Text Classification
Transformers
Safetensors
English
roberta
safety
hate-speech
content-moderation
text-embeddings-inference
Instructions to use mohawwad93/roberta-racial-language-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohawwad93/roberta-racial-language-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mohawwad93/roberta-racial-language-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mohawwad93/roberta-racial-language-detector") model = AutoModelForSequenceClassification.from_pretrained("mohawwad93/roberta-racial-language-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Racial Language & Bias Detection Model
This model is a fine-tuned version of roberta-base trained on a unified dataset combining multiple hate speech and racial bias benchmark datasets.
Label Mapping
NEUTRAL(0): Safe, non-offensive, or general language.FLAGGED(1): Hate speech, racial bias, or offensive targeted language.
Quickstart Inference
from transformers import pipeline
classifier = pipeline("text-classification", model="mohawwad93/roberta-racial-language-detector")
results = classifier("Your input text here...")
print(results)
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