Create README.md

README.md

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+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+- f1
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
+tags:
+- text-classification
+- ai-detection
+- academic-text
+- ai-generated-text-detection
+model-index:
+- name: bert-ai-text-detector
+  results:
+  - task:
+      type: text-classification
+      name: AI-Generated Text Detection
+    dataset:
+      name: Custom Academic Text Dataset
+      type: custom
+    metrics:
+    - type: accuracy
+      value: 0.9957
+    - type: f1
+      value: 0.9958
+    - type: precision
+      value: 0.9923
+    - type: recall
+      value: 0.9994
+---
+# BERT-based AI-Generated Academic Text Detector
+
+A high-accuracy BERT model for detecting AI-generated academic text with **99.57% accuracy** on paragraph-level samples.
+
+## Online Demo
+
+🌐 **Try the model online**: [https://followsci.com/ai-detection](https://followsci.com/ai-detection)
+
+Free web interface with real-time detection, no installation or API key required.
+
+## Model Details
+
+### Model Description
+
+- **Model Type**: BERT-base-uncased fine-tuned for binary text classification
+- **Architecture**: BERT-base-uncased (110M parameters)
+- **Task**: Binary classification (Human-written vs AI-generated text)
+- **Input**: Academic text paragraphs (up to 512 tokens)
+- **Output**: Binary label (0 = Human-written, 1 = AI-generated) with confidence scores
+
+### Training Information
+
+- **Training Samples**: 1,487,400 paragraph-level samples
+- **Validation Samples**: 185,930 paragraph-level samples
+- **Test Samples**: 185,930 paragraph-level samples
+- **Total Dataset**: 1,859,260 paragraphs
+- **Training Data**:
+  - Human-written: Academic papers from arXiv
+  - AI-generated: Text generated by various large language models (GPT, Claude, etc.)
+
+## Performance
+
+### Test Set Results
+
+| Metric | Value |
+|--------|-------|
+| **Accuracy** | **99.57%** |
+| **F1-Score** | **99.58%** |
+| Precision | 99.23% |
+| Recall | 99.94% |
+| False Positive Rate | 0.82% |
+| False Negative Rate | 0.06% |
+
+### Confusion Matrix (Test Set)
+
+| | Predicted: Human | Predicted: AI |
+|---|---|---|
+| **Actual: Human** | 89,740 (TN) | 740 (FP) |
+| **Actual: AI** | 60 (FN) | 95,390 (TP) |
+
+**Inference Speed:** ~20,900 samples/second on RTX 3090 (batch size 64)
+
+## Usage
+
+### Quick Start
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "followsci/bert-ai-text-detector"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name)
+model.eval()
+
+# Detect AI text
+text = "Your academic paragraph here..."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    ai_prob = probs[0][1].item() * 100
+    human_prob = probs[0][0].item() * 100
+    
+    print(f"AI-generated probability: {ai_prob:.1f}%")
+    print(f"Human-written probability: {human_prob:.1f}%")
+    
+    if ai_prob > 50:
+        print("Prediction: AI-generated")
+    else:
+        print("Prediction: Human-written")
+```
+
+### Batch Processing
+
+```python
+texts = [
+    "First paragraph...",
+    "Second paragraph...",
+    # ... more texts
+]
+
+inputs = tokenizer(
+    texts,
+    return_tensors="pt",
+    truncation=True,
+    max_length=512,
+    padding=True
+)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    
+    for i, prob in enumerate(probs):
+        ai_prob = prob[1].item() * 100
+        print(f"Text {i+1}: AI probability = {ai_prob:.1f}%")
+```
+
+### Using with Transformers Pipeline
+
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "text-classification",
+    model="followsci/bert-ai-text-detector",
+    tokenizer="followsci/bert-ai-text-detector"
+)
+
+result = classifier("Your text here...")
+print(result)
+```
+
+## Training Details
+
+### Training Configuration
+
+- **Base Model**: `bert-base-uncased`
+- **Batch Size**: 64
+- **Learning Rate**: 5e-5 (with linear warmup)
+- **Warmup Steps**: 5,000
+- **Max Sequence Length**: 512
+- **Optimizer**: AdamW
+- **Epochs**: 3
+- **Training Time**: ~11 hours (on RTX 3090)
+
+### Dataset Distribution
+
+| Split | Total Samples | Human (Label 0) | AI (Label 1) |
+|-------|--------------|-----------------|--------------|
+| Train | 1,487,400 | 723,780 (48.7%) | 763,620 (51.3%) |
+| Validation | 185,930 | 90,470 (48.7%) | 95,460 (51.3%) |
+| Test | 185,930 | 90,480 (48.7%) | 95,450 (51.3%) |
+
+## Limitations
+
+1. **Domain Specificity**: The model is trained primarily on academic text. Performance may degrade on:
+   - Casual text or social media content
+   - Technical documentation
+   - Creative writing
+
+2. **Binary Classification**: The model only distinguishes between "human" and "AI" text, without:
+   - Identifying which AI model generated the text
+   - Providing confidence intervals
+   - Detecting partially AI-assisted text
+
+3. **Paragraph-Level Detection**: The model is optimized for paragraph-level samples:
+   - Performance on sentence-level or full-document level may vary
+   - Best results achieved with structured academic paragraphs
+
+4. **False Positives**: Approximately 0.82% false positive rate means some human-written text may be flagged as AI-generated.
+
+## Ethical Considerations
+
+- **Use Case**: This model is intended as a tool for academic integrity and research purposes
+- **Bias**: The model may reflect biases present in the training data
+- **Misuse**: Should not be used as the sole criterion for academic misconduct decisions
+- **Transparency**: Results should be interpreted with context and domain expertise
+
+
+## License
+
+This model is licensed under the MIT License.
+
+## Contact
+
+- **Email**: raffoduanedonnenfeld@gmail.com
+
+---
+
+<p align="center">
+  Made with ❤️ for Academic Integrity
+</p>