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title: Oolong Viewer
emoji: 🍵
colorFrom: yellow
colorTo: gray
sdk: static
pinned: false
license: mit
---
# Oolong Viewer
Static viewer for [**Oolong**](https://github.com/abertsch72/oolong)
([paper](https://arxiv.org/abs/2511.02817),
[HF datasets](https://huggingface.co/oolongbench)) — a benchmark for **long-context
reasoning and aggregation**, where a model must reason over a single very long
context window (up to ~132K tokens) to answer counting / timeline / comparison
style questions.
Two datasets, selectable at the top of the sidebar:
- **`synth`** — [oolong-synth](https://huggingface.co/datasets/oolongbench/oolong-synth):
synthetic contexts built from labeled NLP datasets (agnews, imdb, metaphors, …).
6,500 questions over 208 context windows.
- **`real`** — [oolong-real](https://huggingface.co/datasets/oolongbench/oolong-real)
`toy_dnd` config: Critical Role Dungeons & Dragons transcripts (campaign1 = test,
campaign2 = validation). 949 questions over 22 context windows.
Two tabs:
- **📚 Corpus** — the context windows (the long documents). A dropdown selects a
context **by title** (filter by title text, or prev/next). Each shows its
metadata (source dataset / campaign / episodes, token length, how many
questions use it, `context_window_id`) and its full text. Context windows are
reused across many questions, so the corpus is far smaller than the question set.
- **❓ Eval** — the questions. A dropdown selects a **question** (search + one
dropdown per dataset facet — task group / answer type / source, or question
type / split / campaign). Each shows the question, the gold answer, its
**supporting context window** (a clickable chip that jumps to it in the Corpus
tab), and all remaining metadata.
## Long contexts are capped
Oolong is a long-context benchmark: some context windows are up to ~14 MB of
text. To keep the viewer responsive and every file under Hugging Face's 10 MB
non-LFS limit, each context-window shard is **capped at 2 MB** (with a
`truncated in viewer` note); the full contexts live on the HF datasets. The
`context_window_text_with_labels` variant (synth) is not included.
## Data layout
| File | Contents |
|---|---|
| `sets.json` | manifest: per-set question/context counts + filter `facets` |
| `corpus_<set>.json` | context-window **index**: `{cwid, title, size, truncated, n_questions, file, …}` |
| `corpus_<set>/*.txt` | one (capped) context-window **shard**, lazy-loaded |
| `eval_<set>.json` | questions: `{id, context_window_id, question, answer, meta{…}}` |
## Rebuilding the data
```bash
python scripts/build_data.py --data-dir /mnt/tmp/oolong
```
Reads the downloaded `oolong-synth/` (parquet) + `oolong-real/toy_dnd/` (jsonl),
dedupes context windows into shards, projects the questions, and rewrites
`corpus_<set>.json` + `corpus_<set>/` + `eval_<set>.json` + `sets.json`. The raw
datasets (12 GB / 1.4 GB) are **not** committed here. Use `--only synth|real` to
rebuild a single set.
## Local dev
```bash
python -m http.server 8000 # then open http://localhost:8000/
```
Serves statically — no build step. Push to the HF Space remote to deploy.
Dataset license: see the Oolong repo / dataset cards.
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