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vox-parse-distilbert
rajarshi-saha-2314/vox-parse-distilbert
vox-parse-distilbert is a machine learning model from rajarshi-saha-2314. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Fine-tuned 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.
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Aug 12, 2026
- Updated
- Aug 12, 2026
- SHA
- 96870f1d
Base models
Model card
From the Hugging Face model README
VoxParse -- Joint Intent + Slot-Filling (DistilBERT)
Fine-tuned 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.
Part of the VoxParse project: a resume project demonstrating a real-time voice-assistant NLU pipeline. See the project README for full architecture details, training setup, and how to run inference.
Test set metrics
| 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% |
Files
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 files
Usage
This checkpoint is loaded via the project's own model.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.
- License
- apache-2.0
- Created
- Aug 12, 2026
- Updated
- Aug 12, 2026