floxoris/harmony-v2

Harmony v2 — Toxicity Classifier

Lightweight, context-aware toxicity detection that understands intent, not just keywords.

Harmony v2 is a lightweight toxicity classifier fine-tuned from gravitee-io/bert-tiny-toxicity on HarmonyDataset v2, a custom dataset of 7,000 human-like chat messages.

Unlike traditional keyword-based filters, Harmony v2 focuses on who is being targeted, the intent behind the message, and conversational context.

Profanity ≠ Toxicity

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🧠 Overview

Harmony v2 is designed for modern chat moderation, especially for Telegram communities, forums, games, and social platforms.

It distinguishes between:

🚨 Toxic

• Personal attacks
• Harassment
• Threats
• Humiliation
• Dehumanization
• Hate directed at another person

✅ Safe

• Emotional expression
• Frustration
• Profanity without a target
• Friendly banter
• Sarcasm
• Jokes
• Self-directed insults

Instead of blocking words, Harmony v2 evaluates intent.

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✨ Features

Feature	Value
Base model	gravitee-io/bert-tiny-toxicity
Dataset	HarmonyDataset v2
Samples	7,000
Languages	Ukrainian, Russian, mixed UA/RU
Max sequence length	512 tokens
Framework	Hugging Face Transformers
Model format	PyTorch
Quantized size	~9 MB (INT8)
License	Apache-2.0

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⚡ Performance

Metric	Result
Evaluation Loss	0.4255
F1 Score	98.4%
CPU Inference	~11 ms
Precision	High
Recall	High

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📊 Dataset

HarmonyDataset v2 contains 7,000 balanced synthetic chat messages.

Category	Share
Safe	15%
Friendly profanity	10%
Frustration	10%
Self-insult	5%
Joke	10%
Sarcasm	10%
Criticism	5%
Insult	10%
Harassment	15%
Threat	10%

Dataset philosophy:

• realistic conversations
• Telegram-like writing
• slang
• spelling mistakes
• emojis
• mixed Ukrainian/Russian
• contextual toxicity

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🚀 Installation

Clone the repository:

git clone https://github.com/floxoris/harmony-v2
cd harmony-v2

Install dependencies:

pip install -r requirements.txt

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📦 Load the Model

from transformers import (
    AutoTokenizer,
    AutoModelForSequenceClassification
)

model_path = "./Harmony-v2"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)

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🔍 Prediction Example

import torch

def predict(text):
    inputs = tokenizer(
        text,
        return_tensors="pt",
        truncation=True,
        max_length=512
    )

    with torch.no_grad():
        outputs = model(**inputs)

    probs = torch.softmax(outputs.logits, dim=1)
    toxic_score = probs[0][1].item()

    return toxic_score

text = "блін сервер впав, третій раз сьогодні"

score = predict(text)

label = "🚨 Toxic" if score > 0.5 else "✅ Safe"

print(f"""
Text: {text}

Score: {score:.3f}

Prediction: {label}
""")

Example output:

Text: блін сервер впав, третій раз сьогодні

Score: 0.021

Prediction: ✅ Safe

---

🏗 Training

Train locally:

python train.py

Or inside Google Colab:

!python train.py

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📁 Repository Structure

Harmony-v2/
│
├── config.json
├── tokenizer.json
├── tokenizer_config.json
├── special_tokens_map.json
├── model.safetensors
├── train.py
├── requirements.txt
└── README.md

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💡 Intended Use

Harmony v2 is suitable for:

• Telegram bots
• Discord moderation
• Forum moderation
• Live chat filtering
• AI assistants
• Comment moderation
• Social platforms
• Community management

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❌ Not Intended For

Harmony v2 should not be used as the sole decision-maker for:

• legal decisions
• law enforcement
• employment screening
• medical applications

Human review is recommended for critical moderation.

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📚 References

Base model

• gravitee-io/bert-tiny-toxicity

Frameworks

• Hugging Face Transformers
• Hugging Face Datasets
• PyTorch

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📄 License

Licensed under the Apache License 2.0.

You are free to:

• ✅ use commercially
• ✅ modify
• ✅ redistribute
• ✅ include in proprietary software

Subject to the Apache-2.0 license terms.

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🤝 Contributing

Pull requests, bug reports, and suggestions are welcome.

If you find a false positive or false negative, please open an issue.

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🌸 Floxoris Labs

Harmony v2 is developed by Floxoris Labs.

Lightweight AI. Maximum Intelligence.