Merge pull request #428 from QData/api-rework

API Rework

.gitignore

@@ -36,6 +36,9 @@ dist/
 # Weights & Biases outputs
 wandb/
 
+# Tensorboard logs
+runs/
+
 # checkpoints
 checkpoints/
 

docs/1start/basic-Intro.rst → docs/0_get_started/basic-Intro.rst

@@ -17,10 +17,6 @@ Where should I start?
 This is a great question, and one we get a lot. First of all, almost everything in TextAttack can be done in two ways: via the command-line or via the Python API. If you're looking to integrate TextAttack into an existing project, the Python API is likely for you. If you'd prefer to use built-in functionality end-to-end (training a model, running an adversarial attack, augmenting a CSV) then you can just use the command-line API.
 
 
-
-
-For future developers, visit the :ref:`Installation <installation>` page for more details about installing TextAttack onto your own computer. To start making contributions, read the detailed instructions `here <https://github.com/QData/TextAttack/blob/master/CONTRIBUTING.md>`__.
-
 TextAttack does three things very well:
 
 1. Adversarial attacks (Python: ``textattack.shared.Attack``, Bash: ``textattack attack``)

docs/0_get_started/installation.md

@@ -0,0 +1,49 @@
+Installation
+==============
+
+To use TextAttack, you must be running Python 3.6 or above. A CUDA-compatible GPU is optional but will greatly improve speed. 
+
+We recommend installing TextAttack in a virtual environment (check out this [guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)).
+
+There are two ways to install TextAttack. If you want to simply use as it is, install via `pip`. If you want to make any changes and play around, install it from source.
+
+## Install with pip
+Simply run
+
+    pip install textattack 
+
+## Install from Source
+To install TextAttack from source, first clone the repo by running
+
+    git clone https://github.com/QData/TextAttack.git
+    cd TextAttack
+
+Then, install it using `pip`.
+
+    pip install -e . 
+
+To install TextAttack for further development, please run this instead.
+
+    pip install -e .[dev]
+
+This installs additional dependencies required for development.
+
+
+## Optional Dependencies
+For quick installation, TextAttack only installs esssential packages as dependencies (e.g. Transformers, PyTorch). However, you might need to install additional packages to run certain attacks or features.
+For example, Tensorflow and Tensorflow Hub are required to use the TextFooler attack, which was proposed in [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://arxiv.org/abs/1907.11932) by Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolov.
+
+If you attempting to use a feature that requires additional dependencies, TextAttack will let you know which ones you need to install.
+
+However, during installation step, you can also install them together with TextAttack.
+You can install Tensorflow and its related packages by running
+
+    pip install textattack[tensorflow]
+
+You can also install other miscallenous optional dependencies by running
+
+    pip install textattack[optional]
+
+To install both groups of packages, run
+
+    pip install textattack[tensorflow, optional]

docs/0_get_started/quick_api_tour.rst

@@ -0,0 +1,33 @@
+Quick Tour
+==========================
+
+Let us have a quick look at how TextAttack can be used to carry out adversarial attack.
+
+Attacking a BERT model
+------------------------------
+Let us attack a BERT model fine-tuned for sentimental classification task. We are going to use a model that has already been fine-tuned on IMDB dataset using the Transformers library. 
+
+.. code-block::
+
+    >>> import transformers
+    >>> model = transformers.AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-imdb")
+    >>> tokenizer = transformers.AutoTokenizer.from_pretrained("textattack/bert-base-uncased-imdb")
+
+
+TextAttack requires both the model and the tokenizer to be wrapped by a :class:`~transformers.models.wrapper.ModelWrapper` class that implements the forward pass operation given a list of input texts. For models provided by Transformers library, we can also simply use :class:`~transformers.models.wrapper.HuggingFaceModelWrapper` class which implements both the forward pass and tokenization.
+
+.. code-block::
+
+    >>> import textattack
+    >>> model_wrapper = textattack.models.wrappers.HuggingFaceModelWrapper(model, tokenizer)
+
+Next, let's build the attack that we want to use. TextAttack provides prebuilt attacks in the form of :class:`~transformers.attack_recipes.AttackRecipe`. For this example, we will use :ref:TextFooler attack 
+
+Let us also load the IMDB dataset using 🤗 Datasets library. TextAttack also requires that the dataset
+
+.. code-block::
+
+    >>> import datasets
+    >>> dataset = datasets.load_dataset("imdb", split="test")
+
+