| --- |
| base_model: google/bert_uncased_L-2_H-128_A-2 |
| datasets: |
| - THemidli/applied-ner-stage4-improved |
| language: |
| - en |
| library_name: transformers |
| pipeline_tag: token-classification |
| license: apache-2.0 |
| tags: |
| - ner |
| - token-classification |
| --- |
| |
| # 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](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2). Repository: THemidli/applied-ner-stage4-bert-tiny-improved. |
|
|
| ## Results |
|
|
| Exact entity-level seqeval metrics: |
|
|
| | Split | Precision | Recall | F1 | Token accuracy | |
| |---|---:|---:|---:|---:| |
| | Train | 0.9540 | 0.9709 | 0.9624 | 0.9948 | |
| | Test | 0.4261 | 0.5264 | 0.4710 | 0.8332 | |
|
|
| | Label | Precision | Recall | F1 | Support | |
| |---|---:|---:|---:|---:| |
| | PERSON | 0.487 | 0.651 | 0.557 | 195 | |
| | ORGANIZATION | 0.216 | 0.252 | 0.233 | 147 | |
| | LOCATION | 0.436 | 0.545 | 0.484 | 143 | |
| | TIMEDATE | 0.785 | 0.832 | 0.808 | 167 | |
| | PRODUCT | 0.168 | 0.181 | 0.174 | 127 | |
| | WORKOFART | 0.136 | 0.247 | 0.176 | 97 | |
| | JOB | 0.664 | 0.798 | 0.725 | 99 | |
| | AMOUNT | 0.540 | 0.587 | 0.562 | 104 | |
|
|
| 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](https://huggingface.co/datasets/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. |
|
|
