From 02a1188b55263449767ba5c357e88e324019daa9 Mon Sep 17 00:00:00 2001
From: Gema <gema.parreno.piqueras@gmail.com>
Date: Tue, 31 Oct 2023 23:05:14 +0100
Subject: [PATCH] change README and wea

---
 README.md                                    | 20 ++++++++++++--------
 pulling_ace/attacks/word_embedding_attack.py |  6 +++---
 2 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index 69c1a57..dae299e 100644
--- a/README.md
+++ b/README.md
@@ -1,17 +1,21 @@
-# PullingAce: Benchmarking Adversarial Attacks for Hugging Face Models
+# PullingAce: Benchmarking Robustness for Hugging Face Models
 
 > [!NOTE]
 > Repository under active construction
 
 PullingAce is a Python library designed to benchmark adversarial attacks on Hugging Face models. Built on top of TextAttack, PullingAce incorporates a set of recipes to assess the robustness of various natural language processing models. This tool provides a comprehensive evaluation of model vulnerabilities and helps researchers and practitioners in the field of machine learning understand the strengths and weaknesses of different models.
 
-## Features
+## Features for Classification
+
 - **Adversarial Attack Benchmarks**: PullingAce provides a collection of adversarial attack benchmarks tailored for Hugging Face models. Evaluate model robustness against state-of-the-art attacks.
 
 - **Incorporating TextAttack Recipes**: PullingAce integrates TextAttack's powerful attack recipes, making it easy to experiment with different attack strategies and customize evaluations.
 
 - **Effortless Evaluation**: With PullingAce, you can effortlessly assess the performance of your Hugging Face models against adversarial attacks. Obtain detailed insights into your model's strengths and areas for improvement.
 
+## Features for Generative Models
+
+
 ## Installation
 
 To get started with PullingAce, you can install it using pip:
@@ -20,7 +24,7 @@ To get started with PullingAce, you can install it using pip:
 pip install pullingace
 
 ## Try
-beyondthenest --attack tomato --model "textattack/albert-base-v2-ag-news" --dataset "ag_news" --num-examples 5
+pullingace --attack tomato --model "textattack/albert-base-v2-ag-news" --dataset "ag_news" --num-examples 5
 
 ```
 
@@ -29,25 +33,25 @@ This package was created with Cookiecutter and the [sourcery-ai/python-best-prac
 
 
 
-### Builds
+### Notes for Builds
 
 This is right now intented to be a python library 
 Uninstall the Previous Version: If you have a previous version of the package installed, you can uninstall it first to avoid conflicts. You can use the following command for that:
 
-bash
-Copy code
+```bash
 pip uninstall your_package_name
+``````
 Replace your_package_name with the name of your package.
 
 Increment the Version Number: If you've made changes that you want to distribute, it's a good practice to increment the version number in your setup.py file.
 
-python
-Copy code
+```python
 setup(
     name='your_package_name',
     version='0.2',  # Increment this number
     # ...
 )
+``````
 Clear Old Build Directories: Remove old build artifacts to make sure you're starting fresh. Navigate to the folder containing setup.py and run:
 
 ```bash
diff --git a/pulling_ace/attacks/word_embedding_attack.py b/pulling_ace/attacks/word_embedding_attack.py
index 968479d..365cf36 100644
--- a/pulling_ace/attacks/word_embedding_attack.py
+++ b/pulling_ace/attacks/word_embedding_attack.py
@@ -44,15 +44,15 @@
 
 
 def perfom_word_embedding_attack(model_name, dataset_name):
-    falcon = load_model_classification(model_name)
+    model = load_model_classification(model_name)
     # falcon2 = falcon.to_bettertransformer()
     tokenizer = load_tokenizer(model_name)
     tokenizer.pad_token = tokenizer.eos_token
 
-    model_wrapper = HuggingFaceModelWrapper(falcon, tokenizer)
+    model_wrapper = HuggingFaceModelWrapper(model, tokenizer)
     goal_function = UntargetedClassification(model_wrapper)
 
-    ### Unclear if this dataset is going to work with falcon
+    ###
     dataset = HuggingFaceDataset(dataset_name, None, "test")
 
     ## TODO