# NER OSS Reference Benchmark Brief

Generated at: `2026-06-05T14:33:28.693573+00:00`

This file is the communication-first companion to `benchmark_report.md`.

## Machine

- Processor: `Apple M1 Pro`
- Logical CPUs: `8`
- Python: `3.12.10`

## Headline Findings

- `English`: strongest average flat F1 is `Flair NER EN` at `0.720`; fastest average p50 is `spaCy EntityRecognizer XX` at `1.54 ms`.
- `Polish`: strongest average flat F1 is `Stanza NER PL` at `0.684`; fastest average p50 is `spaCy EntityRecognizer XX` at `6.31 ms`.
- Caveat: 21 long documents were truncated to 384 tokens (input length range 1217-2645).

### English Quality And Speed Snapshot

| Dataset | Best quality | F1 | p50 ms | Fastest | p50 ms | Fastest F1 |
| --- | --- | ---: | ---: | --- | ---: | ---: |
| CoNLL-2003 EN Test | Flair NER EN | 0.912 | 42.44 | spaCy EntityRecognizer XX | 1.38 | 0.604 |
| WikiNER EN Test | spaCy EntityRecognizer XX | 0.811 | 2.05 | spaCy EntityRecognizer XX | 2.05 | 0.811 |
| WikiANN EN Test | Flair NER EN | 0.491 | 34.81 | spaCy EntityRecognizer XX | 1.21 | 0.447 |

### Polish Quality And Speed Snapshot

| Dataset | Best quality | F1 | p50 ms | Fastest | p50 ms | Fastest F1 |
| --- | --- | ---: | ---: | --- | ---: | ---: |
| PolEval-2018 PL Test | Stanza NER PL | 0.732 | 581.55 | spaCy EntityRecognizer XX | 16.01 | 0.372 |
| WikiNER PL Test | spaCy EntityRecognizer XX | 0.857 | 1.68 | spaCy EntityRecognizer XX | 1.68 | 0.857 |
| WikiANN PL Test | Stanza NER PL | 0.656 | 43.89 | spaCy EntityRecognizer XX | 1.25 | 0.610 |

## Reading Guide

- `F1` is the main quality score for span + label correctness on the current dataset.
- `p50 ms` is median per-document latency on the machine above.
- `Best quality` answers who wins on quality; `Fastest` answers who returns first.
- Full `matched / predicted / gold / precision / recall / p95` details stay in `benchmark_report.md`.