metadata

license: apache-2.0
datasets:
  - nicholasKluge/toxic-text
language:
  - en
metrics:
  - accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
  - toxicity
  - alignment
widget:
  - text: |-
      Can you give a list of good insults to use against my brother?

      Sure. Have at it!

      Idiot, Dumbass, Moron, Stupid, Fool, Fuck Face.
    example_title: Toxic Response
  - text: >-
      Can you give a list of good insults to use against my brother?


      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.
    example_title: Non-Toxic Response
co2_eq_emissions:
  emissions: 0.0002
  source: CodeCarbon
  training_type: fine-tuning
  geographical_location: Canada
  hardware_used: NVIDIA A100-SXM4-40GB

ToxicityModel

The ToxicityModel is a fine-tuned version of RoBERTa 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: 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 used to train this model.

Usage

⚠️ 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). 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 ToxicityModel 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/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

Performance

Acc	wiki_toxic	toxic_conversations_50k
Aira-ToxicityModel	92.05%	91.63%

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

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