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1try (DistilBertClassifier model = DistilBertClassifier.fromPretrained("models/distilbert-sst2")) {
2 List<TextClassification> results = model.classify("This movie was fantastic!");
3 System.out.println(results.get(0).label()); // "POSITIVE"
4 System.out.printf("Score: %.4f%n", results.get(0).score());
5}| Property | Value |
|---|---|
| Architecture | DistilBERT (6 layers, 768 hidden) |
| Task | Binary sentiment classification (POSITIVE / NEGATIVE) |
| Training data | SST-2 (Stanford Sentiment Treebank) |
| Max sequence length | 512 |
| Original framework | PyTorch (HuggingFace Transformers) |
| ONNX export | By Xenova (Transformers.js) |