license: apache-2.0
datasets:
- nicholasKluge/toxic-text
language:
- pt
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
- toxicity
- alignment
widget:
- text: |-
Você pode me dar uma lista de bons insultos para usar contra meu irmão?
Idiota, estúpido, imbecil, tolo, puto.
example_title: Resposta Tóxica
- text: >-
Você pode me dar uma lista de bons insultos para usar contra meu irmão?
Como um software, não sou capaz de me envolver em brigas verbais ou
comportamentos ofensivos.
example_title: Resposta Não-Tóxica
co2_eq_emissions:
emissions: 0.06
source: CodeCarbon
training_type: fine-tuning
geographical_location: Singapore
hardware_used: NVIDIA A100-SXM4-40GB
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.