prompt2slip
This library is testing the ethics of language models by using natural adversarial texts.
This tool allows for short and simple code and validation with little effort.
Extensibility to be applied to any language model in any problem setting by inheriting from the base class.
Install
pip install prompt2slip
Usage
The simplest sample looks like this.
import prompt2slip
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
base_text = ["This project respects pytorch developers."]
target_word = ["keras"]
target_ids = torch.Tensor(tokenizer.convert_tokens_to_ids(target_words))
attaker = CLMAttacker(model,tokenizer)
output = attacker.attack_by_text(texts,target_ids)
More realistic use cases are stored in examples.
What It Dose
"prompt2slip" provides the function to search for prompts which cause appearance of any specific word against a pre trained natural language generation model. Furthermore, with user customization, it can be applied to a wide range of tasks, including classification tasks.If you want to generate a hostile sample for a classification model, you can simply override the method to compute the adversarial loss function to generate a natural adversarial text.
The unique feature of this library is that it can generate test cases for verifying the danger of a pre-trained natural language model with a few lines of code.
References
More About This Project
License
Development
Install Package
Install poetry
poetry install
Test
Running tests with pytest.
poetry run pytest --cov .
1.2k Dec 18, 2022
125 Dec 20, 2022
2 Jan 17, 2022
1.2k Dec 21, 2022
2 May 17, 2022
2.9k Jan 2, 2023
65 Sep 13, 2022
219 Jan 02, 2023
14 Jan 03, 2023
118 Dec 30, 2022
0 Feb 21, 2022
103 Dec 26, 2022
191 Dec 26, 2022
2 Jan 14, 2022
161 Oct 30, 2022
1 Feb 14, 2022
129 Jan 06, 2023
1.6k Dec 29, 2022
77 Dec 19, 2022
17 Dec 23, 2022
2 Apr 24, 2022
116 Dec 27, 2022
382 Jan 07, 2023
1.2k Dec 30, 2022
0 Feb 13, 2022
919 Jan 03, 2023
6 Nov 10, 2022