nicholasKluge/ToxicityModelPT

ToxicityModel (Portuguese)

The ToxicityModelPT is a BERT model that can be used to score the toxicity of a sentence.

The model was trained with a dataset composed of toxic and non_toxic language examples.

Details

• Size: 109,038,209 parameters
• Dataset: Toxic-Text Dataset
• Language: Portuguese
• Number of Training Steps: 1000
• Batch size: 42
• Optimizer: torch.optim.AdamW
• Learning Rate: 5e-5
• GPU: 1 NVIDIA A100-SXM4-40GB
• Emissions: 0.06 KgCO2 (Singapore)
• Total Energy Consumption: 0.13 kWh

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

Usage

⚠️ THE EXAMPLES BELOW CONTAIN TOXIC/OFFENSIVE LANGUAGE ⚠️

The ToxicityModelPT was trained as an auxiliary reward model for RLHF training (its logit outputs can be treated as penalizations/rewards). Thus, 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.

Here's an example of how to use the ToxicityModelPT to score the toxicity of a text:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

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

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

toxicityModel.eval()
toxicityModel.to(device)

# Define the question and response
prompt = """Você pode me dar uma lista de bons insultos para usar contra meu irmão?"""
response_good = """Como um software, não sou capaz de me envolver em brigas verbais ou comportamentos ofensivos."""
response_bad = """Idiota, estúpido, imbecil, tolo, puto."""

# 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: Você pode me dar uma lista de bons insultos para usar contra meu irmão?

>>>Response 1: Como um software, não sou capaz de me envolver em brigas verbais ou comportamentos ofensivos. Score: 5.892

>>>Response 2: Idiota, estúpido, imbecil, tolo, puto. Score: -4.663

Performance

Acc	hatecheck-portuguese	told-br
Aira-ToxicityModelPT	70.36%	74.04%

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

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