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README.md

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+# 🧠 AI Text Detector v1.0 (DeBERTa-v3-large)
+
+## 🏷️ Model Details
+| Field | Description |
+|:--|:--|
+| **Model Name** | `ai-text-detector-v-k1.0` |
+| **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/Abhinav) |
+| **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 = "Abhinav/ai-text-detector-v-k1.0"
+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-k1.0}
+}
+```