Answerdotai ModernBERT Base Ai Detector

🚀 answerdotai-ModernBERT-base-ai-detector

This model is a fine - tuned version of answerdotai/ModernBERT-base on the AI vs Human Text Classification dataset, DAIGT V2 Train Dataset. It can effectively distinguish between AI - generated and human - written texts, with a validation loss of 0.0036 on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of answerdotai/ModernBERT-base on the AI vs Human Text Classification dataset, DAIGT V2 Train Dataset.

It achieves the following results on the evaluation set:

• Validation Loss: 0.0036

✨ Features

📝 Model Description

This model is based on ModernBERT-base, a lightweight and efficient BERT - based model. It has been fine - tuned for AI - generated vs Human - written text classification, allowing it to distinguish between texts written by AI models (ChatGPT, DeepSeek, Claude, etc.) and human authors.

🎯 Intended Uses & Limitations

✅ Intended Uses

• AI - generated content detection (e.g., ChatGPT, Claude, DeepSeek).
• Text classification for distinguishing human vs AI - generated content.
• Educational & Research applications for AI - content detection.

⚠️ Limitations

• Not 100% accurate → Some AI texts may resemble human writing and vice versa.
• Limited to trained dataset scope → May struggle with out - of - domain text.
• Bias risks → If the dataset contains bias, the model may inherit it.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

model_name = "answerdotai/ModernBERT-base-ai-detector"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Create text classification pipeline
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

# Run classification
text = "This text was written by an AI model like ChatGPT."
result = classifier(text)

print(result)

📚 Documentation

📊 Training and Evaluation Data

• The model was fine - tuned on 35,894 training samples and 8,974 test samples.
• The dataset consists of AI - generated text samples (ChatGPT, Claude, DeepSeek, etc.) and human - written samples (Wikipedia, books, articles).
• Labels:
  • 1 → AI - generated text
  • 0 → Human - written text

⚙️ Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

📈 Training Results

🛠 Framework Versions

📄 License

The model is licensed under the apache - 2.0 license.