---
license: apache-2.0
tags:
  - knowledge-graph
  - wiki5m
  - scaling-law
  - preservation
  - ood
---

# Hypernet Scaling Law Data

Data assets for scaling-law and preservation (catastrophic forgetting) experiments.

## Contents

- **OOD splits**: `train_ood_scaling_law.pq`, `valid_ood_scaling_law.pq`, `eval_ood_scaling_law.pq` — train/valid/eval by domain (eval = held-out domains).
- **Scaling law**: `train_scaling_law.pq`, `valid_scaling_law.pq` — 1hop/2hop/3hop QA.
- **With facts**: `train_scaling_law_with_facts.pq`, `valid_scaling_law_with_facts.pq` — same + `facts` column from relation templates.
- **Preservation**: `preservation_train.pq`, `preservation_eval.pq` (and `preserve_data/`, `preserve_data_2hop/`, `preserve_data_combined/`) — entities not in train, for preservation loss and eval.
- **Relation templates**: `relation_template_mapping.csv` — relation label → question template and noun_template for fact generation.
- **EDA**: `domain_counts_eda.csv`, `figures/` — domain and n_hop stats/plots.

## Schema (parquet)

Canonical columns: `triplet_subject`, `triplet_relation`, `triplet_object`, `question_prompt`, `answer`.  
Some files add `n_hop`, `facts` (list of strings), or `domain`.

## Usage

```python
import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(repo_id="nace-ai/hypernet-scaling-law-data", filename="train_ood_scaling_law.pq")
df = pd.read_parquet(path)
```