---
license: apache-2.0
language:
- en
library_name: opendecision
pipeline_tag: zero-shot-classification
base_model: microsoft/deberta-v3-large
tags:
- decision
- text-classification
- intent-classification
- routing
- zero-shot-classification
- multi-label
- deberta-v3
datasets:
- fastino/fast-decisions
- stanfordnlp/snli
- fancyzhx/dbpedia_14
- mteb/banking77
---

# OpenDecision-Large-Packed

📄 Technical report: [*OpenDecision: Fast Schema-Conditioned Decisions with Packed Candidate Encoding*](https://github.com/tokz-labs/OpenDecision/releases/latest/download/OpenDecision.pdf) (PDF; arXiv version coming soon) · [Code](https://github.com/tokz-labs/OpenDecision)

OpenDecision-Large-Packed is the low-cost variant of
[OpenDecision-Large](https://huggingface.co/Tokz-labs/OpenDecision-Large): the same
433.9M-parameter DeBERTa-v3-large decision model, using a **packed candidate
encoder** that scores up to 16 options in one forward pass. The input text is encoded once
per pack; attention masks and per-sequence relative positions give every option the same
view it would have alone, so answers do not depend on option order.

## Usage

```bash
pip install "opendecision @ git+https://github.com/tokz-labs/OpenDecision"
```

```python
from opendecision import OpenDecision, Choice, Bool, MultiLabel

model = OpenDecision.from_pretrained("Tokz-labs/OpenDecision-Large-Packed")
out = model.decide(
    "I was charged twice for my May invoice and the app keeps logging me out.",
    {
        "queue": Choice(["billing", "technical", "account", "sales"]),
        "tags": MultiLabel(["double_charge", "login_issue", "refund_request", "outage"]),
        "urgent": Bool(),
    },
)
print(out["queue"].best, out["tags"].selected, out["urgent"].policy)
```

## Efficiency

GPU-seconds per 1,000 decisions on the complete classification-suite test sets, one NVIDIA L4:

| Model | All five tasks | Without CLINC150 (151 options) | $ / 1k decisions (L4 list price) |
|---|---|---|---|
| **OpenDecision-Large-Packed** | 69.1 | **34.8** | 0.0153 |
| OpenDecision-Large (cross-encoder) | 77.4 | 58.9 | 0.0172 |
| Laya | 43.5 | 41.2 | 0.0097 |
| JevK5 v0.3 (4B) | 267.4 | 119.9 | 0.0594 |
| SemIf (4B) | 275.0 | 130.4 | 0.0611 |

Packing pays off for questions with up to ~16 options; with many more (CLINC150's 151 intents) the
cross-encoder is faster. Batch-size-1 latency (L4, bf16, p50): 66 ms for a short
text with 10 options; 69 ms for a 512-token text with 10 options
(cross-encoder: 248 ms).

## Benchmarks

Classification suite, complete test sets, macro-F1 (open models, identical inputs; GLiNER2.5 with its
best interface per dataset):

| Model | AG News | CLINC150 | IMDb | Rotten Tomatoes | XNLI | Average |
|---|---|---|---|---|---|---|
| OpenDecision-Large | 87.37 | 76.38 | 94.84 | 92.03 | 88.93* | 87.91 |
| **OpenDecision-Large-Packed** | 86.70 | 75.95 | 94.59 | 89.96 | 88.97* | 87.23 |
| SemIf (Qwen3.5-4B, 4B) | 87.28 | 78.22 | 95.27 | 88.46 | 82.36 | 86.32 |
| JevK5 v0.3 (4B) | 85.45 | 79.86 | 95.06 | 87.60 | 83.40 | 86.27 |
| Laya (421M) | 92.42 | 47.57 | 92.16 | 87.99 | 87.22 | 81.47 |
| GLiFormer large-v1 (576M) | 82.88 | 59.49 | 94.19 | 84.06 | 82.03 | 80.53 |
| GLiNER2.5-Decide (340M) | 71.37 | 66.19 | 90.02 | 85.83 | 47.65 | 72.21 |

\* NLI data is in the training data: XNLI is not zero-shot.

On label sets that no training data targeted, averaged over TREC question types, Emotion and
Subjectivity, it scores 55.64 macro-F1 (OpenDecision-Large 57.00,
Laya 62.11, SemIf 64.29, JevK5 72.07); its final training
stage, which adds 15,942 LLM-invented classification tasks with these task families excluded,
raised this from 45.70. GoEmotions multi-label exact match is low
(13.30; the model selects too many labels). For a new kind of decision, fine-tune on a
few hundred labeled examples (`python -m opendecision.cli.train`).

## Training data

Two stages from DeBERTa-v3-large (the packed stage starts from the clean cross-encoder) on LLM-generated decisions and LLM-invented classification tasks (OpenAI models), the Fast Decisions
public development split, MultiNLI, WANLI and samples of DBpedia-14, BANKING77, SNLI, WikiText-103 and
20 Newsgroups (Mitchell 1997, CC BY 4.0). Every source allows commercial use under its license; sources
and licenses are listed in the technical report.

## Scope and limitations

English only; inputs up to 512 tokens; options truncated to 48 tokens. Not a chat or
reasoning model. The benchmarks above were also used to select training mixtures. Most
training text was generated or labeled by OpenAI models.

## Citation

```bibtex
@misc{alwarawreh2026opendecision,
  title  = {OpenDecision: Fast Schema-Conditioned Decisions with Packed Candidate Encoding},
  author = {Alwarawreh, Abdallah},
  year   = {2026},
  note   = {Tokz Labs technical report},
  url    = {https://github.com/tokz-labs/OpenDecision/releases/latest/download/OpenDecision.pdf}
}
```