---
license: cc-by-sa-4.0
task_categories:
  - question-answering
  - text-retrieval
language:
  - en
tags:
  - long-context
  - ruler
  - nemotron
  - retrieval
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: validation
        path: "*/data/*/validation.jsonl"
---

# RULER-100 — Nemotron-Nano-v3 tokenized

[RULER](https://github.com/NVIDIA/RULER) long-context evaluation data, regenerated with the
**`nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16`** (instruct) tokenizer so the labeled context
lengths are exact for that model — instead of drifting, as they do when RULER data tokenized for a
different model (e.g. Qwen3) is fed to Nemotron.

## What's here

- **7 context lengths:** 4096, 8192, 16384, 32768, 65536, 131072, **262144** (the model's max).
- **13 RULER tasks:** `niah_single_1/2/3`, `niah_multikey_1/2/3`, `niah_multivalue`, `niah_multiquery`, `vt`, `cwe`, `fwe`, `qa_1` (SQuAD), `qa_2` (HotpotQA).
- **100 samples per (length, task)** → 91 files, 9 100 examples total.

### Layout

```
<length>/data/<task>/validation.jsonl
```

Each line has four fields:

| field | type | description |
|---|---|---|
| `index` | int | sample id within the (length, task) file |
| `input` | str | the full RULER task prompt **including the answer prefix** (no chat template) |
| `outputs` | list[str] | gold answer(s); RULER scores with substring match (`all` for niah/vt/cwe/fwe, `part` for qa) |
| `length` | int | tokenized length of the raw input under the Nemotron tokenizer |

## How it was generated (chat-template handling)

The prompts are stored **raw** (no chat template). Generation reserved **16 tokens** of each
length budget for the Nemotron-Nano-v3 chat wrapper that is applied at **inference** time, i.e.
data was produced at `max_seq_length = length − 16`. This mirrors NVIDIA NeMo-Skills'
`nemo_skills/dataset/ruler/prepare.py` (raw data, `max_seq_length -= template_tokens`).

At inference, wrap each `input` as a single user turn with **thinking disabled**:

```python
tok.apply_chat_template(
    [{"role": "user", "content": row["input"]}],
    add_generation_prompt=True,
    enable_thinking=False,   # -> closed <think></think> so the model answers directly
)
# renders: <|im_start|>system\n<|im_end|>\n<|im_start|>user\n{input}<|im_end|>\n<|im_start|>assistant\n<think></think>
```

`enable_thinking=False` is required: the template defaults to `True`, which would spend RULER's
short generation budget (32–128 tokens) on reasoning. With the wrapper applied, prompts land at
≤ `length` tokens for every sample (verified at 4096 and 262144).

Per-task generation budgets (`tokens_to_generate`): niah 128, vt 30, cwe 120, fwe 50, qa 32.

### NIAH "essay" haystack — public-domain (Project Gutenberg)

The NIAH "essay" haystack uses a public-domain [Project Gutenberg](https://www.gutenberg.org)
corpus (7 pre-1929 English novels; PG header/footer/trademark stripped) in place of the
copyrighted Paul Graham essays. The 5 essay tasks (`niah_single_2`, `niah_single_3`,
`niah_multikey_1`, `niah_multivalue`, `niah_multiquery`) were regenerated through the **same
pipeline** as the rest of the dataset — validated to reproduce a non-essay task **100/100**:
RULER `prepare.py` (`--model_template_type base`, `max_seq_length = bucket − 16`, 100 samples,
Nemotron tokenizer, seed 42), with the Gutenberg corpus fed to `niah.py` via its
`RULER_ESSAY_JSON` override, then converted to this 4-field schema. Reproduction scripts are
bundled in [`scripts/`](scripts/): [`build_gutenberg.py`](scripts/build_gutenberg.py),
[`niah.py`](scripts/niah.py), and [`regen_nemotron_essays.py`](scripts/regen_nemotron_essays.py).
The other NIAH tasks use noise/needle haystacks (no third-party text).

**Validation.** All **3,500** regenerated essay records were checked exhaustively: 4-field schema,
sequential `index` (0–99), `length == Nemotron_tokenize(input) + 128 ≤ bucket − 16`, correct answer
count per task, **every gold answer actually inserted as a needle** (`is: <value>`), the answer-prefix
tail intact, **no Paul-Graham / "Project Gutenberg" trademark text**, and each haystack confirmed
drawn from the Gutenberg corpus. The same `length == tokens + tokens_to_generate` convention was
cross-checked against the untouched non-essay files, confirming the regenerated tasks are consistent
with the rest of the dataset.

## Usage

```python
from huggingface_hub import snapshot_download
local = snapshot_download("jet-ai/ruler-100-nemotron", repo_type="dataset")
# point a RULER harness data_dir at `local`; files are <length>/data/<task>/validation.jsonl
```

## Licensing

Released under **CC BY-SA 4.0** — the strongest obligation among the dataset's components (from the
embedded SQuAD / HotpotQA passages). **Every component is openly licensed**, so the dataset is
freely redistributable with attribution + ShareAlike. Per-source breakdown:

| Component | Files | Source / holder | License |
|---|---|---|---|
| RULER pipeline & synthetic structure (task construction, prompts, NIAH/VT/CWE/FWE content) | all files | [NVIDIA RULER](https://github.com/NVIDIA/RULER) | **Apache-2.0** |
| SQuAD v2 passages & questions | `*/data/qa_1/validation.jsonl` | [SQuAD v2](https://rajpurkar.github.io/SQuAD-explorer/) (Rajpurkar et al., Stanford) | **CC BY-SA 4.0** (attribution + ShareAlike) |
| HotpotQA passages & questions | `*/data/qa_2/validation.jsonl` | [HotpotQA](https://hotpotqa.github.io/) (dev distractor) | **CC BY-SA 4.0** (attribution + ShareAlike) |
| Project Gutenberg novels (NIAH "essay" haystack) | `*/data/{niah_single_2, niah_single_3, niah_multikey_1, niah_multivalue, niah_multiquery}/validation.jsonl` | [Project Gutenberg](https://www.gutenberg.org) — pre-1929 English novels, PG header/footer/trademark stripped | **Public domain** |

Notes:

- **Attribution + ShareAlike:** the QA files embed CC BY-SA 4.0 text, so attribute SQuAD / HotpotQA
  and keep adaptations under BY-SA. The RULER pipeline is Apache-2.0 and the Gutenberg haystack is
  public domain (PG's trademarked boilerplate is stripped, leaving only the public-domain works) —
  both are compatible with redistributing the collection under CC BY-SA 4.0.
- **No rights-restricted content** remains. The five essay-haystack NIAH tasks were the *only* ones
  that ever embedded third-party prose, and those now use the public-domain Project Gutenberg corpus
  (previously the all-rights-reserved Paul Graham essays). Every other task was left unchanged and
  never contained third-party text — its haystack is RULER-*generated*: `niah_single_1` uses a
  repeated noise sentence, `niah_multikey_2` / `niah_multikey_3` use synthetic needle lines, and
  `vt` / `cwe` / `fwe` use synthetic chains / word lists.
- This breakdown is **informational, not legal advice** — confirm with your legal / IP office before
  release.

## Provenance

Synthetic data built by [NVIDIA RULER](https://github.com/NVIDIA/RULER). Generation seed `42`,
100 samples per (length, task), `nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16` tokenizer; NIAH
"essay" haystack from a public-domain Project Gutenberg corpus. Reproduction scripts bundled in
[`scripts/`](scripts/): `build_gutenberg.py`, `niah.py` (RULER, with the `RULER_ESSAY_JSON`
override), and `regen_nemotron_essays.py` (the exact generate + 4-field-convert pipeline).

## Added buckets: 98304, 163840, 196608 (2026-07-18/20)

Three buckets were generated AFTER the original publish, to fill the 64k→262k range for a RULER
context-length sweep: **`98304/`, `163840/`, `196608/`**. Pipeline for all three: NVIDIA/RULER @
`38da79d7`, the same patched `niah.py` + Gutenberg haystack + `prepare.py --model_template_type base
--num_samples 100 --max_seq_length <bucket−16>` (98288 / 163824 / 196592), seed 42, Nemotron tokenizer
— IDENTICAL to the original buckets. Fidelity gate: regenerating the published `65536` bucket with this
exact pipeline reproduced all 13 task files **byte-identical**, so the new buckets sit on the same curve
(each = 13 tasks × 100 samples, `length ≤ bucket`). Published to this HF dataset repo alongside the
original lengths; the front-matter `data_files` glob (`*/data/*/validation.jsonl`) includes them
automatically in the `default` config.