---
library_name: onnx
license: apache-2.0
pipeline_tag: token-classification
base_model: fastino/gliner2.5-multi-v1
tags:
- onnx
- gliner2
- gliner2.5
- boundary
- webgpu
- token-classification
---

# gliner2.5-multi-v1-onnx

ONNX export of [fastino/gliner2.5-multi-v1](https://huggingface.co/fastino/gliner2.5-multi-v1) (GLiNER 2.5 `BoundaryExtractor`) for onnxruntime-web / WebGPU. **Revision 2: the graph now contains the full candidate path — sparse proposer, shared candidate pool, and pair reranker — not just the boundary marginals.** Host-side decode is a threshold on the reranked pair scores, matching the Python `AutoExtractor` pipeline (verified to 4 decimals).

## What the graph runs

1. DeBERTa encoder over packed `input_ids` (schema + `[SEP_TEXT]` + words)
2. Gather of word states and query-marker states
3. Boundary encoder + boundary marginals
4. Document candidate pool (top-k endpoints, Cartesian pairing, learned compatibility)
5. Pair reranker (endpoint compatibility + length features + inside evidence + FiLM-conditioned scoring)

Schema packing (entity-type markers) stays on the host; span decode is a host-side threshold.

## Inputs

| Name | Shape | Dtype |
|------|-------|-------|
| input_ids | [B, T] | int64 |
| attention_mask | [B, T] | int64 |
| text_word_indices | [B, L] | int64 |
| text_word_mask | [B, L] | float32 |
| query_marker_indices | [B, Q] | int64 |
| query_marker_mask | [B, Q] | float32 |

## Outputs

| Name | Shape | Dtype | Meaning |
|------|-------|-------|---------|
| start_logits | [B, Q, L+1] | float32 | boundary start marginals |
| end_logits | [B, Q, L+1] | float32 | boundary end marginals |
| pair_indices | [B, Q, C, 2] | int64 | candidate spans, half-open word boundaries (s,e), s<e≤L; C=192 |
| pair_logits | [B, Q, C] | float32 | reranked span logits |
| pair_valid | [B, Q, C] | uint8 | 1 = real candidate (duplicate (s,e) keys can occur; dedupe on the host) |

## Host decode (any language)

```
probs = sigmoid(pair_logits / pair_temperature)          # pair_temperature = 1.0
keep candidates where pair_valid == 1 and probs >= threshold   # 0.5 default
dedupe by (start, end) keeping the max prob
resolve overlaps per label (e.g. max-total-score interval scheduling)
char span = words[s].start .. words[e-1].end
```

Boundary i sits before word i; boundary L sits after the last word. Pair (s, e) covers words s..e-1.

## Verified decode parity

JavaScript decode of this graph (ONNX Runtime Web) vs Python `AutoExtractor` on
`"Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday."` with labels
company/person/product/location:

| Entity | ONNX-web JS | Python AutoExtractor |
|---|---|---|
| Apple | 0.9966 | 0.9966 |
| Tim Cook | 0.9987 | 0.9987 |
| iPhone 15 | 0.9905 | 0.9905 |
| Cupertino | 0.9988 | 0.9988 |

Confidences match to 4 decimals. Reference runtime: [Pastel-Org/gliner2.5-onnx-webgpu](https://github.com/Pastel-Org/gliner2.5-onnx-webgpu) (live demo: [gliner25-onnx-webgpu.pages.dev](https://gliner25-onnx-webgpu.pages.dev)).

## Revision 2 changes

- Graph now ends at the pair reranker instead of the marginals. The previous
  revision required a `min(sigmoid(start), sigmoid(end))` proxy that cost
  precision at low thresholds (0.45 P at 0.3 on our 26-sample suite); this
  revision holds ≥0.77 P across the whole 0.3–0.7 range with flat F1.
- `export_mode="vectorized"` proposer; sort/scatter_reduce internals replaced
  with opset-17-safe `topk` + sentinel padding (constant-k for any input
  length; duplicate candidates possible and deduped on the host).
- Decode parity with the Python pipeline added to the export validation.

## Python check

```python
import onnxruntime as ort
sess = ort.InferenceSession("onnx/model.onnx")
print([o.name for o in sess.get_outputs()])
# ['start_logits', 'end_logits', 'pair_indices', 'pair_logits', 'pair_valid']
```

WebGPU: load `onnx/model.onnx` with `onnxruntime-web` (`webgpu` execution
provider; WASM fallback where WebGPU is unavailable). Int64 inputs are
required.

## Credits

- Base checkpoints and the GLiNER2 reference implementation by
  [Fastino](https://fastino.ai) — Apache-2.0.
- ONNX export + host protocol by [Pastel-Cloud OÜ](https://github.com/Pastel-Org).