README.md

---
language:
  - en
pipeline_tag: text-classification
library_name: peft
base_model: microsoft/deberta-v3-large
datasets:
  - stealthcode/ai-detection
tags:
  - lora
  - ai-detection
  - binary-classification
  - deberta-v3-large
metrics:
  - accuracy
  - f1
  - auroc
  - average_precision
---

# AI Detector LoRA (DeBERTa-v3-large)

LoRA adapter for binary AI-text vs Human-text detection, trained on ~2.3M English samples
(`label: 1 = AI, 0 = Human`) using `microsoft/deberta-v3-large` as the base model.

- **Base model:** `microsoft/deberta-v3-large`
- **Task:** Binary classification (AI vs Human)
- **Head:** Single-logit + `BCEWithLogitsLoss`
- **Adapter type:** LoRA (`peft`)
- **Hardware:** H100 SXM, bf16, multi-GPU
- **Final decision threshold:** **0.9033** (max-F1 on validation)

---

## Files in this repo

- `adapter/` – LoRA weights saved with `peft_model.save_pretrained(...)`
- `threshold.json` – chosen deployment threshold and validation F1
- `results.json` – hyperparameters, validation threshold search, test metrics
- `training_log_history.csv` – raw Trainer log history
- `predictions_val.csv` – validation probabilities and labels
- `predictions_test.csv` – test probabilities and labels
- `figures/` – training and evaluation plots
- `README.md` – this file

---

## Metrics (test set)

Using threshold **0.9033**:

| Metric        | Value   |
|--------------|---------|
| AUROC        | 0.9970  |
| Average Precision (AP) | 0.9966 |
| F1           | 0.9740  |
| Accuracy     | 0.9767  |
| Precision    | 0.9857  |
| Recall       | 0.9625  |
| Specificity  | 0.9884  |

Confusion matrix (test):

- **True Negatives (Human correctly)**: 123,399  
- **False Positives (Human → AI)**: 1,449  
- **False Negatives (AI → Human)**: 3,882  
- **True Positives (AI correctly)**: 99,657  

---

## Plots

### Training & validation

- Learning curves:

  ![Learning curves](./figures/fig_learning_curves.png)

- Eval metrics over time:

  ![Eval metrics](./figures/fig_eval_metrics.png)

### Validation set

- ROC:

  ![ROC (val)](./figures/fig_roc_val.png)

- Precision–Recall:

  ![PR (val)](./figures/fig_pr_val.png)

- Calibration curve:

  ![Calibration (val)](./figures/fig_calibration_val.png)

- F1 vs threshold:

  ![F1 vs threshold (val)](./figures/fig_threshold_f1_val.png)

### Test set

- ROC:

  ![ROC (test)](./figures/fig_roc_test.png)

- Precision–Recall:

  ![PR (test)](./figures/fig_pr_test.png)

- Calibration curve:

  ![Calibration (test)](./figures/fig_calibration_test.png)

- Confusion matrix:

  ![Confusion matrix (test)](./figures/fig_confusion_test.png)

---

## Usage

### Load base + LoRA adapter

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel
import torch
import json

base_model_id = "microsoft/deberta-v3-large"
adapter_id    = "stealthcode/ai-detection"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)

base_model = AutoModelForSequenceClassification.from_pretrained(
    base_model_id,
    num_labels=1,  # single logit for BCEWithLogitsLoss
)
model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()
````

### Inference with threshold

```python
# load threshold
with open("threshold.json") as f:
    thr = json.load(f)["threshold"]  # 0.9033

def predict_proba(texts):
    enc = tokenizer(
        texts,
        padding=True,
        truncation=True,
        max_length=512,
        return_tensors="pt",
    )
    with torch.no_grad():
        logits = model(**enc).logits.squeeze(-1)
        probs = torch.sigmoid(logits)
    return probs.cpu().numpy()

def predict_label(texts, threshold=thr):
    probs = predict_proba(texts)
    return (probs >= threshold).astype(int)

# example
texts = ["Some example text to classify"]
probs = predict_proba(texts)
labels = predict_label(texts)
print(probs, labels)  # label 1 = AI, 0 = Human
```

---

## Notes

* Classifier head is **trainable** together with LoRA layers (unfrozen after applying PEFT).
* Training used:

  * `bf16=True`
  * `optim="adamw_torch_fused"`
  * cosine-with-restarts scheduler
  * LR scaled down from HPO to account for full-dataset (~14k steps).
* Threshold `0.9033` was chosen as the **max-F1** point on the validation set.
  You can adjust it if you prefer fewer false positives or fewer false negatives.