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