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.

📊 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:

Hyperparameter	Value
Learning Rate	`2e-5`
Train Batch Size	`16`
Eval Batch Size	`16`
Optimizer	`AdamW` (`β1=0.9, β2=0.999, ε=1e-08`)
LR Scheduler	`Linear`
Epochs	`3`
Mixed Precision	`Native AMP (fp16)`

📈 Training Results

Training Loss	Epoch	Step	Validation Loss
0.0505	0.22	500	0.0214
0.0114	0.44	1000	0.0110
0.0088	0.66	1500	0.0032
0.0	0.89	2000	0.0048
0.0068	1.11	2500	0.0035
0.0	1.33	3000	0.0040
0.0	1.55	3500	0.0097
0.0053	1.78	4000	0.0101
0.0	2.00	4500	0.0053
0.0	2.22	5000	0.0039
0.0017	2.45	5500	0.0046
0.0	2.67	6000	0.0043
0.0	2.89	6500	0.0036

🛠 Framework Versions

Library	Version
Transformers	`4.48.3`
PyTorch	`2.5.1+cu124`
Datasets	`3.3.2`
Tokenizers	`0.21.0`

📤 Model Usage

To load and use the model for text classification:

python

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)

AICodexLab/answerdotai-ModernBERT-base-ai-detector

answerdotai-ModernBERT-base-ai-detector

📝 Model Description