README.md

---
base_model: prajjwal1/bert-mini
datasets:
- THemidli/applied-ner-stage4-final
language:
- en
library_name: transformers
pipeline_tag: token-classification
license: apache-2.0
tags:
- ner
- token-classification
---

# Applied NER Stage 4 — Final BERT-Mini

An eight-label English token classifier fine-tuned from [prajjwal1/bert-mini](https://huggingface.co/prajjwal1/bert-mini). Repository: THemidli/applied-ner-stage4-bert-mini-final.

## Results

Exact entity-level seqeval metrics:

| Split | Precision | Recall | F1 | Token accuracy |
|---|---:|---:|---:|---:|
| Train | 0.9989 | 0.9987 | 0.9988 | 0.9998 |
| Test | 0.6189 | 0.6747 | 0.6456 | 0.8792 |

| Label | Precision | Recall | F1 | Support |
|---|---:|---:|---:|---:|
| PERSON | 0.673 | 0.769 | 0.718 | 195 |
| ORGANIZATION | 0.470 | 0.429 | 0.448 | 147 |
| LOCATION | 0.686 | 0.748 | 0.716 | 143 |
| TIMEDATE | 0.873 | 0.904 | 0.888 | 167 |
| PRODUCT | 0.316 | 0.339 | 0.327 | 127 |
| WORKOFART | 0.368 | 0.546 | 0.440 | 97 |
| JOB | 0.810 | 0.859 | 0.833 | 99 |
| AMOUNT | 0.725 | 0.733 | 0.729 | 101 |

On 40 fresh, manually gold-labeled wild probes, exact span F1 was 0.7685 (precision 0.7429, recall 0.7959). Test F1 changed by +0.1771 versus Stage 3.

On the MPS backend, repeated runs with identical seed and config showed ±0.01–0.015 F1 variation (0.6331 vs 0.6455 across the two recorded runs); the seed is fixed, and the variation does not change the model ranking (both runs far above BERT-Tiny, slightly below ELECTRA-Small).

## Training

- Dataset: [THemidli/applied-ner-stage4-final](https://huggingface.co/datasets/THemidli/applied-ner-stage4-final)
- Seed: 20260802
- Hardware: Apple MPS (macOS-27.0-arm64-arm-64bit)
- Runtime: 32.908 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: 11,109,137 (44.44 MB tensor storage)
- Saved artifact: 45.16 MB
- Model-load RSS delta: 32.29 MB
- End-to-end inference RSS delta: 58.52 MB
- CPU throughput: 4141.6 examples/s at batch 32 with 8 threads
- Mean latency: 0.2415 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 an 11.11M-parameter uncased four-layer BERT trained on a small, heterogeneous dataset with controlled template additions. It is not a production privacy system. PRODUCT, WORKOFART, ORGANIZATION, title boundaries, and contextual site readings remain weak. The 40-probe wild set is diagnostic, not a population benchmark.