aadiausa/Set_Fit_Ausa

Ausa Hub — intent router

On-device SetFit intent classifier for the Ausa Hub local voice assistant (see engine/router_config.py). Maps a patient utterance to one of 39 intents (appointments, routines, profiles, tests/vitals, messages, meal_time, symptoms, summary, settings, and the system.* catch-alls). Runs as ONNX on-device and also seeds the cloud LLM router's priors.

• —Intents: 39
• —Training examples: ~1699
• —Last updated: 2026-06-25
• —Recent change: conversation-meta / recall utterances ("what did I just say", "what did I tell you my dog's name was", "remind me what I said") and general-knowledge questions now classify as system.oos so they reach the conversational assistant instead of a records intent.

SetFit

This is a SetFit model that can be used for Text Classification. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. 1.Fine-tuning a Sentence Transformer with contrastive learning.
2. 2.Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

• —Model Type: SetFit <!-- - Sentence Transformer: Unknown -->
• —Classification head: a LogisticRegression instance
• —Maximum Sequence Length: 512 tokens
• —Number of Classes: 39 classes <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

• —Repository: SetFit on GitHub
• —Paper: Efficient Few-Shot Learning Without Prompts
• —Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("at what time do I have dinner")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (16, 16)
• —num_epochs: (3, 3)
• —max_steps: -1
• —sampling_strategy: oversampling
• —num_iterations: 20
• —bodylearningrate: (2e-05, 1e-05)
• —headlearningrate: 0.01
• —loss: CosineSimilarityLoss
• —distancemetric: cosinedistance
• —margin: 0.25
• —endtoend: False
• —use_amp: False
• —warmup_proportion: 0.1
• —l2_weight: 0.01
• —seed: 42
• —evaluation_strategy: epoch
• —evalmaxsteps: -1
• —loadbestmodelatend: True

Training Results

Framework Versions

• —Python: 3.12.10
• —SetFit: 1.1.3
• —Sentence Transformers: 5.4.1
• —Transformers: 4.57.6
• —PyTorch: 2.11.0
• —Datasets: 4.8.4
• —Tokenizers: 0.22.2

Citation

BibTeX

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}

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