metadata
license: apache-2.0
language:
- en
metrics:
- accuracy
- f1
- precision
- recall
base_model: FacebookAI/roberta-base
pipeline_tag: text-classification
library_name: transformers
tags:
- roberta
- toxicity-detection
- transformers
- text-classification
- custom-dataset
eval_results:
eval_accuracy: 0.94
eval_f1: 0.93
eval_precision: 0.95
eval_recall: 0.91
🛡️ Toxicity-RoBERTa-Base
A fine-tuned transformer model built on top of roberta-base to detect toxic content in text — including insults, threats, hate speech, and offensive language.
The model is lightweight, accurate, and ideal for real-time moderation tasks.
🧩 Use Cases
This model is designed to flag toxic messages in:
- 🧵 Social media comments and posts
- 🛠️ Developer forums and Discord/Slack bots
- 🧠 LLM output moderation
- 🧩 Community Q&A sites (like Reddit, Stack Overflow)
- 🚨 User-generated content platforms (blogs, review sites, games)
🔍 Model Summary
| Attribute | Details |
|---|---|
| Base Architecture | roberta-base |
| Fine-tuned For | Toxic vs. Non-toxic classification |
| Classes | 0 = Non-toxic, 1 = Toxic |
| Language | English (en) |
| Data Sources | Custom dataset (multi-domain) |
| Framework | 🤗 Transformers |
| Total Parameters | ~125M |
📊 Performance
| Metric | Result |
|---|---|
| Accuracy | 94% |
| F1 Score | 93% |
| Precision | 95% |
| Recall | 91% |
⚙️ Quick Start
💡 Install Required Libraries
pip install transformers torch