[Dataset Request] Add nvidia/Nemotron-PII — PII detection benchmark

Hi! I'd like to request indexing the **Nemotron-PII** dataset and a leaderboard for **PII Detection**.

### Dataset
- **HF:** [`nvidia/Nemotron-PII`](https://huggingface.co/datasets/nvidia/Nemotron-PII)
- **Publisher:** NVIDIA
- **Size:** 200,000 rows (100K train / 100K test), synthetic, ~30 domains
- **Labels:** 50+ PII types; character-level spans (`{start, end, text, label}`) + inline-tagged text; English (US locale)
- **Task:** PII Detection / span extraction (token classification)
- **Primary metric:** token/span-level F1 (micro-averaged)
- **License:** CC-BY-4.0

### Suggested results to seed the leaderboard
⚠️ **Single-source, self-reported.** NVIDIA's dataset card publishes **no** baselines. The only public numbers I could find come from one community author (Kalyan KS), who trained the winning models — treat as a community claim, not an independent benchmark. Micro-F1 on 10K test instances:

| Model | Precision | Recall | F1 |
|---|---|---|---|
| GPT-4o-mini (zero-shot) | 95.21 | 67.05 | 78.69 |
| Ettin-32M (fine-tuned) | 95.96 | 95.49 | 95.73 |
| Ettin-68M (fine-tuned) | 96.35 | 96.19 | 96.27 |

Source: https://huggingface.co/blog/kalyan-ks/tiny-pii-entity-detection-models . Given the single-source provenance, it may be cleaner to **request the dataset/task first and leave results for the community/maintainer to populate** from `pwc-archive` or peer-reviewed work.

### Note
A large, recent, industry-domain PII dataset with fine-grained labels — good complement to AI4Privacy and Privy. Thanks!

Start by inspecting this repository's existing dataset and leaderboard indexing workflow, then review the nvidia/Nemotron-PII dataset card for its schema, labels, license, and evaluation task. Confirm how similar PII or token-classification benchmarks are represented, and leave results unseeded unless an accepted source is available; done means the dataset and PII Detection task are indexed with the stated metric and metadata.

Written by the indexing model from the issue text.