diff --git a/docs/2notebook/1_Introduction_and_Transformations.ipynb b/docs/2notebook/1_Introduction_and_Transformations.ipynb
index 781aa923..6a4db7ec 100644
--- a/docs/2notebook/1_Introduction_and_Transformations.ipynb
+++ b/docs/2notebook/1_Introduction_and_Transformations.ipynb
@@ -77,19 +77,19 @@
    "source": [
     "from textattack.transformations import WordSwap\n",
     "\n",
+    "\n",
     "class BananaWordSwap(WordSwap):\n",
-    "    \"\"\" Transforms an input by replacing any word with 'banana'.\n",
-    "    \"\"\"\n",
-    "    \n",
+    "    \"\"\"Transforms an input by replacing any word with 'banana'.\"\"\"\n",
+    "\n",
     "    # We don't need a constructor, since our class doesn't require any parameters.\n",
     "\n",
     "    def _get_replacement_words(self, word):\n",
-    "        \"\"\" Returns 'banana', no matter what 'word' was originally.\n",
-    "        \n",
-    "            Returns a list with one item, since `_get_replacement_words` is intended to\n",
-    "                return a list of candidate replacement words.\n",
+    "        \"\"\"Returns 'banana', no matter what 'word' was originally.\n",
+    "\n",
+    "        Returns a list with one item, since `_get_replacement_words` is intended to\n",
+    "            return a list of candidate replacement words.\n",
     "        \"\"\"\n",
-    "        return ['banana']"
+    "        return [\"banana\"]"
    ]
   },
   {
@@ -133,17 +133,23 @@
     "import transformers\n",
     "from textattack.models.wrappers import HuggingFaceModelWrapper\n",
     "\n",
-    "model = transformers.AutoModelForSequenceClassification.from_pretrained(\"textattack/bert-base-uncased-ag-news\")\n",
-    "tokenizer = transformers.AutoTokenizer.from_pretrained(\"textattack/bert-base-uncased-ag-news\")\n",
+    "model = transformers.AutoModelForSequenceClassification.from_pretrained(\n",
+    "    \"textattack/bert-base-uncased-ag-news\"\n",
+    ")\n",
+    "tokenizer = transformers.AutoTokenizer.from_pretrained(\n",
+    "    \"textattack/bert-base-uncased-ag-news\"\n",
+    ")\n",
     "\n",
     "model_wrapper = HuggingFaceModelWrapper(model, tokenizer)\n",
     "\n",
     "# Create the goal function using the model\n",
     "from textattack.goal_functions import UntargetedClassification\n",
+    "\n",
     "goal_function = UntargetedClassification(model_wrapper)\n",
     "\n",
     "# Import the dataset\n",
     "from textattack.datasets import HuggingFaceDataset\n",
+    "\n",
     "dataset = HuggingFaceDataset(\"ag_news\", None, \"test\")"
    ]
   },
@@ -166,14 +172,16 @@
    "outputs": [],
    "source": [
     "from textattack.search_methods import GreedySearch\n",
-    "from textattack.constraints.pre_transformation import RepeatModification, StopwordModification\n",
+    "from textattack.constraints.pre_transformation import (\n",
+    "    RepeatModification,\n",
+    "    StopwordModification,\n",
+    ")\n",
     "from textattack import Attack\n",
     "\n",
     "# We're going to use our Banana word swap class as the attack transformation.\n",
-    "transformation = BananaWordSwap() \n",
+    "transformation = BananaWordSwap()\n",
     "# We'll constrain modification of already modified indices and stopwords\n",
-    "constraints = [RepeatModification(),\n",
-    "               StopwordModification()]\n",
+    "constraints = [RepeatModification(), StopwordModification()]\n",
     "# We'll use the Greedy search method\n",
     "search_method = GreedySearch()\n",
     "# Now, let's make the attack from the 4 components:\n",
@@ -517,8 +525,8 @@
     }
    ],
    "source": [
-    "from tqdm import tqdm # tqdm provides us a nice progress bar.\n",
-    "from textattack.loggers import CSVLogger # tracks a dataframe for us.\n",
+    "from tqdm import tqdm  # tqdm provides us a nice progress bar.\n",
+    "from textattack.loggers import CSVLogger  # tracks a dataframe for us.\n",
     "from textattack.attack_results import SuccessfulAttackResult\n",
     "from textattack import Attacker\n",
     "from textattack import AttackArgs\n",
@@ -530,14 +538,14 @@
     "\n",
     "attack_results = attacker.attack_dataset()\n",
     "\n",
-    "#The following legacy tutorial code shows how the Attack API works in detail.\n",
+    "# The following legacy tutorial code shows how the Attack API works in detail.\n",
     "\n",
-    "#logger = CSVLogger(color_method='html')\n",
+    "# logger = CSVLogger(color_method='html')\n",
     "\n",
-    "#num_successes = 0\n",
-    "#i = 0\n",
-    "#while num_successes < 10:\n",
-    "    #result = next(results_iterable)\n",
+    "# num_successes = 0\n",
+    "# i = 0\n",
+    "# while num_successes < 10:\n",
+    "# result = next(results_iterable)\n",
     "#    example, ground_truth_output = dataset[i]\n",
     "#    i += 1\n",
     "#    result = attack.attack(example, ground_truth_output)\n",
@@ -652,15 +660,19 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "pd.options.display.max_colwidth = 480 # increase colum width so we can actually read the examples\n",
     "\n",
-    "logger = CSVLogger(color_method='html')\n",
+    "pd.options.display.max_colwidth = (\n",
+    "    480  # increase colum width so we can actually read the examples\n",
+    ")\n",
+    "\n",
+    "logger = CSVLogger(color_method=\"html\")\n",
     "\n",
     "for result in attack_results:\n",
     "    logger.log_attack_result(result)\n",
     "\n",
     "from IPython.core.display import display, HTML\n",
-    "display(HTML(logger.df[['original_text', 'perturbed_text']].to_html(escape=False)))"
+    "\n",
+    "display(HTML(logger.df[[\"original_text\", \"perturbed_text\"]].to_html(escape=False)))"
    ]
   },
   {
@@ -867,10 +879,10 @@
     "# For AG News, labels are 0: World, 1: Sports, 2: Business, 3: Sci/Tech\n",
     "\n",
     "custom_dataset = [\n",
-    "    ('Malaria deaths in Africa fall by 5% from last year', 0),\n",
-    "    ('Washington Nationals defeat the Houston Astros to win the World Series', 1),\n",
-    "    ('Exxon Mobil hires a new CEO', 2),\n",
-    "    ('Microsoft invests $1 billion in OpenAI', 3),\n",
+    "    (\"Malaria deaths in Africa fall by 5% from last year\", 0),\n",
+    "    (\"Washington Nationals defeat the Houston Astros to win the World Series\", 1),\n",
+    "    (\"Exxon Mobil hires a new CEO\", 2),\n",
+    "    (\"Microsoft invests $1 billion in OpenAI\", 3),\n",
     "]\n",
     "\n",
     "attack_args = AttackArgs(num_examples=4)\n",
@@ -881,14 +893,14 @@
     "\n",
     "results_iterable = attacker.attack_dataset()\n",
     "\n",
-    "logger = CSVLogger(color_method='html')\n",
+    "logger = CSVLogger(color_method=\"html\")\n",
     "\n",
     "for result in results_iterable:\n",
     "    logger.log_attack_result(result)\n",
     "\n",
     "from IPython.core.display import display, HTML\n",
-    "    \n",
-    "display(HTML(logger.df[['original_text', 'perturbed_text']].to_html(escape=False)))"
+    "\n",
+    "display(HTML(logger.df[[\"original_text\", \"perturbed_text\"]].to_html(escape=False)))"
    ]
   }
  ],
diff --git a/docs/2notebook/2_Constraints.ipynb b/docs/2notebook/2_Constraints.ipynb
index 3f384995..b219ca2c 100644
--- a/docs/2notebook/2_Constraints.ipynb
+++ b/docs/2notebook/2_Constraints.ipynb
@@ -100,6 +100,7 @@
    ],
    "source": [
     "import tensorflow as tf\n",
+    "\n",
     "print(tf.__version__)"
    ]
   },
@@ -149,10 +150,11 @@
     "!pip3 install .\n",
     "\n",
     "import nltk\n",
-    "nltk.download('punkt') # The NLTK tokenizer\n",
-    "nltk.download('maxent_ne_chunker') # NLTK named-entity chunker\n",
-    "nltk.download('words') # NLTK list of words\n",
-    "nltk.download('averaged_perceptron_tagger')"
+    "\n",
+    "nltk.download(\"punkt\")  # The NLTK tokenizer\n",
+    "nltk.download(\"maxent_ne_chunker\")  # NLTK named-entity chunker\n",
+    "nltk.download(\"words\")  # NLTK list of words\n",
+    "nltk.download(\"averaged_perceptron_tagger\")"
    ]
   },
   {
@@ -205,8 +207,10 @@
     }
    ],
    "source": [
-    "sentence = ('In 2017, star quarterback Tom Brady led the Patriots to the Super Bowl, '\n",
-    "           'but lost to the Philadelphia Eagles.')\n",
+    "sentence = (\n",
+    "    \"In 2017, star quarterback Tom Brady led the Patriots to the Super Bowl, \"\n",
+    "    \"but lost to the Philadelphia Eagles.\"\n",
+    ")\n",
     "\n",
     "# 1. Tokenize using the NLTK tokenizer.\n",
     "tokens = nltk.word_tokenize(sentence)\n",
@@ -285,6 +289,7 @@
    "source": [
     "import functools\n",
     "\n",
+    "\n",
     "@functools.lru_cache(maxsize=2**14)\n",
     "def get_entities(sentence):\n",
     "    tokens = nltk.word_tokenize(sentence)\n",
@@ -379,9 +384,10 @@
    "source": [
     "from textattack.constraints import Constraint\n",
     "\n",
+    "\n",
     "class NamedEntityConstraint(Constraint):\n",
-    "    \"\"\" A constraint that ensures `transformed_text` only substitutes named entities from `current_text` with other named entities.\n",
-    "    \"\"\"\n",
+    "    \"\"\"A constraint that ensures `transformed_text` only substitutes named entities from `current_text` with other named entities.\"\"\"\n",
+    "\n",
     "    def _check_constraint(self, transformed_text, current_text):\n",
     "        transformed_entities = get_entities(transformed_text.text)\n",
     "        current_entities = get_entities(current_text.text)\n",
@@ -390,26 +396,27 @@
     "        if len(current_entities) == 0:\n",
     "            return False\n",
     "        if len(current_entities) != len(transformed_entities):\n",
-    "            # If the two sentences have a different number of entities, then \n",
-    "            # they definitely don't have the same labels. In this case, the \n",
+    "            # If the two sentences have a different number of entities, then\n",
+    "            # they definitely don't have the same labels. In this case, the\n",
     "            # constraint is violated, and we return False.\n",
     "            return False\n",
     "        else:\n",
     "            # Here we compare all of the words, in order, to make sure that they match.\n",
-    "            # If we find two words that don't match, this means a word was swapped \n",
+    "            # If we find two words that don't match, this means a word was swapped\n",
     "            # between `current_text` and `transformed_text`. That word must be a named entity to fulfill our\n",
     "            # constraint.\n",
     "            current_word_label = None\n",
     "            transformed_word_label = None\n",
-    "            for (word_1, label_1), (word_2, label_2) in zip(current_entities, transformed_entities):\n",
+    "            for (word_1, label_1), (word_2, label_2) in zip(\n",
+    "                current_entities, transformed_entities\n",
+    "            ):\n",
     "                if word_1 != word_2:\n",
-    "                    # Finally, make sure that words swapped between `x` and `x_adv` are named entities. If \n",
+    "                    # Finally, make sure that words swapped between `x` and `x_adv` are named entities. If\n",
     "                    # they're not, then we also return False.\n",
-    "                    if (label_1 not in ['NNP', 'NE']) or (label_2 not in ['NNP', 'NE']):\n",
-    "                        return False            \n",
+    "                    if (label_1 not in [\"NNP\", \"NE\"]) or (label_2 not in [\"NNP\", \"NE\"]):\n",
+    "                        return False\n",
     "            # If we get here, all of the labels match up. Return True!\n",
-    "            return True\n",
-    "    "
+    "            return True"
    ]
   },
   {
@@ -638,17 +645,23 @@
     "import transformers\n",
     "from textattack.models.wrappers import HuggingFaceModelWrapper\n",
     "\n",
-    "model = transformers.AutoModelForSequenceClassification.from_pretrained(\"textattack/albert-base-v2-ag-news\")\n",
-    "tokenizer = transformers.AutoTokenizer.from_pretrained(\"textattack/albert-base-v2-ag-news\")\n",
+    "model = transformers.AutoModelForSequenceClassification.from_pretrained(\n",
+    "    \"textattack/albert-base-v2-ag-news\"\n",
+    ")\n",
+    "tokenizer = transformers.AutoTokenizer.from_pretrained(\n",
+    "    \"textattack/albert-base-v2-ag-news\"\n",
+    ")\n",
     "\n",
     "model_wrapper = HuggingFaceModelWrapper(model, tokenizer)\n",
     "\n",
     "# Create the goal function using the model\n",
     "from textattack.goal_functions import UntargetedClassification\n",
+    "\n",
     "goal_function = UntargetedClassification(model_wrapper)\n",
     "\n",
     "# Import the dataset\n",
     "from textattack.datasets import HuggingFaceDataset\n",
+    "\n",
     "dataset = HuggingFaceDataset(\"ag_news\", None, \"test\")"
    ]
   },
@@ -663,23 +676,27 @@
     "from textattack.transformations import WordSwapEmbedding\n",
     "from textattack.search_methods import GreedyWordSwapWIR\n",
     "from textattack import Attack\n",
-    "from textattack.constraints.pre_transformation import RepeatModification, StopwordModification\n",
+    "from textattack.constraints.pre_transformation import (\n",
+    "    RepeatModification,\n",
+    "    StopwordModification,\n",
+    ")\n",
     "\n",
     "# We're going to the `WordSwapEmbedding` transformation. Using the default settings, this\n",
-    "# will try substituting words with their neighbors in the counter-fitted embedding space. \n",
-    "transformation = WordSwapEmbedding(max_candidates=20) \n",
+    "# will try substituting words with their neighbors in the counter-fitted embedding space.\n",
+    "transformation = WordSwapEmbedding(max_candidates=20)\n",
     "\n",
     "# We'll use the greedy search with word importance ranking method again\n",
     "search_method = GreedyWordSwapWIR()\n",
     "\n",
     "# Our constraints will be the same as Tutorial 1, plus the named entity constraint\n",
-    "constraints = [RepeatModification(),\n",
-    "               StopwordModification(),\n",
-    "               NamedEntityConstraint(False)]\n",
+    "constraints = [\n",
+    "    RepeatModification(),\n",
+    "    StopwordModification(),\n",
+    "    NamedEntityConstraint(False),\n",
+    "]\n",
     "\n",
-    "# Now, let's make the attack using these parameters. \n",
-    "attack = Attack(goal_function, constraints, transformation, search_method)\n",
-    "\n"
+    "# Now, let's make the attack using these parameters.\n",
+    "attack = Attack(goal_function, constraints, transformation, search_method)"
    ]
   },
   {
@@ -800,11 +817,13 @@
     }
    ],
    "source": [
-    "from textattack.loggers import CSVLogger # tracks a dataframe for us.\n",
+    "from textattack.loggers import CSVLogger  # tracks a dataframe for us.\n",
     "from textattack.attack_results import SuccessfulAttackResult\n",
     "from textattack import Attacker, AttackArgs\n",
     "\n",
-    "attack_args = AttackArgs(num_successful_examples=5, log_to_csv=\"results.csv\", csv_coloring_style=\"html\")\n",
+    "attack_args = AttackArgs(\n",
+    "    num_successful_examples=5, log_to_csv=\"results.csv\", csv_coloring_style=\"html\"\n",
+    ")\n",
     "attacker = Attacker(attack, dataset, attack_args)\n",
     "\n",
     "attacker.attack_dataset()"
@@ -833,13 +852,16 @@
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "pd.options.display.max_colwidth = 480 # increase column width so we can actually read the examples\n",
+    "\n",
+    "pd.options.display.max_colwidth = (\n",
+    "    480  # increase column width so we can actually read the examples\n",
+    ")\n",
     "\n",
     "from IPython.core.display import display, HTML\n",
     "\n",
     "logger = attacker.attack_log_manager.loggers[0]\n",
     "successes = logger.df[logger.df[\"result_type\"] == \"Successful\"]\n",
-    "display(HTML(successes[['original_text', 'perturbed_text']].to_html(escape=False)))"
+    "display(HTML(successes[[\"original_text\", \"perturbed_text\"]].to_html(escape=False)))"
    ]
   },
   {
diff --git a/docs/2notebook/3_Augmentations.ipynb b/docs/2notebook/3_Augmentations.ipynb
index 4f72058d..f136fe60 100644
--- a/docs/2notebook/3_Augmentations.ipynb
+++ b/docs/2notebook/3_Augmentations.ipynb
@@ -1,378 +1,392 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Augmentation with TextAttack.ipynb",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.8"
-    }
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "name": "Augmentation with TextAttack.ipynb",
+   "provenance": []
   },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "m83IiqVREJ96"
-      },
-      "source": [
-        "# TextAttack Augmentation"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6UZ0d84hEJ98"
-      },
-      "source": [
-        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QData/TextAttack/blob/master/docs/2notebook/3_Augmentations.ipynb)\n",
-        "\n",
-        "[![View Source on GitHub](https://img.shields.io/badge/github-view%20source-black.svg)](https://github.com/QData/TextAttack/blob/master/docs/2notebook/3_Augmentations.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tjqc2c5_7YaX"
-      },
-      "source": [
-        " Please remember to run the following in your notebook enviroment before running the tutorial codes:\n",
-        "\n",
-        "```\n",
-        "pip3 install textattack[tensorflow]\n",
-        "```\n",
-        "\n",
-        "\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qZ5xnoevEJ99"
-      },
-      "source": [
-        "Augmenting a dataset using TextAttack requries only a few lines of code when it is done right. The `Augmenter` class is created for this purpose to generate augmentations of a string or a list of strings. Augmentation could be done in either python script or command line.\n",
-        "\n",
-        "### Creating an Augmenter\n",
-        "\n",
-        "The **Augmenter** class is essensial for performing data augmentation using TextAttack. It takes in four paramerters in the following order:\n",
-        "\n",
-        "\n",
-        "1.  **transformation**: all [transformations](https://textattack.readthedocs.io/en/latest/apidoc/textattack.transformations.html) implemented by TextAttack can be used to create an `Augmenter`. Note here that if we want to apply multiple transformations in the same time, they first need to be incooporated into a `CompositeTransformation` class.\n",
-        "2.  **constraints**: [constraints](https://textattack.readthedocs.io/en/latest/apidoc/textattack.constraints.html#) determine whether or not a given augmentation is valid, consequently enhancing the quality of the augmentations. The default augmenter does not have any constraints but contraints can be supplied as a list to the Augmenter.\n",
-        "3.  **pct_words_to_swap**:  percentage of words to swap per augmented example. The default is set to 0.1 (10%).\n",
-        "4.  **transformations_per_example** maximum number of augmentations per input. The default is set to 1 (one augmented sentence given one original input)\n",
-        "\n",
-        "An example of creating one's own augmenter is shown below. In this case, we are creating an augmenter with **RandomCharacterDeletion** and **WordSwapQWERTY** transformations, **RepeatModification** and **StopWordModification** constraints. A maximum of **50%** of the words could be purturbed, and 10 augmentations will be generated from each input sentence.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "5AXyxiLD4X93"
-      },
-      "source": [
-        "# import transformations, contraints, and the Augmenter\n",
-        "from textattack.transformations import WordSwapRandomCharacterDeletion\n",
-        "from textattack.transformations import WordSwapQWERTY\n",
-        "from textattack.transformations import CompositeTransformation\n",
-        "\n",
-        "from textattack.constraints.pre_transformation import RepeatModification\n",
-        "from textattack.constraints.pre_transformation import StopwordModification\n",
-        "\n",
-        "from textattack.augmentation import Augmenter"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "wFeXF_OL-vyw",
-        "outputId": "c041e77e-accd-4a58-88be-9b140dd0cd56"
-      },
-      "source": [
-        "# Set up transformation using CompositeTransformation()\n",
-        "transformation = CompositeTransformation([WordSwapRandomCharacterDeletion(), WordSwapQWERTY()])\n",
-        "# Set up constraints\n",
-        "constraints = [RepeatModification(), StopwordModification()]\n",
-        "# Create augmenter with specified parameters\n",
-        "augmenter = Augmenter(transformation=transformation, constraints=constraints, pct_words_to_swap=0.5, transformations_per_example=10)\n",
-        "s = 'What I cannot create, I do not understand.'\n",
-        "# Augment!\n",
-        "augmenter.augment(s)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['Ahat I camnot reate, I do not unerstand.',\n",
-              " 'Ahat I cwnnot crewte, I do not undefstand.',\n",
-              " 'Wat I camnot vreate, I do not undefstand.',\n",
-              " 'Wha I annot crate, I do not unerstand.',\n",
-              " 'Whaf I canno creatr, I do not ynderstand.',\n",
-              " 'Wtat I cannor dreate, I do not understwnd.',\n",
-              " 'Wuat I canno ceate, I do not unferstand.',\n",
-              " 'hat I cnnot ceate, I do not undersand.',\n",
-              " 'hat I cnnot cfeate, I do not undfrstand.',\n",
-              " 'hat I cwnnot crfate, I do not ujderstand.']"
-            ]
-          },
-          "execution_count": 2,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "b7020KtvEJ9-"
-      },
-      "source": [
-        "### Pre-built Augmentation Recipes\n",
-        "\n",
-        "In addition to creating our own augmenter, we could also use pre-built augmentation recipes to perturb datasets. These recipes are implemented from publishded papers and are very convenient to use. The list of available recipes can be found [here](https://textattack.readthedocs.io/en/latest/3recipes/augmenter_recipes.html).\n"
-      ]
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m83IiqVREJ96"
+   },
+   "source": [
+    "# TextAttack Augmentation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6UZ0d84hEJ98"
+   },
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QData/TextAttack/blob/master/docs/2notebook/3_Augmentations.ipynb)\n",
+    "\n",
+    "[![View Source on GitHub](https://img.shields.io/badge/github-view%20source-black.svg)](https://github.com/QData/TextAttack/blob/master/docs/2notebook/3_Augmentations.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tjqc2c5_7YaX"
+   },
+   "source": [
+    " Please remember to run the following in your notebook enviroment before running the tutorial codes:\n",
+    "\n",
+    "```\n",
+    "pip3 install textattack[tensorflow]\n",
+    "```\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qZ5xnoevEJ99"
+   },
+   "source": [
+    "Augmenting a dataset using TextAttack requries only a few lines of code when it is done right. The `Augmenter` class is created for this purpose to generate augmentations of a string or a list of strings. Augmentation could be done in either python script or command line.\n",
+    "\n",
+    "### Creating an Augmenter\n",
+    "\n",
+    "The **Augmenter** class is essensial for performing data augmentation using TextAttack. It takes in four paramerters in the following order:\n",
+    "\n",
+    "\n",
+    "1.  **transformation**: all [transformations](https://textattack.readthedocs.io/en/latest/apidoc/textattack.transformations.html) implemented by TextAttack can be used to create an `Augmenter`. Note here that if we want to apply multiple transformations in the same time, they first need to be incooporated into a `CompositeTransformation` class.\n",
+    "2.  **constraints**: [constraints](https://textattack.readthedocs.io/en/latest/apidoc/textattack.constraints.html#) determine whether or not a given augmentation is valid, consequently enhancing the quality of the augmentations. The default augmenter does not have any constraints but contraints can be supplied as a list to the Augmenter.\n",
+    "3.  **pct_words_to_swap**:  percentage of words to swap per augmented example. The default is set to 0.1 (10%).\n",
+    "4.  **transformations_per_example** maximum number of augmentations per input. The default is set to 1 (one augmented sentence given one original input)\n",
+    "\n",
+    "An example of creating one's own augmenter is shown below. In this case, we are creating an augmenter with **RandomCharacterDeletion** and **WordSwapQWERTY** transformations, **RepeatModification** and **StopWordModification** constraints. A maximum of **50%** of the words could be purturbed, and 10 augmentations will be generated from each input sentence.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "5AXyxiLD4X93"
+   },
+   "source": [
+    "# import transformations, contraints, and the Augmenter\n",
+    "from textattack.transformations import WordSwapRandomCharacterDeletion\n",
+    "from textattack.transformations import WordSwapQWERTY\n",
+    "from textattack.transformations import CompositeTransformation\n",
+    "\n",
+    "from textattack.constraints.pre_transformation import RepeatModification\n",
+    "from textattack.constraints.pre_transformation import StopwordModification\n",
+    "\n",
+    "from textattack.augmentation import Augmenter"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "wFeXF_OL-vyw",
+    "outputId": "c041e77e-accd-4a58-88be-9b140dd0cd56"
+   },
+   "source": [
+    "# Set up transformation using CompositeTransformation()\n",
+    "transformation = CompositeTransformation(\n",
+    "    [WordSwapRandomCharacterDeletion(), WordSwapQWERTY()]\n",
+    ")\n",
+    "# Set up constraints\n",
+    "constraints = [RepeatModification(), StopwordModification()]\n",
+    "# Create augmenter with specified parameters\n",
+    "augmenter = Augmenter(\n",
+    "    transformation=transformation,\n",
+    "    constraints=constraints,\n",
+    "    pct_words_to_swap=0.5,\n",
+    "    transformations_per_example=10,\n",
+    ")\n",
+    "s = \"What I cannot create, I do not understand.\"\n",
+    "# Augment!\n",
+    "augmenter.augment(s)"
+   ],
+   "execution_count": null,
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "pkBqK5wYQKZu"
-      },
-      "source": [
-        "In the following example, we will use the `CheckListAugmenter` to showcase our augmentation recipes. The `CheckListAugmenter` augments words by using the transformation methods provided by CheckList INV testing, which combines **Name Replacement**, **Location Replacement**, **Number Alteration**, and **Contraction/Extension**. The original paper can be found here: [\"Beyond Accuracy: Behavioral Testing of NLP models with CheckList\" (Ribeiro et al., 2020)](https://arxiv.org/abs/2005.04118)"
+     "data": {
+      "text/plain": [
+       "['Ahat I camnot reate, I do not unerstand.',\n",
+       " 'Ahat I cwnnot crewte, I do not undefstand.',\n",
+       " 'Wat I camnot vreate, I do not undefstand.',\n",
+       " 'Wha I annot crate, I do not unerstand.',\n",
+       " 'Whaf I canno creatr, I do not ynderstand.',\n",
+       " 'Wtat I cannor dreate, I do not understwnd.',\n",
+       " 'Wuat I canno ceate, I do not unferstand.',\n",
+       " 'hat I cnnot ceate, I do not undersand.',\n",
+       " 'hat I cnnot cfeate, I do not undfrstand.',\n",
+       " 'hat I cwnnot crfate, I do not ujderstand.']"
       ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b7020KtvEJ9-"
+   },
+   "source": [
+    "### Pre-built Augmentation Recipes\n",
+    "\n",
+    "In addition to creating our own augmenter, we could also use pre-built augmentation recipes to perturb datasets. These recipes are implemented from publishded papers and are very convenient to use. The list of available recipes can be found [here](https://textattack.readthedocs.io/en/latest/3recipes/augmenter_recipes.html).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pkBqK5wYQKZu"
+   },
+   "source": [
+    "In the following example, we will use the `CheckListAugmenter` to showcase our augmentation recipes. The `CheckListAugmenter` augments words by using the transformation methods provided by CheckList INV testing, which combines **Name Replacement**, **Location Replacement**, **Number Alteration**, and **Contraction/Extension**. The original paper can be found here: [\"Beyond Accuracy: Behavioral Testing of NLP models with CheckList\" (Ribeiro et al., 2020)](https://arxiv.org/abs/2005.04118)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "WkYiVH6lQedu",
+    "outputId": "cd5ffc65-ca80-45cd-b3bb-d023bcad09a4"
+   },
+   "source": [
+    "# import the CheckListAugmenter\n",
+    "from textattack.augmentation import CheckListAugmenter\n",
+    "\n",
+    "# Alter default values if desired\n",
+    "augmenter = CheckListAugmenter(pct_words_to_swap=0.2, transformations_per_example=5)\n",
+    "s = \"I'd love to go to Japan but the tickets are 500 dollars\"\n",
+    "# Augment\n",
+    "augmenter.augment(s)"
+   ],
+   "execution_count": null,
+   "outputs": [
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "WkYiVH6lQedu",
-        "outputId": "cd5ffc65-ca80-45cd-b3bb-d023bcad09a4"
-      },
-      "source": [
-        "# import the CheckListAugmenter\n",
-        "from textattack.augmentation import CheckListAugmenter\n",
-        "# Alter default values if desired\n",
-        "augmenter = CheckListAugmenter(pct_words_to_swap=0.2, transformations_per_example=5)\n",
-        "s = \"I'd love to go to Japan but the tickets are 500 dollars\"\n",
-        "# Augment\n",
-        "augmenter.augment(s)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "2021-06-09 16:58:41,816 --------------------------------------------------------------------------------\n",
-            "2021-06-09 16:58:41,817 The model key 'ner' now maps to 'https://huggingface.co/flair/ner-english' on the HuggingFace ModelHub\n",
-            "2021-06-09 16:58:41,817  - The most current version of the model is automatically downloaded from there.\n",
-            "2021-06-09 16:58:41,818  - (you can alternatively manually download the original model at https://nlp.informatik.hu-berlin.de/resources/models/ner/en-ner-conll03-v0.4.pt)\n",
-            "2021-06-09 16:58:41,818 --------------------------------------------------------------------------------\n",
-            "2021-06-09 16:58:41,906 loading file /u/lab/jy2ma/.flair/models/ner-english/4f4cdab26f24cb98b732b389e6cebc646c36f54cfd6e0b7d3b90b25656e4262f.8baa8ae8795f4df80b28e7f7b61d788ecbb057d1dc85aacb316f1bd02837a4a4\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "['I would love to go to Chile but the tickets are 500 dollars',\n",
-              " 'I would love to go to Japan but the tickets are 500 dollars',\n",
-              " 'I would love to go to Japan but the tickets are 75 dollars',\n",
-              " \"I'd love to go to Oman but the tickets are 373 dollars\",\n",
-              " \"I'd love to go to Vietnam but the tickets are 613 dollars\"]"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-06-09 16:58:41,816 --------------------------------------------------------------------------------\n",
+      "2021-06-09 16:58:41,817 The model key 'ner' now maps to 'https://huggingface.co/flair/ner-english' on the HuggingFace ModelHub\n",
+      "2021-06-09 16:58:41,817  - The most current version of the model is automatically downloaded from there.\n",
+      "2021-06-09 16:58:41,818  - (you can alternatively manually download the original model at https://nlp.informatik.hu-berlin.de/resources/models/ner/en-ner-conll03-v0.4.pt)\n",
+      "2021-06-09 16:58:41,818 --------------------------------------------------------------------------------\n",
+      "2021-06-09 16:58:41,906 loading file /u/lab/jy2ma/.flair/models/ner-english/4f4cdab26f24cb98b732b389e6cebc646c36f54cfd6e0b7d3b90b25656e4262f.8baa8ae8795f4df80b28e7f7b61d788ecbb057d1dc85aacb316f1bd02837a4a4\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5vn22xrLST0H"
-      },
-      "source": [
-        "Note that the previous snippet of code is equivalent of running\n",
-        "\n",
-        "```\n",
-        "textattack augment --recipe checklist --pct-words-to-swap .1 --transformations-per-example 5 --exclude-original --interactive\n",
-        "```\n",
-        "in command line.\n"
+     "data": {
+      "text/plain": [
+       "['I would love to go to Chile but the tickets are 500 dollars',\n",
+       " 'I would love to go to Japan but the tickets are 500 dollars',\n",
+       " 'I would love to go to Japan but the tickets are 75 dollars',\n",
+       " \"I'd love to go to Oman but the tickets are 373 dollars\",\n",
+       " \"I'd love to go to Vietnam but the tickets are 613 dollars\"]"
       ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5vn22xrLST0H"
+   },
+   "source": [
+    "Note that the previous snippet of code is equivalent of running\n",
+    "\n",
+    "```\n",
+    "textattack augment --recipe checklist --pct-words-to-swap .1 --transformations-per-example 5 --exclude-original --interactive\n",
+    "```\n",
+    "in command line.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VqfmCKz0XY-Y"
+   },
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "Here's another example of using `WordNetAugmenter`. In this scenario, we enable `enable_advanced_metrics` to acquire perplexity and USE score, and enable `high_yield` to generate more examples in the same running time:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "l2b-4scuXvkA",
+    "outputId": "5a372fd2-226a-4970-a2c9-c09bf2af56c2"
+   },
+   "source": [
+    "from textattack.augmentation import WordNetAugmenter\n",
+    "\n",
+    "augmenter = WordNetAugmenter(\n",
+    "    pct_words_to_swap=0.4,\n",
+    "    transformations_per_example=5,\n",
+    "    high_yield=True,\n",
+    "    enable_advanced_metrics=True,\n",
+    ")\n",
+    "s = \"I'd love to go to Japan but the tickets are 500 dollars\"\n",
+    "results = augmenter.augment(s)\n",
+    "print(f\"Average Original Perplexity Score: {results[1]['avg_original_perplexity']}\\n\")\n",
+    "print(f\"Average Augment Perplexity Score: {results[1]['avg_attack_perplexity']}\\n\")\n",
+    "print(f\"Average Augment USE Score: {results[2]['avg_attack_use_score']}\\n\")\n",
+    "print(f\"Augmentations:\")\n",
+    "results[0]"
+   ],
+   "execution_count": 9,
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "VqfmCKz0XY-Y"
-      },
-      "source": [
-        "\n",
-        "\n",
-        "\n",
-        "Here's another example of using `WordNetAugmenter`. In this scenario, we enable `enable_advanced_metrics` to acquire perplexity and USE score, and enable `high_yield` to generate more examples in the same running time:\n"
-      ]
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Token indices sequence length is longer than the specified maximum sequence length for this model (1091 > 1024). Running this sequence through the model will result in indexing errors\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "l2b-4scuXvkA",
-        "outputId": "5a372fd2-226a-4970-a2c9-c09bf2af56c2"
-      },
-      "source": [
-        "from textattack.augmentation import WordNetAugmenter\n",
-        "augmenter = WordNetAugmenter(pct_words_to_swap=0.4, transformations_per_example=5, high_yield=True, enable_advanced_metrics=True)\n",
-        "s = \"I'd love to go to Japan but the tickets are 500 dollars\"\n",
-        "results = augmenter.augment(s)\n",
-        "print(f\"Average Original Perplexity Score: {results[1]['avg_original_perplexity']}\\n\")\n",
-        "print(f\"Average Augment Perplexity Score: {results[1]['avg_attack_perplexity']}\\n\")\n",
-        "print(f\"Average Augment USE Score: {results[2]['avg_attack_use_score']}\\n\")\n",
-        "print(f\"Augmentations:\")\n",
-        "results[0]"
-      ],
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Token indices sequence length is longer than the specified maximum sequence length for this model (1091 > 1024). Running this sequence through the model will result in indexing errors\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Average Original Perplexity Score: 1.09\n",
-            "\n",
-            "Average Augment Perplexity Score: 3.17\n",
-            "\n",
-            "Average Augment USE Score: 0.72\n",
-            "\n",
-            "Augmentations:\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "[\"I'd bang to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd bang to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd bed to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd bed to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd beloved to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd beloved to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd bonk to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd bonk to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd bonk to travel to Japan but the tag are 500 buck\",\n",
-              " \"I'd bonk to travel to Japan but the tag are 500 clam\",\n",
-              " \"I'd bonk to travel to Japan but the tag are 500 dollar\",\n",
-              " \"I'd bonk to travel to Japan but the tag are 500 dollars\",\n",
-              " \"I'd bonk to travel to Japan but the tag are D dollars\",\n",
-              " \"I'd bonk to travel to Japan but the tag are d dollars\",\n",
-              " \"I'd bonk to travel to Nihon but the tag are 500 dollars\",\n",
-              " \"I'd bonk to travel to Nippon but the tag are 500 dollars\",\n",
-              " \"I'd bonk to travel to japan but the tag are 500 dollars\",\n",
-              " \"I'd dear to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd dear to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd dearest to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd dearest to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd eff to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd eff to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd enjoy to exit to Japan but the fine are 500 buck\",\n",
-              " \"I'd enjoy to exit to Japan but the slate are 500 buck\",\n",
-              " \"I'd enjoy to exit to Japan but the tag are 500 buck\",\n",
-              " \"I'd enjoy to exit to Japan but the ticket are 500 buck\",\n",
-              " \"I'd enjoy to exit to Japan but the tickets are 500 buck\",\n",
-              " \"I'd enjoy to exit to Japan but the tickets are D buck\",\n",
-              " \"I'd enjoy to exit to Japan but the tickets are d buck\",\n",
-              " \"I'd enjoy to exit to Nihon but the tickets are 500 buck\",\n",
-              " \"I'd enjoy to exit to Nippon but the tickets are 500 buck\",\n",
-              " \"I'd enjoy to exit to japan but the tickets are 500 buck\",\n",
-              " \"I'd enjoy to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd enjoy to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd fuck to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd fuck to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd honey to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd honey to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd hump to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd hump to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd jazz to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd jazz to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd know to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd know to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd love to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd love to operate to Japan but the ticket are D buck\",\n",
-              " \"I'd love to operate to Japan but the ticket are d buck\",\n",
-              " \"I'd love to operate to Nihon but the ticket are 500 buck\",\n",
-              " \"I'd love to operate to Nippon but the ticket are 500 buck\",\n",
-              " \"I'd love to operate to japan but the ticket are 500 buck\",\n",
-              " \"I'd love to plump to Nihon but the fine are 500 clam\",\n",
-              " \"I'd love to plump to Nihon but the slate are 500 clam\",\n",
-              " \"I'd love to plump to Nihon but the tag are 500 clam\",\n",
-              " \"I'd love to plump to Nihon but the ticket are 500 clam\",\n",
-              " \"I'd love to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd love to plump to Nihon but the tickets are D clam\",\n",
-              " \"I'd love to plump to Nihon but the tickets are d clam\",\n",
-              " \"I'd lovemaking to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd lovemaking to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd passion to fit to Japan but the fine are 500 buck\",\n",
-              " \"I'd passion to fit to Japan but the fine are 500 clam\",\n",
-              " \"I'd passion to fit to Japan but the fine are 500 dollar\",\n",
-              " \"I'd passion to fit to Japan but the fine are 500 dollars\",\n",
-              " \"I'd passion to fit to Japan but the fine are D dollars\",\n",
-              " \"I'd passion to fit to Japan but the fine are d dollars\",\n",
-              " \"I'd passion to fit to Nihon but the fine are 500 dollars\",\n",
-              " \"I'd passion to fit to Nippon but the fine are 500 dollars\",\n",
-              " \"I'd passion to fit to japan but the fine are 500 dollars\",\n",
-              " \"I'd passion to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd passion to plump to Nihon but the tickets are 500 clam\",\n",
-              " \"I'd screw to operate to Japan but the ticket are 500 buck\",\n",
-              " \"I'd screw to plump to Nihon but the tickets are 500 clam\"]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 9
-        }
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average Original Perplexity Score: 1.09\n",
+      "\n",
+      "Average Augment Perplexity Score: 3.17\n",
+      "\n",
+      "Average Augment USE Score: 0.72\n",
+      "\n",
+      "Augmentations:\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "whvwbHLVEJ-S"
-      },
-      "source": [
-        "### Conclusion\n",
-        "We have now went through the basics in running `Augmenter` by either creating a new augmenter from scratch or using a pre-built augmenter. This could be done in as few as 4 lines of code so please give it a try if you haven't already! 🐙"
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[\"I'd bang to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd bang to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd bed to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd bed to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd beloved to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd beloved to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd bonk to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd bonk to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd bonk to travel to Japan but the tag are 500 buck\",\n",
+       " \"I'd bonk to travel to Japan but the tag are 500 clam\",\n",
+       " \"I'd bonk to travel to Japan but the tag are 500 dollar\",\n",
+       " \"I'd bonk to travel to Japan but the tag are 500 dollars\",\n",
+       " \"I'd bonk to travel to Japan but the tag are D dollars\",\n",
+       " \"I'd bonk to travel to Japan but the tag are d dollars\",\n",
+       " \"I'd bonk to travel to Nihon but the tag are 500 dollars\",\n",
+       " \"I'd bonk to travel to Nippon but the tag are 500 dollars\",\n",
+       " \"I'd bonk to travel to japan but the tag are 500 dollars\",\n",
+       " \"I'd dear to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd dear to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd dearest to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd dearest to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd eff to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd eff to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd enjoy to exit to Japan but the fine are 500 buck\",\n",
+       " \"I'd enjoy to exit to Japan but the slate are 500 buck\",\n",
+       " \"I'd enjoy to exit to Japan but the tag are 500 buck\",\n",
+       " \"I'd enjoy to exit to Japan but the ticket are 500 buck\",\n",
+       " \"I'd enjoy to exit to Japan but the tickets are 500 buck\",\n",
+       " \"I'd enjoy to exit to Japan but the tickets are D buck\",\n",
+       " \"I'd enjoy to exit to Japan but the tickets are d buck\",\n",
+       " \"I'd enjoy to exit to Nihon but the tickets are 500 buck\",\n",
+       " \"I'd enjoy to exit to Nippon but the tickets are 500 buck\",\n",
+       " \"I'd enjoy to exit to japan but the tickets are 500 buck\",\n",
+       " \"I'd enjoy to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd enjoy to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd fuck to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd fuck to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd honey to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd honey to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd hump to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd hump to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd jazz to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd jazz to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd know to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd know to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd love to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd love to operate to Japan but the ticket are D buck\",\n",
+       " \"I'd love to operate to Japan but the ticket are d buck\",\n",
+       " \"I'd love to operate to Nihon but the ticket are 500 buck\",\n",
+       " \"I'd love to operate to Nippon but the ticket are 500 buck\",\n",
+       " \"I'd love to operate to japan but the ticket are 500 buck\",\n",
+       " \"I'd love to plump to Nihon but the fine are 500 clam\",\n",
+       " \"I'd love to plump to Nihon but the slate are 500 clam\",\n",
+       " \"I'd love to plump to Nihon but the tag are 500 clam\",\n",
+       " \"I'd love to plump to Nihon but the ticket are 500 clam\",\n",
+       " \"I'd love to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd love to plump to Nihon but the tickets are D clam\",\n",
+       " \"I'd love to plump to Nihon but the tickets are d clam\",\n",
+       " \"I'd lovemaking to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd lovemaking to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd passion to fit to Japan but the fine are 500 buck\",\n",
+       " \"I'd passion to fit to Japan but the fine are 500 clam\",\n",
+       " \"I'd passion to fit to Japan but the fine are 500 dollar\",\n",
+       " \"I'd passion to fit to Japan but the fine are 500 dollars\",\n",
+       " \"I'd passion to fit to Japan but the fine are D dollars\",\n",
+       " \"I'd passion to fit to Japan but the fine are d dollars\",\n",
+       " \"I'd passion to fit to Nihon but the fine are 500 dollars\",\n",
+       " \"I'd passion to fit to Nippon but the fine are 500 dollars\",\n",
+       " \"I'd passion to fit to japan but the fine are 500 dollars\",\n",
+       " \"I'd passion to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd passion to plump to Nihon but the tickets are 500 clam\",\n",
+       " \"I'd screw to operate to Japan but the ticket are 500 buck\",\n",
+       " \"I'd screw to plump to Nihon but the tickets are 500 clam\"]"
       ]
+     },
+     "metadata": {},
+     "execution_count": 9
     }
-  ]
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "whvwbHLVEJ-S"
+   },
+   "source": [
+    "### Conclusion\n",
+    "We have now went through the basics in running `Augmenter` by either creating a new augmenter from scratch or using a pre-built augmenter. This could be done in as few as 4 lines of code so please give it a try if you haven't already! 🐙"
+   ]
+  }
+ ]
 }
\ No newline at end of file
diff --git a/docs/2notebook/4_Custom_Datasets_Word_Embedding.ipynb b/docs/2notebook/4_Custom_Datasets_Word_Embedding.ipynb
index 6788b8d2..506b705a 100644
--- a/docs/2notebook/4_Custom_Datasets_Word_Embedding.ipynb
+++ b/docs/2notebook/4_Custom_Datasets_Word_Embedding.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# TextAttack with Custom Dataset and Word Embedding.\n", 
+    "# TextAttack with Custom Dataset and Word Embedding.\n",
     "\n",
     "This tutorial will show you how to use textattack with any dataset and word embedding you may want to use\n"
    ]
@@ -291,7 +291,9 @@
     "from textattack.models.wrappers import HuggingFaceModelWrapper\n",
     "\n",
     "# https://huggingface.co/textattack\n",
-    "model = transformers.AutoModelForSequenceClassification.from_pretrained(\"textattack/albert-base-v2-imdb\")\n",
+    "model = transformers.AutoModelForSequenceClassification.from_pretrained(\n",
+    "    \"textattack/albert-base-v2-imdb\"\n",
+    ")\n",
     "tokenizer = transformers.AutoTokenizer.from_pretrained(\"textattack/albert-base-v2-imdb\")\n",
     "# We wrap the model so it can be used by textattack\n",
     "model_wrapper = HuggingFaceModelWrapper(model, tokenizer)"
@@ -319,13 +321,13 @@
    "outputs": [],
    "source": [
     "# dataset: An iterable of (text, ground_truth_output) pairs.\n",
-    "#0 means the review is negative\n",
-    "#1 means the review is positive\n",
+    "# 0 means the review is negative\n",
+    "# 1 means the review is positive\n",
     "custom_dataset = [\n",
-    "    ('I hate this movie', 0), #A negative comment, with a negative label\n",
-    "    ('I hate this movie', 1), #A negative comment, with a positive label\n",
-    "    ('I love this movie', 0), #A positive comment, with a negative label\n",
-    "    ('I love this movie', 1), #A positive comment, with a positive label\n",
+    "    (\"I hate this movie\", 0),  # A negative comment, with a negative label\n",
+    "    (\"I hate this movie\", 1),  # A negative comment, with a positive label\n",
+    "    (\"I love this movie\", 0),  # A positive comment, with a negative label\n",
+    "    (\"I love this movie\", 1),  # A positive comment, with a positive label\n",
     "]"
    ]
   },
@@ -360,7 +362,10 @@
    "source": [
     "from textattack import Attack\n",
     "from textattack.search_methods import GreedySearch\n",
-    "from textattack.constraints.pre_transformation import RepeatModification, StopwordModification\n",
+    "from textattack.constraints.pre_transformation import (\n",
+    "    RepeatModification,\n",
+    "    StopwordModification,\n",
+    ")\n",
     "from textattack.goal_functions import UntargetedClassification\n",
     "from textattack.transformations import WordSwapEmbedding\n",
     "from textattack.constraints.pre_transformation import RepeatModification\n",
@@ -369,10 +374,9 @@
     "# We'll use untargeted classification as the goal function.\n",
     "goal_function = UntargetedClassification(model_wrapper)\n",
     "# We'll to use our WordSwapEmbedding as the attack transformation.\n",
-    "transformation = WordSwapEmbedding() \n",
+    "transformation = WordSwapEmbedding()\n",
     "# We'll constrain modification of already modified indices and stopwords\n",
-    "constraints = [RepeatModification(),\n",
-    "               StopwordModification()]\n",
+    "constraints = [RepeatModification(), StopwordModification()]\n",
     "# We'll use the Greedy search method\n",
     "search_method = GreedySearch()\n",
     "# Now, let's make the attack from the 4 components:\n",
@@ -429,7 +433,7 @@
    "source": [
     "for example, label in custom_dataset:\n",
     "    result = attack.attack(example, label)\n",
-    "    print(result.__str__(color_method='ansi'))"
+    "    print(result.__str__(color_method=\"ansi\"))"
    ]
   },
   {
@@ -453,10 +457,30 @@
    "source": [
     "from textattack.shared import WordEmbedding\n",
     "\n",
-    "embedding_matrix = [[1.0], [2.0], [3.0], [4.0]] #2-D array of shape N x D where N represents size of vocab and D is the dimension of embedding vectors.\n",
-    "word2index = {\"hate\":0, \"despise\":1, \"like\":2, \"love\":3} #dictionary that maps word to its index with in the embedding matrix.\n",
-    "index2word = {0:\"hate\", 1: \"despise\", 2:\"like\", 3:\"love\"} #dictionary that maps index to its word.\n",
-    "nn_matrix = [[0, 1, 2, 3], [1, 0, 2, 3], [2, 1, 3, 0], [3, 2, 1, 0]] #2-D integer array of shape N x K where N represents size of vocab and K is the top-K nearest neighbours.\n",
+    "embedding_matrix = [\n",
+    "    [1.0],\n",
+    "    [2.0],\n",
+    "    [3.0],\n",
+    "    [4.0],\n",
+    "]  # 2-D array of shape N x D where N represents size of vocab and D is the dimension of embedding vectors.\n",
+    "word2index = {\n",
+    "    \"hate\": 0,\n",
+    "    \"despise\": 1,\n",
+    "    \"like\": 2,\n",
+    "    \"love\": 3,\n",
+    "}  # dictionary that maps word to its index with in the embedding matrix.\n",
+    "index2word = {\n",
+    "    0: \"hate\",\n",
+    "    1: \"despise\",\n",
+    "    2: \"like\",\n",
+    "    3: \"love\",\n",
+    "}  # dictionary that maps index to its word.\n",
+    "nn_matrix = [\n",
+    "    [0, 1, 2, 3],\n",
+    "    [1, 0, 2, 3],\n",
+    "    [2, 1, 3, 0],\n",
+    "    [3, 2, 1, 0],\n",
+    "]  # 2-D integer array of shape N x K where N represents size of vocab and K is the top-K nearest neighbours.\n",
     "\n",
     "embedding = WordEmbedding(embedding_matrix, word2index, index2word, nn_matrix)"
    ]
@@ -509,13 +533,13 @@
    "source": [
     "from textattack.attack_results import SuccessfulAttackResult\n",
     "\n",
-    "transformation = WordSwapEmbedding(3, embedding) \n",
+    "transformation = WordSwapEmbedding(3, embedding)\n",
     "\n",
     "attack = Attack(goal_function, constraints, transformation, search_method)\n",
     "\n",
     "for example, label in custom_dataset:\n",
     "    result = attack.attack(example, label)\n",
-    "    print(result.__str__(color_method='ansi'))"
+    "    print(result.__str__(color_method=\"ansi\"))"
    ]
   }
  ],
diff --git a/docs/2notebook/Example_0_tensorflow.ipynb b/docs/2notebook/Example_0_tensorflow.ipynb
index f16aa295..6c1fd55e 100644
--- a/docs/2notebook/Example_0_tensorflow.ipynb
+++ b/docs/2notebook/Example_0_tensorflow.ipynb
@@ -232,22 +232,26 @@
     "print(\"Version: \", tf.__version__)\n",
     "print(\"Eager mode: \", tf.executing_eagerly())\n",
     "print(\"Hub version: \", hub.__version__)\n",
-    "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")\n",
+    "print(\n",
+    "    \"GPU is\", \"available\" if tf.config.list_physical_devices(\"GPU\") else \"NOT AVAILABLE\"\n",
+    ")\n",
     "\n",
-    "train_data, test_data = tfds.load(name=\"imdb_reviews\", split=[\"train\", \"test\"], \n",
-    "                                  batch_size=-1, as_supervised=True)\n",
+    "train_data, test_data = tfds.load(\n",
+    "    name=\"imdb_reviews\", split=[\"train\", \"test\"], batch_size=-1, as_supervised=True\n",
+    ")\n",
     "\n",
     "train_examples, train_labels = tfds.as_numpy(train_data)\n",
     "test_examples, test_labels = tfds.as_numpy(test_data)\n",
     "\n",
     "model = \"https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1\"\n",
-    "hub_layer = hub.KerasLayer(model, output_shape=[20], input_shape=[], \n",
-    "                           dtype=tf.string, trainable=True)\n",
+    "hub_layer = hub.KerasLayer(\n",
+    "    model, output_shape=[20], input_shape=[], dtype=tf.string, trainable=True\n",
+    ")\n",
     "hub_layer(train_examples[:3])\n",
     "\n",
     "model = tf.keras.Sequential()\n",
     "model.add(hub_layer)\n",
-    "model.add(tf.keras.layers.Dense(16, activation='relu'))\n",
+    "model.add(tf.keras.layers.Dense(16, activation=\"relu\"))\n",
     "model.add(tf.keras.layers.Dense(1))\n",
     "\n",
     "model.summary()\n",
@@ -258,16 +262,20 @@
     "y_val = train_labels[:10000]\n",
     "partial_y_train = train_labels[10000:]\n",
     "\n",
-    "model.compile(optimizer='adam',\n",
-    "              loss=tf.losses.BinaryCrossentropy(from_logits=True),\n",
-    "              metrics=['accuracy'])\n",
+    "model.compile(\n",
+    "    optimizer=\"adam\",\n",
+    "    loss=tf.losses.BinaryCrossentropy(from_logits=True),\n",
+    "    metrics=[\"accuracy\"],\n",
+    ")\n",
     "\n",
-    "history = model.fit(partial_x_train,\n",
-    "                    partial_y_train,\n",
-    "                    epochs=40,\n",
-    "                    batch_size=512,\n",
-    "                    validation_data=(x_val, y_val),\n",
-    "                    verbose=1)"
+    "history = model.fit(\n",
+    "    partial_x_train,\n",
+    "    partial_y_train,\n",
+    "    epochs=40,\n",
+    "    batch_size=512,\n",
+    "    validation_data=(x_val, y_val),\n",
+    "    verbose=1,\n",
+    ")"
    ]
   },
   {
@@ -300,6 +308,7 @@
     "\n",
     "from textattack.models.wrappers import ModelWrapper\n",
     "\n",
+    "\n",
     "class CustomTensorFlowModelWrapper(ModelWrapper):\n",
     "    def __init__(self, model):\n",
     "        self.model = model\n",
@@ -312,8 +321,8 @@
     "        logits = logits.squeeze(dim=-1)\n",
     "        # Since this model only has a single output (between 0 or 1),\n",
     "        # we have to add the second dimension.\n",
-    "        final_preds = torch.stack((1-logits, logits), dim=1)\n",
-    "        return final_preds\n"
+    "        final_preds = torch.stack((1 - logits, logits), dim=1)\n",
+    "        return final_preds"
    ]
   },
   {
@@ -350,7 +359,7 @@
     }
    ],
    "source": [
-    "CustomTensorFlowModelWrapper(model)(['I hate you so much', 'I love you'])"
+    "CustomTensorFlowModelWrapper(model)([\"I hate you so much\", \"I love you\"])"
    ]
   },
   {
diff --git a/docs/2notebook/Example_1_sklearn.ipynb b/docs/2notebook/Example_1_sklearn.ipynb
index b50d5298..7826f18f 100644
--- a/docs/2notebook/Example_1_sklearn.ipynb
+++ b/docs/2notebook/Example_1_sklearn.ipynb
@@ -119,8 +119,9 @@
     }
    ],
    "source": [
-    "import nltk # the Natural Language Toolkit\n",
-    "nltk.download('punkt') # The NLTK tokenizer"
+    "import nltk  # the Natural Language Toolkit\n",
+    "\n",
+    "nltk.download(\"punkt\")  # The NLTK tokenizer"
    ]
   },
   {
@@ -259,102 +260,139 @@
     "# Nice to see additional metrics\n",
     "from sklearn.metrics import classification_report\n",
     "\n",
-    "def load_data(dataset_split='train'):\n",
-    "    dataset = datasets.load_dataset('rotten_tomatoes')[dataset_split]\n",
+    "\n",
+    "def load_data(dataset_split=\"train\"):\n",
+    "    dataset = datasets.load_dataset(\"rotten_tomatoes\")[dataset_split]\n",
     "    # Open and import positve data\n",
     "    df = pd.DataFrame()\n",
-    "    df['Review'] = [review['text'] for review in dataset]\n",
-    "    df['Sentiment'] = [review['label'] for review in dataset]\n",
+    "    df[\"Review\"] = [review[\"text\"] for review in dataset]\n",
+    "    df[\"Sentiment\"] = [review[\"label\"] for review in dataset]\n",
     "    # Remove non-alphanumeric characters\n",
-    "    df['Review'] = df['Review'].apply(lambda x: re.sub(\"[^a-zA-Z]\", ' ', str(x)))\n",
+    "    df[\"Review\"] = df[\"Review\"].apply(lambda x: re.sub(\"[^a-zA-Z]\", \" \", str(x)))\n",
     "    # Tokenize the training and testing data\n",
     "    df_tokenized = tokenize_review(df)\n",
     "    return df_tokenized\n",
     "\n",
+    "\n",
     "def tokenize_review(df):\n",
     "    # Tokenize Reviews in training\n",
-    "    tokened_reviews = [word_tokenize(rev) for rev in df['Review']]\n",
+    "    tokened_reviews = [word_tokenize(rev) for rev in df[\"Review\"]]\n",
     "    # Create word stems\n",
     "    stemmed_tokens = []\n",
     "    porter = PorterStemmer()\n",
     "    for i in range(len(tokened_reviews)):\n",
     "        stems = [porter.stem(token) for token in tokened_reviews[i]]\n",
-    "        stems = ' '.join(stems)\n",
+    "        stems = \" \".join(stems)\n",
     "        stemmed_tokens.append(stems)\n",
-    "    df.insert(1, column='Stemmed', value=stemmed_tokens)\n",
+    "    df.insert(1, column=\"Stemmed\", value=stemmed_tokens)\n",
     "    return df\n",
     "\n",
+    "\n",
     "def transform_BOW(training, testing, column_name):\n",
-    "    vect = CountVectorizer(max_features=100, ngram_range=(1,3), stop_words=ENGLISH_STOP_WORDS)\n",
+    "    vect = CountVectorizer(\n",
+    "        max_features=100, ngram_range=(1, 3), stop_words=ENGLISH_STOP_WORDS\n",
+    "    )\n",
     "    vectFit = vect.fit(training[column_name])\n",
     "    BOW_training = vectFit.transform(training[column_name])\n",
-    "    BOW_training_df = pd.DataFrame(BOW_training.toarray(), columns=vect.get_feature_names())\n",
+    "    BOW_training_df = pd.DataFrame(\n",
+    "        BOW_training.toarray(), columns=vect.get_feature_names()\n",
+    "    )\n",
     "    BOW_testing = vectFit.transform(testing[column_name])\n",
-    "    BOW_testing_Df = pd.DataFrame(BOW_testing.toarray(), columns=vect.get_feature_names())\n",
+    "    BOW_testing_Df = pd.DataFrame(\n",
+    "        BOW_testing.toarray(), columns=vect.get_feature_names()\n",
+    "    )\n",
     "    return vectFit, BOW_training_df, BOW_testing_Df\n",
     "\n",
+    "\n",
     "def transform_tfidf(training, testing, column_name):\n",
-    "    Tfidf = TfidfVectorizer(ngram_range=(1,3), max_features=100, stop_words=ENGLISH_STOP_WORDS)\n",
+    "    Tfidf = TfidfVectorizer(\n",
+    "        ngram_range=(1, 3), max_features=100, stop_words=ENGLISH_STOP_WORDS\n",
+    "    )\n",
     "    Tfidf_fit = Tfidf.fit(training[column_name])\n",
     "    Tfidf_training = Tfidf_fit.transform(training[column_name])\n",
-    "    Tfidf_training_df = pd.DataFrame(Tfidf_training.toarray(), columns=Tfidf.get_feature_names())\n",
+    "    Tfidf_training_df = pd.DataFrame(\n",
+    "        Tfidf_training.toarray(), columns=Tfidf.get_feature_names()\n",
+    "    )\n",
     "    Tfidf_testing = Tfidf_fit.transform(testing[column_name])\n",
-    "    Tfidf_testing_df = pd.DataFrame(Tfidf_testing.toarray(), columns=Tfidf.get_feature_names())\n",
+    "    Tfidf_testing_df = pd.DataFrame(\n",
+    "        Tfidf_testing.toarray(), columns=Tfidf.get_feature_names()\n",
+    "    )\n",
     "    return Tfidf_fit, Tfidf_training_df, Tfidf_testing_df\n",
     "\n",
+    "\n",
     "def add_augmenting_features(df):\n",
-    "    tokened_reviews = [word_tokenize(rev) for rev in df['Review']]\n",
+    "    tokened_reviews = [word_tokenize(rev) for rev in df[\"Review\"]]\n",
     "    # Create feature that measures length of reviews\n",
     "    len_tokens = []\n",
     "    for i in range(len(tokened_reviews)):\n",
     "        len_tokens.append(len(tokened_reviews[i]))\n",
     "    len_tokens = preprocessing.scale(len_tokens)\n",
-    "    df.insert(0, column='Lengths', value=len_tokens)\n",
+    "    df.insert(0, column=\"Lengths\", value=len_tokens)\n",
     "\n",
     "    # Create average word length (training)\n",
-    "    Average_Words = [len(x)/(len(x.split())) for x in df['Review'].tolist()]\n",
+    "    Average_Words = [len(x) / (len(x.split())) for x in df[\"Review\"].tolist()]\n",
     "    Average_Words = preprocessing.scale(Average_Words)\n",
-    "    df['averageWords'] = Average_Words\n",
+    "    df[\"averageWords\"] = Average_Words\n",
     "    return df\n",
     "\n",
+    "\n",
     "def build_model(X_train, y_train, X_test, y_test, name_of_test):\n",
     "    log_reg = LogisticRegression(C=30, max_iter=200).fit(X_train, y_train)\n",
     "    y_pred = log_reg.predict(X_test)\n",
-    "    print('Training accuracy of '+name_of_test+': ', log_reg.score(X_train, y_train))\n",
-    "    print('Testing accuracy of '+name_of_test+': ', log_reg.score(X_test, y_test))\n",
+    "    print(\n",
+    "        \"Training accuracy of \" + name_of_test + \": \", log_reg.score(X_train, y_train)\n",
+    "    )\n",
+    "    print(\"Testing accuracy of \" + name_of_test + \": \", log_reg.score(X_test, y_test))\n",
     "    print(classification_report(y_test, y_pred))  # Evaluating prediction ability\n",
     "    return log_reg\n",
     "\n",
+    "\n",
     "# Load training and test sets\n",
     "# Loading reviews into DF\n",
-    "df_train = load_data('train')\n",
+    "df_train = load_data(\"train\")\n",
     "\n",
-    "print('...successfully loaded training data')\n",
-    "print('Total length of training data: ', len(df_train))\n",
+    "print(\"...successfully loaded training data\")\n",
+    "print(\"Total length of training data: \", len(df_train))\n",
     "# Add augmenting features\n",
     "df_train = add_augmenting_features(df_train)\n",
-    "print('...augmented data with len_tokens and average_words')\n",
+    "print(\"...augmented data with len_tokens and average_words\")\n",
     "\n",
     "# Load test DF\n",
-    "df_test = load_data('test')\n",
+    "df_test = load_data(\"test\")\n",
     "\n",
-    "print('...successfully loaded testing data')\n",
-    "print('Total length of testing data: ', len(df_test))\n",
+    "print(\"...successfully loaded testing data\")\n",
+    "print(\"Total length of testing data: \", len(df_test))\n",
     "df_test = add_augmenting_features(df_test)\n",
-    "print('...augmented data with len_tokens and average_words')\n",
+    "print(\"...augmented data with len_tokens and average_words\")\n",
     "\n",
     "# Create unstemmed BOW features for training set\n",
-    "unstemmed_BOW_vect_fit, df_train_bow_unstem, df_test_bow_unstem = transform_BOW(df_train, df_test, 'Review')\n",
-    "print('...successfully created the unstemmed BOW data')\n",
+    "unstemmed_BOW_vect_fit, df_train_bow_unstem, df_test_bow_unstem = transform_BOW(\n",
+    "    df_train, df_test, \"Review\"\n",
+    ")\n",
+    "print(\"...successfully created the unstemmed BOW data\")\n",
     "\n",
     "# Create TfIdf features for training set\n",
-    "unstemmed_tfidf_vect_fit, df_train_tfidf_unstem, df_test_tfidf_unstem = transform_tfidf(df_train, df_test, 'Review')\n",
-    "print('...successfully created the unstemmed TFIDF data')\n",
+    "unstemmed_tfidf_vect_fit, df_train_tfidf_unstem, df_test_tfidf_unstem = transform_tfidf(\n",
+    "    df_train, df_test, \"Review\"\n",
+    ")\n",
+    "print(\"...successfully created the unstemmed TFIDF data\")\n",
     "\n",
     "# Running logistic regression on dataframes\n",
-    "bow_unstemmed = build_model(df_train_bow_unstem, df_train['Sentiment'], df_test_bow_unstem, df_test['Sentiment'], 'BOW Unstemmed')\n",
+    "bow_unstemmed = build_model(\n",
+    "    df_train_bow_unstem,\n",
+    "    df_train[\"Sentiment\"],\n",
+    "    df_test_bow_unstem,\n",
+    "    df_test[\"Sentiment\"],\n",
+    "    \"BOW Unstemmed\",\n",
+    ")\n",
     "\n",
-    "tfidf_unstemmed = build_model(df_train_tfidf_unstem, df_train['Sentiment'], df_test_tfidf_unstem, df_test['Sentiment'], 'TFIDF Unstemmed')"
+    "tfidf_unstemmed = build_model(\n",
+    "    df_train_tfidf_unstem,\n",
+    "    df_train[\"Sentiment\"],\n",
+    "    df_test_tfidf_unstem,\n",
+    "    df_test[\"Sentiment\"],\n",
+    "    \"TFIDF Unstemmed\",\n",
+    ")"
    ]
   },
   {
diff --git a/docs/2notebook/Example_2_allennlp.ipynb b/docs/2notebook/Example_2_allennlp.ipynb
index 928c7dd3..99694ddc 100644
--- a/docs/2notebook/Example_2_allennlp.ipynb
+++ b/docs/2notebook/Example_2_allennlp.ipynb
@@ -1,3144 +1,3148 @@
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+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JPVBc5ndpFIX"
+   },
+   "source": [
+    "# TextAttack & AllenNLP \n",
+    "\n",
+    "This is an example of testing adversarial attacks from TextAttack on pretrained models provided by AllenNLP. \n",
+    "\n",
+    "In a few lines of code, we load a sentiment analysis model trained on the Stanford Sentiment Treebank and configure it with a TextAttack model wrapper. Then, we initialize the TextBugger attack and run the attack on a few samples from the SST-2 train set.\n",
+    "\n",
+    "For more information on AllenNLP pre-trained models: https://docs.allennlp.org/models/main/\n",
+    "\n",
+    "For more information about the TextBugger attack: https://arxiv.org/abs/1812.05271"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AyPMGcz0qLfK"
+   },
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QData/TextAttack/blob/master/docs/2notebook/Example_2_allennlp.ipynb)\n",
+    "\n",
+    "[![View Source on GitHub](https://img.shields.io/badge/github-view%20source-black.svg)](https://github.com/QData/TextAttack/blob/master/docs/2notebook/Example_2_allennlp.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "My9oy5iBSKfb"
+   },
+   "source": [
+    "!pip install allennlp allennlp_models > /dev/null"
+   ],
+   "execution_count": 4,
+   "outputs": []
+  },
+  {
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+   "metadata": {
+    "id": "z8wAb0BcSg8W",
+    "outputId": "8cc26ced-6f03-433c-97d2-72037c606fde",
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-          "model_module": "@jupyter-widgets/controls",
-          "model_name": "DescriptionStyleModel",
-          "model_module_version": "1.5.0",
-          "state": {
-            "_view_name": "StyleView",
-            "_model_name": "DescriptionStyleModel",
-            "description_width": "",
-            "_view_module": "@jupyter-widgets/base",
-            "_model_module_version": "1.5.0",
-            "_view_count": null,
-            "_view_module_version": "1.2.0",
-            "_model_module": "@jupyter-widgets/controls"
-          }
-        },
-        "57c72b3beb6c422690e0be7ba8c583c5": {
-          "model_module": "@jupyter-widgets/base",
-          "model_name": "LayoutModel",
-          "model_module_version": "1.2.0",
-          "state": {
-            "_view_name": "LayoutView",
-            "grid_template_rows": null,
-            "right": null,
-            "justify_content": null,
-            "_view_module": "@jupyter-widgets/base",
-            "overflow": null,
-            "_model_module_version": "1.2.0",
-            "_view_count": null,
-            "flex_flow": null,
-            "width": null,
-            "min_width": null,
-            "border": null,
-            "align_items": null,
-            "bottom": null,
-            "_model_module": "@jupyter-widgets/base",
-            "top": null,
-            "grid_column": null,
-            "overflow_y": null,
-            "overflow_x": null,
-            "grid_auto_flow": null,
-            "grid_area": null,
-            "grid_template_columns": null,
-            "flex": null,
-            "_model_name": "LayoutModel",
-            "justify_items": null,
-            "grid_row": null,
-            "max_height": null,
-            "align_content": null,
-            "visibility": null,
-            "align_self": null,
-            "height": null,
-            "min_height": null,
-            "padding": null,
-            "grid_auto_rows": null,
-            "grid_gap": null,
-            "max_width": null,
-            "order": null,
-            "_view_module_version": "1.2.0",
-            "grid_template_areas": null,
-            "object_position": null,
-            "object_fit": null,
-            "grid_auto_columns": null,
-            "margin": null,
-            "display": null,
-            "left": null
-          }
-        }
-      }
+     "base_uri": "https://localhost:8080/"
+    }
+   },
+   "source": [
+    "!pip3 install textattack[tensorflow]"
+   ],
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: textattack[tensorflow] in /usr/local/lib/python3.7/dist-packages (0.3.3)\n",
+      "Requirement already satisfied: language-tool-python in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (2.6.1)\n",
+      "Requirement already satisfied: terminaltables in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (3.1.0)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (3.0.12)\n",
+      "Requirement already satisfied: numpy>=1.19.2 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.19.5)\n",
+      "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.4.1)\n",
+      "Requirement already satisfied: word2number in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.1)\n",
+      "Requirement already satisfied: lru-dict in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.1.7)\n",
+      "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (3.2.5)\n",
+      "Requirement already satisfied: pandas>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.1.5)\n",
+      "Requirement already satisfied: num2words in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (0.5.10)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.7.1)\n",
+      "Requirement already satisfied: transformers>=3.3.0 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (4.5.1)\n",
+      "Requirement already satisfied: lemminflect in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (0.2.2)\n",
+      "Requirement already satisfied: more-itertools in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (8.8.0)\n",
+      "Requirement already satisfied: editdistance in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (0.5.3)\n",
+      "Requirement already satisfied: bert-score>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (0.3.10)\n",
+      "Requirement already satisfied: torch!=1.8,>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.9.0+cu111)\n",
+      "Requirement already satisfied: tqdm<4.50.0,>=4.27 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (4.49.0)\n",
+      "Requirement already satisfied: flair in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (0.9)\n",
+      "Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.11.0)\n",
+      "Requirement already satisfied: tensorflow>=2 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (2.6.0)\n",
+      "Requirement already satisfied: tensorflow-hub in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (0.12.0)\n",
+      "Requirement already satisfied: tensorboardX in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (2.4)\n",
+      "Collecting tensorflow-text>=2\n",
+      "  Downloading tensorflow_text-2.6.0-cp37-cp37m-manylinux1_x86_64.whl (4.4 MB)\n",
+      "\u001b[K     |████████████████████████████████| 4.4 MB 5.4 MB/s \n",
+      "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from bert-score>=0.3.5->textattack[tensorflow]) (2.26.0)\n",
+      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from bert-score>=0.3.5->textattack[tensorflow]) (3.2.2)\n",
+      "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from bert-score>=0.3.5->textattack[tensorflow]) (21.0)\n",
+      "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->bert-score>=0.3.5->textattack[tensorflow]) (2.4.7)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.1->textattack[tensorflow]) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.1->textattack[tensorflow]) (2018.9)\n",
+      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0.1->textattack[tensorflow]) (1.15.0)\n",
+      "Requirement already satisfied: grpcio<2.0,>=1.37.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (1.41.0)\n",
+      "Requirement already satisfied: tensorflow-estimator~=2.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (2.6.0)\n",
+      "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (3.17.3)\n",
+      "Requirement already satisfied: google-pasta~=0.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (0.2.0)\n",
+      "Requirement already satisfied: gast==0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (0.4.0)\n",
+      "Requirement already satisfied: flatbuffers~=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (1.12)\n",
+      "Requirement already satisfied: termcolor~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (1.1.0)\n",
+      "Requirement already satisfied: tensorboard~=2.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (2.6.0)\n",
+      "Requirement already satisfied: keras~=2.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (2.6.0)\n",
+      "Requirement already satisfied: clang~=5.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (5.0)\n",
+      "Requirement already satisfied: absl-py~=0.10 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (0.12.0)\n",
+      "Requirement already satisfied: wrapt~=1.12.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (1.12.1)\n",
+      "Requirement already satisfied: astunparse~=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (1.6.3)\n",
+      "Requirement already satisfied: wheel~=0.35 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (0.37.0)\n",
+      "Requirement already satisfied: typing-extensions~=3.7.4 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (3.7.4.3)\n",
+      "Requirement already satisfied: opt-einsum~=3.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (3.3.0)\n",
+      "Requirement already satisfied: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (1.1.2)\n",
+      "Requirement already satisfied: h5py~=3.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2->textattack[tensorflow]) (3.1.0)\n",
+      "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py~=3.1.0->tensorflow>=2->textattack[tensorflow]) (1.5.2)\n",
+      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (0.6.1)\n",
+      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (0.4.6)\n",
+      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (3.3.4)\n",
+      "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (57.4.0)\n",
+      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (1.8.0)\n",
+      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (1.0.1)\n",
+      "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (1.35.0)\n",
+      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (0.2.8)\n",
+      "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (4.7.2)\n",
+      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (4.2.4)\n",
+      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (1.3.0)\n",
+      "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (3.10.1)\n",
+      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (0.4.8)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests->bert-score>=0.3.5->textattack[tensorflow]) (2.0.6)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->bert-score>=0.3.5->textattack[tensorflow]) (1.25.11)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->bert-score>=0.3.5->textattack[tensorflow]) (2.10)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->bert-score>=0.3.5->textattack[tensorflow]) (2021.5.30)\n",
+      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (3.1.1)\n",
+      "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers>=3.3.0->textattack[tensorflow]) (0.0.46)\n",
+      "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers>=3.3.0->textattack[tensorflow]) (2019.12.20)\n",
+      "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers>=3.3.0->textattack[tensorflow]) (0.10.3)\n",
+      "Requirement already satisfied: xxhash in /usr/local/lib/python3.7/dist-packages (from datasets->textattack[tensorflow]) (2.0.2)\n",
+      "Requirement already satisfied: fsspec>=2021.05.0 in /usr/local/lib/python3.7/dist-packages (from datasets->textattack[tensorflow]) (2021.10.1)\n",
+      "Requirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets->textattack[tensorflow]) (0.70.12.2)\n",
+      "Requirement already satisfied: huggingface-hub<0.1.0 in /usr/local/lib/python3.7/dist-packages (from datasets->textattack[tensorflow]) (0.0.19)\n",
+      "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from datasets->textattack[tensorflow]) (0.3.4)\n",
+      "Requirement already satisfied: pyarrow!=4.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from datasets->textattack[tensorflow]) (3.0.0)\n",
+      "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<0.1.0->datasets->textattack[tensorflow]) (3.13)\n",
+      "Requirement already satisfied: segtok>=1.5.7 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (1.5.10)\n",
+      "Requirement already satisfied: hyperopt>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (0.1.2)\n",
+      "Requirement already satisfied: langdetect in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (1.0.9)\n",
+      "Requirement already satisfied: konoha<5.0.0,>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (4.6.5)\n",
+      "Requirement already satisfied: bpemb>=0.3.2 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (0.3.3)\n",
+      "Requirement already satisfied: gdown==3.12.2 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (3.12.2)\n",
+      "Requirement already satisfied: ftfy in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (6.0.3)\n",
+      "Requirement already satisfied: conllu>=4.0 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (4.4.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (0.22.2.post1)\n",
+      "Requirement already satisfied: sqlitedict>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (1.7.0)\n",
+      "Requirement already satisfied: mpld3==0.3 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (0.3)\n",
+      "Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (4.2.6)\n",
+      "Requirement already satisfied: janome in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (0.4.1)\n",
+      "Requirement already satisfied: gensim<=3.8.3,>=3.4.0 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (3.6.0)\n",
+      "Requirement already satisfied: sentencepiece==0.1.95 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (0.1.95)\n",
+      "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (0.8.9)\n",
+      "Requirement already satisfied: wikipedia-api in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (0.5.4)\n",
+      "Requirement already satisfied: deprecated>=1.2.4 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (1.2.13)\n",
+      "Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from gensim<=3.8.3,>=3.4.0->flair->textattack[tensorflow]) (5.2.1)\n",
+      "Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.1.1->flair->textattack[tensorflow]) (2.6.3)\n",
+      "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.1.1->flair->textattack[tensorflow]) (0.16.0)\n",
+      "Requirement already satisfied: pymongo in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.1.1->flair->textattack[tensorflow]) (3.12.0)\n",
+      "Requirement already satisfied: overrides<4.0.0,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from konoha<5.0.0,>=4.0.0->flair->textattack[tensorflow]) (3.1.0)\n",
+      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (3.6.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->bert-score>=0.3.5->textattack[tensorflow]) (1.3.2)\n",
+      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->bert-score>=0.3.5->textattack[tensorflow]) (0.10.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->flair->textattack[tensorflow]) (1.0.1)\n",
+      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from ftfy->flair->textattack[tensorflow]) (0.2.5)\n",
+      "Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.7/dist-packages (from num2words->textattack[tensorflow]) (0.6.2)\n",
+      "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers>=3.3.0->textattack[tensorflow]) (7.1.2)\n",
+      "Installing collected packages: tensorflow-text\n",
+      "Successfully installed tensorflow-text-2.6.0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "_br6Xvsif9SA",
+    "outputId": "1025399f-9f63-4d9a-e854-e4f92ba24c45"
+   },
+   "source": [
+    "from allennlp.predictors import Predictor\n",
+    "import allennlp_models.classification\n",
+    "\n",
+    "import textattack\n",
+    "\n",
+    "\n",
+    "class AllenNLPModel(textattack.models.wrappers.ModelWrapper):\n",
+    "    def __init__(self):\n",
+    "        self.predictor = Predictor.from_path(\n",
+    "            \"https://storage.googleapis.com/allennlp-public-models/basic_stanford_sentiment_treebank-2020.06.09.tar.gz\"\n",
+    "        )\n",
+    "        self.model = self.predictor._model\n",
+    "        self.tokenizer = self.predictor._dataset_reader._tokenizer\n",
+    "\n",
+    "    def __call__(self, text_input_list):\n",
+    "        outputs = []\n",
+    "        for text_input in text_input_list:\n",
+    "            outputs.append(self.predictor.predict(sentence=text_input))\n",
+    "        # For each output, outputs['logits'] contains the logits where\n",
+    "        # index 0 corresponds to the positive and index 1 corresponds\n",
+    "        # to the negative score. We reverse the outputs (by reverse slicing,\n",
+    "        # [::-1]) so that negative comes first and positive comes second.\n",
+    "        return [output[\"logits\"][::-1] for output in outputs]\n",
+    "\n",
+    "\n",
+    "model_wrapper = AllenNLPModel()"
+   ],
+   "execution_count": 8,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "textattack: Updating TextAttack package dependencies.\n",
+      "textattack: Downloading NLTK required packages.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[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]   Unzipping corpora/omw.zip.\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]   Unzipping corpora/wordnet.zip.\n",
+      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "textattack: Downloading https://textattack.s3.amazonaws.com/word_embeddings/paragramcf.\n",
+      "100%|██████████| 481M/481M [00:14<00:00, 33.6MB/s]\n",
+      "textattack: Unzipping file /root/.cache/textattack/tmp7xfefu5f.zip to /root/.cache/textattack/word_embeddings/paragramcf.\n",
+      "textattack: Successfully saved word_embeddings/paragramcf to cache.\n",
+      "Plugin allennlp_models could not be loaded: No module named 'nltk.translate.meteor_score'\n",
+      "downloading: 100%|##########| 37033341/37033341 [00:01<00:00, 27735821.99B/s]\n"
+     ]
     }
+   ]
   },
-  "cells": [
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
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+      "73a5417a077f4f7e82e7f11d7f4fefba",
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+      "3221c018b6604cada04f2710fd00e750",
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+      "bf95592be6084cb782f11e2957120215",
+      "660a5285054b439496a555cdfef285b8",
+      "1aa35c99719544d995629b2c797b6813",
+      "da770238262140a881a3ba9f7c9a6187",
+      "7a55e9a929c7489fbed6a3fd970c0621",
+      "d5e2ca12c98b4cb1b7e7f869ee1e549d",
+      "57c72b3beb6c422690e0be7ba8c583c5"
+     ]
+    },
+    "id": "MDRWI5Psb85g",
+    "outputId": "e66ec3d6-53d4-4e74-d6da-01a5f285ea98"
+   },
+   "source": [
+    "from textattack.datasets import HuggingFaceDataset\n",
+    "from textattack.attack_recipes import TextBuggerLi2018\n",
+    "from textattack.attacker import Attacker\n",
+    "\n",
+    "\n",
+    "dataset = HuggingFaceDataset(\"glue\", \"sst2\", \"train\")\n",
+    "attack = TextBuggerLi2018.build(model_wrapper)\n",
+    "\n",
+    "attacker = Attacker(attack, dataset)\n",
+    "attacker.attack_dataset()"
+   ],
+   "execution_count": 9,
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JPVBc5ndpFIX"
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b448a4eedc844ef840ca70aa997d02b",
+       "version_minor": 0,
+       "version_major": 2
       },
-      "source": [
-        "# TextAttack & AllenNLP \n",
-        "\n",
-        "This is an example of testing adversarial attacks from TextAttack on pretrained models provided by AllenNLP. \n",
-        "\n",
-        "In a few lines of code, we load a sentiment analysis model trained on the Stanford Sentiment Treebank and configure it with a TextAttack model wrapper. Then, we initialize the TextBugger attack and run the attack on a few samples from the SST-2 train set.\n",
-        "\n",
-        "For more information on AllenNLP pre-trained models: https://docs.allennlp.org/models/main/\n",
-        "\n",
-        "For more information about the TextBugger attack: https://arxiv.org/abs/1812.05271"
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/7.78k [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "AyPMGcz0qLfK"
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cc82d8fd98749e7b160ac4dae04c9d8",
+       "version_minor": 0,
+       "version_major": 2
       },
-      "source": [
-        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QData/TextAttack/blob/master/docs/2notebook/Example_2_allennlp.ipynb)\n",
-        "\n",
-        "[![View Source on GitHub](https://img.shields.io/badge/github-view%20source-black.svg)](https://github.com/QData/TextAttack/blob/master/docs/2notebook/Example_2_allennlp.ipynb)"
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/4.47k [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "My9oy5iBSKfb"
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Downloading and preparing dataset glue/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown size, total: 11.90 MiB) to /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af00f37ede9e45f0a59fdf54711cf985",
+       "version_minor": 0,
+       "version_major": 2
       },
-      "source": [
-        "!pip install allennlp allennlp_models > /dev/null"
-      ],
-      "execution_count": 4,
-      "outputs": []
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/7.44M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "id": "z8wAb0BcSg8W",
-        "outputId": "8cc26ced-6f03-433c-97d2-72037c606fde",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "424af94826664dc1a8b38f252c4e047f",
+       "version_minor": 0,
+       "version_major": 2
       },
-      "source": [
-        "!pip3 install textattack[tensorflow]"
-      ],
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Requirement already satisfied: textattack[tensorflow] in /usr/local/lib/python3.7/dist-packages (0.3.3)\n",
-            "Requirement already satisfied: language-tool-python in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (2.6.1)\n",
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-            "Requirement already satisfied: numpy>=1.19.2 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.19.5)\n",
-            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.4.1)\n",
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-            "Requirement already satisfied: pandas>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.1.5)\n",
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-            "Requirement already satisfied: bert-score>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (0.3.10)\n",
-            "Requirement already satisfied: torch!=1.8,>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.9.0+cu111)\n",
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-            "Requirement already satisfied: flair in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (0.9)\n",
-            "Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (1.11.0)\n",
-            "Requirement already satisfied: tensorflow>=2 in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (2.6.0)\n",
-            "Requirement already satisfied: tensorflow-hub in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (0.12.0)\n",
-            "Requirement already satisfied: tensorboardX in /usr/local/lib/python3.7/dist-packages (from textattack[tensorflow]) (2.4)\n",
-            "Collecting tensorflow-text>=2\n",
-            "  Downloading tensorflow_text-2.6.0-cp37-cp37m-manylinux1_x86_64.whl (4.4 MB)\n",
-            "\u001b[K     |████████████████████████████████| 4.4 MB 5.4 MB/s \n",
-            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from bert-score>=0.3.5->textattack[tensorflow]) (2.26.0)\n",
-            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from bert-score>=0.3.5->textattack[tensorflow]) (3.2.2)\n",
-            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from bert-score>=0.3.5->textattack[tensorflow]) (21.0)\n",
-            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->bert-score>=0.3.5->textattack[tensorflow]) (2.4.7)\n",
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-            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.1->textattack[tensorflow]) (2018.9)\n",
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-            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (0.4.6)\n",
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-            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->bert-score>=0.3.5->textattack[tensorflow]) (1.25.11)\n",
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-            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->bert-score>=0.3.5->textattack[tensorflow]) (2021.5.30)\n",
-            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (3.1.1)\n",
-            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers>=3.3.0->textattack[tensorflow]) (0.0.46)\n",
-            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers>=3.3.0->textattack[tensorflow]) (2019.12.20)\n",
-            "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers>=3.3.0->textattack[tensorflow]) (0.10.3)\n",
-            "Requirement already satisfied: xxhash in /usr/local/lib/python3.7/dist-packages (from datasets->textattack[tensorflow]) (2.0.2)\n",
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-            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets->textattack[tensorflow]) (0.70.12.2)\n",
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-            "Requirement already satisfied: conllu>=4.0 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (4.4.1)\n",
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-            "Requirement already satisfied: sqlitedict>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (1.7.0)\n",
-            "Requirement already satisfied: mpld3==0.3 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (0.3)\n",
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-            "Requirement already satisfied: gensim<=3.8.3,>=3.4.0 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (3.6.0)\n",
-            "Requirement already satisfied: sentencepiece==0.1.95 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (0.1.95)\n",
-            "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (0.8.9)\n",
-            "Requirement already satisfied: wikipedia-api in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (0.5.4)\n",
-            "Requirement already satisfied: deprecated>=1.2.4 in /usr/local/lib/python3.7/dist-packages (from flair->textattack[tensorflow]) (1.2.13)\n",
-            "Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from gensim<=3.8.3,>=3.4.0->flair->textattack[tensorflow]) (5.2.1)\n",
-            "Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.1.1->flair->textattack[tensorflow]) (2.6.3)\n",
-            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.1.1->flair->textattack[tensorflow]) (0.16.0)\n",
-            "Requirement already satisfied: pymongo in /usr/local/lib/python3.7/dist-packages (from hyperopt>=0.1.1->flair->textattack[tensorflow]) (3.12.0)\n",
-            "Requirement already satisfied: overrides<4.0.0,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from konoha<5.0.0,>=4.0.0->flair->textattack[tensorflow]) (3.1.0)\n",
-            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard~=2.6->tensorflow>=2->textattack[tensorflow]) (3.6.0)\n",
-            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->bert-score>=0.3.5->textattack[tensorflow]) (1.3.2)\n",
-            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->bert-score>=0.3.5->textattack[tensorflow]) (0.10.0)\n",
-            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->flair->textattack[tensorflow]) (1.0.1)\n",
-            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from ftfy->flair->textattack[tensorflow]) (0.2.5)\n",
-            "Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.7/dist-packages (from num2words->textattack[tensorflow]) (0.6.2)\n",
-            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers>=3.3.0->textattack[tensorflow]) (7.1.2)\n",
-            "Installing collected packages: tensorflow-text\n",
-            "Successfully installed tensorflow-text-2.6.0\n"
-          ]
-        }
+      "text/plain": [
+       "0 examples [00:00, ? examples/s]"
       ]
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "_br6Xvsif9SA",
-        "outputId": "1025399f-9f63-4d9a-e854-e4f92ba24c45"
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6ad5b1ec3f64ddbbf0d3bdb6d567658",
+       "version_minor": 0,
+       "version_major": 2
       },
-      "source": [
-        "from allennlp.predictors import Predictor\n",
-        "import allennlp_models.classification\n",
-        "\n",
-        "import textattack\n",
-        "\n",
-        "class AllenNLPModel(textattack.models.wrappers.ModelWrapper):\n",
-        "    def __init__(self):\n",
-        "        self.predictor = Predictor.from_path(\"https://storage.googleapis.com/allennlp-public-models/basic_stanford_sentiment_treebank-2020.06.09.tar.gz\")\n",
-        "        self.model = self.predictor._model\n",
-        "        self.tokenizer = self.predictor._dataset_reader._tokenizer\n",
-        "\n",
-        "    def __call__(self, text_input_list):\n",
-        "        outputs = []\n",
-        "        for text_input in text_input_list:\n",
-        "            outputs.append(self.predictor.predict(sentence=text_input))\n",
-        "        # For each output, outputs['logits'] contains the logits where\n",
-        "        # index 0 corresponds to the positive and index 1 corresponds \n",
-        "        # to the negative score. We reverse the outputs (by reverse slicing,\n",
-        "        # [::-1]) so that negative comes first and positive comes second.\n",
-        "        return [output['logits'][::-1] for output in outputs]\n",
-        "\n",
-        "model_wrapper = AllenNLPModel()"
-      ],
-      "execution_count": 8,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "textattack: Updating TextAttack package dependencies.\n",
-            "textattack: Downloading NLTK required packages.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "[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]   Unzipping corpora/omw.zip.\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]   Unzipping corpora/wordnet.zip.\n",
-            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
-            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "textattack: Downloading https://textattack.s3.amazonaws.com/word_embeddings/paragramcf.\n",
-            "100%|██████████| 481M/481M [00:14<00:00, 33.6MB/s]\n",
-            "textattack: Unzipping file /root/.cache/textattack/tmp7xfefu5f.zip to /root/.cache/textattack/word_embeddings/paragramcf.\n",
-            "textattack: Successfully saved word_embeddings/paragramcf to cache.\n",
-            "Plugin allennlp_models could not be loaded: No module named 'nltk.translate.meteor_score'\n",
-            "downloading: 100%|##########| 37033341/37033341 [00:01<00:00, 27735821.99B/s]\n"
-          ]
-        }
+      "text/plain": [
+       "0 examples [00:00, ? examples/s]"
       ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a0bd608a44944fda41042d07d54b076",
+       "version_minor": 0,
+       "version_major": 2
+      },
+      "text/plain": [
+       "0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Dataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000,
-          "referenced_widgets": [
-            "6b448a4eedc844ef840ca70aa997d02b",
-            "bd686416d53a4d88b3ae1e357c4f0e71",
-            "3b3da3896eca40caac9561b1979c90ba",
-            "47c06887d2aa477a820737eda5fb3ad4",
-            "aaed99b5432b47508d5090a8df7c24bc",
-            "f59ffe8c7da14da08c861235cf2d9ea7",
-            "869e01668ff342178f40f385a0bc3366",
-            "266f90eabfea46e1ae5ee4bc22f711ee",
-            "dff48b2efd70497ba4b28ca6bd1499d9",
-            "6439f6674b484b14b4e9bf21497efc56",
-            "45c77d8e79d14fffab433e78b86048ce",
-            "2cc82d8fd98749e7b160ac4dae04c9d8",
-            "60b5c7c86aa94936b06981c65b9db3e8",
-            "d26fbb35af5f45d7ad75977ea9c5ffad",
-            "9e0287b81c6f45a386858de9c8e8735e",
-            "b60a716e37964537b122ce1116e002d0",
-            "516f9277541e4c199a2fa125a75f8bdb",
-            "6f0f652f722f4827ba1eab9fb081d8d2",
-            "fd5086beccb6431fa907d90d7168f79f",
-            "daa2c6454a704b84bd7e2525a52dba0c",
-            "f7f132d7b56b4bb9b950b09ad27ca115",
-            "fa983f4a063b4a83856b5d219e3ed04b",
-            "af00f37ede9e45f0a59fdf54711cf985",
-            "d88766c2c3bc4e7e83530b7ae6647ffd",
-            "38c0d380e8ac4290b6d82979d0bb131a",
-            "c93c7594ee084a4283144933bfcafefd",
-            "0150a0c8ed674464874ba83453e0ddbd",
-            "0c8e9d22a63644cd88dd5aa7ba08a21f",
-            "603c23100ac54d01ada1ebbba7bb5fc0",
-            "25f91f41a1de48498ebd248a3cce85a8",
-            "d1b6bdc47c544e84ae3ba3b584c7afa3",
-            "138cf20c691d4e9784adefea3ceecd1d",
-            "5d02bf542c7c4c289722e03e56f5d56c",
-            "424af94826664dc1a8b38f252c4e047f",
-            "6e779ad14425452aa70f0efbf40f99b4",
-            "264ab6ca60db4c29ad45830ab9de40ef",
-            "627d891d68474869a38e1801afe63b89",
-            "48ac05c8ff36473e8896be8a47d876a4",
-            "eef891ab6af04cd8ad39e50109c15bba",
-            "743a844b407e41e7b9a84cc5feb1b7d0",
-            "d2c4e06c58174175baa80d3c316dcc09",
-            "5bd2eabf4cd343cf8a6056e8535d3150",
-            "2accb80f9e0440328199a278739c2d67",
-            "cb1c32ecba014b84bf5832fff6732526",
-            "f6ad5b1ec3f64ddbbf0d3bdb6d567658",
-            "af907b5540244cd4a38d9deeccbba57a",
-            "0a273d61ed62463daee40739cd52ae28",
-            "f1c36b2e6651488b900ea7659a10ff4c",
-            "519fb7f7926c4a31a561153deec61bc1",
-            "5e009457ac3d4ef5a3b3fb6560b3c80f",
-            "d8a9cfa29033467c8201007c05897627",
-            "b8640741b3404eb2956a5b60a377db06",
-            "810727ad014a42aa8788a03578b8ee52",
-            "2c2f360da4a64a0b8f21a28774ede852",
-            "36a2c00fefb64582b09eca3c02a33956",
-            "2a0bd608a44944fda41042d07d54b076",
-            "6f5f6167aba247458fa8371416cd27d1",
-            "182aa21c1cab4699b21c89babf8b92ab",
-            "37816bf4761c448c9bde942c8a7e4c7e",
-            "c921586e8995495f8c8313da78382ff7",
-            "fcca6c8857ea4edfbda08ed390747ad8",
-            "83b50dba703b4eb6a3f488083a341dbc",
-            "530f8a34716e4790b161f578ca592602",
-            "ecd1834382af47f48ccaed3d3e13b348",
-            "4632d5700b7a4180b4ebef6ed36019c1",
-            "40f8c3c973034a7288156a727d84e1fc",
-            "73a5417a077f4f7e82e7f11d7f4fefba",
-            "787e669ab19f4b3694b7560dd9012b68",
-            "3221c018b6604cada04f2710fd00e750",
-            "068d9c59920d48b188b7e52c9117b6e6",
-            "bf95592be6084cb782f11e2957120215",
-            "660a5285054b439496a555cdfef285b8",
-            "1aa35c99719544d995629b2c797b6813",
-            "da770238262140a881a3ba9f7c9a6187",
-            "7a55e9a929c7489fbed6a3fd970c0621",
-            "d5e2ca12c98b4cb1b7e7f869ee1e549d",
-            "57c72b3beb6c422690e0be7ba8c583c5"
-          ]
-        },
-        "id": "MDRWI5Psb85g",
-        "outputId": "e66ec3d6-53d4-4e74-d6da-01a5f285ea98"
+     "output_type": "display_data",
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73a5417a077f4f7e82e7f11d7f4fefba",
+       "version_minor": 0,
+       "version_major": 2
       },
-      "source": [
-        "from textattack.datasets import HuggingFaceDataset\n",
-        "from textattack.attack_recipes import TextBuggerLi2018\n",
-        "from textattack.attacker import Attacker\n",
-        "\n",
-        "\n",
-        "dataset = HuggingFaceDataset(\"glue\", \"sst2\", \"train\")\n",
-        "attack = TextBuggerLi2018.build(model_wrapper)\n",
-        "\n",
-        "attacker = Attacker(attack, dataset)\n",
-        "attacker.attack_dataset()"
-      ],
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "6b448a4eedc844ef840ca70aa997d02b",
-              "version_minor": 0,
-              "version_major": 2
-            },
-            "text/plain": [
-              "Downloading:   0%|          | 0.00/7.78k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "2cc82d8fd98749e7b160ac4dae04c9d8",
-              "version_minor": 0,
-              "version_major": 2
-            },
-            "text/plain": [
-              "Downloading:   0%|          | 0.00/4.47k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Downloading and preparing dataset glue/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown size, total: 11.90 MiB) to /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "af00f37ede9e45f0a59fdf54711cf985",
-              "version_minor": 0,
-              "version_major": 2
-            },
-            "text/plain": [
-              "Downloading:   0%|          | 0.00/7.44M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "424af94826664dc1a8b38f252c4e047f",
-              "version_minor": 0,
-              "version_major": 2
-            },
-            "text/plain": [
-              "0 examples [00:00, ? examples/s]"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "f6ad5b1ec3f64ddbbf0d3bdb6d567658",
-              "version_minor": 0,
-              "version_major": 2
-            },
-            "text/plain": [
-              "0 examples [00:00, ? examples/s]"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "2a0bd608a44944fda41042d07d54b076",
-              "version_minor": 0,
-              "version_major": 2
-            },
-            "text/plain": [
-              "0 examples [00:00, ? examples/s]"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Dataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "73a5417a077f4f7e82e7f11d7f4fefba",
-              "version_minor": 0,
-              "version_major": 2
-            },
-            "text/plain": [
-              "  0%|          | 0/3 [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "textattack: Loading \u001b[94mdatasets\u001b[0m dataset \u001b[94mglue\u001b[0m, subset \u001b[94msst2\u001b[0m, split \u001b[94mtrain\u001b[0m.\n",
-            "textattack: Unknown if model of class <class 'allennlp.models.basic_classifier.BasicClassifier'> compatible with goal function <class 'textattack.goal_functions.classification.untargeted_classification.UntargetedClassification'>.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Attack(\n",
-            "  (search_method): GreedyWordSwapWIR(\n",
-            "    (wir_method):  delete\n",
-            "  )\n",
-            "  (goal_function):  UntargetedClassification\n",
-            "  (transformation):  CompositeTransformation(\n",
-            "    (0): WordSwapRandomCharacterInsertion(\n",
-            "        (random_one):  True\n",
-            "      )\n",
-            "    (1): WordSwapRandomCharacterDeletion(\n",
-            "        (random_one):  True\n",
-            "      )\n",
-            "    (2): WordSwapNeighboringCharacterSwap(\n",
-            "        (random_one):  True\n",
-            "      )\n",
-            "    (3): WordSwapHomoglyphSwap\n",
-            "    (4): WordSwapEmbedding(\n",
-            "        (max_candidates):  5\n",
-            "        (embedding):  WordEmbedding\n",
-            "      )\n",
-            "    )\n",
-            "  (constraints): \n",
-            "    (0): UniversalSentenceEncoder(\n",
-            "        (metric):  angular\n",
-            "        (threshold):  0.8\n",
-            "        (window_size):  inf\n",
-            "        (skip_text_shorter_than_window):  False\n",
-            "        (compare_against_original):  True\n",
-            "      )\n",
-            "    (1): RepeatModification\n",
-            "    (2): StopwordModification\n",
-            "  (is_black_box):  True\n",
-            ") \n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\r  0%|          | 0/10 [00:00<?, ?it/s]Using /tmp/tfhub_modules to cache modules.\n",
-            "Downloading TF-Hub Module 'https://tfhub.dev/google/universal-sentence-encoder/4'.\n",
-            "Downloaded https://tfhub.dev/google/universal-sentence-encoder/4, Total size: 987.47MB\n",
-            "Downloaded TF-Hub Module 'https://tfhub.dev/google/universal-sentence-encoder/4'.\n",
-            "[Succeeded / Failed / Skipped / Total] 1 / 1 / 0 / 2:  20%|██        | 2/10 [01:27<05:48, 43.58s/it]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 1 ---------------------------------------------\n",
-            "[[Negative (95%)]] --> [[Positive (93%)]]\n",
-            "\n",
-            "[[hide]] new secretions from the parental units \n",
-            "\n",
-            "[[concealing]] new secretions from the parental units \n",
-            "\n",
-            "\n",
-            "--------------------------------------------- Result 2 ---------------------------------------------\n",
-            "[[Negative (96%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "contains no wit , only labored gags \n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[Succeeded / Failed / Skipped / Total] 1 / 2 / 1 / 4:  40%|████      | 4/10 [01:27<02:11, 21.91s/it]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 3 ---------------------------------------------\n",
-            "[[Positive (100%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "that loves its characters and communicates something rather beautiful about human nature \n",
-            "\n",
-            "\n",
-            "--------------------------------------------- Result 4 ---------------------------------------------\n",
-            "[[Positive (82%)]] --> [[[SKIPPED]]]\n",
-            "\n",
-            "remains utterly satisfied to remain the same throughout \n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[Succeeded / Failed / Skipped / Total] 1 / 3 / 1 / 5:  50%|█████     | 5/10 [01:28<01:28, 17.62s/it]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 5 ---------------------------------------------\n",
-            "[[Negative (98%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "on the worst revenge-of-the-nerds clichés the filmmakers could dredge up \n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\r[Succeeded / Failed / Skipped / Total] 1 / 4 / 1 / 6:  60%|██████    | 6/10 [01:28<00:59, 14.75s/it]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 6 ---------------------------------------------\n",
-            "[[Negative (99%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "that 's far too tragic to merit such superficial treatment \n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[Succeeded / Failed / Skipped / Total] 2 / 5 / 1 / 8:  80%|████████  | 8/10 [01:29<00:22, 11.24s/it]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 7 ---------------------------------------------\n",
-            "[[Positive (98%)]] --> [[Negative (62%)]]\n",
-            "\n",
-            "[[demonstrates]] that the [[director]] of such [[hollywood]] blockbusters as patriot games can still [[turn]] out a [[small]] , personal [[film]] with an emotional [[wallop]] . \n",
-            "\n",
-            "[[shows]] that the [[directors]] of such [[tinseltown]] blockbusters as patriot games can still [[turning]] out a [[tiny]] , personal [[movies]] with an emotional [[batting]] . \n",
-            "\n",
-            "\n",
-            "--------------------------------------------- Result 8 ---------------------------------------------\n",
-            "[[Positive (90%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "of saucy \n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[Succeeded / Failed / Skipped / Total] 2 / 6 / 1 / 9:  90%|█████████ | 9/10 [01:30<00:10, 10.03s/it]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 9 ---------------------------------------------\n",
-            "[[Negative (99%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "a depressed fifteen-year-old 's suicidal poetry \n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[Succeeded / Failed / Skipped / Total] 3 / 6 / 1 / 10: 100%|██████████| 10/10 [01:30<00:00,  9.05s/it]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 10 ---------------------------------------------\n",
-            "[[Positive (79%)]] --> [[Negative (65%)]]\n",
-            "\n",
-            "are more [[deeply]] thought through than in most ` right-thinking ' films \n",
-            "\n",
-            "are more [[seriously]] thought through than in most ` right-thinking ' films \n",
-            "\n",
-            "\n",
-            "\n",
-            "+-------------------------------+--------+\n",
-            "| Attack Results                |        |\n",
-            "+-------------------------------+--------+\n",
-            "| Number of successful attacks: | 3      |\n",
-            "| Number of failed attacks:     | 6      |\n",
-            "| Number of skipped attacks:    | 1      |\n",
-            "| Original accuracy:            | 90.0%  |\n",
-            "| Accuracy under attack:        | 60.0%  |\n",
-            "| Attack success rate:          | 33.33% |\n",
-            "| Average perturbed word %:     | 17.94% |\n",
-            "| Average num. words per input: | 9.5    |\n",
-            "| Avg num queries:              | 35.11  |\n",
-            "+-------------------------------+--------+\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "[<textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fab125a1810>,\n",
-              " <textattack.attack_results.failed_attack_result.FailedAttackResult at 0x7fab19420c10>,\n",
-              " <textattack.attack_results.failed_attack_result.FailedAttackResult at 0x7fab0bb0f4d0>,\n",
-              " <textattack.attack_results.skipped_attack_result.SkippedAttackResult at 0x7fab0f3cd610>,\n",
-              " <textattack.attack_results.failed_attack_result.FailedAttackResult at 0x7fab192d4790>,\n",
-              " <textattack.attack_results.failed_attack_result.FailedAttackResult at 0x7fab0b7ba190>,\n",
-              " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fab0bd2ee90>,\n",
-              " <textattack.attack_results.failed_attack_result.FailedAttackResult at 0x7fab09731590>,\n",
-              " <textattack.attack_results.failed_attack_result.FailedAttackResult at 0x7fab097e9610>,\n",
-              " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fab13265450>]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 9
-        }
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "textattack: Loading \u001b[94mdatasets\u001b[0m dataset \u001b[94mglue\u001b[0m, subset \u001b[94msst2\u001b[0m, split \u001b[94mtrain\u001b[0m.\n",
+      "textattack: Unknown if model of class <class 'allennlp.models.basic_classifier.BasicClassifier'> compatible with goal function <class 'textattack.goal_functions.classification.untargeted_classification.UntargetedClassification'>.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Attack(\n",
+      "  (search_method): GreedyWordSwapWIR(\n",
+      "    (wir_method):  delete\n",
+      "  )\n",
+      "  (goal_function):  UntargetedClassification\n",
+      "  (transformation):  CompositeTransformation(\n",
+      "    (0): WordSwapRandomCharacterInsertion(\n",
+      "        (random_one):  True\n",
+      "      )\n",
+      "    (1): WordSwapRandomCharacterDeletion(\n",
+      "        (random_one):  True\n",
+      "      )\n",
+      "    (2): WordSwapNeighboringCharacterSwap(\n",
+      "        (random_one):  True\n",
+      "      )\n",
+      "    (3): WordSwapHomoglyphSwap\n",
+      "    (4): WordSwapEmbedding(\n",
+      "        (max_candidates):  5\n",
+      "        (embedding):  WordEmbedding\n",
+      "      )\n",
+      "    )\n",
+      "  (constraints): \n",
+      "    (0): UniversalSentenceEncoder(\n",
+      "        (metric):  angular\n",
+      "        (threshold):  0.8\n",
+      "        (window_size):  inf\n",
+      "        (skip_text_shorter_than_window):  False\n",
+      "        (compare_against_original):  True\n",
+      "      )\n",
+      "    (1): RepeatModification\n",
+      "    (2): StopwordModification\n",
+      "  (is_black_box):  True\n",
+      ") \n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\r  0%|          | 0/10 [00:00<?, ?it/s]Using /tmp/tfhub_modules to cache modules.\n",
+      "Downloading TF-Hub Module 'https://tfhub.dev/google/universal-sentence-encoder/4'.\n",
+      "Downloaded https://tfhub.dev/google/universal-sentence-encoder/4, Total size: 987.47MB\n",
+      "Downloaded TF-Hub Module 'https://tfhub.dev/google/universal-sentence-encoder/4'.\n",
+      "[Succeeded / Failed / Skipped / Total] 1 / 1 / 0 / 2:  20%|██        | 2/10 [01:27<05:48, 43.58s/it]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 1 ---------------------------------------------\n",
+      "[[Negative (95%)]] --> [[Positive (93%)]]\n",
+      "\n",
+      "[[hide]] new secretions from the parental units \n",
+      "\n",
+      "[[concealing]] new secretions from the parental units \n",
+      "\n",
+      "\n",
+      "--------------------------------------------- Result 2 ---------------------------------------------\n",
+      "[[Negative (96%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "contains no wit , only labored gags \n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[Succeeded / Failed / Skipped / Total] 1 / 2 / 1 / 4:  40%|████      | 4/10 [01:27<02:11, 21.91s/it]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 3 ---------------------------------------------\n",
+      "[[Positive (100%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "that loves its characters and communicates something rather beautiful about human nature \n",
+      "\n",
+      "\n",
+      "--------------------------------------------- Result 4 ---------------------------------------------\n",
+      "[[Positive (82%)]] --> [[[SKIPPED]]]\n",
+      "\n",
+      "remains utterly satisfied to remain the same throughout \n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[Succeeded / Failed / Skipped / Total] 1 / 3 / 1 / 5:  50%|█████     | 5/10 [01:28<01:28, 17.62s/it]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 5 ---------------------------------------------\n",
+      "[[Negative (98%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "on the worst revenge-of-the-nerds clichés the filmmakers could dredge up \n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\r[Succeeded / Failed / Skipped / Total] 1 / 4 / 1 / 6:  60%|██████    | 6/10 [01:28<00:59, 14.75s/it]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 6 ---------------------------------------------\n",
+      "[[Negative (99%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "that 's far too tragic to merit such superficial treatment \n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[Succeeded / Failed / Skipped / Total] 2 / 5 / 1 / 8:  80%|████████  | 8/10 [01:29<00:22, 11.24s/it]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 7 ---------------------------------------------\n",
+      "[[Positive (98%)]] --> [[Negative (62%)]]\n",
+      "\n",
+      "[[demonstrates]] that the [[director]] of such [[hollywood]] blockbusters as patriot games can still [[turn]] out a [[small]] , personal [[film]] with an emotional [[wallop]] . \n",
+      "\n",
+      "[[shows]] that the [[directors]] of such [[tinseltown]] blockbusters as patriot games can still [[turning]] out a [[tiny]] , personal [[movies]] with an emotional [[batting]] . \n",
+      "\n",
+      "\n",
+      "--------------------------------------------- Result 8 ---------------------------------------------\n",
+      "[[Positive (90%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "of saucy \n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[Succeeded / Failed / Skipped / Total] 2 / 6 / 1 / 9:  90%|█████████ | 9/10 [01:30<00:10, 10.03s/it]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 9 ---------------------------------------------\n",
+      "[[Negative (99%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "a depressed fifteen-year-old 's suicidal poetry \n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[Succeeded / Failed / Skipped / Total] 3 / 6 / 1 / 10: 100%|██████████| 10/10 [01:30<00:00,  9.05s/it]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 10 ---------------------------------------------\n",
+      "[[Positive (79%)]] --> [[Negative (65%)]]\n",
+      "\n",
+      "are more [[deeply]] thought through than in most ` right-thinking ' films \n",
+      "\n",
+      "are more [[seriously]] thought through than in most ` right-thinking ' films \n",
+      "\n",
+      "\n",
+      "\n",
+      "+-------------------------------+--------+\n",
+      "| Attack Results                |        |\n",
+      "+-------------------------------+--------+\n",
+      "| Number of successful attacks: | 3      |\n",
+      "| Number of failed attacks:     | 6      |\n",
+      "| Number of skipped attacks:    | 1      |\n",
+      "| Original accuracy:            | 90.0%  |\n",
+      "| Accuracy under attack:        | 60.0%  |\n",
+      "| Attack success rate:          | 33.33% |\n",
+      "| Average perturbed word %:     | 17.94% |\n",
+      "| Average num. words per input: | 9.5    |\n",
+      "| Avg num queries:              | 35.11  |\n",
+      "+-------------------------------+--------+\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fab125a1810>,\n",
+       " <textattack.attack_results.failed_attack_result.FailedAttackResult at 0x7fab19420c10>,\n",
+       " <textattack.attack_results.failed_attack_result.FailedAttackResult at 0x7fab0bb0f4d0>,\n",
+       " <textattack.attack_results.skipped_attack_result.SkippedAttackResult at 0x7fab0f3cd610>,\n",
+       " <textattack.attack_results.failed_attack_result.FailedAttackResult at 0x7fab192d4790>,\n",
+       " <textattack.attack_results.failed_attack_result.FailedAttackResult at 0x7fab0b7ba190>,\n",
+       " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fab0bd2ee90>,\n",
+       " <textattack.attack_results.failed_attack_result.FailedAttackResult at 0x7fab09731590>,\n",
+       " <textattack.attack_results.failed_attack_result.FailedAttackResult at 0x7fab097e9610>,\n",
+       " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fab13265450>]"
       ]
+     },
+     "metadata": {},
+     "execution_count": 9
     }
-  ]
+   ]
+  }
+ ]
 }
\ No newline at end of file
diff --git a/docs/2notebook/Example_3_Keras.ipynb b/docs/2notebook/Example_3_Keras.ipynb
index b4df581f..af5a4e70 100644
--- a/docs/2notebook/Example_3_Keras.ipynb
+++ b/docs/2notebook/Example_3_Keras.ipynb
@@ -66,7 +66,7 @@
     "from keras.layers import Flatten\n",
     "from keras.layers import Dropout\n",
     "\n",
-    "from nltk.tokenize import word_tokenize, RegexpTokenizer\n"
+    "from nltk.tokenize import word_tokenize, RegexpTokenizer"
    ]
   },
   {
@@ -99,7 +99,6 @@
     }
    ],
    "source": [
-    "\n",
     "NUM_WORDS = 1000\n",
     "\n",
     "(x_train_tokens, y_train), (x_test_tokens, y_test) = tf.keras.datasets.imdb.load_data(\n",
@@ -110,19 +109,20 @@
     "    seed=113,\n",
     "    start_char=1,\n",
     "    oov_char=2,\n",
-    "    index_from=3\n",
+    "    index_from=3,\n",
     ")\n",
     "\n",
+    "\n",
     "def transform(x):\n",
-    "  x_transform = []\n",
-    "  for i, word_indices in enumerate(x):\n",
-    "    BoW_array = np.zeros((NUM_WORDS,))\n",
-    "    for index in word_indices:\n",
-    "      if index < len(BoW_array):\n",
-    "        BoW_array[index] += 1\n",
-    "    x_transform.append(BoW_array)\n",
-    "  return np.array(x_transform)\n",
-    "    \n",
+    "    x_transform = []\n",
+    "    for i, word_indices in enumerate(x):\n",
+    "        BoW_array = np.zeros((NUM_WORDS,))\n",
+    "        for index in word_indices:\n",
+    "            if index < len(BoW_array):\n",
+    "                BoW_array[index] += 1\n",
+    "        x_transform.append(BoW_array)\n",
+    "    return np.array(x_transform)\n",
+    "\n",
     "\n",
     "index = int(0.9 * len(x_train_tokens))\n",
     "x_train = transform(x_train_tokens)[:index]\n",
@@ -132,9 +132,7 @@
     "y_train = to_categorical(y_train)\n",
     "y_test = to_categorical(y_test)\n",
     "\n",
-    "vocabulary = tf.keras.datasets.imdb.get_word_index(\n",
-    "    path='imdb_word_index.json'\n",
-    ")"
+    "vocabulary = tf.keras.datasets.imdb.get_word_index(path=\"imdb_word_index.json\")"
    ]
   },
   {
@@ -202,30 +200,23 @@
     }
    ],
    "source": [
-    "#Model Created with Keras\n",
+    "# Model Created with Keras\n",
     "model = Sequential()\n",
-    "model.add(Dense(512, activation='relu', input_dim=NUM_WORDS))\n",
+    "model.add(Dense(512, activation=\"relu\", input_dim=NUM_WORDS))\n",
     "model.add(Dropout(0.3))\n",
-    "model.add(Dense(100, activation='relu'))\n",
-    "model.add(Dense(2, activation='sigmoid'))\n",
+    "model.add(Dense(100, activation=\"relu\"))\n",
+    "model.add(Dense(2, activation=\"sigmoid\"))\n",
     "opt = keras.optimizers.Adam(learning_rate=0.00001)\n",
     "\n",
-    "model.compile(\n",
-    " optimizer = opt,\n",
-    " loss = \"binary_crossentropy\",\n",
-    " metrics = [\"accuracy\"]\n",
-    ")\n",
+    "model.compile(optimizer=opt, loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
     "\n",
     "\n",
     "results = model.fit(\n",
-    " x_train, y_train,\n",
-    " epochs= 18,\n",
-    " batch_size = 512,\n",
-    " validation_data = (x_test, y_test)\n",
+    "    x_train, y_train, epochs=18, batch_size=512, validation_data=(x_test, y_test)\n",
     ")\n",
     "\n",
     "\n",
-    "print(results.history)\n"
+    "print(results.history)"
    ]
   },
   {
@@ -268,19 +259,19 @@
     "        self.model = model\n",
     "\n",
     "    def __call__(self, text_input_list):\n",
-    "      \n",
-    "      x_transform = []\n",
-    "      for i, review in enumerate(text_input_list):\n",
-    "        tokens = [x.strip(\",\") for x in review.split()]\n",
-    "        BoW_array = np.zeros((NUM_WORDS,))\n",
-    "        for word in tokens:\n",
-    "          if word in vocabulary:\n",
-    "            if vocabulary[word] < len(BoW_array):\n",
-    "              BoW_array[vocabulary[word]] += 1            \n",
-    "        x_transform.append(BoW_array)\n",
-    "      x_transform = np.array(x_transform)\n",
-    "      prediction = self.model.predict(x_transform)\n",
-    "      return prediction\n",
+    "\n",
+    "        x_transform = []\n",
+    "        for i, review in enumerate(text_input_list):\n",
+    "            tokens = [x.strip(\",\") for x in review.split()]\n",
+    "            BoW_array = np.zeros((NUM_WORDS,))\n",
+    "            for word in tokens:\n",
+    "                if word in vocabulary:\n",
+    "                    if vocabulary[word] < len(BoW_array):\n",
+    "                        BoW_array[vocabulary[word]] += 1\n",
+    "            x_transform.append(BoW_array)\n",
+    "        x_transform = np.array(x_transform)\n",
+    "        prediction = self.model.predict(x_transform)\n",
+    "        return prediction\n",
     "\n",
     "\n",
     "CustomKerasModelWrapper(model)([\"bad bad bad bad bad\", \"good good good good\"])"
diff --git a/docs/2notebook/Example_4_CamemBERT.ipynb b/docs/2notebook/Example_4_CamemBERT.ipynb
index 04744625..83268dd9 100644
--- a/docs/2notebook/Example_4_CamemBERT.ipynb
+++ b/docs/2notebook/Example_4_CamemBERT.ipynb
@@ -50,32 +50,35 @@
     "\n",
     "# Quiet TensorFlow.\n",
     "import os\n",
+    "\n",
     "if \"TF_CPP_MIN_LOG_LEVEL\" not in os.environ:\n",
     "    os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n",
     "\n",
     "\n",
     "class HuggingFaceSentimentAnalysisPipelineWrapper(ModelWrapper):\n",
-    "    \"\"\" Transformers sentiment analysis pipeline returns a list of responses\n",
-    "        like \n",
-    "        \n",
-    "            [{'label': 'POSITIVE', 'score': 0.7817379832267761}]\n",
-    "            \n",
-    "        We need to convert that to a format TextAttack understands, like\n",
-    "        \n",
-    "            [[0.218262017, 0.7817379832267761]\n",
+    "    \"\"\"Transformers sentiment analysis pipeline returns a list of responses\n",
+    "    like\n",
+    "\n",
+    "        [{'label': 'POSITIVE', 'score': 0.7817379832267761}]\n",
+    "\n",
+    "    We need to convert that to a format TextAttack understands, like\n",
+    "\n",
+    "        [[0.218262017, 0.7817379832267761]\n",
     "    \"\"\"\n",
+    "\n",
     "    def __init__(self, model):\n",
-    "        self.model = model#pipeline = pipeline\n",
+    "        self.model = model  # pipeline = pipeline\n",
+    "\n",
     "    def __call__(self, text_inputs):\n",
     "        raw_outputs = self.model(text_inputs)\n",
     "        outputs = []\n",
     "        for output in raw_outputs:\n",
-    "            score = output['score']\n",
-    "            if output['label'] == 'POSITIVE':\n",
-    "                outputs.append([1-score, score])\n",
+    "            score = output[\"score\"]\n",
+    "            if output[\"label\"] == \"POSITIVE\":\n",
+    "                outputs.append([1 - score, score])\n",
     "            else:\n",
-    "                outputs.append([score, 1-score])\n",
-    "        return np.array(outputs)\n"
+    "                outputs.append([score, 1 - score])\n",
+    "        return np.array(outputs)"
    ]
   },
   {
@@ -581,7 +584,7 @@
     "# see https://github.com/TheophileBlard/french-sentiment-analysis-with-bert\n",
     "model = TFAutoModelForSequenceClassification.from_pretrained(\"tblard/tf-allocine\")\n",
     "tokenizer = AutoTokenizer.from_pretrained(\"tblard/tf-allocine\")\n",
-    "pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)\n",
+    "pipeline = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer)\n",
     "\n",
     "model_wrapper = HuggingFaceSentimentAnalysisPipelineWrapper(pipeline)\n",
     "\n",
@@ -590,15 +593,15 @@
     "#\n",
     "# WordNet defaults to english. Set the default language to French ('fra')\n",
     "#\n",
-    "# See \"Building a free French wordnet from multilingual resources\", \n",
-    "# E. L. R. A. (ELRA) (ed.), \n",
+    "# See \"Building a free French wordnet from multilingual resources\",\n",
+    "# E. L. R. A. (ELRA) (ed.),\n",
     "# Proceedings of the Sixth International Language Resources and Evaluation (LREC’08).\n",
-    "recipe.transformation.language = 'fra'\n",
+    "recipe.transformation.language = \"fra\"\n",
     "\n",
-    "dataset = HuggingFaceDataset('allocine', split='test')\n",
+    "dataset = HuggingFaceDataset(\"allocine\", split=\"test\")\n",
     "\n",
     "attacker = Attacker(recipe, dataset)\n",
-    "attacker.attack_dataset()\n"
+    "attacker.attack_dataset()"
    ]
   }
  ],
diff --git a/docs/2notebook/Example_5_Explain_BERT.ipynb b/docs/2notebook/Example_5_Explain_BERT.ipynb
index f56e398d..a6316fdc 100644
--- a/docs/2notebook/Example_5_Explain_BERT.ipynb
+++ b/docs/2notebook/Example_5_Explain_BERT.ipynb
@@ -87,7 +87,7 @@
     }
    ],
    "source": [
-    "#Optional: Install dependency CAptum\n",
+    "# Optional: Install dependency CAptum\n",
     "!pip3 install captum"
    ]
   },
@@ -99,7 +99,14 @@
    },
    "outputs": [],
    "source": [
-    "from captum.attr import IntegratedGradients, LayerConductance, LayerIntegratedGradients, LayerDeepLiftShap, InternalInfluence, LayerGradientXActivation\n",
+    "from captum.attr import (\n",
+    "    IntegratedGradients,\n",
+    "    LayerConductance,\n",
+    "    LayerIntegratedGradients,\n",
+    "    LayerDeepLiftShap,\n",
+    "    InternalInfluence,\n",
+    "    LayerGradientXActivation,\n",
+    ")\n",
     "from captum.attr import visualization as viz"
    ]
   },
@@ -125,9 +132,9 @@
    "source": [
     "if torch.cuda.is_available():\n",
     "    device = torch.device(\"cuda:0\")\n",
-    "else: \n",
+    "else:\n",
     "    device = torch.device(\"cpu\")\n",
-    "    \n",
+    "\n",
     "print(device)"
    ]
   },
@@ -245,9 +252,13 @@
    ],
    "source": [
     "dataset = HuggingFaceDataset(\"ag_news\", None, \"train\")\n",
-    "original_model = AutoModelForSequenceClassification.from_pretrained(\"textattack/bert-base-uncased-ag-news\")\n",
-    "original_tokenizer = AutoTokenizer.from_pretrained(\"textattack/bert-base-uncased-ag-news\")\n",
-    "model = HuggingFaceModelWrapper(original_model,original_tokenizer)"
+    "original_model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "    \"textattack/bert-base-uncased-ag-news\"\n",
+    ")\n",
+    "original_tokenizer = AutoTokenizer.from_pretrained(\n",
+    "    \"textattack/bert-base-uncased-ag-news\"\n",
+    ")\n",
+    "model = HuggingFaceModelWrapper(original_model, original_tokenizer)"
    ]
   },
   {
@@ -258,45 +269,64 @@
    },
    "outputs": [],
    "source": [
-    "def get_text(tokenizer,input_ids,token_type_ids,attention_mask):\n",
+    "def get_text(tokenizer, input_ids, token_type_ids, attention_mask):\n",
     "    list_of_text = []\n",
     "    number = input_ids.size()[0]\n",
     "    for i in range(number):\n",
-    "        ii = input_ids[i,].cpu().numpy()\n",
-    "        tt = token_type_ids[i,]\n",
-    "        am = attention_mask[i,]\n",
+    "        ii = (\n",
+    "            input_ids[\n",
+    "                i,\n",
+    "            ]\n",
+    "            .cpu()\n",
+    "            .numpy()\n",
+    "        )\n",
+    "        tt = token_type_ids[\n",
+    "            i,\n",
+    "        ]\n",
+    "        am = attention_mask[\n",
+    "            i,\n",
+    "        ]\n",
     "        txt = tokenizer.decode(ii, skip_special_tokens=True)\n",
     "        list_of_text.append(txt)\n",
     "    return list_of_text\n",
-    "    \n",
-    "sel =2\n",
-    "batch_encoded = model.tokenizer([dataset[i][0]['text'] for i in range(sel)], padding=True, return_tensors=\"pt\")\n",
+    "\n",
+    "\n",
+    "sel = 2\n",
+    "batch_encoded = model.tokenizer(\n",
+    "    [dataset[i][0][\"text\"] for i in range(sel)], padding=True, return_tensors=\"pt\"\n",
+    ")\n",
     "batch_encoded.to(device)\n",
     "labels = [dataset[i][1] for i in range(sel)]\n",
     "\n",
     "clone = deepcopy(model)\n",
     "clone.model.to(device)\n",
     "\n",
-    "def calculate(input_ids,token_type_ids,attention_mask):\n",
-    "    #convert back to list of text\n",
-    "    return clone.model(input_ids,token_type_ids,attention_mask)[0]\n",
-    "    \n",
-    "# x = calculate(**batch_encoded)    \n",
+    "\n",
+    "def calculate(input_ids, token_type_ids, attention_mask):\n",
+    "    # convert back to list of text\n",
+    "    return clone.model(input_ids, token_type_ids, attention_mask)[0]\n",
+    "\n",
+    "\n",
+    "# x = calculate(**batch_encoded)\n",
     "\n",
     "lig = LayerIntegratedGradients(calculate, clone.model.bert.embeddings)\n",
     "# lig = InternalInfluence(calculate, clone.model.bert.embeddings)\n",
     "# lig = LayerGradientXActivation(calculate, clone.model.bert.embeddings)\n",
     "\n",
-    "bsl = torch.zeros(batch_encoded['input_ids'].size()).type(torch.LongTensor).to(device)\n",
+    "bsl = torch.zeros(batch_encoded[\"input_ids\"].size()).type(torch.LongTensor).to(device)\n",
     "labels = torch.tensor(labels).to(device)\n",
     "\n",
-    "attributions,delta = lig.attribute(inputs=batch_encoded['input_ids'],\n",
-    "                              baselines=bsl,\n",
-    "                              additional_forward_args=(batch_encoded['token_type_ids'], batch_encoded['attention_mask']),\n",
-    "                              n_steps = 10,\n",
-    "                              target = labels,\n",
-    "                              return_convergence_delta=True\n",
-    "                              )\n",
+    "attributions, delta = lig.attribute(\n",
+    "    inputs=batch_encoded[\"input_ids\"],\n",
+    "    baselines=bsl,\n",
+    "    additional_forward_args=(\n",
+    "        batch_encoded[\"token_type_ids\"],\n",
+    "        batch_encoded[\"attention_mask\"],\n",
+    "    ),\n",
+    "    n_steps=10,\n",
+    "    target=labels,\n",
+    "    return_convergence_delta=True,\n",
+    ")\n",
     "atts = attributions.sum(dim=-1).squeeze(0)\n",
     "atts = atts / torch.norm(atts)"
    ]
@@ -334,6 +364,7 @@
    ],
    "source": [
     "from textattack.attack_recipes import PWWSRen2019\n",
+    "\n",
     "attack = PWWSRen2019.build(model)"
    ]
   },
diff --git a/docs/2notebook/Example_6_Chinese_Attack.ipynb b/docs/2notebook/Example_6_Chinese_Attack.ipynb
index b032306c..66e93918 100644
--- a/docs/2notebook/Example_6_Chinese_Attack.ipynb
+++ b/docs/2notebook/Example_6_Chinese_Attack.ipynb
@@ -1,2258 +1,3073 @@
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-        "version": 3
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-      "file_extension": ".py",
-      "mimetype": "text/x-python",
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-        " Please remember to run the following in your notebook enviroment before running the tutorial codes:\n",
-        "\n",
-        "```\n",
-        "pip3 install textattack[tensorflow]\n",
-        "```\n",
-        "\n",
-        "\n",
-        "\n"
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-        "With a few additional modifications to the standard TextAttack commands, lanaguage models in Chinese can be attacked just as English models. Four transformations are available for either Chinese attack or augmentation:\n",
-        "\n",
-        "1. **ChineseHomophoneCharacterSwap**: transforms an input by replacing its words with substitions that share similar/identical pronounciation.\n",
-        "2. **ChineseMorphonymCharacterSwap**: transforms an input by replacing its words with substitions that share similar glyph structures.\n",
-        "3. **ChineseWordSwapHowNet**: transforms an input by replacing its words with synonyms provided by [OpenHownet](http://nlp.csai.tsinghua.edu.cn/).\n",
-        "4. **ChineseWordSwapMaskedLM**: transforms an input with potential replacements using a masked language model."
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-        "# Import required packages\n",
-        "import transformers\n",
-        "import string\n",
-        "import os\n",
-        "import pandas as pd\n",
-        "import datasets\n",
-        "\n",
-        "# Import classes required to build an Attacker\n",
-        "from textattack.models.wrappers import HuggingFaceModelWrapper\n",
-        "from textattack.search_methods import GreedyWordSwapWIR\n",
-        "from textattack.constraints.pre_transformation import RepeatModification, StopwordModification\n",
-        "from textattack.goal_functions import UntargetedClassification\n",
-        "\n",
-        "from textattack import Attack, Attacker, AttackArgs\n",
-        "from textattack.loggers import CSVLogger\n",
-        "from textattack.datasets import Dataset, HuggingFaceDataset\n",
-        "\n",
-        "# Import optional MUSE for higher quality examples\n",
-        "from textattack.constraints.semantics.sentence_encoders import MultilingualUniversalSentenceEncoder\n",
-        "muse = MultilingualUniversalSentenceEncoder(\n",
-        "    threshold=0.9,\n",
-        "    metric=\"cosine\",\n",
-        "    compare_against_original=True,\n",
-        "    window_size=15,\n",
-        "    skip_text_shorter_than_window=True,\n",
-        ")\n",
-        "\n",
-        "# Import the transformations\n",
-        "\n",
-        "from textattack.transformations import CompositeTransformation\n",
-        "from textattack.transformations import ChineseWordSwapMaskedLM\n",
-        "from textattack.transformations import ChineseMorphonymCharacterSwap\n",
-        "from textattack.transformations import ChineseWordSwapHowNet\n",
-        "from textattack.transformations import ChineseHomophoneCharacterSwap"
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+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m83IiqVREJ96"
+   },
+   "source": [
+    "# Chinese Attack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6UZ0d84hEJ98"
+   },
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QData/TextAttack/blob/master/docs/2notebook/Example_6_Chinese%20Attack.ipynb)\n",
+    "\n",
+    "\n",
+    "[![View Source on GitHub](https://img.shields.io/badge/github-view%20source-black.svg)](https://github.com/QData/TextAttack/blob/master/docs/2notebook/Example_6_Chinese%20Attack.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tjqc2c5_7YaX"
+   },
+   "source": [
+    " Please remember to run the following in your notebook enviroment before running the tutorial codes:\n",
+    "\n",
+    "```\n",
+    "pip3 install textattack[tensorflow]\n",
+    "```\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qZ5xnoevEJ99"
+   },
+   "source": [
+    "With a few additional modifications to the standard TextAttack commands, lanaguage models in Chinese can be attacked just as English models. Four transformations are available for either Chinese attack or augmentation:\n",
+    "\n",
+    "1. **ChineseHomophoneCharacterSwap**: transforms an input by replacing its words with substitions that share similar/identical pronounciation.\n",
+    "2. **ChineseMorphonymCharacterSwap**: transforms an input by replacing its words with substitions that share similar glyph structures.\n",
+    "3. **ChineseWordSwapHowNet**: transforms an input by replacing its words with synonyms provided by [OpenHownet](http://nlp.csai.tsinghua.edu.cn/).\n",
+    "4. **ChineseWordSwapMaskedLM**: transforms an input with potential replacements using a masked language model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We begin with imports:"
+   ],
+   "metadata": {
+    "id": "2EP1DJylSfkD"
+   }
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "5AXyxiLD4X93"
+   },
+   "source": [
+    "# Import required packages\n",
+    "import transformers\n",
+    "import string\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import datasets\n",
+    "\n",
+    "# Import classes required to build an Attacker\n",
+    "from textattack.models.wrappers import HuggingFaceModelWrapper\n",
+    "from textattack.search_methods import GreedyWordSwapWIR\n",
+    "from textattack.constraints.pre_transformation import (\n",
+    "    RepeatModification,\n",
+    "    StopwordModification,\n",
+    ")\n",
+    "from textattack.goal_functions import UntargetedClassification\n",
+    "\n",
+    "from textattack import Attack, Attacker, AttackArgs\n",
+    "from textattack.loggers import CSVLogger\n",
+    "from textattack.datasets import Dataset, HuggingFaceDataset\n",
+    "\n",
+    "# Import optional MUSE for higher quality examples\n",
+    "from textattack.constraints.semantics.sentence_encoders import (\n",
+    "    MultilingualUniversalSentenceEncoder,\n",
+    ")\n",
+    "\n",
+    "muse = MultilingualUniversalSentenceEncoder(\n",
+    "    threshold=0.9,\n",
+    "    metric=\"cosine\",\n",
+    "    compare_against_original=True,\n",
+    "    window_size=15,\n",
+    "    skip_text_shorter_than_window=True,\n",
+    ")\n",
+    "\n",
+    "# Import the transformations\n",
+    "\n",
+    "from textattack.transformations import CompositeTransformation\n",
+    "from textattack.transformations import ChineseWordSwapMaskedLM\n",
+    "from textattack.transformations import ChineseMorphonymCharacterSwap\n",
+    "from textattack.transformations import ChineseWordSwapHowNet\n",
+    "from textattack.transformations import ChineseHomophoneCharacterSwap"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Models and datasets would also need to be set up:"
+   ],
+   "metadata": {
+    "id": "1mSvCqhHSi0h"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# In this example, we will attack a pre-trained entailment model from HugginFace (https://huggingface.co/uer/roberta-base-finetuned-chinanews-chinese)\n",
+    "tokenizer = transformers.AutoTokenizer.from_pretrained(\n",
+    "    \"uer/roberta-base-finetuned-chinanews-chinese\"\n",
+    ")\n",
+    "model = transformers.AutoModelForSequenceClassification.from_pretrained(\n",
+    "    \"uer/roberta-base-finetuned-chinanews-chinese\"\n",
+    ")\n",
+    "model_wrapper = HuggingFaceModelWrapper(model, tokenizer)\n",
+    "\n",
+    "# Set goal function\n",
+    "goal_function = UntargetedClassification(model_wrapper, query_budget=10000)\n",
+    "\n",
+    "# Set dataset from which we will generate adversraial examples\n",
+    "path = os.path.abspath(\"\")\n",
+    "path_list = path.split(os.sep)\n",
+    "temppath = os.path.normpath(\"examples/dataset/zh_sentiment/entailment_dataset.tsv\")\n",
+    "dataset = datasets.load_dataset(\"csv\", data_files=temppath, delimiter=\"\\t\")[\"train\"]\n",
+    "dataset = HuggingFaceDataset(\n",
+    "    dataset,\n",
+    "    dataset_columns=([\"text\"], \"label\"),\n",
+    "    label_names=[\n",
+    "        \"Mainland China politics\",\n",
+    "        \"Hong Kong - Macau politics\",\n",
+    "        \"International news\",\n",
+    "        \"Financial news\",\n",
+    "        \"Culture\",\n",
+    "        \"Entertainment\",\n",
+    "        \"Sports\",\n",
+    "    ],\n",
+    ")"
+   ],
+   "metadata": {
+    "id": "CfnC9qUFPq9h"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "If this is your first time running Hownet, run this code block"
+   ],
+   "metadata": {
+    "id": "XfJVzCdRSr3d"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import OpenHowNet\n",
+    "\n",
+    "OpenHowNet.download()"
+   ],
+   "metadata": {
+    "id": "Hgal-PHeQwys"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "\n",
+    "\n",
+    "Now we are ready to attack! With goal function, transformation, constraints, search method, and goal function, we create the Attacker as any other TextAttack attacks\n"
+   ],
+   "metadata": {
+    "id": "SrtoxdrMSZ0X"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# transformation, using ChineseWordSwapMaskedLM transformation in this example\n",
+    "\n",
+    "transformation = ChineseWordSwapMaskedLM()\n",
+    "\n",
+    "# constraint\n",
+    "stopwords = set(\n",
+    "    [\n",
+    "        \"、\",\n",
+    "        \"。\",\n",
+    "        \"〈\",\n",
+    "        \"〉\",\n",
+    "        \"《\",\n",
+    "        \"》\",\n",
+    "        \"一\",\n",
+    "        \"一个\",\n",
+    "        \"一些\",\n",
+    "        \"一何\",\n",
+    "        \"一切\",\n",
+    "        \"一则\",\n",
+    "        \"一方面\",\n",
+    "        \"一旦\",\n",
+    "        \"一来\",\n",
+    "        \"一样\",\n",
+    "        \"一种\",\n",
+    "        \"一般\",\n",
+    "        \"一转眼\",\n",
+    "        \"七\",\n",
+    "        \"万一\",\n",
+    "        \"三\",\n",
+    "        \"上\",\n",
+    "        \"上下\",\n",
+    "        \"下\",\n",
+    "        \"不\",\n",
+    "        \"不仅\",\n",
+    "        \"不但\",\n",
+    "        \"不光\",\n",
+    "        \"不单\",\n",
+    "        \"不只\",\n",
+    "        \"不外乎\",\n",
+    "        \"不如\",\n",
+    "        \"不妨\",\n",
+    "        \"不尽\",\n",
+    "        \"不尽然\",\n",
+    "        \"不得\",\n",
+    "        \"不怕\",\n",
+    "        \"不惟\",\n",
+    "        \"不成\",\n",
+    "        \"不拘\",\n",
+    "        \"不料\",\n",
+    "        \"不是\",\n",
+    "        \"不比\",\n",
+    "        \"不然\",\n",
+    "        \"不特\",\n",
+    "        \"不独\",\n",
+    "        \"不管\",\n",
+    "        \"不至于\",\n",
+    "        \"不若\",\n",
+    "        \"不论\",\n",
+    "        \"不过\",\n",
+    "        \"不问\",\n",
+    "        \"与\",\n",
+    "        \"与其\",\n",
+    "        \"与其说\",\n",
+    "        \"与否\",\n",
+    "        \"与此同时\",\n",
+    "        \"且\",\n",
+    "        \"且不说\",\n",
+    "        \"且说\",\n",
+    "        \"两者\",\n",
+    "        \"个\",\n",
+    "        \"个别\",\n",
+    "        \"中\",\n",
+    "        \"临\",\n",
+    "        \"为\",\n",
+    "        \"为了\",\n",
+    "        \"为什么\",\n",
+    "        \"为何\",\n",
+    "        \"为止\",\n",
+    "        \"为此\",\n",
+    "        \"为着\",\n",
+    "        \"乃\",\n",
+    "        \"乃至\",\n",
+    "        \"乃至于\",\n",
+    "        \"么\",\n",
+    "        \"之\",\n",
+    "        \"之一\",\n",
+    "        \"之所以\",\n",
+    "        \"之类\",\n",
+    "        \"乌乎\",\n",
+    "        \"乎\",\n",
+    "        \"乘\",\n",
+    "        \"九\",\n",
+    "        \"也\",\n",
+    "        \"也好\",\n",
+    "        \"也罢\",\n",
+    "        \"了\",\n",
+    "        \"二\",\n",
+    "        \"二来\",\n",
+    "        \"于\",\n",
+    "        \"于是\",\n",
+    "        \"于是乎\",\n",
+    "        \"云云\",\n",
+    "        \"云尔\",\n",
+    "        \"五\",\n",
+    "        \"些\",\n",
+    "        \"亦\",\n",
+    "        \"人\",\n",
+    "        \"人们\",\n",
+    "        \"人家\",\n",
+    "        \"什\",\n",
+    "        \"什么\",\n",
+    "        \"什么样\",\n",
+    "        \"今\",\n",
+    "        \"介于\",\n",
+    "        \"仍\",\n",
+    "        \"仍旧\",\n",
+    "        \"从\",\n",
+    "        \"从此\",\n",
+    "        \"从而\",\n",
+    "        \"他\",\n",
+    "        \"他人\",\n",
+    "        \"他们\",\n",
+    "        \"他们们\",\n",
+    "        \"以\",\n",
+    "        \"以上\",\n",
+    "        \"以为\",\n",
+    "        \"以便\",\n",
+    "        \"以免\",\n",
+    "        \"以及\",\n",
+    "        \"以故\",\n",
+    "        \"以期\",\n",
+    "        \"以来\",\n",
+    "        \"以至\",\n",
+    "        \"以至于\",\n",
+    "        \"以致\",\n",
+    "        \"们\",\n",
+    "        \"任\",\n",
+    "        \"任何\",\n",
+    "        \"任凭\",\n",
+    "        \"会\",\n",
+    "        \"似的\",\n",
+    "        \"但\",\n",
+    "        \"但凡\",\n",
+    "        \"但是\",\n",
+    "        \"何\",\n",
+    "        \"何以\",\n",
+    "        \"何况\",\n",
+    "        \"何处\",\n",
+    "        \"何时\",\n",
+    "        \"余外\",\n",
+    "        \"作为\",\n",
+    "        \"你\",\n",
+    "        \"你们\",\n",
+    "        \"使\",\n",
+    "        \"使得\",\n",
+    "        \"例如\",\n",
+    "        \"依\",\n",
+    "        \"依据\",\n",
+    "        \"依照\",\n",
+    "        \"便于\",\n",
+    "        \"俺\",\n",
+    "        \"俺们\",\n",
+    "        \"倘\",\n",
+    "        \"倘使\",\n",
+    "        \"倘或\",\n",
+    "        \"倘然\",\n",
+    "        \"倘若\",\n",
+    "        \"借\",\n",
+    "        \"借傥然\",\n",
+    "        \"假使\",\n",
+    "        \"假如\",\n",
+    "        \"假若\",\n",
+    "        \"做\",\n",
+    "        \"像\",\n",
+    "        \"儿\",\n",
+    "        \"先不先\",\n",
+    "        \"光\",\n",
+    "        \"光是\",\n",
+    "        \"全体\",\n",
+    "        \"全部\",\n",
+    "        \"八\",\n",
+    "        \"六\",\n",
+    "        \"兮\",\n",
+    "        \"共\",\n",
+    "        \"关于\",\n",
+    "        \"关于具体地说\",\n",
+    "        \"其\",\n",
+    "        \"其一\",\n",
+    "        \"其中\",\n",
+    "        \"其二\",\n",
+    "        \"其他\",\n",
+    "        \"其余\",\n",
+    "        \"其它\",\n",
+    "        \"其次\",\n",
+    "        \"具体地说\",\n",
+    "        \"具体说来\",\n",
+    "        \"兼之\",\n",
+    "        \"内\",\n",
+    "        \"再\",\n",
+    "        \"再其次\",\n",
+    "        \"再则\",\n",
+    "        \"再有\",\n",
+    "        \"再者\",\n",
+    "        \"再者说\",\n",
+    "        \"再说\",\n",
+    "        \"冒\",\n",
+    "        \"冲\",\n",
+    "        \"况且\",\n",
+    "        \"几\",\n",
+    "        \"几时\",\n",
+    "        \"凡\",\n",
+    "        \"凡是\",\n",
+    "        \"凭\",\n",
+    "        \"凭借\",\n",
+    "        \"出于\",\n",
+    "        \"出来\",\n",
+    "        \"分\",\n",
+    "        \"分别\",\n",
+    "        \"则\",\n",
+    "        \"则甚\",\n",
+    "        \"别\",\n",
+    "        \"别人\",\n",
+    "        \"别处\",\n",
+    "        \"别是\",\n",
+    "        \"别的\",\n",
+    "        \"别管\",\n",
+    "        \"别说\",\n",
+    "        \"到\",\n",
+    "        \"前后\",\n",
+    "        \"前此\",\n",
+    "        \"前者\",\n",
+    "        \"加之\",\n",
+    "        \"加以\",\n",
+    "        \"区\",\n",
+    "        \"即\",\n",
+    "        \"即令\",\n",
+    "        \"即使\",\n",
+    "        \"即便\",\n",
+    "        \"即如\",\n",
+    "        \"即或\",\n",
+    "        \"即若\",\n",
+    "        \"却\",\n",
+    "        \"去\",\n",
+    "        \"又\",\n",
+    "        \"又及\",\n",
+    "        \"及\",\n",
+    "        \"及其\",\n",
+    "        \"及至\",\n",
+    "        \"反之\",\n",
+    "        \"反而\",\n",
+    "        \"反过来\",\n",
+    "        \"反过来说\",\n",
+    "        \"受到\",\n",
+    "        \"另\",\n",
+    "        \"另一方面\",\n",
+    "        \"另外\",\n",
+    "        \"另悉\",\n",
+    "        \"只\",\n",
+    "        \"只当\",\n",
+    "        \"只怕\",\n",
+    "        \"只是\",\n",
+    "        \"只有\",\n",
+    "        \"只消\",\n",
+    "        \"只要\",\n",
+    "        \"只限\",\n",
+    "        \"叫\",\n",
+    "        \"叮咚\",\n",
+    "        \"可\",\n",
+    "        \"可以\",\n",
+    "        \"可是\",\n",
+    "        \"可见\",\n",
+    "        \"各\",\n",
+    "        \"各个\",\n",
+    "        \"各位\",\n",
+    "        \"各种\",\n",
+    "        \"各自\",\n",
+    "        \"同\",\n",
+    "        \"同时\",\n",
+    "        \"后\",\n",
+    "        \"后者\",\n",
+    "        \"向\",\n",
+    "        \"向使\",\n",
+    "        \"向着\",\n",
+    "        \"吓\",\n",
+    "        \"吗\",\n",
+    "        \"否则\",\n",
+    "        \"吧\",\n",
+    "        \"吧哒\",\n",
+    "        \"含\",\n",
+    "        \"吱\",\n",
+    "        \"呀\",\n",
+    "        \"呃\",\n",
+    "        \"呕\",\n",
+    "        \"呗\",\n",
+    "        \"呜\",\n",
+    "        \"呜呼\",\n",
+    "        \"呢\",\n",
+    "        \"呵\",\n",
+    "        \"呵呵\",\n",
+    "        \"呸\",\n",
+    "        \"呼哧\",\n",
+    "        \"咋\",\n",
+    "        \"和\",\n",
+    "        \"咚\",\n",
+    "        \"咦\",\n",
+    "        \"咧\",\n",
+    "        \"咱\",\n",
+    "        \"咱们\",\n",
+    "        \"咳\",\n",
+    "        \"哇\",\n",
+    "        \"哈\",\n",
+    "        \"哈哈\",\n",
+    "        \"哉\",\n",
+    "        \"哎\",\n",
+    "        \"哎呀\",\n",
+    "        \"哎哟\",\n",
+    "        \"哗\",\n",
+    "        \"哟\",\n",
+    "        \"哦\",\n",
+    "        \"哩\",\n",
+    "        \"哪\",\n",
+    "        \"哪个\",\n",
+    "        \"哪些\",\n",
+    "        \"哪儿\",\n",
+    "        \"哪天\",\n",
+    "        \"哪年\",\n",
+    "        \"哪怕\",\n",
+    "        \"哪样\",\n",
+    "        \"哪边\",\n",
+    "        \"哪里\",\n",
+    "        \"哼\",\n",
+    "        \"哼唷\",\n",
+    "        \"唉\",\n",
+    "        \"唯有\",\n",
+    "        \"啊\",\n",
+    "        \"啐\",\n",
+    "        \"啥\",\n",
+    "        \"啦\",\n",
+    "        \"啪达\",\n",
+    "        \"啷当\",\n",
+    "        \"喂\",\n",
+    "        \"喏\",\n",
+    "        \"喔唷\",\n",
+    "        \"喽\",\n",
+    "        \"嗡\",\n",
+    "        \"嗡嗡\",\n",
+    "        \"嗬\",\n",
+    "        \"嗯\",\n",
+    "        \"嗳\",\n",
+    "        \"嘎\",\n",
+    "        \"嘎登\",\n",
+    "        \"嘘\",\n",
+    "        \"嘛\",\n",
+    "        \"嘻\",\n",
+    "        \"嘿\",\n",
+    "        \"嘿嘿\",\n",
+    "        \"四\",\n",
+    "        \"因\",\n",
+    "        \"因为\",\n",
+    "        \"因了\",\n",
+    "        \"因此\",\n",
+    "        \"因着\",\n",
+    "        \"因而\",\n",
+    "        \"固然\",\n",
+    "        \"在\",\n",
+    "        \"在下\",\n",
+    "        \"在于\",\n",
+    "        \"地\",\n",
+    "        \"基于\",\n",
+    "        \"处在\",\n",
+    "        \"多\",\n",
+    "        \"多么\",\n",
+    "        \"多少\",\n",
+    "        \"大\",\n",
+    "        \"大家\",\n",
+    "        \"她\",\n",
+    "        \"她们\",\n",
+    "        \"好\",\n",
+    "        \"如\",\n",
+    "        \"如上\",\n",
+    "        \"如上所述\",\n",
+    "        \"如下\",\n",
+    "        \"如何\",\n",
+    "        \"如其\",\n",
+    "        \"如同\",\n",
+    "        \"如是\",\n",
+    "        \"如果\",\n",
+    "        \"如此\",\n",
+    "        \"如若\",\n",
+    "        \"始而\",\n",
+    "        \"孰料\",\n",
+    "        \"孰知\",\n",
+    "        \"宁\",\n",
+    "        \"宁可\",\n",
+    "        \"宁愿\",\n",
+    "        \"宁肯\",\n",
+    "        \"它\",\n",
+    "        \"它们\",\n",
+    "        \"对\",\n",
+    "        \"对于\",\n",
+    "        \"对待\",\n",
+    "        \"对方\",\n",
+    "        \"对比\",\n",
+    "        \"将\",\n",
+    "        \"小\",\n",
+    "        \"尔\",\n",
+    "        \"尔后\",\n",
+    "        \"尔尔\",\n",
+    "        \"尚且\",\n",
+    "        \"就\",\n",
+    "        \"就是\",\n",
+    "        \"就是了\",\n",
+    "        \"就是说\",\n",
+    "        \"就算\",\n",
+    "        \"就要\",\n",
+    "        \"尽\",\n",
+    "        \"尽管\",\n",
+    "        \"尽管如此\",\n",
+    "        \"岂但\",\n",
+    "        \"己\",\n",
+    "        \"已\",\n",
+    "        \"已矣\",\n",
+    "        \"巴\",\n",
+    "        \"巴巴\",\n",
+    "        \"年\",\n",
+    "        \"并\",\n",
+    "        \"并且\",\n",
+    "        \"庶乎\",\n",
+    "        \"庶几\",\n",
+    "        \"开外\",\n",
+    "        \"开始\",\n",
+    "        \"归\",\n",
+    "        \"归齐\",\n",
+    "        \"当\",\n",
+    "        \"当地\",\n",
+    "        \"当然\",\n",
+    "        \"当着\",\n",
+    "        \"彼\",\n",
+    "        \"彼时\",\n",
+    "        \"彼此\",\n",
+    "        \"往\",\n",
+    "        \"待\",\n",
+    "        \"很\",\n",
+    "        \"得\",\n",
+    "        \"得了\",\n",
+    "        \"怎\",\n",
+    "        \"怎么\",\n",
+    "        \"怎么办\",\n",
+    "        \"怎么样\",\n",
+    "        \"怎奈\",\n",
+    "        \"怎样\",\n",
+    "        \"总之\",\n",
+    "        \"总的来看\",\n",
+    "        \"总的来说\",\n",
+    "        \"总的说来\",\n",
+    "        \"总而言之\",\n",
+    "        \"恰恰相反\",\n",
+    "        \"您\",\n",
+    "        \"惟其\",\n",
+    "        \"慢说\",\n",
+    "        \"我\",\n",
+    "        \"我们\",\n",
+    "        \"或\",\n",
+    "        \"或则\",\n",
+    "        \"或是\",\n",
+    "        \"或曰\",\n",
+    "        \"或者\",\n",
+    "        \"截至\",\n",
+    "        \"所\",\n",
+    "        \"所以\",\n",
+    "        \"所在\",\n",
+    "        \"所幸\",\n",
+    "        \"所有\",\n",
+    "        \"才\",\n",
+    "        \"才能\",\n",
+    "        \"打\",\n",
+    "        \"打从\",\n",
+    "        \"把\",\n",
+    "        \"抑或\",\n",
+    "        \"拿\",\n",
+    "        \"按\",\n",
+    "        \"按照\",\n",
+    "        \"换句话说\",\n",
+    "        \"换言之\",\n",
+    "        \"据\",\n",
+    "        \"据此\",\n",
+    "        \"接着\",\n",
+    "        \"故\",\n",
+    "        \"故此\",\n",
+    "        \"故而\",\n",
+    "        \"旁人\",\n",
+    "        \"无\",\n",
+    "        \"无宁\",\n",
+    "        \"无论\",\n",
+    "        \"既\",\n",
+    "        \"既往\",\n",
+    "        \"既是\",\n",
+    "        \"既然\",\n",
+    "        \"日\",\n",
+    "        \"时\",\n",
+    "        \"时候\",\n",
+    "        \"是\",\n",
+    "        \"是以\",\n",
+    "        \"是的\",\n",
+    "        \"更\",\n",
+    "        \"曾\",\n",
+    "        \"替\",\n",
+    "        \"替代\",\n",
+    "        \"最\",\n",
+    "        \"月\",\n",
+    "        \"有\",\n",
+    "        \"有些\",\n",
+    "        \"有关\",\n",
+    "        \"有及\",\n",
+    "        \"有时\",\n",
+    "        \"有的\",\n",
+    "        \"望\",\n",
+    "        \"朝\",\n",
+    "        \"朝着\",\n",
+    "        \"本\",\n",
+    "        \"本人\",\n",
+    "        \"本地\",\n",
+    "        \"本着\",\n",
+    "        \"本身\",\n",
+    "        \"来\",\n",
+    "        \"来着\",\n",
+    "        \"来自\",\n",
+    "        \"来说\",\n",
+    "        \"极了\",\n",
+    "        \"果然\",\n",
+    "        \"果真\",\n",
+    "        \"某\",\n",
+    "        \"某个\",\n",
+    "        \"某些\",\n",
+    "        \"某某\",\n",
+    "        \"根据\",\n",
+    "        \"欤\",\n",
+    "        \"正值\",\n",
+    "        \"正如\",\n",
+    "        \"正巧\",\n",
+    "        \"正是\",\n",
+    "        \"此\",\n",
+    "        \"此地\",\n",
+    "        \"此处\",\n",
+    "        \"此外\",\n",
+    "        \"此时\",\n",
+    "        \"此次\",\n",
+    "        \"此间\",\n",
+    "        \"毋宁\",\n",
+    "        \"每\",\n",
+    "        \"每当\",\n",
+    "        \"比\",\n",
+    "        \"比及\",\n",
+    "        \"比如\",\n",
+    "        \"比方\",\n",
+    "        \"没奈何\",\n",
+    "        \"沿\",\n",
+    "        \"沿着\",\n",
+    "        \"漫说\",\n",
+    "        \"点\",\n",
+    "        \"焉\",\n",
+    "        \"然则\",\n",
+    "        \"然后\",\n",
+    "        \"然而\",\n",
+    "        \"照\",\n",
+    "        \"照着\",\n",
+    "        \"犹且\",\n",
+    "        \"犹自\",\n",
+    "        \"甚且\",\n",
+    "        \"甚么\",\n",
+    "        \"甚或\",\n",
+    "        \"甚而\",\n",
+    "        \"甚至\",\n",
+    "        \"甚至于\",\n",
+    "        \"用\",\n",
+    "        \"用来\",\n",
+    "        \"由\",\n",
+    "        \"由于\",\n",
+    "        \"由是\",\n",
+    "        \"由此\",\n",
+    "        \"由此可见\",\n",
+    "        \"的\",\n",
+    "        \"的确\",\n",
+    "        \"的话\",\n",
+    "        \"直到\",\n",
+    "        \"相对而言\",\n",
+    "        \"省得\",\n",
+    "        \"看\",\n",
+    "        \"眨眼\",\n",
+    "        \"着\",\n",
+    "        \"着呢\",\n",
+    "        \"矣\",\n",
+    "        \"矣乎\",\n",
+    "        \"矣哉\",\n",
+    "        \"离\",\n",
+    "        \"秒\",\n",
+    "        \"称\",\n",
+    "        \"竟而\",\n",
+    "        \"第\",\n",
+    "        \"等\",\n",
+    "        \"等到\",\n",
+    "        \"等等\",\n",
+    "        \"简言之\",\n",
+    "        \"管\",\n",
+    "        \"类如\",\n",
+    "        \"紧接着\",\n",
+    "        \"纵\",\n",
+    "        \"纵令\",\n",
+    "        \"纵使\",\n",
+    "        \"纵然\",\n",
+    "        \"经\",\n",
+    "        \"经过\",\n",
+    "        \"结果\",\n",
+    "        \"给\",\n",
+    "        \"继之\",\n",
+    "        \"继后\",\n",
+    "        \"继而\",\n",
+    "        \"综上所述\",\n",
+    "        \"罢了\",\n",
+    "        \"者\",\n",
+    "        \"而\",\n",
+    "        \"而且\",\n",
+    "        \"而况\",\n",
+    "        \"而后\",\n",
+    "        \"而外\",\n",
+    "        \"而已\",\n",
+    "        \"而是\",\n",
+    "        \"而言\",\n",
+    "        \"能\",\n",
+    "        \"能否\",\n",
+    "        \"腾\",\n",
+    "        \"自\",\n",
+    "        \"自个儿\",\n",
+    "        \"自从\",\n",
+    "        \"自各儿\",\n",
+    "        \"自后\",\n",
+    "        \"自家\",\n",
+    "        \"自己\",\n",
+    "        \"自打\",\n",
+    "        \"自身\",\n",
+    "        \"至\",\n",
+    "        \"至于\",\n",
+    "        \"至今\",\n",
+    "        \"至若\",\n",
+    "        \"致\",\n",
+    "        \"般的\",\n",
+    "        \"若\",\n",
+    "        \"若夫\",\n",
+    "        \"若是\",\n",
+    "        \"若果\",\n",
+    "        \"若非\",\n",
+    "        \"莫不然\",\n",
+    "        \"莫如\",\n",
+    "        \"莫若\",\n",
+    "        \"虽\",\n",
+    "        \"虽则\",\n",
+    "        \"虽然\",\n",
+    "        \"虽说\",\n",
+    "        \"被\",\n",
+    "        \"要\",\n",
+    "        \"要不\",\n",
+    "        \"要不是\",\n",
+    "        \"要不然\",\n",
+    "        \"要么\",\n",
+    "        \"要是\",\n",
+    "        \"譬喻\",\n",
+    "        \"譬如\",\n",
+    "        \"让\",\n",
+    "        \"许多\",\n",
+    "        \"论\",\n",
+    "        \"设使\",\n",
+    "        \"设或\",\n",
+    "        \"设若\",\n",
+    "        \"诚如\",\n",
+    "        \"诚然\",\n",
+    "        \"该\",\n",
+    "        \"说\",\n",
+    "        \"说来\",\n",
+    "        \"请\",\n",
+    "        \"诸\",\n",
+    "        \"诸位\",\n",
+    "        \"诸如\",\n",
+    "        \"谁\",\n",
+    "        \"谁人\",\n",
+    "        \"谁料\",\n",
+    "        \"谁知\",\n",
+    "        \"贼死\",\n",
+    "        \"赖以\",\n",
+    "        \"赶\",\n",
+    "        \"起\",\n",
+    "        \"起见\",\n",
+    "        \"趁\",\n",
+    "        \"趁着\",\n",
+    "        \"越是\",\n",
+    "        \"距\",\n",
+    "        \"跟\",\n",
+    "        \"较\",\n",
+    "        \"较之\",\n",
+    "        \"边\",\n",
+    "        \"过\",\n",
+    "        \"还\",\n",
+    "        \"还是\",\n",
+    "        \"还有\",\n",
+    "        \"还要\",\n",
+    "        \"这\",\n",
+    "        \"这一来\",\n",
+    "        \"这个\",\n",
+    "        \"这么\",\n",
+    "        \"这么些\",\n",
+    "        \"这么样\",\n",
+    "        \"这么点儿\",\n",
+    "        \"这些\",\n",
+    "        \"这会儿\",\n",
+    "        \"这儿\",\n",
+    "        \"这就是说\",\n",
+    "        \"这时\",\n",
+    "        \"这样\",\n",
+    "        \"这次\",\n",
+    "        \"这般\",\n",
+    "        \"这边\",\n",
+    "        \"这里\",\n",
+    "        \"进而\",\n",
+    "        \"连\",\n",
+    "        \"连同\",\n",
+    "        \"逐步\",\n",
+    "        \"通过\",\n",
+    "        \"遵循\",\n",
+    "        \"遵照\",\n",
+    "        \"那\",\n",
+    "        \"那个\",\n",
+    "        \"那么\",\n",
+    "        \"那么些\",\n",
+    "        \"那么样\",\n",
+    "        \"那些\",\n",
+    "        \"那会儿\",\n",
+    "        \"那儿\",\n",
+    "        \"那时\",\n",
+    "        \"那样\",\n",
+    "        \"那般\",\n",
+    "        \"那边\",\n",
+    "        \"那里\",\n",
+    "        \"都\",\n",
+    "        \"鄙人\",\n",
+    "        \"鉴于\",\n",
+    "        \"针对\",\n",
+    "        \"阿\",\n",
+    "        \"除\",\n",
+    "        \"除了\",\n",
+    "        \"除外\",\n",
+    "        \"除开\",\n",
+    "        \"除此之外\",\n",
+    "        \"除非\",\n",
+    "        \"随\",\n",
+    "        \"随后\",\n",
+    "        \"随时\",\n",
+    "        \"随着\",\n",
+    "        \"难道说\",\n",
+    "        \"零\",\n",
+    "        \"非\",\n",
+    "        \"非但\",\n",
+    "        \"非徒\",\n",
+    "        \"非特\",\n",
+    "        \"非独\",\n",
+    "        \"靠\",\n",
+    "        \"顺\",\n",
+    "        \"顺着\",\n",
+    "        \"首先\",\n",
+    "        \"︿\",\n",
+    "        \"！\",\n",
+    "        \"＃\",\n",
+    "        \"＄\",\n",
+    "        \"％\",\n",
+    "        \"＆\",\n",
+    "        \"（\",\n",
+    "        \"）\",\n",
+    "        \"＊\",\n",
+    "        \"＋\",\n",
+    "        \"，\",\n",
+    "        \"０\",\n",
+    "        \"１\",\n",
+    "        \"２\",\n",
+    "        \"３\",\n",
+    "        \"４\",\n",
+    "        \"５\",\n",
+    "        \"６\",\n",
+    "        \"７\",\n",
+    "        \"８\",\n",
+    "        \"９\",\n",
+    "        \"：\",\n",
+    "        \"；\",\n",
+    "        \"＜\",\n",
+    "        \"＞\",\n",
+    "        \"？\",\n",
+    "        \"＠\",\n",
+    "        \"［\",\n",
+    "        \"］\",\n",
+    "        \"｛\",\n",
+    "        \"｜\",\n",
+    "        \"｝\",\n",
+    "        \"～\",\n",
+    "        \"￥\",\n",
+    "    ]\n",
+    ")\n",
+    "stopwords = stopwords.union(set(string.punctuation))\n",
+    "constraints = [RepeatModification(), StopwordModification(stopwords=stopwords)]\n",
+    "\n",
+    "# search method\n",
+    "search_method = GreedyWordSwapWIR(wir_method=\"weighted-saliency\")\n",
+    "\n",
+    "# attack!\n",
+    "attack = Attack(goal_function, constraints, transformation, search_method)\n",
+    "attack_args = AttackArgs(num_examples=20)\n",
+    "attacker = Attacker(attack, dataset, attack_args)\n",
+    "attack_results = attacker.attack_dataset()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
+      "4b423038915e40158f9da4c07d09aad3",
+      "3711cf0a18994cee8fc840d9a93cf5d3",
+      "7f77bd7b8e5f45ae94cfc45f915c0c72",
+      "fe0ca6138bc54b628c03e590c6e96aed",
+      "8b39363f69eb46009c5357263a65248c",
+      "6b976fd913584da69456c1b6d53483cb",
+      "ea568ab2407f474da3b1f1b2540fa3a8",
+      "ff6b34a7e75b443593f3dca5d050cd52",
+      "4f31972fd2fd44bbac063bb4b5075e98",
+      "7de1551891ec447ab6d80ea1de145f16",
+      "e5e2c0507c834887b80f5717c1e6d5f3",
+      "588b1321a9274de6a8a9e86622d90be4",
+      "2436b07259a34ee18fe9c1007f7b615b",
+      "98aac5a0baee4930bd461f2c5fd73f4a",
+      "34607a8556794a5a86c18abe5bd7e5a5",
+      "f78f6701ce4f4b3b9ff0af925620f261",
+      "a1e3fb5cceed4e95957a17192a641b69",
+      "83e9b14c4d354fdc80db4f8a881f19f3",
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+      "be070cb4a1624b0bb8f9b594c6b951a5",
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+      "5ae4c618f75d4ef9b65e5020fccb6d72",
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+      "d7621b5c619a4ce38ebe63924374cf78",
+      "1b208b6df75f4a9e97faa4e3705a9442",
+      "a7871b8ec3ec40e7bbbe6a5f40b79f4a",
+      "aeb7ee752d834b4cbaa189419fd75dd4",
+      "b47dfff73e73410aa89f65e3c5b0c366",
+      "bdf3571e59ef4a688ab89d4badda27b1",
+      "d3bab427b92144d6b9ce96eac18ceb89"
+     ]
+    },
+    "id": "C_0Z8njnRblT",
+    "outputId": "3890d784-de7f-4b70-f984-cbc9e0c7f700"
+   },
+   "execution_count": null,
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "# In this example, we will attack a pre-trained entailment model from HugginFace (https://huggingface.co/uer/roberta-base-finetuned-chinanews-chinese)\n",
-        "tokenizer = transformers.AutoTokenizer.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese')\n",
-        "model = transformers.AutoModelForSequenceClassification.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese')\n",
-        "model_wrapper = HuggingFaceModelWrapper(model, tokenizer)\n",
-        "\n",
-        "# Set goal function\n",
-        "goal_function = UntargetedClassification(model_wrapper, query_budget=10000)\n",
-        "\n",
-        "# Set dataset from which we will generate adversraial examples\n",
-        "path = os.path.abspath('')\n",
-        "path_list = path.split(os.sep)\n",
-        "temppath = os.path.normpath('examples/dataset/zh_sentiment/entailment_dataset.tsv')\n",
-        "dataset = datasets.load_dataset('csv', data_files=temppath, delimiter=\"\\t\")[\"train\"]\n",
-        "dataset = HuggingFaceDataset(\n",
-        "        dataset,\n",
-        "        dataset_columns=([\"text\"], \"label\"),\n",
-        "        label_names=[\"Mainland China politics\", \"Hong Kong - Macau politics\", \"International news\", \"Financial news\", \"Culture\", \"Entertainment\", \"Sports\"]\n",
-        "    )"
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/615 [00:00<?, ?B/s]"
       ],
-      "metadata": {
-        "id": "CfnC9qUFPq9h"
-      },
-      "execution_count": null,
-      "outputs": []
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "4b423038915e40158f9da4c07d09aad3"
+      }
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "If this is your first time running Hownet, run this code block"
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/1.04G [00:00<?, ?B/s]"
       ],
-      "metadata": {
-        "id": "XfJVzCdRSr3d"
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "588b1321a9274de6a8a9e86622d90be4"
       }
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "source": [
-        "import OpenHowNet\n",
-        "OpenHowNet.download()"
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/4.83M [00:00<?, ?B/s]"
       ],
-      "metadata": {
-        "id": "Hgal-PHeQwys"
-      },
-      "execution_count": null,
-      "outputs": []
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "a132f09845a54cbe865cbe8159bb693e"
+      }
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "\n",
-        "\n",
-        "Now we are ready to attack! With goal function, transformation, constraints, search method, and goal function, we create the Attacker as any other TextAttack attacks\n"
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/8.68M [00:00<?, ?B/s]"
       ],
-      "metadata": {
-        "id": "SrtoxdrMSZ0X"
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "be070cb4a1624b0bb8f9b594c6b951a5"
       }
+     },
+     "metadata": {}
     },
     {
-      "cell_type": "code",
-      "source": [
-        "# transformation, using ChineseWordSwapMaskedLM transformation in this example\n",
-        "\n",
-        "transformation = ChineseWordSwapMaskedLM()\n",
-        "\n",
-        "# constraint\n",
-        "stopwords = set(\n",
-        "        [\"、\", \"。\", \"〈\", \"〉\", \"《\", \"》\", \"一\", \"一个\", \"一些\", \"一何\", \"一切\", \"一则\", \"一方面\", \"一旦\", \"一来\", \"一样\", \"一种\", \"一般\", \"一转眼\", \"七\", \"万一\", \"三\", \"上\", \"上下\", \"下\", \"不\", \"不仅\", \"不但\", \"不光\", \"不单\", \"不只\", \"不外乎\", \"不如\", \"不妨\", \"不尽\", \"不尽然\", \"不得\", \"不怕\", \"不惟\", \"不成\", \"不拘\", \"不料\", \"不是\", \"不比\", \"不然\", \"不特\", \"不独\", \"不管\", \"不至于\", \"不若\", \"不论\", \"不过\", \"不问\", \"与\", \"与其\", \"与其说\", \"与否\", \"与此同时\", \"且\", \"且不说\", \"且说\", \"两者\", \"个\", \"个别\", \"中\", \"临\", \"为\", \"为了\", \"为什么\", \"为何\", \"为止\", \"为此\", \"为着\", \"乃\", \"乃至\", \"乃至于\", \"么\", \"之\", \"之一\", \"之所以\", \"之类\", \"乌乎\", \"乎\", \"乘\", \"九\", \"也\", \"也好\", \"也罢\", \"了\", \"二\", \"二来\", \"于\", \"于是\", \"于是乎\", \"云云\", \"云尔\", \"五\", \"些\", \"亦\", \"人\", \"人们\", \"人家\", \"什\", \"什么\", \"什么样\", \"今\", \"介于\", \"仍\", \"仍旧\", \"从\", \"从此\", \"从而\", \"他\", \"他人\", \"他们\", \"他们们\", \"以\", \"以上\", \"以为\", \"以便\", \"以免\", \"以及\", \"以故\", \"以期\", \"以来\", \"以至\", \"以至于\", \"以致\", \"们\", \"任\", \"任何\", \"任凭\", \"会\", \"似的\", \"但\", \"但凡\", \"但是\", \"何\", \"何以\", \"何况\", \"何处\", \"何时\", \"余外\", \"作为\", \"你\", \"你们\", \"使\", \"使得\", \"例如\", \"依\", \"依据\", \"依照\", \"便于\", \"俺\", \"俺们\", \"倘\", \"倘使\", \"倘或\", \"倘然\", \"倘若\", \"借\", \"借傥然\", \"假使\", \"假如\", \"假若\", \"做\", \"像\", \"儿\", \"先不先\", \"光\", \"光是\", \"全体\", \"全部\", \"八\", \"六\", \"兮\", \"共\", \"关于\", \"关于具体地说\", \"其\", \"其一\", \"其中\", \"其二\", \"其他\", \"其余\", \"其它\", \"其次\", \"具体地说\", \"具体说来\", \"兼之\", \"内\", \"再\", \"再其次\", \"再则\", \"再有\", \"再者\", \"再者说\", \"再说\", \"冒\", \"冲\", \"况且\", \"几\", \"几时\", \"凡\", \"凡是\", \"凭\", \"凭借\", \"出于\", \"出来\", \"分\", \"分别\", \"则\", \"则甚\", \"别\", \"别人\", \"别处\", \"别是\", \"别的\", \"别管\", \"别说\", \"到\", \"前后\", \"前此\", \"前者\", \"加之\", \"加以\", \"区\", \"即\", \"即令\", \"即使\", \"即便\", \"即如\", \"即或\", \"即若\", \"却\", \"去\", \"又\", \"又及\", \"及\", \"及其\", \"及至\", \"反之\", \"反而\", \"反过来\", \"反过来说\", \"受到\", \"另\", \"另一方面\", \"另外\", \"另悉\", \"只\", \"只当\", \"只怕\", \"只是\", \"只有\", \"只消\", \"只要\", \"只限\", \"叫\", \"叮咚\", \"可\", \"可以\", \"可是\", \"可见\", \"各\", \"各个\", \"各位\", \"各种\", \"各自\", \"同\", \"同时\", \"后\", \"后者\", \"向\", \"向使\", \"向着\", \"吓\", \"吗\", \"否则\", \"吧\", \"吧哒\", \"含\", \"吱\", \"呀\", \"呃\", \"呕\", \"呗\", \"呜\", \"呜呼\", \"呢\", \"呵\", \"呵呵\", \"呸\", \"呼哧\", \"咋\", \"和\", \"咚\", \"咦\", \"咧\", \"咱\", \"咱们\", \"咳\", \"哇\", \"哈\", \"哈哈\", \"哉\", \"哎\", \"哎呀\", \"哎哟\", \"哗\", \"哟\", \"哦\", \"哩\", \"哪\", \"哪个\", \"哪些\", \"哪儿\", \"哪天\", \"哪年\", \"哪怕\", \"哪样\", \"哪边\", \"哪里\", \"哼\", \"哼唷\", \"唉\", \"唯有\", \"啊\", \"啐\", \"啥\", \"啦\", \"啪达\", \"啷当\", \"喂\", \"喏\", \"喔唷\", \"喽\", \"嗡\", \"嗡嗡\", \"嗬\", \"嗯\", \"嗳\", \"嘎\", \"嘎登\", \"嘘\", \"嘛\", \"嘻\", \"嘿\", \"嘿嘿\", \"四\", \"因\", \"因为\", \"因了\", \"因此\", \"因着\", \"因而\", \"固然\", \"在\", \"在下\", \"在于\", \"地\", \"基于\", \"处在\", \"多\", \"多么\", \"多少\", \"大\", \"大家\", \"她\", \"她们\", \"好\", \"如\", \"如上\", \"如上所述\", \"如下\", \"如何\", \"如其\", \"如同\", \"如是\", \"如果\", \"如此\", \"如若\", \"始而\", \"孰料\", \"孰知\", \"宁\", \"宁可\", \"宁愿\", \"宁肯\", \"它\", \"它们\", \"对\", \"对于\", \"对待\", \"对方\", \"对比\", \"将\", \"小\", \"尔\", \"尔后\", \"尔尔\", \"尚且\", \"就\", \"就是\", \"就是了\", \"就是说\", \"就算\", \"就要\", \"尽\", \"尽管\", \"尽管如此\", \"岂但\", \"己\", \"已\", \"已矣\", \"巴\", \"巴巴\", \"年\", \"并\", \"并且\", \"庶乎\", \"庶几\", \"开外\", \"开始\", \"归\", \"归齐\", \"当\", \"当地\", \"当然\", \"当着\", \"彼\", \"彼时\", \"彼此\", \"往\", \"待\", \"很\", \"得\", \"得了\", \"怎\", \"怎么\", \"怎么办\", \"怎么样\", \"怎奈\", \"怎样\", \"总之\", \"总的来看\", \"总的来说\", \"总的说来\", \"总而言之\", \"恰恰相反\", \"您\", \"惟其\", \"慢说\", \"我\", \"我们\", \"或\", \"或则\", \"或是\", \"或曰\", \"或者\", \"截至\", \"所\", \"所以\", \"所在\", \"所幸\", \"所有\", \"才\", \"才能\", \"打\", \"打从\", \"把\", \"抑或\", \"拿\", \"按\", \"按照\", \"换句话说\", \"换言之\", \"据\", \"据此\", \"接着\", \"故\", \"故此\", \"故而\", \"旁人\", \"无\", \"无宁\", \"无论\", \"既\", \"既往\", \"既是\", \"既然\", \"日\", \"时\", \"时候\", \"是\", \"是以\", \"是的\", \"更\", \"曾\", \"替\", \"替代\", \"最\", \"月\", \"有\", \"有些\", \"有关\", \"有及\", \"有时\", \"有的\", \"望\", \"朝\", \"朝着\", \"本\", \"本人\", \"本地\", \"本着\", \"本身\", \"来\", \"来着\", \"来自\", \"来说\", \"极了\", \"果然\", \"果真\", \"某\", \"某个\", \"某些\", \"某某\", \"根据\", \"欤\", \"正值\", \"正如\", \"正巧\", \"正是\", \"此\", \"此地\", \"此处\", \"此外\", \"此时\", \"此次\", \"此间\", \"毋宁\", \"每\", \"每当\", \"比\", \"比及\", \"比如\", \"比方\", \"没奈何\", \"沿\", \"沿着\", \"漫说\", \"点\", \"焉\", \"然则\", \"然后\", \"然而\", \"照\", \"照着\", \"犹且\", \"犹自\", \"甚且\", \"甚么\", \"甚或\", \"甚而\", \"甚至\", \"甚至于\", \"用\", \"用来\", \"由\", \"由于\", \"由是\", \"由此\", \"由此可见\", \"的\", \"的确\", \"的话\", \"直到\", \"相对而言\", \"省得\", \"看\", \"眨眼\", \"着\", \"着呢\", \"矣\", \"矣乎\", \"矣哉\", \"离\", \"秒\", \"称\", \"竟而\", \"第\", \"等\", \"等到\", \"等等\", \"简言之\", \"管\", \"类如\", \"紧接着\", \"纵\", \"纵令\", \"纵使\", \"纵然\", \"经\", \"经过\", \"结果\", \"给\", \"继之\", \"继后\", \"继而\", \"综上所述\", \"罢了\", \"者\", \"而\", \"而且\", \"而况\", \"而后\", \"而外\", \"而已\", \"而是\", \"而言\", \"能\", \"能否\", \"腾\", \"自\", \"自个儿\", \"自从\", \"自各儿\", \"自后\", \"自家\", \"自己\", \"自打\", \"自身\", \"至\", \"至于\", \"至今\", \"至若\", \"致\", \"般的\", \"若\", \"若夫\", \"若是\", \"若果\", \"若非\", \"莫不然\", \"莫如\", \"莫若\", \"虽\", \"虽则\", \"虽然\", \"虽说\", \"被\", \"要\", \"要不\", \"要不是\", \"要不然\", \"要么\", \"要是\", \"譬喻\", \"譬如\", \"让\", \"许多\", \"论\", \"设使\", \"设或\", \"设若\", \"诚如\", \"诚然\", \"该\", \"说\", \"说来\", \"请\", \"诸\", \"诸位\", \"诸如\", \"谁\", \"谁人\", \"谁料\", \"谁知\", \"贼死\", \"赖以\", \"赶\", \"起\", \"起见\", \"趁\", \"趁着\", \"越是\", \"距\", \"跟\", \"较\", \"较之\", \"边\", \"过\", \"还\", \"还是\", \"还有\", \"还要\", \"这\", \"这一来\", \"这个\", \"这么\", \"这么些\", \"这么样\", \"这么点儿\", \"这些\", \"这会儿\", \"这儿\", \"这就是说\", \"这时\", \"这样\", \"这次\", \"这般\", \"这边\", \"这里\", \"进而\", \"连\", \"连同\", \"逐步\", \"通过\", \"遵循\", \"遵照\", \"那\", \"那个\", \"那么\", \"那么些\", \"那么样\", \"那些\", \"那会儿\", \"那儿\", \"那时\", \"那样\", \"那般\", \"那边\", \"那里\", \"都\", \"鄙人\", \"鉴于\", \"针对\", \"阿\", \"除\", \"除了\", \"除外\", \"除开\", \"除此之外\", \"除非\", \"随\", \"随后\", \"随时\", \"随着\", \"难道说\", \"零\", \"非\", \"非但\", \"非徒\", \"非特\", \"非独\", \"靠\", \"顺\", \"顺着\", \"首先\", \"︿\", \"！\", \"＃\", \"＄\", \"％\", \"＆\", \"（\", \"）\", \"＊\", \"＋\", \"，\", \"０\", \"１\", \"２\", \"３\", \"４\", \"５\", \"６\", \"７\", \"８\", \"９\", \"：\", \"；\", \"＜\", \"＞\", \"？\", \"＠\", \"［\", \"］\", \"｛\", \"｜\", \"｝\", \"～\", \"￥\"]\n",
-        "    )\n",
-        "stopwords = stopwords.union(set(string.punctuation))\n",
-        "constraints = [RepeatModification(),\n",
-        "               StopwordModification(stopwords = stopwords)]\n",
-        "\n",
-        "# search method\n",
-        "search_method = GreedyWordSwapWIR(wir_method=\"weighted-saliency\")\n",
-        "\n",
-        "# attack!\n",
-        "attack = Attack(goal_function, constraints, transformation, search_method)\n",
-        "attack_args = AttackArgs(num_examples=20)\n",
-        "attacker = Attacker(attack, dataset, attack_args)\n",
-        "attack_results = attacker.attack_dataset()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000,
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-        },
-        "id": "C_0Z8njnRblT",
-        "outputId": "3890d784-de7f-4b70-f984-cbc9e0c7f700"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading:   0%|          | 0.00/615 [00:00<?, ?B/s]"
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-              "Downloading:   0%|          | 0.00/4.83M [00:00<?, ?B/s]"
-            ],
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-              "Downloading:   0%|          | 0.00/8.68M [00:00<?, ?B/s]"
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-          "output_type": "stream",
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-          "text": [
-            "Attack(\n",
-            "  (search_method): GreedyWordSwapWIR(\n",
-            "    (wir_method):  weighted-saliency\n",
-            "  )\n",
-            "  (goal_function):  UntargetedClassification\n",
-            "  (transformation):  ChineseWordSwapMaskedLM\n",
-            "  (constraints): \n",
-            "    (0): RepeatModification\n",
-            "    (1): StopwordModification\n",
-            "  (is_black_box):  True\n",
-            ") \n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "  0%|          | 0/20 [00:00<?, ?it/s]\u001b[A\n",
-            "  5%|▌         | 1/20 [03:10<1:00:26, 190.86s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 0 / 1 / 0 / 1:   5%|▌         | 1/20 [03:10<1:00:26, 190.86s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 1 ---------------------------------------------\n",
-            "[[Sports (100%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "林书豪新秀赛上甘心\"跑龙套\" 自称仍是底薪球员\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 0 / 1 / 0 / 1:  10%|█         | 2/20 [06:55<1:02:18, 207.69s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 0 / 2 / 0 / 2:  10%|█         | 2/20 [06:55<1:02:18, 207.70s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 2 ---------------------------------------------\n",
-            "[[Culture (100%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "成都现“真人图书馆”：无书“借人”给你读\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 0 / 2 / 0 / 2:  15%|█▌        | 3/20 [07:01<39:50, 140.61s/it]  \u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 0 / 2 / 1 / 3:  15%|█▌        | 3/20 [07:01<39:50, 140.61s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 3 ---------------------------------------------\n",
-            "[[Mainland china politics (57%)]] --> [[[SKIPPED]]]\n",
-            "\n",
-            "中国经济走向更趋稳健务实\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 0 / 2 / 1 / 3:  20%|██        | 4/20 [11:33<46:12, 173.28s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 0 / 3 / 1 / 4:  20%|██        | 4/20 [11:33<46:12, 173.28s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 4 ---------------------------------------------\n",
-            "[[Sports (100%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "国际田联世界挑战赛 罗伯斯迎来赛季第三冠\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 0 / 3 / 1 / 4:  25%|██▌       | 5/20 [14:52<44:36, 178.44s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 5 ---------------------------------------------\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 1 / 3 / 1 / 5:  25%|██▌       | 5/20 [14:53<44:39, 178.62s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "[[International news (66%)]] --> [[Entertainment (68%)]]\n",
-            "\n",
-            "德国一电视台合成“默克尔头巾照”惹争议\n",
-            "\n",
-            "德国一电视台合成“性感头巾照”惹争议\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 1 / 3 / 1 / 5:  30%|███       | 6/20 [14:57<34:55, 149.65s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 1 / 3 / 2 / 6:  30%|███       | 6/20 [14:57<34:55, 149.65s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 6 ---------------------------------------------\n",
-            "[[Mainland china politics (80%)]] --> [[[SKIPPED]]]\n",
-            "\n",
-            "朴槿惠今访华 韩媒称访西安可能为增进与习近平友谊\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 1 / 3 / 2 / 6:  35%|███▌      | 7/20 [15:04<27:59, 129.16s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 1 / 3 / 3 / 7:  35%|███▌      | 7/20 [15:04<27:59, 129.16s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 7 ---------------------------------------------\n",
-            "[[Mainland china politics (59%)]] --> [[[SKIPPED]]]\n",
-            "\n",
-            "中国驻休斯敦总领馆举办春节招待会向华裔拜年\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 1 / 3 / 3 / 7:  40%|████      | 8/20 [15:08<22:43, 113.60s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 1 / 3 / 4 / 8:  40%|████      | 8/20 [15:08<22:43, 113.61s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 8 ---------------------------------------------\n",
-            "[[Culture (93%)]] --> [[[SKIPPED]]]\n",
-            "\n",
-            "NASA发现“地球兄弟” 具备生命存活条件\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 1 / 3 / 4 / 8:  45%|████▌     | 9/20 [15:13<18:36, 101.52s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 1 / 3 / 5 / 9:  45%|████▌     | 9/20 [15:13<18:36, 101.52s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 9 ---------------------------------------------\n",
-            "[[Culture (53%)]] --> [[[SKIPPED]]]\n",
-            "\n",
-            "儿子去世后社交网站账号停用 父亲请求保留记忆\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 1 / 3 / 5 / 9:  50%|█████     | 10/20 [18:20<18:20, 110.06s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 2 / 3 / 5 / 10:  50%|█████     | 10/20 [18:20<18:20, 110.06s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 10 ---------------------------------------------\n",
-            "[[Culture (100%)]] --> [[Entertainment (72%)]]\n",
-            "\n",
-            "第六届鲁迅文学奖颁发 格非等35位获奖者领奖\n",
-            "\n",
-            "第六届决赛颁发 格非等35位获奖者领奖\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 2 / 3 / 5 / 10:  55%|█████▌    | 11/20 [22:44<18:36, 124.02s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 3 / 3 / 5 / 11:  55%|█████▌    | 11/20 [22:44<18:36, 124.02s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 11 ---------------------------------------------\n",
-            "[[Hong kong - macau politics (96%)]] --> [[Culture (79%)]]\n",
-            "\n",
-            "东莞台商欲借“台博会”搭建内销平台\n",
-            "\n",
-            "东莞讯欲借“艺博会”搭建内销平台\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 3 / 3 / 5 / 11:  60%|██████    | 12/20 [22:48<15:12, 114.07s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 3 / 3 / 6 / 12:  60%|██████    | 12/20 [22:48<15:12, 114.07s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 12 ---------------------------------------------\n",
-            "[[Financial news (56%)]] --> [[[SKIPPED]]]\n",
-            "\n",
-            "日本网友买扇贝当下酒菜 发现内有真正珍珠(图)\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 3 / 3 / 6 / 12:  65%|██████▌   | 13/20 [28:59<15:36, 133.78s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 3 / 4 / 6 / 13:  65%|██████▌   | 13/20 [28:59<15:36, 133.78s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 13 ---------------------------------------------\n",
-            "[[Sports (100%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "篮球热潮席卷张江 NBA中投王与拉拉队鼎力加盟\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 3 / 4 / 6 / 13:  70%|███████   | 14/20 [33:40<14:26, 144.34s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 3 / 5 / 6 / 14:  70%|███████   | 14/20 [33:40<14:26, 144.34s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 14 ---------------------------------------------\n",
-            "[[Sports (100%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "UFC终极格斗冠军赛开打 \"草原狼\"遭遇三连败\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 3 / 5 / 6 / 14:  75%|███████▌  | 15/20 [33:45<11:15, 135.04s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 3 / 5 / 7 / 15:  75%|███████▌  | 15/20 [33:45<11:15, 135.04s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 15 ---------------------------------------------\n",
-            "[[Culture (92%)]] --> [[[SKIPPED]]]\n",
-            "\n",
-            "水果style：心形水果惹人爱 骰子西瓜乐趣多(图)\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 3 / 5 / 7 / 15:  80%|████████  | 16/20 [40:09<10:02, 150.60s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 3 / 6 / 7 / 16:  80%|████████  | 16/20 [40:09<10:02, 150.60s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 16 ---------------------------------------------\n",
-            "[[Sports (100%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "同里杯中国天元赛前瞻：芈昱廷李钦诚争挑战权\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 3 / 6 / 7 / 16:  85%|████████▌ | 17/20 [43:32<07:41, 153.67s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 4 / 6 / 7 / 17:  85%|████████▌ | 17/20 [43:32<07:41, 153.67s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 17 ---------------------------------------------\n",
-            "[[Entertainment (100%)]] --> [[Financial news (99%)]]\n",
-            "\n",
-            "桂纶镁为戏体验生活 东北洗衣店当店员\n",
-            "\n",
-            "桂纶品牌为首体验生活 东北洗衣店当家\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 4 / 6 / 7 / 17:  90%|█████████ | 18/20 [44:01<04:53, 146.75s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 4 / 7 / 7 / 18:  90%|█████████ | 18/20 [44:01<04:53, 146.75s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 18 ---------------------------------------------\n",
-            "[[Culture (95%)]] --> [[[FAILED]]]\n",
-            "\n",
-            "河南羲皇故都朝祖会流传6000年 一天游客80万人\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 4 / 7 / 7 / 18:  95%|█████████▌| 19/20 [44:07<02:19, 139.35s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 4 / 7 / 8 / 19:  95%|█████████▌| 19/20 [44:07<02:19, 139.35s/it]\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 19 ---------------------------------------------\n",
-            "[[Culture (92%)]] --> [[[SKIPPED]]]\n",
-            "\n",
-            "辛柏青谈追求妻子：用1袋洗衣粉、2块肥皂打动她的\n",
-            "\n",
-            "\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "[Succeeded / Failed / Skipped / Total] 4 / 7 / 8 / 19: 100%|██████████| 20/20 [49:19<00:00, 147.96s/it]\u001b[A\n",
-            "[Succeeded / Failed / Skipped / Total] 5 / 7 / 8 / 20: 100%|██████████| 20/20 [49:19<00:00, 147.96s/it]"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--------------------------------------------- Result 20 ---------------------------------------------\n",
-            "[[International news (100%)]] --> [[Mainland china politics (66%)]]\n",
-            "\n",
-            "朝鲜谴责韩国前方部队打出反朝口号\n",
-            "\n",
-            "中国谴责日本前方部队打出侵略口号\n",
-            "\n",
-            "\n",
-            "\n",
-            "+-------------------------------+--------+\n",
-            "| Attack Results                |        |\n",
-            "+-------------------------------+--------+\n",
-            "| Number of successful attacks: | 5      |\n",
-            "| Number of failed attacks:     | 7      |\n",
-            "| Number of skipped attacks:    | 8      |\n",
-            "| Original accuracy:            | 60.0%  |\n",
-            "| Accuracy under attack:        | 35.0%  |\n",
-            "| Attack success rate:          | 41.67% |\n",
-            "| Average perturbed word %:     | 36.39% |\n",
-            "| Average num. words per input: | 9.3    |\n",
-            "| Avg num queries:              | 45.5   |\n",
-            "+-------------------------------+--------+\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n"
-          ]
-        }
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Attack(\n",
+      "  (search_method): GreedyWordSwapWIR(\n",
+      "    (wir_method):  weighted-saliency\n",
+      "  )\n",
+      "  (goal_function):  UntargetedClassification\n",
+      "  (transformation):  ChineseWordSwapMaskedLM\n",
+      "  (constraints): \n",
+      "    (0): RepeatModification\n",
+      "    (1): StopwordModification\n",
+      "  (is_black_box):  True\n",
+      ") \n",
+      "\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "As aforementioned, we can also augment Chinese sentences with the provided transformation. A quick examples is shown below:"
-      ],
-      "metadata": {
-        "id": "3e_tQiHWS-Pb"
-      }
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "  0%|          | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "  5%|▌         | 1/20 [03:10<1:00:26, 190.86s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 0 / 1 / 0 / 1:   5%|▌         | 1/20 [03:10<1:00:26, 190.86s/it]\u001b[A"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": [
-        "from textattack.constraints.pre_transformation import RepeatModification\n",
-        "from textattack.constraints.pre_transformation import StopwordModification\n",
-        "from textattack.augmentation import Augmenter\n",
-        "\n",
-        "# transformation\n",
-        "transformation = ChineseMorphonymCharacterSwap()\n",
-        "\n",
-        "# constraints\n",
-        "constraints = [RepeatModification(), StopwordModification()]\n",
-        "\n",
-        "# Create augmenter with specified parameters\n",
-        "augmenter = Augmenter(transformation=transformation, pct_words_to_swap = 0.1, transformations_per_example=2)\n",
-        "s = '听见树林的呢喃，发现溪流中的知识。'\n",
-        "\n",
-        "# Augment!\n",
-        "augmenter.augment(s)"
-      ],
-      "metadata": {
-        "id": "43MCRE0pqVM0",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "2ad12bf5-3bd8-4c8d-913c-949fcae787d3"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Building prefix dict from the default dictionary ...\n",
-            "DEBUG:jieba:Building prefix dict from the default dictionary ...\n",
-            "Dumping model to file cache /tmp/jieba.cache\n",
-            "DEBUG:jieba:Dumping model to file cache /tmp/jieba.cache\n",
-            "Loading model cost 0.888 seconds.\n",
-            "DEBUG:jieba:Loading model cost 0.888 seconds.\n",
-            "Prefix dict has been built successfully.\n",
-            "DEBUG:jieba:Prefix dict has been built successfully.\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "['听见树林的呢喃，发现溪流中的知织。', '听见树林的呢喃，发视溪流中的知识。']"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 11
-        }
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 1 ---------------------------------------------\n",
+      "[[Sports (100%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "林书豪新秀赛上甘心\"跑龙套\" 自称仍是底薪球员\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 0 / 1 / 0 / 1:  10%|█         | 2/20 [06:55<1:02:18, 207.69s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 0 / 2 / 0 / 2:  10%|█         | 2/20 [06:55<1:02:18, 207.70s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 2 ---------------------------------------------\n",
+      "[[Culture (100%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "成都现“真人图书馆”：无书“借人”给你读\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 0 / 2 / 0 / 2:  15%|█▌        | 3/20 [07:01<39:50, 140.61s/it]  \u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 0 / 2 / 1 / 3:  15%|█▌        | 3/20 [07:01<39:50, 140.61s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 3 ---------------------------------------------\n",
+      "[[Mainland china politics (57%)]] --> [[[SKIPPED]]]\n",
+      "\n",
+      "中国经济走向更趋稳健务实\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 0 / 2 / 1 / 3:  20%|██        | 4/20 [11:33<46:12, 173.28s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 0 / 3 / 1 / 4:  20%|██        | 4/20 [11:33<46:12, 173.28s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 4 ---------------------------------------------\n",
+      "[[Sports (100%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "国际田联世界挑战赛 罗伯斯迎来赛季第三冠\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 0 / 3 / 1 / 4:  25%|██▌       | 5/20 [14:52<44:36, 178.44s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 5 ---------------------------------------------\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 1 / 3 / 1 / 5:  25%|██▌       | 5/20 [14:53<44:39, 178.62s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[[International news (66%)]] --> [[Entertainment (68%)]]\n",
+      "\n",
+      "德国一电视台合成“默克尔头巾照”惹争议\n",
+      "\n",
+      "德国一电视台合成“性感头巾照”惹争议\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 1 / 3 / 1 / 5:  30%|███       | 6/20 [14:57<34:55, 149.65s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 1 / 3 / 2 / 6:  30%|███       | 6/20 [14:57<34:55, 149.65s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 6 ---------------------------------------------\n",
+      "[[Mainland china politics (80%)]] --> [[[SKIPPED]]]\n",
+      "\n",
+      "朴槿惠今访华 韩媒称访西安可能为增进与习近平友谊\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 1 / 3 / 2 / 6:  35%|███▌      | 7/20 [15:04<27:59, 129.16s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 1 / 3 / 3 / 7:  35%|███▌      | 7/20 [15:04<27:59, 129.16s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 7 ---------------------------------------------\n",
+      "[[Mainland china politics (59%)]] --> [[[SKIPPED]]]\n",
+      "\n",
+      "中国驻休斯敦总领馆举办春节招待会向华裔拜年\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 1 / 3 / 3 / 7:  40%|████      | 8/20 [15:08<22:43, 113.60s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 1 / 3 / 4 / 8:  40%|████      | 8/20 [15:08<22:43, 113.61s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 8 ---------------------------------------------\n",
+      "[[Culture (93%)]] --> [[[SKIPPED]]]\n",
+      "\n",
+      "NASA发现“地球兄弟” 具备生命存活条件\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 1 / 3 / 4 / 8:  45%|████▌     | 9/20 [15:13<18:36, 101.52s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 1 / 3 / 5 / 9:  45%|████▌     | 9/20 [15:13<18:36, 101.52s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 9 ---------------------------------------------\n",
+      "[[Culture (53%)]] --> [[[SKIPPED]]]\n",
+      "\n",
+      "儿子去世后社交网站账号停用 父亲请求保留记忆\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 1 / 3 / 5 / 9:  50%|█████     | 10/20 [18:20<18:20, 110.06s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 2 / 3 / 5 / 10:  50%|█████     | 10/20 [18:20<18:20, 110.06s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 10 ---------------------------------------------\n",
+      "[[Culture (100%)]] --> [[Entertainment (72%)]]\n",
+      "\n",
+      "第六届鲁迅文学奖颁发 格非等35位获奖者领奖\n",
+      "\n",
+      "第六届决赛颁发 格非等35位获奖者领奖\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 2 / 3 / 5 / 10:  55%|█████▌    | 11/20 [22:44<18:36, 124.02s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 3 / 3 / 5 / 11:  55%|█████▌    | 11/20 [22:44<18:36, 124.02s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 11 ---------------------------------------------\n",
+      "[[Hong kong - macau politics (96%)]] --> [[Culture (79%)]]\n",
+      "\n",
+      "东莞台商欲借“台博会”搭建内销平台\n",
+      "\n",
+      "东莞讯欲借“艺博会”搭建内销平台\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 3 / 3 / 5 / 11:  60%|██████    | 12/20 [22:48<15:12, 114.07s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 3 / 3 / 6 / 12:  60%|██████    | 12/20 [22:48<15:12, 114.07s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 12 ---------------------------------------------\n",
+      "[[Financial news (56%)]] --> [[[SKIPPED]]]\n",
+      "\n",
+      "日本网友买扇贝当下酒菜 发现内有真正珍珠(图)\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 3 / 3 / 6 / 12:  65%|██████▌   | 13/20 [28:59<15:36, 133.78s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 3 / 4 / 6 / 13:  65%|██████▌   | 13/20 [28:59<15:36, 133.78s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 13 ---------------------------------------------\n",
+      "[[Sports (100%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "篮球热潮席卷张江 NBA中投王与拉拉队鼎力加盟\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 3 / 4 / 6 / 13:  70%|███████   | 14/20 [33:40<14:26, 144.34s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 3 / 5 / 6 / 14:  70%|███████   | 14/20 [33:40<14:26, 144.34s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 14 ---------------------------------------------\n",
+      "[[Sports (100%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "UFC终极格斗冠军赛开打 \"草原狼\"遭遇三连败\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 3 / 5 / 6 / 14:  75%|███████▌  | 15/20 [33:45<11:15, 135.04s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 3 / 5 / 7 / 15:  75%|███████▌  | 15/20 [33:45<11:15, 135.04s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 15 ---------------------------------------------\n",
+      "[[Culture (92%)]] --> [[[SKIPPED]]]\n",
+      "\n",
+      "水果style：心形水果惹人爱 骰子西瓜乐趣多(图)\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 3 / 5 / 7 / 15:  80%|████████  | 16/20 [40:09<10:02, 150.60s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 3 / 6 / 7 / 16:  80%|████████  | 16/20 [40:09<10:02, 150.60s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 16 ---------------------------------------------\n",
+      "[[Sports (100%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "同里杯中国天元赛前瞻：芈昱廷李钦诚争挑战权\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 3 / 6 / 7 / 16:  85%|████████▌ | 17/20 [43:32<07:41, 153.67s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 4 / 6 / 7 / 17:  85%|████████▌ | 17/20 [43:32<07:41, 153.67s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 17 ---------------------------------------------\n",
+      "[[Entertainment (100%)]] --> [[Financial news (99%)]]\n",
+      "\n",
+      "桂纶镁为戏体验生活 东北洗衣店当店员\n",
+      "\n",
+      "桂纶品牌为首体验生活 东北洗衣店当家\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 4 / 6 / 7 / 17:  90%|█████████ | 18/20 [44:01<04:53, 146.75s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 4 / 7 / 7 / 18:  90%|█████████ | 18/20 [44:01<04:53, 146.75s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 18 ---------------------------------------------\n",
+      "[[Culture (95%)]] --> [[[FAILED]]]\n",
+      "\n",
+      "河南羲皇故都朝祖会流传6000年 一天游客80万人\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 4 / 7 / 7 / 18:  95%|█████████▌| 19/20 [44:07<02:19, 139.35s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 4 / 7 / 8 / 19:  95%|█████████▌| 19/20 [44:07<02:19, 139.35s/it]\u001b[A"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 19 ---------------------------------------------\n",
+      "[[Culture (92%)]] --> [[[SKIPPED]]]\n",
+      "\n",
+      "辛柏青谈追求妻子：用1袋洗衣粉、2块肥皂打动她的\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "[Succeeded / Failed / Skipped / Total] 4 / 7 / 8 / 19: 100%|██████████| 20/20 [49:19<00:00, 147.96s/it]\u001b[A\n",
+      "[Succeeded / Failed / Skipped / Total] 5 / 7 / 8 / 20: 100%|██████████| 20/20 [49:19<00:00, 147.96s/it]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "--------------------------------------------- Result 20 ---------------------------------------------\n",
+      "[[International news (100%)]] --> [[Mainland china politics (66%)]]\n",
+      "\n",
+      "朝鲜谴责韩国前方部队打出反朝口号\n",
+      "\n",
+      "中国谴责日本前方部队打出侵略口号\n",
+      "\n",
+      "\n",
+      "\n",
+      "+-------------------------------+--------+\n",
+      "| Attack Results                |        |\n",
+      "+-------------------------------+--------+\n",
+      "| Number of successful attacks: | 5      |\n",
+      "| Number of failed attacks:     | 7      |\n",
+      "| Number of skipped attacks:    | 8      |\n",
+      "| Original accuracy:            | 60.0%  |\n",
+      "| Accuracy under attack:        | 35.0%  |\n",
+      "| Attack success rate:          | 41.67% |\n",
+      "| Average perturbed word %:     | 36.39% |\n",
+      "| Average num. words per input: | 9.3    |\n",
+      "| Avg num queries:              | 45.5   |\n",
+      "+-------------------------------+--------+\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "As aforementioned, we can also augment Chinese sentences with the provided transformation. A quick examples is shown below:"
+   ],
+   "metadata": {
+    "id": "3e_tQiHWS-Pb"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "from textattack.constraints.pre_transformation import RepeatModification\n",
+    "from textattack.constraints.pre_transformation import StopwordModification\n",
+    "from textattack.augmentation import Augmenter\n",
+    "\n",
+    "# transformation\n",
+    "transformation = ChineseMorphonymCharacterSwap()\n",
+    "\n",
+    "# constraints\n",
+    "constraints = [RepeatModification(), StopwordModification()]\n",
+    "\n",
+    "# Create augmenter with specified parameters\n",
+    "augmenter = Augmenter(\n",
+    "    transformation=transformation, pct_words_to_swap=0.1, transformations_per_example=2\n",
+    ")\n",
+    "s = \"听见树林的呢喃，发现溪流中的知识。\"\n",
+    "\n",
+    "# Augment!\n",
+    "augmenter.augment(s)"
+   ],
+   "metadata": {
+    "id": "43MCRE0pqVM0",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "2ad12bf5-3bd8-4c8d-913c-949fcae787d3"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Building prefix dict from the default dictionary ...\n",
+      "DEBUG:jieba:Building prefix dict from the default dictionary ...\n",
+      "Dumping model to file cache /tmp/jieba.cache\n",
+      "DEBUG:jieba:Dumping model to file cache /tmp/jieba.cache\n",
+      "Loading model cost 0.888 seconds.\n",
+      "DEBUG:jieba:Loading model cost 0.888 seconds.\n",
+      "Prefix dict has been built successfully.\n",
+      "DEBUG:jieba:Prefix dict has been built successfully.\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "['听见树林的呢喃，发现溪流中的知织。', '听见树林的呢喃，发视溪流中的知识。']"
       ]
+     },
+     "metadata": {},
+     "execution_count": 11
     }
-  ]
+   ]
+  }
+ ]
 }
\ No newline at end of file