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README.md

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+---
+language: en
+tags:
+- ad-creative
+- ctr-prediction
+- survival-analysis
+- multi-task-learning
+- clip
+- fatigue-prediction
+license: mit
+---
+
+# Creative Intelligence Scorer
+
+**Multi-Task Creative Lifespan Prediction** — predicts ad creative CTR score and
+fatigue half-life from a raw image using a frozen CLIP backbone and a trainable
+multi-task head.
+
+## Architecture
+
+```
+Input image (224×224 RGB)
+        ↓
+[FROZEN] CLIP-ViT-B/32 (openai/clip-vit-base-patch32)
+        ↓  512-dim embedding
+Projection: Linear(512→256) → ReLU → Dropout(0.2)
+        ↓  256-dim shared representation
+  ┌─────────────────────┐
+  ↓                     ↓
+CTR head             Fatigue head
+Linear(256→1)        Linear(256→2)
+Sigmoid              Weibull params (log_scale, log_shape)
+```
+
+Loss = 0.5 × BCELoss(ctr) + 0.5 × WeibullNLLLoss(fatigue, right-censored)
+
+## Training data
+
+- Meta Ad Library (Apify scrape): 3,502 real ad images — gaming, ecommerce, finance verticals
+- PIL-generated synthetic ads: 18,746 images with rule-based CTR and half-life labels
+- Total: 22,248 images | 80/10/10 train/val/test split
+
+## Metrics (test set)
+
+| Metric | Value | Target |
+|--------|-------|--------|
+| Spearman r (CTR ranking) | TBD | > 0.30 |
+| MAE (CTR calibration) | TBD | < 0.15 |
+
+## Limitations
+
+- **CTR labels are proxy scores**, not real click-through rates — derived from ad
+  activity signals, not A/B test data.
+- **GradCAM is a spatial approximation** — CLIP's pooler_output discards spatial
+  structure; the 16×16 heatmap is gradient-weighted feature attribution on the
+  projection layer, not true spatial GradCAM.
+- Trained on a dataset with known label imbalance (wear-out >> cut-out).
+
+## Intended use
+
+Portfolio project demonstrating Multi-Task Creative Lifespan Prediction for ad
+creative scoring. Not intended for production ad serving decisions.

model.safetensors

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processor_config.json

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+{
+  "image_processor": {
+    "crop_size": {
+      "height": 224,
+      "width": 224
+    },
+    "do_center_crop": true,
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.48145466,
+      0.4578275,
+      0.40821073
+    ],
+    "image_processor_type": "CLIPImageProcessor",
+    "image_std": [
+      0.26862954,
+      0.26130258,
+      0.27577711
+    ],
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+      "shortest_edge": 224
+    }
+  },
+  "processor_class": "CLIPProcessor"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|startoftext|>",
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "processor_class": "CLIPProcessor",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}

training_args.bin

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