Add discoverability frontmatter to top-level model card

README.md

@@ -1,5 +1,21 @@
 ---
 license: apache-2.0
+pipeline_tag: image-classification
+language:
+- en
+tags:
+- anime
+- anime-tagger
+- tagger
+- image-tagging
+- multi-label
+- multi-label-classification
+- vision-transformer
+- vit
+- illustration
+- danbooru
+- safetensors
+- onnx
 ---
 
 
@@ -15,6 +31,8 @@ A multi-label anime tagger trained from scratch on a \~5.9M image dataset that r
 
 **V1** (the from-scratch 320×320 model) is shipping now. **V1.1** (a 448×448 fine-tune of V1) is **planned for release the week of 2026-05-15** and is expected to outperform V1 on this evaluation set; final numbers will be filled in once training and eval are complete. Pick the checkpoint whose native resolution matches the resolution you intend to feed it (see *Variants* below).
 
+A live demo is available on the companion Space: [Grio43/OppaiOracle](https://huggingface.co/spaces/Grio43/OppaiOracle).
+
 ---
 
 ## Variants — which checkpoint should I use?
@@ -30,6 +48,12 @@ Two practical notes:
 - **V1 is not deprecated by V1.1.** They are siblings with different operating points, not generations of the same model.
 - **Until V1.1 ships, V1 is the only released checkpoint.** Numbers and recommendations in this card refer to V1 unless explicitly labeled V1.1.
 
+### Files in this repo
+
+- `V1_safetensors/` — V1 in `safetensors` format with `config.json` and `preprocessing.json`. Use this for PyTorch / custom inference.
+- `V1_onnx/` — V1 exported to ONNX. Use this for ONNX Runtime inference (CPU, DirectML, CUDA EP).
+- Each variant directory ships `vocabulary.json`, `selected_tags.csv`, `pr_thresholds.json`, and a copy of this README.
+
 ---
 
 ## How this model came to be
@@ -212,4 +236,8 @@ V1 ships with the noise it ships with. V2 is where I plan to do something about
 
 ## License / usage
 
-_TODO: fill in license, intended-use statement, and out-of-scope use list before publishing._
+Released under the **Apache License 2.0**. You may use, modify, and redistribute the model and accompanying files for personal, research, or commercial purposes, provided you retain the license notice and attribution.
+
+**Intended use.** Research and downstream tooling for multi-label tagging of anime / illustration imagery.
+
+**Out-of-scope use.** Decisions about real people; safety-critical pipelines that depend on label correctness without human review; training a downstream model on raw outputs without manual review (the missing-tag bias described above will propagate).