---
license: mit
library_name: onnx
tags:
- token-classification
- ner
- resume
- distillation
base_model: microsoft/deberta-v3-xsmall
---

# onnx-ner-student

DeBERTa-v3-xsmall BIO token classifier distilled from the production **GLiNER2**
teacher (`fastino/gliner2-base-v1`) for résumé entity extraction. Replaces the
~1 GB GLiNER2 runtime in curriculo-ai to fit the t3.medium memory budget.

**16 entity types**, each an independent BIO sequence (a token may be B for
several types at once — e.g. `CI/CD` is both `technical_skill` and `framework`).

## Files
- `model.onnx` — FP32
- `model_quantized.onnx` — INT8 dynamic (runtime default)
- `labels.json` — the 16 type names, index-aligned to the output head
- tokenizer files (fast/`tokenizers`-loadable, torch-free)

## I/O
`input_ids`, `attention_mask` `[B, T]` → `logits` `[B, T, 16, 3]`
(argmax over the last dim → per-type BIO tag `0=O,1=B,2=I`; decode with
`ner_dataset.decode_spans`).

## Results (held-out test vs teacher, best `ct0.5_lr2e-04_ep24`)
micro-F1 **0.8924**, precision 0.8881, recall 0.8968.

| type | F1 |
|------|----|
| award | 0.9474 |
| certification | 0.5689 |
| degree | 0.9064 |
| field_of_study | 0.8165 |
| framework | 0.7987 |
| industry | 0.8319 |
| interest | 0.9818 |
| job_title | 0.932 |
| language | 0.9515 |
| location | 0.8925 |
| organization | 0.9726 |
| person_name | 0.9655 |
| soft_skill | 0.8108 |
| technical_skill | 0.8536 |
| technology | 0.92 |
| tool | 0.8772 |