applied-ner-stage3-bert-tiny

Applied NER Stage 3 — BERT Tiny

An eight-label English token classifier fine-tuned from google/bert_uncased_L-2_H-128_A-2. Repository: THemidli/applied-ner-stage3-bert-tiny.

Results

Exact entity-level seqeval metrics:

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.