Multilingual Hate Speech Classifier for Social Media with Disagreement-Aware Training

A multilingual XLM-R-based (100 languages) hate speech classification model fine-tuned on English, Italian and Slovenian with inter-annotator disagreement-aware training.

The details of the model and the disagreement-aware training are described in our paper:

 @inproceedings{
  grigor2024multilingual,
  title={Multilingual Hate Speech Modeling by Leveraging Inter-Annotator Disagreement},
  author={Grigor, Patricia-Carla and Evkoski, Bojan and Kralj Novak, Petra},
  url={http://dx.doi.org/10.70314/is.2024.sikdd.7},
  DOI={10.70314/is.2024.sikdd.7},
  booktitle={Proceedings of Data Mining and Data Warehouses – Sikdd 2024},
  publisher={Jožef Stefan Instutute},
  year={2024}
 }

Authors: Patricia-Carla Grigor, Bojan Evkoski, Petra Kralj Novak

Data available here: English; Italian; Slovenian

Model output The model classifies each input into one of four distinct classes:

• 0 - appropriate
• 1 - inappropriate
• 2 - offensive
• 3 - violent

Training data*

• 51k English Youtube comments
• 60k Italian Youtube comments
• 50k Slovenian Twitter comments

Evaluation data*

• 10k English Youtube comments
• 10k Italian Youtube comments
• 10k Slovenian Twitter comments

* each comment is manually labeled by two different annotators

Fine-tuning hyperparameters

  num_train_epochs=3,
  train_batch_size=8,
  learning_rate=6e-6

Evaluation Results Model agreement (accuracy) vs. Inter-annotator agreement (0 - no agreement; 100 - perfect agreement):

Class-specific model F1-scores:

Usage

from transformers import AutoModelForSequenceClassification, TextClassificationPipeline, AutoTokenizer, AutoConfig

MODEL = "IMSyPP/hate_speech_multilingual"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
config = AutoConfig.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)

pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True,
task='sentiment_analysis', device=0, function_to_apply="none")
pipe([
"Thank you for using our model",
"Grazie per aver utilizzato il nostro modello"
"Hvala za uporabo našega modela"
])