diff --git a/.gitignore b/.gitignore index dbf6f51e9..880868351 100644 --- a/.gitignore +++ b/.gitignore @@ -48,3 +48,4 @@ checkpoints/ .vscode *.csv !tests/sample_outputs/csv_attack_log.csv +tests/test_command_line/attack_log.txt diff --git a/docs/2notebook/1_Introduction_and_Transformations.ipynb b/docs/2notebook/1_Introduction_and_Transformations.ipynb index 781aa923d..6a4db7ec3 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 3f384995b..b219ca2c3 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 4f72058df..f136fe609 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 6788b8d20..506b705ac 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 f16aa295a..6c1fd55e6 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 b50d52982..7826f18ff 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 928c7dd3d..99694ddc6 100644 --- a/docs/2notebook/Example_2_allennlp.ipynb +++ b/docs/2notebook/Example_2_allennlp.ipynb @@ -1,3144 +1,3148 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "accelerator": "GPU", + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "[TextAttack] Model Example: AllenNLP", + 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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": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "z8wAb0BcSg8W", + "outputId": "8cc26ced-6f03-433c-97d2-72037c606fde", "colab": { - 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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": [ + "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" + }, + "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 /dev/null" - ], - "execution_count": 4, - "outputs": [] + "text/plain": [ + "Downloading: 0%| | 0.00/7.44M [00:00=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", - 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"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 compatible with goal function .\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 [[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": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "metadata": {}, - "execution_count": 9 - } + "text/plain": [ + " 0%| | 0/3 [00:00 compatible with goal function .\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 [[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": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" ] + }, + "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 b4df581f3..af5a4e709 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 04744625c..83268dd96 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 f56e398d2..a6316fdca 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 b032306c7..66e93918f 100644 --- a/docs/2notebook/Example_6_Chinese_Attack.ipynb +++ b/docs/2notebook/Example_6_Chinese_Attack.ipynb @@ -1,2258 +1,3073 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - 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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." - ] + "fe0ca6138bc54b628c03e590c6e96aed": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": 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"model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "cells": [ + { + "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", + "5f5457f292284dd8b914f45e26b2f749", + "2bb72191846f49528663680a315d8b01", + "83eff532314e4edcbfe648b321e9a310", + "3d30e700d32443fdb37b5ab934d2d70a", + "a132f09845a54cbe865cbe8159bb693e", + "0af0e1eaea2f48c5b0fec6e550bd1baa", + "dd6b0a5d9db245338a8fdb2ef5b29bf9", + "58fc309041b54e94ae265167fa20d8d7", + "89dfd3fdc41e417a870901bc79e47495", + "21472d1c4c8b494a8d3660b3320e9d4b", + "7511bb9ca5424674bb2350dff63c468a", + "f6dd2c2cb4e346fe9af7026b5d2162e9", + "a34ad57624fc422aa4832db3963298e6", + "5167daffe92e44d2acc2af2d9b9738df", + "acbfb34a353f41649675bd104069d14e", + "be070cb4a1624b0bb8f9b594c6b951a5", + "2edb7130713d4e10a07bbf808abb9771", + "5ae4c618f75d4ef9b65e5020fccb6d72", + "138d8260e67f4bc58106b9b42f7abd12", + "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 [[[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 [[[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 diff --git a/requirements.txt b/requirements.txt index 34f4ecd9f..4dd1ad244 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,20 +5,20 @@ filelock language_tool_python lemminflect lru-dict -datasets==2.4.0 +datasets>=2.4.0 nltk numpy>=1.21.0 pandas>=1.0.1 scipy>=1.4.1 torch>=1.7.0,!=1.8 -transformers==4.30.0 +transformers>=4.30.0 terminaltables tqdm word2number num2words more-itertools PySocks!=1.5.7,>=1.5.6 -pinyin==0.4.0 +pinyin>=0.4.0 jieba OpenHowNet pycld2 diff --git a/tests/test_command_line/test_loggers.py b/tests/test_command_line/test_loggers.py deleted file mode 100644 index c6589f60a..000000000 --- a/tests/test_command_line/test_loggers.py +++ /dev/null @@ -1,101 +0,0 @@ -import json -import os - -from helpers import run_command_and_get_result -import pytest - -DEBUG = False - -""" -Attack command-line tests in the format (name, args, sample_output_file) -""" - -""" - list_test_params data structure requires - 1) test name - 2) logger filetype - json/text/csv. # Future Work : Tests for Wandb and Visdom - 3) logger file name - 4) sample log file -""" - -list_test_params = [ - ( - "json_summary_logger", - "json", - "textattack attack --recipe deepwordbug --model lstm-mr --num-examples 2 --log-summary-to-json attack_summary.json", - "attack_summary.json", - "tests/sample_outputs/json_attack_summary.json", - ), - ( - "txt_logger", - "txt", - "textattack attack --recipe deepwordbug --model lstm-mr --num-examples 2 --log-to-txt attack_log.txt", - "attack_log.txt", - "tests/sample_outputs/txt_attack_log.txt", - ), - # Removing CSV Logging Test for time-being , will redo CSV test in separate PR. - # ( - # "csv_logger", - # "csv", - # "textattack attack --recipe deepwordbug --model lstm-mr --num-examples 2 --log-to-csv attack_log.csv", - # "attack_log.csv", - # "tests/sample_outputs/csv_attack_log.csv", - # ), -] - - -@pytest.mark.parametrize( - "name, filetype, command, test_log_file, sample_log_file", list_test_params -) -def test_logger(name, filetype, command, test_log_file, sample_log_file): - # Run command and validate outputs. - result = run_command_and_get_result(command) - - assert result.stdout is not None - assert result.stderr is not None - assert result.returncode == 0 - assert os.path.exists(test_log_file), f"{test_log_file} did not get generated" - - if filetype == "json": - with open(sample_log_file) as f: - desired_dictionary = json.load(f) - - with open(test_log_file) as f: - test_dictionary = json.load(f) - - assert ( - desired_dictionary == test_dictionary - ), f"{filetype} file {test_log_file} differs from {sample_log_file}" - - elif filetype == "txt": - assert ( - os.system(f"diff {test_log_file} {sample_log_file}") == 0 - ), f"{filetype} file {test_log_file} differs from {sample_log_file}" - - elif filetype == "csv": - import pandas as pd - - # Convert them into dataframes and compare. - test_df = pd.read_csv(test_log_file) - sample_df = pd.read_csv(sample_log_file) - try: - test_df = test_df[sorted(list(test_df.columns.values))] - sample_df = sample_df[sorted(list(test_df.columns.values))] - - for c in test_df.columns: - if test_df[c].dtype == int: - test_df[c] = test_df[c].astype(float) - - if sample_df[c].dtype == int: - sample_df[c] = sample_df[c].astype(float) - except KeyError: - assert ( - False - ), f"{filetype} file {test_log_file} differs from {sample_log_file}" - - assert sample_df.equals( - test_df - ), f"{filetype} file {test_log_file} differs from {sample_log_file}" - - # cleanup - os.remove(test_log_file) diff --git a/textattack/attack_recipes/a2t_yoo_2021.py b/textattack/attack_recipes/a2t_yoo_2021.py index 2c0919e77..faf24e95f 100644 --- a/textattack/attack_recipes/a2t_yoo_2021.py +++ b/textattack/attack_recipes/a2t_yoo_2021.py @@ -69,6 +69,14 @@ def build(model_wrapper, mlm=False): # # Greedily swap words with "Word Importance Ranking". # - search_method = GreedyWordSwapWIR(wir_method="gradient") + + max_len = getattr(model_wrapper, "max_length", None) or min( + 1024, + model_wrapper.tokenizer.model_max_length, + model_wrapper.model.config.max_position_embeddings - 2, + ) + search_method = GreedyWordSwapWIR( + wir_method="gradient", truncate_words_to=max_len + ) return Attack(goal_function, constraints, transformation, search_method) diff --git a/textattack/loggers/csv_logger.py b/textattack/loggers/csv_logger.py index c739d2c10..ee7f008fd 100644 --- a/textattack/loggers/csv_logger.py +++ b/textattack/loggers/csv_logger.py @@ -21,6 +21,7 @@ def __init__(self, filename="results.csv", color_method="file"): self.color_method = color_method self.row_list = [] self._flushed = True + self.df = pd.DataFrame() def log_attack_result(self, result): original_text, perturbed_text = result.diff_color(self.color_method) @@ -39,10 +40,10 @@ def log_attack_result(self, result): "result_type": result_type, } self.row_list.append(row) + self.df = pd.DataFrame.from_records(self.row_list) self._flushed = False def flush(self): - self.df = pd.DataFrame.from_records(self.row_list) self.df.to_csv(self.filename, quoting=csv.QUOTE_NONNUMERIC, index=False) self._flushed = True diff --git a/textattack/search_methods/greedy_word_swap_wir.py b/textattack/search_methods/greedy_word_swap_wir.py index 5721ce6b6..e1369809b 100644 --- a/textattack/search_methods/greedy_word_swap_wir.py +++ b/textattack/search_methods/greedy_word_swap_wir.py @@ -35,7 +35,7 @@ def __init__(self, wir_method="unk", unk_token="[UNK]"): self.wir_method = wir_method self.unk_token = unk_token - def _get_index_order(self, initial_text): + def _get_index_order(self, initial_text, max_len=-1): """Returns word indices of ``initial_text`` in descending order of importance.""" diff --git a/textattack/shared/validators.py b/textattack/shared/validators.py index fcf08e150..4d9611d5a 100644 --- a/textattack/shared/validators.py +++ b/textattack/shared/validators.py @@ -24,10 +24,7 @@ r"^textattack.models.helpers.word_cnn_for_classification.*", r"^transformers.modeling_\w*\.\w*ForSequenceClassification$", ], - ( - NonOverlappingOutput, - MinimizeBleu, - ): [ + (NonOverlappingOutput, MinimizeBleu,): [ r"^textattack.models.helpers.t5_for_text_to_text.*", ], }