abhi099k/ai-text-detector-v-n4.0

🧠 AI Text Detector v1.0 (DeBERTa-v3-large)

🏷️ Model Details

Field	Description
Model Name	ai-text-detector-v-n4.0
Base Model	microsoft/deberta-v3-large
Task	Text Classification (Human-written vs AI-generated)
Language	English
Framework	PyTorch, Transformers
Trained by	Abhinav
Fine-tuned using	Hugging Face Trainer API with early stopping, mixed precision (fp16), and F1 optimization.

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📖 Model Description

This model fine-tunes DeBERTa-v3-large for detecting whether a given text is written by a Human or generated by an AI.
It was trained on a custom dataset containing 10,000+ samples of diverse text across multiple topics, labeled as:

• 0 → Human-written text
• 1 → AI-generated text

The goal is to identify subtle linguistic differences and stylistic cues between natural human writing and machine-generated content.

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⚙️ Training Configuration

Parameter	Value
Epochs	4
Batch size	8
Learning Rate	2e-5
Max Sequence Length	256
Optimizer	AdamW
Scheduler	Linear decay
Weight Decay	0.01
Seed	42
Mixed Precision	✅ Yes (fp16)
Gradient Accumulation Steps	2
Frameworks	PyTorch, Transformers, Datasets

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🧾 Dataset

Field	Value
Source	Custom dataset (gpt_5_with_10k.csv)
Columns	text, label
Labels	0 = Human, 1 = AI
Split	90% Train / 10% Test
Cleaning	Removed special characters, normalized whitespace, and standardized punctuation.

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📊 Evaluation Results

Metric	Score
Accuracy	~0.97
F1 Score	~0.97
Precision / Recall	Balanced

Confusion Matrix Example:

[[4800   90]    → True Human
 [ 110 5000]]   → True AI

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🔍 Usage Example

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "abhi099k/ai-text-detector-v-n4.0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

text = "This article explores the evolution of large language models..."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
pred = torch.argmax(outputs.logits, dim=1).item()

print("🧑 Human" if pred == 0 else "🤖 AI")

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📈 Intended Use

• Detect AI-generated content for moderation, academic integrity, or authenticity verification.
• Use as a foundation model for fine-tuning on domain-specific datasets (e.g., essays, reviews, research papers).

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⚠️ Limitations

• May misclassify paraphrased AI text or human text with robotic phrasing.
• Primarily trained on English — not guaranteed for other languages.
• Should not be used for punitive or high-stakes decisions without human review.

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🏆 Future Improvements

• Multi-language support (Hindi, Spanish, etc.)
• Add stylistic embeddings for cross-model generalization.
• Robustness testing against prompt-engineering and obfuscation.

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🧩 Technical Summary

Component	Library
Tokenization	AutoTokenizer
Model	AutoModelForSequenceClassification
Trainer	transformers.Trainer
Metrics	evaluate (accuracy, f1)
Visualization	matplotlib (confusion matrix)

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📬 Citation

If you use this model, please cite:

@model{abhinav_ai_text_detector_v1,
  title     = {AI Text Detector v1.0 – DeBERTa-v3-large Fine-tune},
  author    = {Abhinav},
  year      = {2025},
  url       = {https://huggingface.co/Abhinav/ai-text-detector-v-n4.0}
}