| --- |
| 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) |
| ``` |
|
|
