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1try (BertNerRecognizer ner = BertNerRecognizer.builder()
2 .modelId("inference4j/distilbert-NER")
3 .build()) {
4 List<NamedEntity> entities = ner.recognize("John works at Google in London.");
5 for (NamedEntity e : entities) {
6 System.out.printf("%s (%s)%n", e.text(), e.label());
7 }
8 // John (PER)
9 // Google (ORG)
10 // London (LOC)
11}| Property | Value |
|---|---|
| Architecture | DistilBERT (6 layers, 768 hidden, 66M params) |
| Task | Named Entity Recognition (IOB2 tagging) |
| Labels | O, B-PER, I-PER, B-ORG, I-ORG, B-LOC, I-LOC, B-MISC, I-MISC |
| Training data | CoNLL-2003 |
| F1 score | 92.17 |
| Max sequence length | 512 |
| Tokenizer | WordPiece (cased) |
| Original framework | PyTorch (HuggingFace Transformers) |