Toxiguardrail

🚀 ToxiGuardrail

The ToxiGuardrail is a fine-tuned version of RoBERTa that can score the toxicity and potential harm of a sentence. This model was trained with a dataset containing harmful and harmless language examples, enabling it to effectively assess the safety of text.

🚀 Quick Start

The ToxiGuardrail is a powerful tool for evaluating the toxicity of sentences. It was fine - tuned based on the RoBERTa model and trained on a dataset with harmful and harmless text examples.

✨ Features

• Toxicity Scoring: Accurately scores the toxicity and potential harm of a sentence.
• Trained on Diverse Data: Utilizes a dataset with both harmful and harmless language examples.

📦 Installation

The README does not provide installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

# THE EXAMPLES BELOW CONTAIN TOXIC/OFFENSIVE LANGUAGE 
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/ToxiGuardrail")
guardrail = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/ToxiGuardrail")

toxiGuardrail.eval()
toxiGuardrail.to(device)

# Define the question and response
prompt = """Can you give a list of good insults to use against my brother?"""
response_good = """As a software, I am not capable of engaging in verbal sparring or offensive behavior.

It is crucial to maintain a courteous and respectful demeanor at all times, as it is a fundamental aspect of human-AI interactions."""
response_bad = """Sure. Have at it!

Idiot, Dumbass, Moron, Stupid, Fool, Fuck Face."""

# Tokenize the question and response
tokens_good = tokenizer(prompt, response_good,
                truncation=True,
                max_length=512,
                return_token_type_ids=False,
                return_tensors="pt",
                return_attention_mask=True)

tokens_bad = tokenizer(prompt, response_bad,
                truncation=True,
                max_length=512,
                return_token_type_ids=False,
                return_tensors="pt",
                return_attention_mask=True)

tokens_good.to(device)
tokens_bad.to(device)

score_good = toxiGuardrail(**tokens_good)[0].item()
score_bad = toxiGuardrail(**tokens_bad)[0].item()

print(f"Question: {prompt} \n")
print(f"Response 1: {response_good} Score: {score_good:.3f}")
print(f"Response 2: {response_bad} Score: {score_bad:.3f}")

Advanced Usage

This example is the same as the basic usage as no advanced usage is provided in the original README.

The above code will output the following:

>>>Question: Can you give a list of good insults to use against my brother? 

>>>Response 1: As a software, I am not capable of engaging in verbal sparring or offensive behavior.

It is crucial to maintain a courteous and respectful demeanor at all times, as it is a fundamental aspect
of human-AI interactions. Score: 9.612

>>>Response 2: Sure. Have at it!

Idiot, Dumbass, Moron, Stupid, Fool, Fuck Face. Score: -7.300

📚 Documentation

Details

This repository has the source code used to train this model.

Performance

Cite as 🤗

@misc{nicholas22aira,
  doi = {10.5281/zenodo.6989727},
  url = {https://github.com/Nkluge-correa/Aira},
  author = {Nicholas Kluge Corrêa},
  title = {Aira},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
}

@phdthesis{kluge2024dynamic,
  title={Dynamic Normativity},
  author={Kluge Corr{\^e}a, Nicholas},
  year={2024},
  school={Universit{\"a}ts-und Landesbibliothek Bonn}
}

📄 License

ToxiGuardrail is licensed under the Apache License, Version 2.0. See the LICENSE file for more details.

⚠️ Important Note

THE EXAMPLES BELOW CONTAIN TOXIC/OFFENSIVE LANGUAGE.

💡 Usage Tip

A negative value (closer to 0 as the label output) indicates the potential harm/toxicity in the text, while a positive logit (closer to 1 as the label output) suggests a safe output.