---
license: apache-2.0
language:
- en
- fr
- it
- ru
- pt
- es
- tr
base_model:
- unitary/toxic-bert
pipeline_tag: text-classification
tags:
- toxicity
- BERT
- gravitee-io
- ai-gateway
---
# Detoxify ONNX 🚀

This project provides an ONNX-exported and quantized version of the [Detoxify](https://github.com/unitaryai/detoxify) multilingual model, optimized for runtime inference.  
It enables faster and lighter toxicity detection using ONNX Runtime.

## 🧪 ONNX Evaluation Results

### Original Model (using Detoxify lib and ONNX):

| Threshold | Accuracy | Precision | Recall | F1     | AUC-ROC |
|----------:|---------:|----------:|-------:|-------:|--------:|
| **0.2**   | 0.8408   | 0.4899    | 0.8659 | 0.6257 | 0.9345  |
| **0.4**   | 0.8723   | 0.5628    | 0.7577 | 0.6459 | 0.9345  |
| **0.5**   | 0.8845   | 0.6073    | 0.7041 | 0.6521 | 0.9345  |
| **0.7**   | 0.8954   | 0.6951    | 0.5691 | 0.6258 | 0.9345  |
| **0.9**   | 0.8941   | 0.8501    | 0.3780 | 0.5234 | 0.9345  |

Time for 1 threshold evaluation =~ 3 min 30s 

### Quantized model:

| Threshold | Accuracy | Precision | Recall | F1     | AUC-ROC |
|----------:|---------:|----------:|-------:|-------:|--------:|
| **0.2**   | 0.8581   | 0.5249    | 0.8154 | 0.6387 | 0.9306  |
| **0.4**   | 0.8809   | 0.6001    | 0.6748 | 0.6353 | 0.9306  |
| **0.5**   | 0.8880   | 0.6408    | 0.6179 | 0.6291 | 0.9306  |
| **0.7**   | 0.8969   | 0.7467    | 0.4984 | 0.5978 | 0.9306  |
| **0.9**   | 0.8869   | 0.8878    | 0.3024 | 0.4512 | 0.9306  |

Time for 1 threshold evaluation =~ 2 min 41s 

## 🤗 Usage

```python
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification
import numpy as np

# Load model and tokenizer using optimum
model = ORTModelForSequenceClassification.from_pretrained("gravitee-io/detoxify-onnx", file_name="model.quant.onnx")
tokenizer = AutoTokenizer.from_pretrained("gravitee-io/detoxify-onnx")

# Tokenize input
text = "Your comment here"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)

# Run inference
outputs = model(**inputs)
logits = outputs.logits

# Optional: convert to probabilities
probs = 1 / (1 + np.exp(-logits))
print(probs)
```

## 🐙 GitHub Repository: 

You can find the full source code, CLI tools, and evaluation scripts in the official [GitHub repository](https://github.com/gravitee-io-labs/detoxify-onnx).