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---
license: mit
language:
- en
base_model:
- microsoft/deberta-v3-large
pipeline_tag: text-classification
metrics:
- accuracy
---
# 🧠 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](https://huggingface.co/microsoft/deberta-v3-large) |
| **Task** | Text Classification (Human-written vs AI-generated) |
| **Language** | English |
| **Framework** | PyTorch, Transformers |
| **Trained by** | [Abhinav](https://huggingface.co/abhi099k) |
| **Fine-tuned using** | Hugging Face `Trainer` API with early stopping, mixed precision (fp16), and F1 optimization. |
---
## 📖 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.
---
## ⚙️ 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 |
---
## 🧾 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. |
---
## 📊 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
```
---
## 🔍 Usage Example
```python
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")
```
---
## 📈 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).
---
## ⚠️ 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.
---
## 🏆 Future Improvements
- Multi-language support (Hindi, Spanish, etc.)
- Add stylistic embeddings for cross-model generalization.
- Robustness testing against prompt-engineering and obfuscation.
---
## 🧩 Technical Summary
| Component | Library |
|:--|:--|
| Tokenization | `AutoTokenizer` |
| Model | `AutoModelForSequenceClassification` |
| Trainer | `transformers.Trainer` |
| Metrics | `evaluate` (accuracy, f1) |
| Visualization | `matplotlib` (confusion matrix) |
---
## 📬 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}
}
``` |