Label	Precision	Recall	F1	Support
PERSON	0.461	0.641	0.536	195
ORGANIZATION	0.200	0.218	0.208	147
LOCATION	0.432	0.552	0.485	143
TIMEDATE	0.792	0.844	0.817	167
PRODUCT	0.171	0.189	0.180	127
WORKOFART	0.128	0.247	0.168	97
JOB	0.699	0.798	0.745	99
AMOUNT	0.556	0.625	0.588	104

On 40 fresh, manually gold-labeled wild probes, exact span F1 was 0.4785 (precision 0.4505, recall 0.5102).

Training

Dataset: THemidli/applied-ner-stage2-expanded
Seed: 20260802
Hardware: Apple MPS (macOS-27.0-arm64-arm-64bit)
Runtime: 17.780 seconds
Records/chunks: 641/665 train; 159/165 test
Maximum length: 256; fast-tokenizer overflow chunks, no overlapping stride
Hyperparameters: {"epochs": 20, "eval_batch_size": 64, "learning_rate": 0.0005, "scheduler": "linear", "train_batch_size": 32, "warmup_steps": 42, "weight_decay": 0.01}
No validation split and no test-driven checkpoint selection

Footprint and CPU benchmark

Parameters: 4,371,601 (17.49 MB tensor storage)
Saved artifact: 18.21 MB
Model-load RSS delta: 31.85 MB
End-to-end inference RSS delta: 41.30 MB
CPU throughput: 11459.9 examples/s at batch 32 with 8 threads
Mean latency: 0.0873 ms/example at that batch size

The benchmark covers tokenizer plus PyTorch CPU forward pass over 40 short probes, repeated 50 times. It is workload- and hardware-specific, not single-request latency.

Labels

PERSON, ORGANIZATION, LOCATION, TIMEDATE, PRODUCT, WORKOFART, JOB, AMOUNT using BIO encoding.

Limitations

This is a 4.37M-parameter uncased two-layer BERT trained on a small, heterogeneous dataset. It is a compact baseline, not a production privacy system. Rare works/products, company-versus-product context, exact boundaries, and subword-heavy names remain weak. The 40-probe wild set is diagnostic, not a population benchmark.