
    "    original_output = make_prediction(target_tokenizer, target_model, original_text)\n",
    "    perturbed_output = make_prediction(target_tokenizer, target_model, perturbed_text)\n",
    "\n",
    "    new_row = {\n",
    "        'original_text': original_text,\n",
    "        'perturbed_text': perturbed_text,\n",
    "        'ground_truth_output': ground_truth_output,\n",
    "        'original_output': original_output,\n",
    "        'perturbed_output': perturbed_output,\n",
    "    }\n",
    "    target_results = target_results.append(new_row, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3RJMLzIpvMBv",
    "outputId": "2ae2a8d4-1e55-4436-e70e-d9a3876a7607"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original accuracy: 80.0\n",
      "Perturbed accuracy: 44.0\n"
     ]
    }
   ],
   "source": [
    "original_accuracy = (target_results['ground_truth_output'] == target_results['original_output']).sum() / len(target_results)\n",
    "perturbed_accuracy = (target_results['ground_truth_output'] == target_results['perturbed_output']).sum() / len(target_results)\n",
    "\n",
    "print(f\"Original accuracy: {100 * original_accuracy}\")\n",
    "print(f\"Perturbed accuracy: {100 * perturbed_accuracy}\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hHNUeBKNB50X"
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