Identifikasi <strong>orang</strong>, <strong>organisasi</strong>, <strong>tempat</strong>, dan entitas bernama lainnya dalam teks apa pun. Berjalan lokal.
100% Privat. Teks Anda tidak pernah diunggah.
Berjalan sepenuhnya di browser Anda. Data Anda tidak pernah keluar dari perangkat Anda.
BERT-base-NER clasifica cada token en BIO-tags (B-PER, I-PER, O, etc.) y agrupa entidades consecutivas.
The BERT-base-NER model extracts 4 entity types: PER (Person — names of people), ORG (Organization — companies, institutions), LOC (Location — cities, countries, landmarks), and MISC (Miscellaneous — other proper nouns like titles, events, products).
No. The NER model runs entirely in your browser using WebAssembly. Your text never leaves your device. The ~108MB model downloads once from CDN and caches locally in IndexedDB.
BERT-base-NER achieves ~89% F1-score on the CoNLL-2003 benchmark for 4-class NER. Performance may vary for short text, informal writing, or non-English content.
Yes. Copy entities as JSON (with text, type, confidence, start/end positions) or CSV for spreadsheet analysis.