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distilbert-base-uncased with two task heads -- intent classification
on [CLS] and BIO slot tagging on every token -- trained jointly on the
SNIPS NLU benchmark.| task | metric | value |
|---|---|---|
| Intent classification | accuracy | 97.71% |
| Intent classification | macro F1 | 97.77% |
| Slot filling (entity-level, seqeval) | precision | 92.83% |
| Slot filling (entity-level, seqeval) | recall | 94.69% |
| Slot filling (entity-level, seqeval) | F1 | 93.75% |
model.pt -- PyTorch state dict for the joint model (encoder + intent head + slot head)config.json -- architecture config (encoder name, label vocab sizes, max sequence length)tokenizer.json, vocab.txt, tokenizer_config.json, special_tokens_map.json -- DistilBERT tokenizer filesmodel.py (not a standard
AutoModelForSequenceClassification, since it's a custom joint architecture
with two heads). See src/model.py::load_checkpoint and src/predict.py in
the VoxParse repo for the loading code.