GTox — XLM-RoBERTa Multilingual Offensive Speech Classifier (v1)

A fine-tuned XLM-RoBERTa base model for detecting offensive speech in English and German. This is a binary classifier (0 = safe, 1 = offensive) trained to catch a broad range of harmful language — from explicit hate speech to subtle microaggressions.

Model Description

Categories of Offensive Speech

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Training Data

The model was trained on a combined multilingual dataset assembled from the following sources:

German:

• GermEval 2018 — Offensive language detection in German tweets
• GermEval 2019 — Offensive language detection in German tweets
• HASOC 2019 DE — Hate speech and offensive content in German
• HASOC 2020 DE — Hate speech and offensive content in German

English:

• HASOC 2019 EN — Hate speech and offensive content in English
• HASOC 2020 EN — Hate speech and offensive content in English
• Jigsaw Toxicity — Toxic comment classification

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Evaluation Results

Results will be filled in after evaluation is completed.

GermEval 2018 Test Set (DE)

GermEval 2019 Test Set (DE)

HASOC 2019 Test Set (DE)

HASOC 2020 Test Set (DE)

HASOC 2019 Test Set (EN)

HASOC 2020 Test Set (EN)

HateCheck (DE)

HateCheck (EN)

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Usage

With Hugging Face pipeline

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Horbee/xlm-roberta-base-offensive-comment-classifier",
    tokenizer="Horbee/xlm-roberta-base-offensive-comment-classifier",
)

# English
result = classifier("You are such an idiot!")
print(result)
# [{'label': 'LABEL_1', 'score': 0.99}]  →  Offensive

# German
result = classifier("Du bist ein Idiot!")
print(result)
# [{'label': 'LABEL_1', 'score': 0.99}]  →  Offensive

# Safe example
result = classifier("This is a completely normal and friendly comment.")
print(result)
# [{'label': 'LABEL_0', 'score': 0.99}]  →  Safe

The label mapping is:

• LABEL_0 → Safe (not offensive)
• LABEL_1 → Offensive

Batch inference

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Horbee/xlm-roberta-base-offensive-comment-classifier",
    device=0,  # use GPU; set to -1 or omit for CPU
)

texts = [
    "I hate all of you!",
    "Have a nice day!",
    "Das ist wirklich schrecklich.",
    "Schönen guten Morgen!",
]

results = classifier(texts, batch_size=8)
for text, result in zip(texts, results):
    label = "Offensive" if result["label"] == "LABEL_1" else "Safe"
    print(f"{label} ({result['score']:.2f}): {text}")

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ONNX Usage

The model is also available as an INT8 quantized ONNX model (onnx/model_quantized.onnx) for fast CPU inference without a PyTorch dependency.

Installation

pip install onnxruntime tokenizers numpy

Inference

from pathlib import Path

import numpy as np
import onnxruntime as ort
from tokenizers import Tokenizer

MODEL_DIR = Path("onnx")

# Load tokenizer and ONNX session
tokenizer = Tokenizer.from_file(str(MODEL_DIR / "tokenizer.json"))
tokenizer.enable_truncation(max_length=256)
tokenizer.enable_padding(pad_id=1, pad_token="<pad>", length=256)

session = ort.InferenceSession(
    str(MODEL_DIR / "model.onnx"),
    providers=["CPUExecutionProvider"],
)

def classify(text: str, threshold: float = 0.5) -> tuple[int, list[float]]:
    encoded = tokenizer.encode(text)
    inputs = {
        "input_ids": np.array([encoded.ids], dtype=np.int64),
        "attention_mask": np.array([encoded.attention_mask], dtype=np.int64),
    }
    logits = session.run(None, inputs)[0]
    probs = np.exp(logits) / np.sum(np.exp(logits), axis=-1, keepdims=True)
    label = int(probs[0][1] >= threshold)
    return label, probs[0].tolist()

# Example
label, probs = classify("Du bist ein Idiot!")
print(f"Label: {label} — {'Offensive' if label == 1 else 'Safe'}")
print(f"Probabilities: safe={probs[0]:.3f}, offensive={probs[1]:.3f}")

ONNX Model Files

The quantized model uses dynamic INT8 quantization (QInt8 weights, QUInt8 activations) applied to MatMul, Attention, Gather, and embedding layers, resulting in significantly reduced model size and faster CPU throughput with minimal accuracy loss.

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Model Files

xlm-roberta-base-offensive-comment-classifier/
├── config.json
├── model.safetensors
├── tokenizer.json
├── tokenizer_config.json
├── onnx/
│   ├── model.onnx          # FP32 ONNX export
│   ├── model_quantized.onnx          # INT8 quantized ONNX (recommended for production)
│   └── tokenizer.json

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Limitations and Bias

• The model was primarily trained on social media text (tweets, forum comments). Performance may degrade on formal or domain-specific text.
• Implicit offensive speech (microaggressions, sarcasm) remains the hardest category to detect reliably.
• The model supports English and German only. Using it on other languages may produce unreliable results even though XLM-RoBERTa has multilingual pretraining.
• As with all classifiers trained on human-annotated data, the model reflects the biases present in the annotation guidelines and annotator demographics.

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Citation

If you use this model in your research, please cite this repository:

@misc{gtox2024,
  title  = {GTox: Multilingual Offensive Speech Classifier},
  year   = {2026},
  url    = {https://github.com/Horbee/gtox-offensive-comment-classifier}
}