Applied NER Stage 4 — Improved BERT Tiny

An eight-label English token classifier fine-tuned from google/bert_uncased_L-2_H-128_A-2. Repository: THemidli/applied-ner-stage4-bert-tiny-improved.

Results

Exact entity-level seqeval metrics:

On 40 fresh, manually gold-labeled wild probes, exact span F1 was 0.5849 (precision 0.5439, recall 0.6327). Test F1 changed by +0.0025 versus Stage 3.

Training

• Dataset: THemidli/applied-ner-stage4-improved
• Seed: 20260802
• Hardware: Apple MPS (macOS-27.0-arm64-arm-64bit)
• Runtime: 14.937 seconds
• Records/chunks: 841/865 train; 159/165 test
• Maximum length: 256; fast-tokenizer overflow chunks, no overlapping stride
• Hyperparameters: {"attention_dropout": 0.1, "classifier_dropout": 0.1, "epochs": 16, "eval_batch_size": 64, "hidden_dropout": 0.1, "label_smoothing_factor": 0.0, "learning_rate": 0.0005, "scheduler": "linear", "train_batch_size": 32, "warmup_steps": 45, "weight_decay": 0.02}
• 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: 31.82 MB
• End-to-end inference RSS delta: 41.48 MB
• CPU throughput: 11610.4 examples/s at batch 32 with 8 threads
• Mean latency: 0.0861 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.