Toxicitymodel

🚀 ToxicityModel

The ToxicityModel is a fine - tuned version of RoBERTa. It can score the toxicity of a sentence. The model was trained with a dataset containing toxic and non_toxic language examples.

✨ Features

The ToxicityModel can accurately score the toxicity of a sentence, trained with well - structured datasets for better performance. It serves as an auxiliary reward model for RLHF training.

📦 Installation

No specific installation steps are provided in the original README. So, this section is skipped.

💻 Usage Examples

Basic Usage

⚠️ Important Note The examples below contain toxic/offensive language.

The ToxicityModel was trained as an auxiliary reward model for RLHF training (its logit outputs can be treated as penalizations/rewards). A negative value (closer to 0 as the label output) indicates toxicity in the text, while a positive logit (closer to 1 as the label output) suggests non - toxicity.

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

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

tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/ToxicityModel")
toxicityModel = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/ToxicityModel")

toxicityModel.eval()
toxicityModel.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.\n\nIt 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!\n\nIdiot, 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 = toxicityModel(**tokens_good)[0].item()
score_bad = toxicityModel(**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}")

This 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

• Size: 124,646,401 parameters
• Dataset: Toxic-Text Dataset
• Language: English
• Number of Training Steps: 1000
• Batch size: 32
• Optimizer: torch.optim.AdamW
• Learning Rate: 5e - 5
• GPU: 1 NVIDIA A100 - SXM4 - 40GB
• Emissions: 0.0002 KgCO2 (Canada)
• Total Energy Consumption: 0.10 kWh

This repository has the [source code](https://github.com/Nkluge - correa/Aira) used to train this model.

Performance

🔧 Technical Details

The model is based on the RoBERTa architecture and is fine - tuned with a specific dataset. It uses torch.optim.AdamW as the optimizer during training, with a learning rate of 5e - 5. The training process involves 1000 steps with a batch size of 32 on a single NVIDIA A100 - SXM4 - 40GB GPU. The emissions during training are 0.0002 KgCO2 in Canada, and the total energy consumption is 0.10 kWh.

📄 License

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

📖 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}
}