Model improved

README.md

@@ -16,19 +16,35 @@ metrics:
   - f1
   - auroc
   - average_precision
+model-index:
+  - name: AI Detector LoRA (DeBERTa-v3-large)
+    results:
+      - task:
+          type: text-classification
+          name: AI Text Detection
+        dataset:
+          name: stealthcode/ai-detection
+          type: stealthcode/ai-detection
+        metrics:
+          - type: auroc
+            value: 0.9985
+          - type: f1
+            value: 0.9812
+          - type: accuracy
+            value: 0.9814
 ---
 
 # AI Detector LoRA (DeBERTa-v3-large)
 
-LoRA adapter for binary AI-text vs Human-text detection, trained on ~2.3M English samples
+LoRA adapter for binary AI-text vs Human-text detection, trained on ~2.7M 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:** **0.9284** (max-F1 on calibration set)
+- **Hardware:** 8 x RTX 5090, bf16, multi-GPU
+- **Final decision threshold:** **0.8697** (max-F1 on calibration set)
 
 ---
 
@@ -47,34 +63,35 @@ LoRA adapter for binary AI-text vs Human-text detection, trained on ~2.3M Englis
 
 ---
 
-## Metrics (test set)
+## Metrics (test set, n=279,241)
 
-Using threshold **0.9284**:
+Using threshold **0.8697**:
 
 | Metric                 | Value  |
 | ---------------------- | ------ |
-| AUROC                  | 0.9979 |
-| Average Precision (AP) | 0.9977 |
-| F1                     | 0.9773 |
-| Accuracy               | 0.9797 |
-| Precision              | 0.9909 |
-| Recall                 | 0.9640 |
-| Specificity            | 0.9927 |
+| AUROC                  | 0.9985 |
+| Average Precision (AP) | 0.9985 |
+| F1                     | 0.9812 |
+| Accuracy               | 0.9814 |
+| Precision (AI)         | 0.9902 |
+| Recall (AI)            | 0.9724 |
+| Precision (Human)      | 0.9728 |
+| Recall (Human)         | 0.9904 |
 
 Confusion matrix (test):
 
-- **True Negatives (Human correctly)**: 123,936
-- **False Positives (Human → AI)**: 912
-- **False Negatives (AI → Human)**: 3,723
-- **True Positives (AI correctly)**: 99,816
+- **True Negatives (Human correctly)**: 138,276
+- **False Positives (Human → AI)**: 1,345
+- **False Negatives (AI → Human)**: 3,859
+- **True Positives (AI correctly)**: 135,761
 
 ### Calibration
 
 - **Method:** temperature scaling
-- **Temperature (T):** 1.2807
+- **Temperature (T):** 1.4437
 - **Calibration set:** calibration
-- Test ECE: 0.0119 → 0.0159 (after calibration)
-- Test Brier: 0.01812 → 0.01829 (after calibration)
+- Test ECE: 0.0075 → 0.0116 (after calibration)
+- Test Brier: 0.0157 → 0.0156 (after calibration)
 
 ---
 
@@ -156,7 +173,7 @@ model.eval()
 ```python
 # load threshold
 with open("threshold.json") as f:
-    thr = json.load(f)["threshold"]  # 0.9284
+    thr = json.load(f)["threshold"]  # 0.8697
 
 def predict_proba(texts):
     enc = tokenizer(
@@ -194,7 +211,7 @@ model = AutoModelForSequenceClassification.from_pretrained(model_dir)
 model.eval()
 
 with open("threshold.json") as f:
-    thr = json.load(f)["threshold"]  # 0.9284
+    thr = json.load(f)["threshold"]  # 0.8697
 
 def predict_proba(texts):
     enc = tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors="pt")
@@ -209,7 +226,7 @@ def predict_proba(texts):
 ```python
 import json
 with open("calibration.json") as f:
-    T = json.load(f)["temperature"]  # e.g., 1.2807
+    T = json.load(f)["temperature"]  # e.g., 1.4437
 
 def predict_proba_calibrated(texts):
     enc = tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors="pt")
@@ -224,12 +241,17 @@ def predict_proba_calibrated(texts):
 ## Notes
 
 - Classifier head is **trainable** together with LoRA layers (unfrozen after applying PEFT).
-- Training used:
+- **LoRA config:**
+  - `r=32`, `alpha=128`, `dropout=0.0`
+  - Target modules: `query_proj`, `key_proj`, `value_proj`
+- **Training config:**
 
   - `bf16=True`
   - `optim="adamw_torch_fused"`
-  - cosine-with-restarts scheduler
-  - LR scaled down from HPO to account for full-dataset (~14k steps).
+  - `lr_scheduler_type="cosine_with_restarts"`
+  - `num_train_epochs=2`
+  - `per_device_train_batch_size=8`, `gradient_accumulation_steps=4`
+  - `max_grad_norm=0.5`
 
-- Threshold `0.9284` was chosen as the **max-F1** point on the calibration set.
+- Threshold `0.8697` was chosen as the **max-F1** point on the calibration set.
   You can adjust it if you prefer fewer false positives or fewer false negatives.

adapter/adapter_config.json

@@ -34,9 +34,9 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
+    "key_proj",
     "query_proj",
-    "value_proj",
-    "key_proj"
+    "value_proj"
   ],
   "target_parameters": null,
   "task_type": "SEQ_CLS",

adapter/adapter_model.safetensors

@@ -1,3 +1,3 @@
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calibration.json

@@ -1,26 +1,26 @@
 {
-  "temperature": 1.2806789875030518,
+  "temperature": 1.4436575174331665,
   "method": "temperature_scaling",
   "calibration_set": "calibration",
   "calibration_metrics": {
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+    "temperature": 1.4436575174331665,
     "optimization_method": "LBFGS_logspace",
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-    "calibrated_nll": 0.06279846573841724,
-    "uncalibrated_ece": 0.012124871567496009,
-    "calibrated_ece": 0.016240862688628014,
-    "uncalibrated_brier": 0.01822748637167701,
-    "calibrated_brier": 0.018437309998858068,
-    "nll_improvement": 0.001806140874052481,
-    "ece_improvement": -0.004115991121132005,
-    "brier_improvement": -0.00020982362718105843
+    "uncalibrated_nll": 0.057230830731130305,
+    "calibrated_nll": 0.05340311260808736,
+    "uncalibrated_ece": 0.007595386161633095,
+    "calibrated_ece": 0.011707928851842823,
+    "uncalibrated_brier": 0.01589206575792085,
+    "calibrated_brier": 0.015775692446082124,
+    "nll_improvement": 0.0038277181230429447,
+    "ece_improvement": -0.004112542690209728,
+    "brier_improvement": 0.00011637331183872793
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   "test_metrics": {
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-    "ece_after": 0.015908939599173937,
-    "ece_improvement": -0.004046765290468848,
-    "brier_before": 0.01812282837704726,
-    "brier_after": 0.018294590049400802,
-    "brier_improvement": -0.0001717616723535438
+    "ece_before": 0.007462335961689493,
+    "ece_after": 0.011600581100766194,
+    "ece_improvement": -0.004138245139076701,
+    "brier_before": 0.015727129447539786,
+    "brier_after": 0.0156334356493489,
+    "brier_improvement": 9.369379819088725e-05
   }
 }

merged_model/model.safetensors

@@ -1,3 +1,3 @@
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results.json

@@ -16,105 +16,105 @@
       "key_proj",
       "value_proj"
     ],
-    "learning_rate": 0.0001554357238163802,
+    "learning_rate": 0.00014057133690327707,
     "lr_scheduler_type": "cosine_with_restarts",
     "max_grad_norm": 0.5,
     "optim": "adamw_torch_fused"
   },
   "threshold_optimization": {
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+      "threshold": 0.869714617729187,
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+        "precision_ai": 0.9900603673104631,
+        "recall_ai": 0.9720312276178198,
         "confusion_matrix": {
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-          "false_negative": 2993,
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+          "true_negative": 110607,
+          "false_positive": 1090,
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+          "true_positive": 108572
         }
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+    "temperature": 1.4436575174331665,
     "method": "temperature_scaling",
     "calibration_set": "calibration",
     "calibration_metrics": {
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-  "timestamp": "20251115_090814",
+  "timestamp": "20251124_170935",
   "seed": 42
 }

threshold.json

@@ -1,9 +1,9 @@
 {
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 }

training_log_history.csv

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 loss,grad_norm,learning_rate,epoch,step,eval_loss,eval_auroc,eval_ap,eval_f1,eval_max_f1,eval_best_threshold,eval_accuracy,eval_precision_human,eval_recall_human,eval_precision_ai,eval_recall_ai,eval_runtime,eval_samples_per_second,eval_steps_per_second,train_runtime,train_samples_per_second,train_steps_per_second,total_flos,train_loss
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