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license: mit
tags:
- osu
- representation-learning
- similarity-search
---
# BoBERT · v15
Hit-object sequence encoders and retrieval indices for osu! modes. Each mode has its own tokenizer, encoder weights, centering statistics and optional collection-trained linear adapter.
| Mode | Variant | Maps | Dimensions | Layers |
|---|---|---:|---:|---:|
| std | adapted | 498,962 | 384 | 9 |
| taiko | adapted | 95,415 | 384 | 9 |
| catch | adapted | 24,550 | 256 | 9 |
| mania | adapted | 314,997 | 384 | 9 |
The release manifest selects one model/index pair per mode. Base and adapted files use the same mode directory; adapted files have an `.a` suffix. The index records its model's SHA256 checksum. Catalogs are shared in `data/`.
Export workspace: `f77d80d8442a535c3d7700f245a11022157dc89c`.
```sh
uv run fetch-run --repo token03/bobert --revision v15
docker compose up --build api
```
From a source checkout:
```python
import torch
from core.model import BobertEncoder
from core.artifacts import resolve_artifacts
artifacts = resolve_artifacts("runs/current", "mania")
model = BobertEncoder.from_pretrained(artifacts.model, torch.device("cpu"))
```
[Source, training and evaluation](https://github.com/token03/bobert)
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