---
license: odbl
language:
- en
tags:
- graph
- link-prediction
- benchmark
- model-dataset
size_categories:
- 10K<n<100K
configs:
- config_name: eval_edges
  data_files: data/eval_edges.jsonl
---

# ArtifactBench

A heterogeneous graph of HuggingFace **model / dataset / paper / codebase**
nodes (14,053) with observed **(model, dataset, performance-metric)**
evaluation edges (51,337 relations), for benchmarking link prediction and
attribute (metric-value) regression, plus an agent-based verification suite.

## License

Released under the **Open Database License (ODbL) v1.0** — see `LICENSE` or
<https://opendatacommons.org/licenses/odbl/1-0/>. Share/modify/use freely
with attribution; keep derivative databases open under ODbL.

## Dataset Viewer

The viewer renders `data/eval_edges.jsonl` (config `eval_edges`): one row per
`(model_id, dataset_id, metric, value)` over all 30,499 normalized evaluation
edges. The full graph (embeddings, adjacency, splits) lives in the directories
below as `.npz` / `.json` and is loaded programmatically.

## Layout

| path | contents |
|---|---|
| `full/` | unsplit graph: `node_metadata.json`, `node_mappings.json`, `node_embeddings_{voyage,random}.npy` (N×1024), `edges{,_eval,_base_model,_resource}.npz`, matching `edge_metadata*.json` |
| `transductive/` | edge-level split (all nodes in train & test); `{train,test}_split/{edges,pos_edges}.npz`, `node_metadata.json`, `edge_metadata_normalized.json`, `node_embeddings_*.npy`, `split_info.json` |
| `inductive/` | disjoint-node split (test models unseen in train); same layout + `node_split.json` |
| `verification_bench/` | `bench.json` (263 (model,dataset,metric) triples) + `agent_results/<cell>/` reference agent outputs |
| `case_study_nli/` | frozen 48-model × 12-dataset NLI grid + aggregate & plotting scripts |

Node types: `model`, `dataset`, `paper`, `codebase`. Edge types: model↔dataset
(eval, metrics normalized to `[0,1]`), model↔{paper,codebase},
dataset↔{paper,codebase} (resource), model↔model (base-model/fine-tune).

## Usage

```python
from huggingface_hub import snapshot_download
import numpy as np, json
p = snapshot_download("lwaekfjlk/artifact-bench", repo_type="dataset")
emb = np.load(f"{p}/transductive/node_embeddings_voyage.npy")          # (N, 1024)
nm  = json.load(open(f"{p}/transductive/train_split/node_metadata.json"))
pos = np.load(f"{p}/transductive/train_split/pos_edges.npz")["edges"]  # (2, E)
```

## Notes

- `case_study_nli`: 9 bug cells use `previous_accuracy`; 3 binary-output
  models are masked on 3-way NLI sets; 2 cells are true failures. Scripts in
  `case_study_nli/scripts/` regenerate the aggregate + figures.
- `verification_bench`: 263/266 cells have a complete `results.json`; cell
  dirs named `<model>_<dataset>_<metric>` with `/`→`_`.