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README.md

@@ -1,16 +1,67 @@
----
-license: mit
-pipeline_tag: image-classification
-tags:
-- aesthetic
-- image-scoring
-- model_hub_mixin
-- ordinal-regression
-- pytorch_model_hub_mixin
-- siglip2
----
-
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: https://github.com/Jannchie/silva
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+---
+library_name: silva
+pipeline_tag: image-classification
+license: mit
+base_model: google/siglip2-so400m-patch14-384
+base_model_relation: adapter
+tags:
+  - aesthetic
+  - siglip2
+  - ordinal-regression
+metrics:
+  - spearmanr
+  - pearsonr
+  - mae
+model-index:
+  - name: silva-aesthetic
+    results:
+      - task:
+          type: image-classification
+          name: Personal Aesthetic Scoring
+        metrics:
+          - type: spearmanr
+            value: 0.7576
+          - type: pearsonr
+            value: 0.7496
+          - type: mae
+            value: 0.6619
+---
+
+# SILVA — Personal Aesthetic Head
+
+Scores an illustration by **one specific person's** taste — not a universal quality
+model, so it won't match anyone else's preferences. Output is a single number in
+`[0, 1]`; higher means more to this person's liking.
+
+**Only the head ships here (~7 MB), not an image model.** It runs on top of the frozen
+`google/siglip2-so400m-patch14-384` backbone, which `silva[backbone]` installs and loads for you.
+
+## Quickstart
+
+```python
+# pip install "silva-scorer[backbone] @ git+https://github.com/Jannchie/silva"
+from silva import SilvaScorer
+
+scorer = SilvaScorer.from_pretrained("Jannchie/silva-aesthetic")
+print(scorer.score("your_image.jpg"))     # 0.73
+print(scorer.score(["a.jpg", "b.jpg"]))    # [0.73, 0.41]
+```
+
+Already have `google/siglip2-so400m-patch14-384` embeddings? Skip the backbone and score them directly:
+
+```python
+# pip install "silva-scorer @ git+https://github.com/Jannchie/silva"
+from silva import EmbeddingAestheticModel
+
+head = EmbeddingAestheticModel.from_pretrained("Jannchie/silva-aesthetic").eval()
+score = head(embedding)["calibrated_score"]  # calibrated to the label distribution; ["score"] for raw. embedding: [B, 1152] pooler_output
+```
+
+## Scores (held-out test split)
+
+| Spearman | Pearson | MAE (1–5) | Top-5% |
+|---|---|---|---|
+| 0.7576 | 0.7496 | 0.6619 | 0.4206 |
+
+Architecture: `embedding[1152] → LayerNorm → MLP [1024, 512, 256] → ordinal head`. Trained on one
+person's private 1–5 ratings; labels and images not released. [Source](https://github.com/Jannchie/silva)