---
configs:
- config_name: models
  data_files:
  - split: models
    path: models.jsonl
tags:
- benchmarks
- evaluation
- decision-models
- leaderboard
---

# Decision Model Benchmark Usage

Which evaluation benchmarks labs use to evaluate their **decision models** (single-pass,
zero-output-token classifiers/raters — routing, triage, moderation, reranking, LLM-judge,
visual inspection — not chat LLMs). Sister dataset to
[`SaylorTwift/llm-benchmark-usage`](https://huggingface.co/datasets/SaylorTwift/llm-benchmark-usage),
which covers LLM/agent evaluation suites; benchmark names are canonicalized against
that dataset so overlapping benchmarks (e.g. `AI2D`, `MMBench`, `BoolQ`) share one spelling
across both.

Hand-built from papers, technical reports, system cards, model cards, and blog posts.

```python
from datasets import load_dataset

models = load_dataset("SaylorTwift/decision-model-benchmark-usage", "models")["models"]
```

## `models`

One row per model, fully self-contained.

| column | type | description |
|---|---|---|
| `model_id` | string | Hugging Face repo id for open-weight models (e.g. `LiquidAI/d1-3B`), or a plain slug for closed/API models |
| `lab` | string | Organization/lab that released the model |
| `release_date` | timestamp | Release date. HF repo creation date for open-weight models (a proxy, not always the exact announcement date); hand-researched announcement date for closed models |
| `source` | string | Link to the paper/report/card/blog this model's evaluation suite was extracted from |
| `benchmarks` | list | The evaluation suite: `{name, category}` objects. Names are canonicalized (e.g. always `CVBench`, never `CV-Bench`); genuinely distinct variants are kept separate (`SQuAD` vs `SQuAD 2.0`). Categories are per-source free-text groupings, not a controlled vocabulary |

Richer per-source metadata (paper type, title, notes, confidence flags) lives in a local
`sources` table and is not published as of this schema version; it may move to a separate
Hub dataset later.