| --- |
| base_model: google/bert_uncased_L-2_H-128_A-2 |
| datasets: |
| - THemidli/applied-ner-stage2-expanded |
| language: |
| - en |
| library_name: transformers |
| pipeline_tag: token-classification |
| license: apache-2.0 |
| tags: |
| - ner |
| - token-classification |
| --- |
| |
| # Applied NER Stage 3 — BERT Tiny |
|
|
| An eight-label English token classifier fine-tuned from [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2). Repository: THemidli/applied-ner-stage3-bert-tiny. |
|
|
| ## Results |
|
|
| Exact entity-level seqeval metrics: |
|
|
| | Split | Precision | Recall | F1 | Token accuracy | |
| |---|---:|---:|---:|---:| |
| | Train | 0.9581 | 0.9726 | 0.9653 | 0.9957 | |
| | Test | 0.4215 | 0.5273 | 0.4685 | 0.8269 | |
|
|
| | 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](https://huggingface.co/datasets/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. |
| |
