Merge branch 'master' into s3-model-fix

Author: jinyongyoo

README.md

@@ -296,14 +296,15 @@ textattack attack --model bert-base-uncased-sst2 --recipe textfooler --num-examp
 ### Augmenting Text: `textattack augment`
 
 Many of the components of TextAttack are useful for data augmentation. The `textattack.Augmenter` class
-uses a transformation and a list of constraints to augment data. We also offer five built-in recipes
+uses a transformation and a list of constraints to augment data. We also offer  built-in recipes
 for data augmentation:
-- `textattack.WordNetAugmenter` augments text by replacing words with WordNet synonyms
-- `textattack.EmbeddingAugmenter` augments text by replacing words with neighbors in the counter-fitted embedding space, with a constraint to ensure their cosine similarity is at least 0.8
-- `textattack.CharSwapAugmenter` augments text by substituting, deleting, inserting, and swapping adjacent characters
-- `textattack.EasyDataAugmenter` augments text with a combination of word insertions, substitutions and deletions.
-- `textattack.CheckListAugmenter` augments text by contraction/extension and by substituting names, locations, numbers.
-- `textattack.CLAREAugmenter` augments text by replacing, inserting, and merging with a pre-trained masked language model.
+- `wordnet` augments text by replacing words with WordNet synonyms
+- `embedding` augments text by replacing words with neighbors in the counter-fitted embedding space, with a constraint to ensure their cosine similarity is at least 0.8
+- `charswap` augments text by substituting, deleting, inserting, and swapping adjacent characters
+- `eda` augments text with a combination of word insertions, substitutions and deletions.
+- `checklist` augments text by contraction/extension and by substituting names, locations, numbers.
+- `clare` augments text by replacing, inserting, and merging with a pre-trained masked language model.
+
 
 #### Augmentation Command-Line Interface
 The easiest way to use our data augmentation tools is with `textattack augment <args>`. `textattack augment`
@@ -380,24 +381,23 @@ automatically loaded using the `datasets` package.
 #### Training Examples
 *Train our default LSTM for 50 epochs on the Yelp Polarity dataset:*
 ```bash
-textattack train --model lstm --dataset yelp_polarity --batch-size 64 --epochs 50 --learning-rate 1e-5
+textattack train --model-name-or-path lstm --dataset yelp_polarity  --epochs 50 --learning-rate 1e-5
 ```
 
-The training process has data augmentation built-in:
-```bash
-textattack train --model lstm --dataset rotten_tomatoes --augment eda --pct-words-to-swap .1 --transformations-per-example 4
-```
-This uses the `EasyDataAugmenter` recipe to augment the `rotten_tomatoes` dataset before training.
 
 *Fine-Tune `bert-base` on the `CoLA` dataset for 5 epochs**:
 ```bash
-textattack train --model bert-base-uncased --dataset glue^cola --batch-size 32 --epochs 5
+textattack train --model-name-or-path bert-base-uncased --dataset glue^cola --per-device-train-batch-size 8 --epochs 5
 ```
 
 
 ### To check datasets: `textattack peek-dataset`
 
-To take a closer look at a dataset, use `textattack peek-dataset`. TextAttack will print some cursory statistics about the inputs and outputs from the dataset. For example, `textattack peek-dataset --dataset-from-huggingface snli` will show information about the SNLI dataset from the NLP package.
+To take a closer look at a dataset, use `textattack peek-dataset`. TextAttack will print some cursory statistics about the inputs and outputs from the dataset. For example, 
+```bash
+textattack peek-dataset --dataset-from-huggingface snli
+```
+will show information about the SNLI dataset from the NLP package.
 
 
 ### To list functional components: `textattack list`
@@ -547,6 +547,11 @@ A `SearchMethod` takes as input an initial `GoalFunctionResult` and returns a fi
 
 ## Multi-lingual Support
 
+
+- see example code: [https://github.com/QData/TextAttack/blob/master/examples/attack/attack_camembert.py](https://github.com/QData/TextAttack/blob/master/examples/attack/attack_camembert.py) for using our framework to attack French-BERT. 
+
+- see tutorial notebook: [https://textattack.readthedocs.io/en/latest/2notebook/Example_4_CamemBERT.html](https://textattack.readthedocs.io/en/latest/2notebook/Example_4_CamemBERT.html) for using our framework to attack French-BERT. 
+
 - See [README_ZH.md](https://github.com/QData/TextAttack/blob/master/README_ZH.md) for our README in Chinese 
 
 

README_ZH.md

@@ -292,11 +292,14 @@ textattack attack --model bert-base-uncased-sst2 --recipe textfooler --num-examp
 ### 增强文本数据：`textattack augment`
 
 TextAttack 的组件中，有很多易用的数据增强工具。`textattack.Augmenter` 类使用 *变换* 与一系列的 *约束* 进行数据增强。我们提供了 5 中内置的数据增强策略：
-- `textattack.WordNetAugmenter` 通过基于 WordNet 同义词替换的方式增强文本
-- `textattack.EmbeddingAugmenter` 通过邻近词替换的方式增强文本，使用 counter-fitted 词嵌入空间中的邻近词进行替换，约束二者的 cosine 相似度不低于 0.8
-- `textattack.CharSwapAugmenter` 通过字符的增删改，以及临近字符交换的方式增强文本
-- `textattack.EasyDataAugmenter` 通过对词的增删改来增强文本
-- `textattack.CheckListAugmenter` 通过简写，扩写以及对实体、地点、数字的替换来增强文本
+- `wordnet` 通过基于 WordNet 同义词替换的方式增强文本
+- `embedding` 通过邻近词替换的方式增强文本，使用 counter-fitted 词嵌入空间中的邻近词进行替换，约束二者的 cosine 相似度不低于 0.8
+- `charswap` 通过字符的增删改，以及临近字符交换的方式增强文本
+- `eda` 通过对词的增删改来增强文本
+- `checklist` 通过简写，扩写以及对实体、地点、数字的替换来增强文本
+- `clare` 使用 pre-trained masked language model, 通过对词的增删改来增强文本
+
+
 
 #### 数据增强的命令行接口
 使用 textattack 来进行数据增强，最快捷的方法是通过 `textattack augment <args>` 命令行接口。 `textattack augment` 使用 CSV 文件作为输入，在参数中设置需要增强的文本列，每个样本允许改变的比例，以及对于每个输入样本生成多少个增强样本。输出的结果保存为与输入文件格式一致的 CSV 文件，结果文件中为对指定的文本列生成的增强样本。
@@ -362,18 +365,13 @@ it's a enigma how the filmmaking wo be publicized in this condition .,0
 #### 运行训练的例子
 *在 Yelp 分类数据集上对 TextAttack 中默认的 LSTM 模型训练 50 个 epoch：*
 ```bash
-textattack train --model lstm --dataset yelp_polarity --batch-size 64 --epochs 50 --learning-rate 1e-5
+textattack train --model-name-or-path lstm --dataset yelp_polarity  --epochs 50 --learning-rate 1e-5
 ```
 
-训练接口中同样内置了数据增强功能：
-```bash
-textattack train --model lstm --dataset rotten_tomatoes --augment eda --pct-words-to-swap .1 --transformations-per-example 4
-```
-上面这个例子在训练之前使用 `EasyDataAugmenter` 策略对 `rotten_tomatoes` 数据集进行数据增强。
 
 *在 `CoLA` 数据集上对 `bert-base` 模型精调 5 个 epoch：*
 ```bash
-textattack train --model bert-base-uncased --dataset glue^cola --batch-size 32 --epochs 5
+textattack train --model-name-or-path bert-base-uncased --dataset glue^cola --per-device-train-batch-size 8 --epochs 5
 ```
 
 

docs/1start/FAQ.md

@@ -42,22 +42,25 @@ conda activate textattackenv
 conda env list
 ```
 
+If you want to use the most-up-to-date version of textattack (normally with newer bug fixes), you can run the following: 
+```bash
+git clone https://github.com/QData/TextAttack.git
+cd TextAttack
+pip install .[dev]
+```
+
+
 ### 1. How to Train
 
 For example, you can *Train our default LSTM for 50 epochs on the Yelp Polarity dataset:*
 ```bash
-textattack train --model lstm --dataset yelp_polarity --batch-size 64 --epochs 50 --learning-rate 1e-5
+textattack train --model-name-or-path lstm --dataset yelp_polarity  --epochs 50 --learning-rate 1e-5
 ```
 
-The training process has data augmentation built-in:
-```bash
-textattack train --model lstm --dataset rotten_tomatoes --augment eda --pct-words-to-swap .1 --transformations-per-example 4
-```
-This uses the `EasyDataAugmenter` recipe to augment the `rotten_tomatoes` dataset before training.
 
 *Fine-Tune `bert-base` on the `CoLA` dataset for 5 epochs**:
 ```bash
-textattack train --model bert-base-uncased --dataset glue^cola --batch-size 32 --epochs 5
+textattack train --model-name-or-path bert-base-uncased --dataset glue^cola --per-device-train-batch-size 8 --epochs 5
 ```
 
 

docs/1start/api-design-tips.md

@@ -7,6 +7,24 @@ Lessons learned in designing TextAttack
 
 TextAttack is an open-source Python toolkit for adversarial attacks, adversarial training, and data augmentation in NLP. TextAttack unites 15+ papers from the NLP adversarial attack literature into a single shared framework, with many components reused across attacks. This framework allows both researchers and developers to test and study the weaknesses of their NLP models. 
 
+
+## Presentations on TextAttack 
+
+### 2020: Jack Morris' summary tutorial talk on TextAttack 
+
+- On Jul 31, 2020, Jack Morries gave an invited talk at Weights & Biases research salon on " TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP"
+
+- [Youtube Talk link](https://www.youtube.com/watch?v=22Q3f7Fb110)
+
+
+### 2021: Dr. Qi's summary tutorial talk on TextAttack 
+
+- On April 14 2021, Prof. Qi gave an invited talk at the UVA Human and Machine Intelligence Seminar on "Generalizing Adversarial Examples to Natural Language Processing"
+
+- [TalkSlide](https://qdata.github.io/qdata-page/pic/20210414-HMI-textAttack.pdf)
+
+
+
 ## Challenges in Design
 
 

docs/1start/multilingual-visualization.md

@@ -0,0 +1,16 @@
+TextAttack Extended Functions (Multilingual)
+============================================
+
+
+
+## Multilingual Supports
+
+- see example code: [https://github.com/QData/TextAttack/blob/master/examples/attack/attack_camembert.py](https://github.com/QData/TextAttack/blob/master/examples/attack/attack_camembert.py) for using our framework to attack French-BERT. 
+
+- see tutorial notebook: [https://textattack.readthedocs.io/en/latest/2notebook/Example_4_CamemBERT.html](https://textattack.readthedocs.io/en/latest/2notebook/Example_4_CamemBERT.html) for using our framework to attack French-BERT. 
+
+
+
+## We have built a new WebDemo For Visulizing TextAttack generated Examples; 
+
+- [TextAttack-WebDemo Github](https://github.com/QData/TextAttack-WebDemo)