Earlybird-fast-ONNX

Earlybird-fast (ONNX)

This is an ONNX version of noumenon-labs/Earlybird-fast. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js

See the pipeline documentation for text-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TextClassificationPipeline

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🦅 Earlybird: Fast & Accurate AI Text Detection

Earlybird is a lightweight, high-speed AI text detection model designed to classify text as either Human-Written or AI-Generated.

Built on the efficient DistilRoBERTa architecture, it was fine-tuned on the W.O.R.M. (Wait, Original or Machine) dataset.

⚡ Model Stats

• Architecture: DistilRoBERTa (82M parameters)
• Primary Task: Binary Classification (Human vs. AI)
• Context Window: 512 Tokens
• Inference Speed: <50ms (CPU) / <5ms (GPU)

🚀 Overview

Earlybird is designed for rapid, real-time detection. Unlike generative Large Language Models (LLMs) that are slow and resource-heavy, Earlybird uses a distilled encoder architecture. This allows it to process text in milliseconds, making it ideal for high-volume applications like content moderation, academic integrity checks, and spam filtering.

The model analyzes stylistic patterns, perplexity, and token transitions to determine if a text was written by a human or generated by models like GPT-4, Claude, Llama, or Mistral.

📚 Training Data

Earlybird was trained on Mega-WORM, a unified dataset curated from four major open-source collections. The training data was rigorously filtered to ensure high-quality prose, focusing on texts with sufficient context (essays, blog posts, articles).

📊 Performance Benchmarks

The model excels at identifying AI-generated content in Medium and Long-form text (over 100 words). However, users should be aware of limitations regarding very short texts.

Detailed Length Breakdown

Overall Metrics

⚠️ Important Limitations

• Short Text Instability: As shown in the benchmarks, the model's accuracy drops significantly (to ~76%) on texts under 100 words (e.g., short tweets, single sentences). It is not recommended for use on short social media comments without human review.
• Context Requirement: The model relies on analyzing sentence structure and paragraph flow. Without enough words, it lacks the context needed to make a high-confidence prediction.
• False Positives: Highly formal, academic human writing can occasionally be flagged as AI due to its rigid structure.