Text Classification
Transformers
PyTorch
English
roberta
text classification
hate speech
offensive language
hatecheck
text-embeddings-inference
Instructions to use henrystoll/hatespeech-refugees with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henrystoll/hatespeech-refugees with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henrystoll/hatespeech-refugees")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henrystoll/hatespeech-refugees") model = AutoModelForSequenceClassification.from_pretrained("henrystoll/hatespeech-refugees") - Notebooks
- Google Colab
- Kaggle
Frederik Gaasdal Jensen • Henry Stoll • Sippo Rossi • Raghava Rao Mukkamala
UNHCR Hate Speech Detection Model
This is a transformer model that can detect hate and offensive speech for English text. The primary use-case of this model is to detect hate speech targeted at refugees. The model is based on roberta-uncased and was fine-tuned on 12 abusive language datasets.
The model has been developed as a collaboration between UNHCR, the UN Refugee Agency, and Copenhagen Business School.
- F1-score on test set (10% of the overall dataset): 81%
- Hatecheck score: 90.3%
Labels
{
0: "Normal",
1: "Offensive",
2: "Hate speech",
}
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