Datasets:

zoom-ai
/

contested-fast-decisions

---
license: apache-2.0
language:
- en
task_categories:
- text-classification
pretty_name: Contested Fast Decisions
size_categories:
- n<1K
tags:
- classification
- multi-task
- decision-model
- routing
- adversarial-filtering
source_datasets:
- fastino/fast-decisions
configs:
- config_name: default
  data_files: "data/train.jsonl"
  default: true
---
# Contested Fast Decisions: A Benchmark of Contested Operational Decisions

**Contested Fast Decisions** is a benchmark of **818 operational decisions** (routing, triage and
classification) over **696 texts** in 17 domains, on which current models do not reliably agree. Every label
was re-checked. It is derived from the development split of
[`fastino/fast-decisions`](https://huggingface.co/datasets/fastino/fast-decisions).

## Leaderboard

Decision models, queried through their API with one `choice` question per single-label decision and one yes/no `noul`
question per label for multi-label decisions, given only the task name and candidate labels (no label descriptions).

| Model | Accuracy | Single-label | Multi-label |
|:---|---:|---:|---:|
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/6594b1bb57a556fbe162915e/cPgoupfEcYW0KjxyNEKsc.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> **Zoom Decisions Pro** | **73.7%** | **73.7%** | **73.8%** |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/6594b1bb57a556fbe162915e/cPgoupfEcYW0KjxyNEKsc.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> **Zoom Decisions** | **69.3%** | **67.9%** | **74.3%** |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/61b8e2ba285851687028d395/EsTgVtnM2IqVRKgPdfqcB.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> LiquidAI d1-ultra | 66.5% | 67.6% | 62.8% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/64b89bf66b5ee8c38859cbd6/l_27fD52uFMZUXdFdY9fR.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Perplexity Decision (v1.2 27B) | 66.3% | 64.4% | 72.7% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/66410d9f0e40c1344674729b/VIwpYwu8btQBGQhZH2zbz.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> JEV (jev-1.14.0) | 66.0% | 68.5% | 57.4% |
| <img src="https://www.startlux.com/assets/favicon.svg" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> StartLux Decision 27B Instant | 65.2% | 66.6% | 60.1% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/64b89bf66b5ee8c38859cbd6/l_27fD52uFMZUXdFdY9fR.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Perplexity Decision (v1.1 27B) | 63.2% | 64.4% | 59.0% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/68783facef79a05727260de3/UPX5RQxiPGA-ZbBmArIKq.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> OpenAI Decisions API (gpt-6-luna) | 62.7% | 63.5% | 60.1% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/66410d9f0e40c1344674729b/VIwpYwu8btQBGQhZH2zbz.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> JEV (jev-1.13.0) | 62.6% | 65.5% | 52.5% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/1583646260758-5e64858c87403103f9f1055d.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Microsoft Decision 1 | 61.0% | 64.4% | 49.2% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/66f8caead3186746f4524419/hEhRBJQ2FfsIPGdREBr5u.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> vLLM-SR d3 (26B) | 60.6% | 61.4% | 57.9% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/61b8e2ba285851687028d395/EsTgVtnM2IqVRKgPdfqcB.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> LiquidAI d1 | 60.1% | 60.3% | 59.6% |
| <img src="https://www.startlux.com/assets/favicon.svg" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> StartLux Decision 9B Instant | 60.1% | 63.1% | 49.7% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/650bb539ba3bc336065efadd/kMhNB7BzPxsPBeg0Kc982.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Cloudflare Clef | 57.2% | 58.4% | 53.0% |
| <img src="https://www.startlux.com/assets/favicon.svg" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> StartLux Decision 4B Instant | 56.2% | 58.3% | 49.2% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/66f8caead3186746f4524419/hEhRBJQ2FfsIPGdREBr5u.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> vLLM-SR d3-flash (8B) | 56.0% | 57.2% | 51.9% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/66f8caead3186746f4524419/hEhRBJQ2FfsIPGdREBr5u.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> vLLM-SR d3-mini (5B) | 55.7% | 59.1% | 44.3% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/655e1c11accde1bbc8c4034b/gan0PsimqPMgUjtXQByoi.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Bespoke Nimble 9B v3 | 54.4% | 60.9% | 31.7% |
| <img src="https://www.startlux.com/assets/favicon.svg" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> StartLux Decision 2B Instant | 49.9% | 54.0% | 35.5% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/650bb539ba3bc336065efadd/kMhNB7BzPxsPBeg0Kc982.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Cloudflare Clef-Flash | 46.2% | 51.3% | 28.4% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/66f8caead3186746f4524419/hEhRBJQ2FfsIPGdREBr5u.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> vLLM-SR d3-nano (2B) | 45.8% | 49.1% | 34.4% |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/645bc01df6ce7202d776f68f/rVvt1tvnZ9Xg56TbGn1Pq.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> GLiNER2.5-Decide | 45.1% | 55.4% | 9.3% |

## How it was built

Starting from the `fastino/fast-decisions` dev split (1,700 texts, 2,900 decisions):

1. **Keep the contested decisions.** Eight fixed filter models answer every decision (gpt-6-luna, Gemma 4 31B-it, gpt-5.6-luna, JEV (jev-1.13.0), Qwen3.8-27B, Qwen3.6-27B, Qwen3.5-9B, Qwen3.6-35B-A3B; all without
   extended thinking). Decisions on which all eight give the same answer are discarded; the 983 on which
   they do not all agree are kept.
2. **Relabel them.** The original labels are unreliable (for example, a tennis recap with a clear winner labelled
   `draw`), so each kept decision is labelled again, independently, by `gpt-6-sol` (default reasoning) and Claude
   Opus 5 (default thinking), from the same prompt and without seeing the original label or any model's answer. They
   agree on 686; that label is used.
3. **Resolve disagreements.** The remaining 297 (the labellers disagreed, or Opus 5 declined to answer) were
   reviewed one at a time by Claude Opus 5.5, which set the label for 132 and judged 165 too
   ambiguous to label; those are dropped.

Because every decision was contested, each is answered wrong by at least one filter model. The filter models only
select the data; any model can be evaluated on exactly these decisions.

**424 of the 818 labels (51.8%) differ from the original
dataset.** Each decision keeps its original label in `original_label`.

## Data format

A single file, `data/train.jsonl`, with one JSON object per text. Only the text's hard decisions are included, so a text can
carry fewer decisions than in the original dataset.

```json
{
  "id": "sports_recap/2",
  "category": "sports_recap",
  "input": "<free text>",
  "output": {
    "classifications": [
      {
        "task": "sport",
        "true_label": [
          "basketball"
        ],
        "labels": [
          "football",
          "basketball",
          "baseball",
          "soccer",
          "tennis",
          "cricket",
          "motorsport",
          "other"
        ],
        "multi_label": false,
        "original_label": [
          "other"
        ],
        "label_source": "labeller_agreement"
      }
    ]
  }
}
```

| Field | Meaning |
|---|---|
| `id` | `<category>/<line>`: the text's line number (0-based) in `fastino/fast-decisions` `<category>.jsonl`; the category is also in the `category` field |
| `input` | the text, unchanged from the source dataset |
| `task`, `labels`, `multi_label` | the decision and its full candidate label set, unchanged from the source dataset |
| `true_label` | the verified gold label(s); always a list |
| `original_label` | the label in `fastino/fast-decisions` |
| `label_source` | `labeller_agreement` (gpt-6-sol and Claude Opus 5 agreed; 686 decisions) or `adjudication` (set by Claude Opus 5.5; 132 decisions) |

| Domain | Texts | Decisions |
|---|---:|---:|
| `ticket_route` | 77 | 121 |
| `email_triage` | 62 | 101 |
| `restaurant_review` | 79 | 86 |
| `product_feedback` | 71 | 78 |
| `screen_tags` | 53 | 64 |
| `clinic_request` | 41 | 47 |
| `sports_recap` | 37 | 44 |
| `benefits_request` | 43 | 44 |
| `news_topic` | 35 | 35 |
| `support_topic` | 34 | 34 |
| `travel_request` | 34 | 34 |
| `support_intent` | 30 | 30 |
| `banking_intent` | 28 | 28 |
| `paper_field` | 23 | 23 |
| `agent_handoff` | 18 | 18 |
| `review_sentiment` | 16 | 16 |
| `document_type` | 15 | 15 |
| **Total** | **696** | **818** |

635 decisions are single-label and 183 are multi-label (`product_area`, `aspects`,
`genres`).

## Loading

```python
from datasets import load_dataset
ds = load_dataset("zoom-ai/contested-fast-decisions", split="train")                # all domains
email = ds.filter(lambda r: r["category"] == "email_triage")
```

## Evaluation

Exact match per decision: a prediction is correct only if the predicted label set equals `true_label` (compared as
sets). Report plain accuracy over all 818 decisions; per-domain counts are small (15 to 121), so per-domain
averages are noisy.

## License and attribution

Apache 2.0, following the source dataset. If you use this dataset, please cite it:

```bibtex
@misc{xu2026contestedfastdecisions,
      title={Contested Fast Decisions: A Benchmark of Contested Operational Decisions},
      author={Silei Xu, Linyong Nan},
      year={2026},
      publisher={Hugging Face},
      howpublished={\url{https://huggingface.co/datasets/zoom-ai/contested-fast-decisions}},
}
```

Texts, tasks and candidate labels come from
[`fastino/fast-decisions`](https://huggingface.co/datasets/fastino/fast-decisions); please cite it as well:

```bibtex
@misc{zaratiana2025gliner2efficientmultitaskinformation,
      title={GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface},
      author={Urchade Zaratiana and Gil Pasternak and Oliver Boyd and George Hurn-Maloney and Ash Lewis},
      year={2025},
      eprint={2507.18546},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.18546},
}
```

Last updated: 10/16/2026

	---
	license: apache-2.0
	language:
	- en
	task_categories:
	- text-classification
	pretty_name: Contested Fast Decisions
	size_categories:
	- n<1K
	tags:
	- classification
	- multi-task
	- decision-model
	- routing
	- adversarial-filtering
	source_datasets:
	- fastino/fast-decisions
	configs:
	- config_name: default
	data_files: "data/train.jsonl"
	default: true
	---

	# Contested Fast Decisions: A Benchmark of Contested Operational Decisions

	Contested Fast Decisions is a benchmark of 818 operational decisions (routing, triage and
	classification) over 696 texts in 17 domains, on which current models do not reliably agree. Every label
	was re-checked. It is derived from the development split of
	[`fastino/fast-decisions`](https://huggingface.co/datasets/fastino/fast-decisions).

	## Leaderboard

	Decision models, queried through their API with one `choice` question per single-label decision and one yes/no `noul`
	question per label for multi-label decisions, given only the task name and candidate labels (no label descriptions).

	\| Model \| Accuracy \| Single-label \| Multi-label \|
	\|:---\|---:\|---:\|---:\|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/6594b1bb57a556fbe162915e/cPgoupfEcYW0KjxyNEKsc.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Zoom Decisions Pro \| 73.7% \| 73.7% \| 73.8% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/6594b1bb57a556fbe162915e/cPgoupfEcYW0KjxyNEKsc.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Zoom Decisions \| 69.3% \| 67.9% \| 74.3% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/61b8e2ba285851687028d395/EsTgVtnM2IqVRKgPdfqcB.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> LiquidAI d1-ultra \| 66.5% \| 67.6% \| 62.8% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/64b89bf66b5ee8c38859cbd6/l_27fD52uFMZUXdFdY9fR.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Perplexity Decision (v1.2 27B) \| 66.3% \| 64.4% \| 72.7% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/66410d9f0e40c1344674729b/VIwpYwu8btQBGQhZH2zbz.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> JEV (jev-1.14.0) \| 66.0% \| 68.5% \| 57.4% \|
	\| <img src="https://www.startlux.com/assets/favicon.svg" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> StartLux Decision 27B Instant \| 65.2% \| 66.6% \| 60.1% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/64b89bf66b5ee8c38859cbd6/l_27fD52uFMZUXdFdY9fR.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Perplexity Decision (v1.1 27B) \| 63.2% \| 64.4% \| 59.0% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/68783facef79a05727260de3/UPX5RQxiPGA-ZbBmArIKq.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> OpenAI Decisions API (gpt-6-luna) \| 62.7% \| 63.5% \| 60.1% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/66410d9f0e40c1344674729b/VIwpYwu8btQBGQhZH2zbz.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> JEV (jev-1.13.0) \| 62.6% \| 65.5% \| 52.5% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/1583646260758-5e64858c87403103f9f1055d.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Microsoft Decision 1 \| 61.0% \| 64.4% \| 49.2% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/66f8caead3186746f4524419/hEhRBJQ2FfsIPGdREBr5u.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> vLLM-SR d3 (26B) \| 60.6% \| 61.4% \| 57.9% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/61b8e2ba285851687028d395/EsTgVtnM2IqVRKgPdfqcB.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> LiquidAI d1 \| 60.1% \| 60.3% \| 59.6% \|
	\| <img src="https://www.startlux.com/assets/favicon.svg" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> StartLux Decision 9B Instant \| 60.1% \| 63.1% \| 49.7% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/650bb539ba3bc336065efadd/kMhNB7BzPxsPBeg0Kc982.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Cloudflare Clef \| 57.2% \| 58.4% \| 53.0% \|
	\| <img src="https://www.startlux.com/assets/favicon.svg" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> StartLux Decision 4B Instant \| 56.2% \| 58.3% \| 49.2% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/66f8caead3186746f4524419/hEhRBJQ2FfsIPGdREBr5u.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> vLLM-SR d3-flash (8B) \| 56.0% \| 57.2% \| 51.9% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/66f8caead3186746f4524419/hEhRBJQ2FfsIPGdREBr5u.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> vLLM-SR d3-mini (5B) \| 55.7% \| 59.1% \| 44.3% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/655e1c11accde1bbc8c4034b/gan0PsimqPMgUjtXQByoi.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Bespoke Nimble 9B v3 \| 54.4% \| 60.9% \| 31.7% \|
	\| <img src="https://www.startlux.com/assets/favicon.svg" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> StartLux Decision 2B Instant \| 49.9% \| 54.0% \| 35.5% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/650bb539ba3bc336065efadd/kMhNB7BzPxsPBeg0Kc982.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> Cloudflare Clef-Flash \| 46.2% \| 51.3% \| 28.4% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/66f8caead3186746f4524419/hEhRBJQ2FfsIPGdREBr5u.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> vLLM-SR d3-nano (2B) \| 45.8% \| 49.1% \| 34.4% \|
	\| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/645bc01df6ce7202d776f68f/rVvt1tvnZ9Xg56TbGn1Pq.png" width="16" height="16" style="display:inline-block;width:1.15em;height:1.15em;max-width:none;margin:0 0.3em 0 0;vertical-align:-0.2em;"> GLiNER2.5-Decide \| 45.1% \| 55.4% \| 9.3% \|

	## How it was built

	Starting from the `fastino/fast-decisions` dev split (1,700 texts, 2,900 decisions):

	1. Keep the contested decisions. Eight fixed filter models answer every decision (gpt-6-luna, Gemma 4 31B-it, gpt-5.6-luna, JEV (jev-1.13.0), Qwen3.8-27B, Qwen3.6-27B, Qwen3.5-9B, Qwen3.6-35B-A3B; all without
	extended thinking). Decisions on which all eight give the same answer are discarded; the 983 on which
	they do not all agree are kept.
	2. Relabel them. The original labels are unreliable (for example, a tennis recap with a clear winner labelled
	`draw`), so each kept decision is labelled again, independently, by `gpt-6-sol` (default reasoning) and Claude
	Opus 5 (default thinking), from the same prompt and without seeing the original label or any model's answer. They
	agree on 686; that label is used.
	3. Resolve disagreements. The remaining 297 (the labellers disagreed, or Opus 5 declined to answer) were
	reviewed one at a time by Claude Opus 5.5, which set the label for 132 and judged 165 too
	ambiguous to label; those are dropped.

	Because every decision was contested, each is answered wrong by at least one filter model. The filter models only
	select the data; any model can be evaluated on exactly these decisions.

	**424 of the 818 labels (51.8%) differ from the original
	dataset.** Each decision keeps its original label in `original_label`.

	## Data format

	A single file, `data/train.jsonl`, with one JSON object per text. Only the text's hard decisions are included, so a text can
	carry fewer decisions than in the original dataset.

	```json
	{
	"id": "sports_recap/2",
	"category": "sports_recap",
	"input": "<free text>",
	"output": {
	"classifications": [
	{
	"task": "sport",
	"true_label": [
	"basketball"
	],
	"labels": [
	"football",
	"basketball",
	"baseball",
	"soccer",
	"tennis",
	"cricket",
	"motorsport",
	"other"
	],
	"multi_label": false,
	"original_label": [
	"other"
	],
	"label_source": "labeller_agreement"
	}
	]
	}
	}
	```

	\| Field \| Meaning \|
	\|---\|---\|
	\| `id` \| `<category>/<line>`: the text's line number (0-based) in `fastino/fast-decisions` `<category>.jsonl`; the category is also in the `category` field \|
	\| `input` \| the text, unchanged from the source dataset \|
	\| `task`, `labels`, `multi_label` \| the decision and its full candidate label set, unchanged from the source dataset \|
	\| `true_label` \| the verified gold label(s); always a list \|
	\| `original_label` \| the label in `fastino/fast-decisions` \|
	\| `label_source` \| `labeller_agreement` (gpt-6-sol and Claude Opus 5 agreed; 686 decisions) or `adjudication` (set by Claude Opus 5.5; 132 decisions) \|

	\| Domain \| Texts \| Decisions \|
	\|---\|---:\|---:\|
	\| `ticket_route` \| 77 \| 121 \|
	\| `email_triage` \| 62 \| 101 \|
	\| `restaurant_review` \| 79 \| 86 \|
	\| `product_feedback` \| 71 \| 78 \|
	\| `screen_tags` \| 53 \| 64 \|
	\| `clinic_request` \| 41 \| 47 \|
	\| `sports_recap` \| 37 \| 44 \|
	\| `benefits_request` \| 43 \| 44 \|
	\| `news_topic` \| 35 \| 35 \|
	\| `support_topic` \| 34 \| 34 \|
	\| `travel_request` \| 34 \| 34 \|
	\| `support_intent` \| 30 \| 30 \|
	\| `banking_intent` \| 28 \| 28 \|
	\| `paper_field` \| 23 \| 23 \|
	\| `agent_handoff` \| 18 \| 18 \|
	\| `review_sentiment` \| 16 \| 16 \|
	\| `document_type` \| 15 \| 15 \|
	\| Total \| 696 \| 818 \|

	635 decisions are single-label and 183 are multi-label (`product_area`, `aspects`,
	`genres`).

	## Loading

	```python
	from datasets import load_dataset

	ds = load_dataset("zoom-ai/contested-fast-decisions", split="train") # all domains
	email = ds.filter(lambda r: r["category"] == "email_triage")
	```

	## Evaluation

	Exact match per decision: a prediction is correct only if the predicted label set equals `true_label` (compared as
	sets). Report plain accuracy over all 818 decisions; per-domain counts are small (15 to 121), so per-domain
	averages are noisy.

	## License and attribution

	Apache 2.0, following the source dataset. If you use this dataset, please cite it:

	```bibtex
	@misc{xu2026contestedfastdecisions,
	title={Contested Fast Decisions: A Benchmark of Contested Operational Decisions},
	author={Silei Xu, Linyong Nan},
	year={2026},
	publisher={Hugging Face},
	howpublished={\url{https://huggingface.co/datasets/zoom-ai/contested-fast-decisions}},
	}
	```

	Texts, tasks and candidate labels come from
	[`fastino/fast-decisions`](https://huggingface.co/datasets/fastino/fast-decisions); please cite it as well:

	```bibtex
	@misc{zaratiana2025gliner2efficientmultitaskinformation,
	title={GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface},
	author={Urchade Zaratiana and Gil Pasternak and Oliver Boyd and George Hurn-Maloney and Ash Lewis},
	year={2025},
	eprint={2507.18546},
	archivePrefix={arXiv},
	primaryClass={cs.CL},
	url={https://arxiv.org/abs/2507.18546},
	}
	```

	Last updated: 10/16/2026