This library is testing the ethics of language models by using natural adversarial texts.

Related tags

Text Data & NLPpythonnlpmachine-learningdeep-learningpytorchtesting-toolsadversarial-examples
Overview

prompt2slip

prompt2slip Logo

pytest PyPI version License: MIT

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

MIT License

Development

Install Package

Install poetry

poetry install

Test

Running tests with pytest.

poetry run pytest --cov .
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