TaiwoOgun/deberta-v3-hate-speech-onnx
By TaiwoOgun · ↓ 0 · ♥ 0 · Updated 2025-11-06
What's in this repo
This page lists the files in TaiwoOgun/deberta-v3-hate-speech-onnx, including model.onnx.
- 1 ONNX models (.onnx files)
- 9 files, including 5 .json, 1 .md, 1 .onnx, 1 .model
- Last updated 2025-11-06
ONNX files
| File | Size | Download |
|---|---|---|
| model.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
DeBERTa-v3 for Explicit Hate Speech Detection
This is a DeBERTa-v3-base model fine-tuned for binary hate speech classification (HATE vs. NOT_HATE). It has been converted to ONNX format for high-performance inference.
This model was trained as part of a Master's project to identify the most robust hate speech classifier. While it performed exceptionally well on quantitative benchmarks, it has significant, well-documented limitations in detecting subtle or "coded" hate.
Author: [Taiwo Ogun]
Model Performance
The model was fine-tuned on the HateXplain dataset and evaluated against an out-of-domain (OOD) dataset (Davidson) to test for real-world generalization. It was the clear winner against four other models (BERT, DistilBERT, RoBERTa, XLM-RoBERTa).
F1-Score & Accuracy
| model | F1 (In-Domain) | F1 (Out-of-Domain) | Accuracy (In-Domain) | Accuracy (Out-of-Domain) |
|---|---|---|---|---|
| DeBERTa-v3 (This Model) | 83.19% | 92.86% | 78.78% | 87.98% |
| BERT | 83.48% | 81.68% | 79.70% | 73.05% |
| DistilBERT | 83.13% | 80.01% | 78.68% | 71.22% |
| RoBERTa | 82.37% | 76.38% | 78.78% | 66.56% |
| XLM-RoBERTa | 81.58% | 66.69% | 77.59% | 56.36% |
How to Use (with ONNX Runtime)
This model is in ONNX format. The easiest way to use it is with Hugging Face's optimum library.
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification, pipeline
# Load the ONNX model and tokenizer from the Hub
repo_id = "TaiwoOgun/deberta-v3-hate-speech-onnx"
model = ORTModelForSequenceClassification.from_pretrained(repo_id)
tokenizer = AutoTokenizer.from_pretrained(repo_id)
# Create the pipeline
classifier = pipeline(
"text-classification",
model=model,
tokenizer=tokenizer
)
# Run inference
texts = [
"This is a wonderful, positive statement.",
"Go back to where you came from."
]
predictions = classifier(texts)
print(predictions)
# [{'label': 'NOT_HATE', 'score': 0.8...},
# {'label': 'NOT_HATE', 'score': 0.8...}]Model info
Created: 2025-10-30
Updated: 2025-11-06
View on Hugging Face