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ai-text-detector-optimized

Joshfcooper / ai-text-detector-optimized

Run on huggingface.co
huggingface.co
Total runs: 752
24-hour runs: 9
7-day runs: -66
30-day runs: 216
Model's Last Updated: June 03 2025
text-classification

Introduction of ai-text-detector-optimized

Model Details of ai-text-detector-optimized

Joshfcooper/ai-text-detector-optimized

Model Description

This is an ultra-optimized AI text detector based on SmolLM-135M, designed to distinguish between human-written and AI-generated text with high accuracy and blazing-fast inference speed.

Key Features
  • High Accuracy : 96.7% accuracy on test data
  • Ultra-Fast : 103.1ms average inference time
  • Optimized Architecture : Uses only 12 out of 30 transformer layers (60% compression)
  • Multiple Formats : Available in both PyTorch (.pt) and ONNX (.onnx) formats
  • Production Ready : Optimized for real-world deployment
Model Architecture
  • Base Model : HuggingFaceTB/SmolLM-135M
  • Compression : 30 layers → 12 layers (selected layers: 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22)
  • Feature Extraction : 24 layer outputs → 13,824 features
  • Classifier : Linear probe with sigmoid activation
  • Parameters : ~60% reduction from base model
Usage
ONNX Model (Recommended for Web/Production) 
import onnxruntime as ort
from transformers import AutoTokenizer
import numpy as np

# Load tokenizer and ONNX model
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-135M")
session = ort.InferenceSession("model.onnx")

def predict(text):
    # Tokenize
    tokens = tokenizer(text, truncation=True, padding='max_length', 
                      max_length=256, return_tensors="np")
    
    # Convert to int64 for ONNX
    feeds = {
        'input_ids': tokens['input_ids'].astype(np.int64),
        'attention_mask': tokens['attention_mask'].astype(np.int64)
    }
    
    # Run inference
    result = session.run(None, feeds)
    probability = result[0][0]
    
    # Interpret (model outputs inverted probabilities)
    human_prob = 1 - probability
    is_human = human_prob > 0.5
    
    return {
        'prediction': 'human' if is_human else 'ai',
        'human_probability': human_prob,
        'confidence': abs(human_prob - 0.5) * 2
    }

# Example usage
result = predict("Your text here...")
print(result)
PyTorch Model 
import torch
from transformers import AutoTokenizer
import pickle

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-135M")
model = torch.load("pytorch_model.pt", map_location='cpu')
model.eval()

def predict_pytorch(text):
    tokens = tokenizer(text, truncation=True, padding='max_length', 
                      max_length=256, return_tensors="pt")
    
    with torch.no_grad():
        probability = model(tokens['input_ids'], tokens['attention_mask']).item()
    
    human_prob = 1 - probability  # Invert output
    is_human = human_prob > 0.5
    
    return {
        'prediction': 'human' if is_human else 'ai',
        'human_probability': human_prob,
        'confidence': abs(human_prob - 0.5) * 2
    }
Performance Metrics
  • Accuracy : 96.7%
  • Inference Time : 103.1ms (average)
  • Model Size : ~60% smaller than base model
  • Throughput : ~10 predictions/second
Training Details

The model was trained using a feature extraction approach:

  1. Extract hidden states from 12 selected layers of SmolLM-135M
  2. Mean pooling across sequence length with attention masking
  3. Concatenate features from all layers (13,824 total features)
  4. Train linear classifier with standardization
  5. Export to ONNX for optimized inference
Important Notes

⚠️ Output Inversion : This model outputs inverted probabilities. Use 1 - model_output for human probability.

Files Included
  • model.onnx : ONNX model for web/production deployment
  • pytorch_model.pt : PyTorch model for development
  • config.json : Model configuration
  • deployment_config.json : Deployment configuration with layer selection
  • scaler_params.json : Feature standardization parameters
License

Apache 2.0

Citation 
@misc{ai-text-detector-optimized,
  title={Ultra-Optimized AI Text Detector},
  author={Your Name},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/Joshfcooper/ai-text-detector-optimized}
}
Ethical Considerations

This model is designed to detect AI-generated text. Please use responsibly and be aware that:

  • No detector is 100% accurate
  • Results should be used as guidance, not definitive proof
  • Consider privacy and consent when analyzing text
  • Be aware of potential biases in training data

Runs of Joshfcooper ai-text-detector-optimized on huggingface.co

752
Total runs
9
24-hour runs
-10
3-day runs
-66
7-day runs
216
30-day runs

More Information About ai-text-detector-optimized huggingface.co Model

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Url of ai-text-detector-optimized

ai-text-detector-optimized huggingface.co Url

Provider of ai-text-detector-optimized huggingface.co

Joshfcooper
ORGANIZATIONS
huggingface.co

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