{
  "task": "Bashkir person named entity recognition",
  "metric": "exact person-span micro F1 with orphan I-PER treated as a new span",
  "evaluation_protocol": "public runtime.py, one sentence per ONNX invocation, CPU",
  "gold": {
    "description": "human-verified held-out Bashkir person-entity benchmark",
    "sentences": 1013,
    "gold_entities": 3027,
    "fp32": {"tp": 2542, "fp": 347, "fn": 485, "f1": 0.8593644354293442, "perfect_sentences": 746},
    "int8": {"tp": 2539, "fp": 346, "fn": 488, "f1": 0.8589309878213802, "perfect_sentences": 745}
  },
  "authored_stress": {
    "description": "100 authored diagnostic sentences in ten categories; not a representative population sample",
    "sentences": 100,
    "gold_entities": 85,
    "exact_sentence_overlap_with_checked_training_and_gold_files": 0,
    "fp32": {"tp": 79, "fp": 6, "fn": 6, "f1": 0.9294117647058824, "perfect_sentences": 92},
    "int8": {"tp": 78, "fp": 6, "fn": 7, "f1": 0.9230769230769231, "perfect_sentences": 92}
  },
  "inference": {
    "backend": "ONNX Runtime CPU",
    "export": "FP32 ONNX opset 17 and dynamic MatMul/Gemm INT8 quantization",
    "input": "int64 input_ids with dynamic batch and sequence dimensions",
    "output": "float logits [batch, sequence, 3]",
    "tested_max_length": 256,
    "note": "Tokenization and first-subword label alignment are identical across formats. Dynamic INT8 scores can vary with batch composition; this release summary measures the public single-sentence runtime. A separate batch-16 experiment measured INT8 gold F1 0.8582051715396316. Timings vary by host and are not a portable speed guarantee."
  }
}