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Set_Fit_Ausa

Text Classification
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Retrain: recall/meta + general questions -> system.oos (2026-06-25); refresh model card
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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: do I still need to take my medicine?
- text: send me another verification email
- text: at what time do I have dinner
- text: is it safe to take my insulin if my sugar is normal?
- text: turn on AR guides for test sessions
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
---
## 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](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 39 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| messages.send | <ul><li>'should I take a half dose of my statin?'</li><li>'message aakash hi'</li><li>'Should I be worried about this reading?'</li></ul> |
| appointments.read | <ul><li>'what appointments do I have this week'</li><li>'are there any earlier slots'</li><li>'what times are available tomorrow'</li></ul> |
| profiles.care_team | <ul><li>'does my doctor have any openings this week'</li><li>"what is my doctor's phone number"</li><li>'list my assigned medical professionals'</li></ul> |
| system.oos | <ul><li>'twice a day'</li><li>'update that one'</li><li>'what is your name'</li></ul> |
| routines.update | <ul><li>'update my meditation routine duration from 10 to 20 minutes'</li><li>'update my yoga routine to include weekends'</li><li>"I need to push my morning jog routine back by an hour to 7 AM because it's getting too cold."</li></ul> |
| summary.read | <ul><li>"what's happening for me today"</li><li>'fill me in on my day'</li><li>'give me a rundown of my day'</li></ul> |
| tests.take_test | <ul><li>'let me do a temperature check'</li><li>'glucose test'</li><li>'begin a health test right now'</li></ul> |
| tests.test_history | <ul><li>'are my vitals normal'</li><li>'show me my vitals'</li><li>'is my blood sugar too high'</li></ul> |
| profiles.family.update | <ul><li>"extend my mother's data sharing for 90 more days"</li><li>"extend my son's data sharing access for another 30 days"</li><li>"update my brother-in-law's sharing to permanent"</li></ul> |
| symptoms.report | <ul><li>"I'm getting these chest palpitations"</li><li>'I have a migraine'</li><li>"i'm not well"</li></ul> |
| meal_time.update | <ul><li>'push my breakfast back to 7:30'</li><li>'set my dinner meal time to 8pm'</li><li>'set breakfast to 8am'</li></ul> |
| meal_time.read | <ul><li>'do I have a meal coming up'</li><li>'what time do I have my meals'</li><li>'what time is breakfast'</li></ul> |
| profiles.personal_info.picture | <ul><li>'choose a different photo for my profile'</li><li>'I need a new avatar picture'</li><li>'Let me snap a quick selfie to replace my current avatar.'</li></ul> |
| settings.calls | <ul><li>'warn me about poor connection during calls'</li><li>'enable background blur and closed captions'</li><li>'keep camera off when joining a call'</li></ul> |
| routines.read | <ul><li>'show my routines for today'</li><li>'what is the schedule for my gym routine'</li><li>'cancel this, show my routine schedule'</li></ul> |
| profiles.read | <ul><li>'what conditions have been diagnosed for me'</li><li>'what is my medical history'</li><li>'what medical issues are on my record'</li></ul> |
| profiles.personal_info.read | <ul><li>'what email is on my account'</li><li>'show me my email address'</li><li>'what is my email'</li></ul> |
| profiles.allergies.update | <ul><li>'cancel, I want to update my allergy info'</li><li>'My reaction to dairy has gotten worse, change the severity to high and add stomach cramps.'</li><li>'update my cat allergy to include asthma symptoms'</li></ul> |
| appointments.create | <ul><li>'arrange a visit for next Friday I need a prescription refill'</li><li>'leave it, I want to schedule a doctor visit instead'</li><li>'I want to schedule a checkup'</li></ul> |
| profiles.update | <ul><li>'I need to change my primary care physician'</li><li>'update my emergency contact to my wife'</li><li>'update my insurance plan details'</li></ul> |
| routines.create | <ul><li>'set up a blood pressure testing routine'</li><li>'create a routine to take my aspirin 81mg every day after lunch'</li><li>'create a monthly routine for my doctor follow-up on the first Monday'</li></ul> |
| settings.wifi | <ul><li>'I need to switch wifi networks'</li><li>'configure my wifi settings'</li><li>'show me my saved wifi networks'</li></ul> |
| profiles.family.read | <ul><li>'list all family members connected'</li><li>'show me who I am sharing my health data with'</li><li>'show me my family'</li></ul> |
| profiles.family.create | <ul><li>'invite someone to my account'</li><li>'I want to invite my husband to my care circle so he can keep track of my appointments.'</li><li>'let someone see my health information'</li></ul> |
| messages.read | <ul><li>'show my messages'</li><li>'show me my recent messages'</li><li>'what did Dr. Chen say'</li></ul> |
| profiles.personal_info.verify_email | <ul><li>'validate my email with a code'</li><li>'verify my email address'</li><li>'resend the email confirmation link'</li></ul> |
| profiles.family.delete | <ul><li>'delete my sister from family sharing'</li><li>'delete my neighbor from my care circle'</li><li>'delete all sharing with my in-laws'</li></ul> |
| appointments.delete | <ul><li>'take my Thursday appointment off the calendar'</li><li>"Scrap the follow-up appointment with the dermatologist, I won't be needing it."</li><li>'cancel the telehealth call I scheduled'</li></ul> |
| routines.delete | <ul><li>'take off my insulin shot reminder'</li><li>'erase this routine from my list forever'</li><li>'I do not need this routine anymore'</li></ul> |
| settings.display | <ul><li>'what is my current brightness level'</li><li>'brighten the display'</li><li>'dim the screen brightness'</li></ul> |
| settings.notifications | <ul><li>'show me my current notification settings'</li><li>'I want to make sure I get pinged whenever my care team sends a message, turn those on.'</li><li>'disable care team notifications'</li></ul> |
| system.cancel | <ul><li>'disregard'</li><li>'can we just stop'</li><li>"let's not"</li></ul> |
| profiles.allergies.read | <ul><li>'check if I have a sulfa drug allergy'</li><li>'tell me about my allergy details'</li><li>'is there an egg allergy on my profile'</li></ul> |
| profiles.allergies.delete | <ul><li>'delete an old allergy that is not relevant'</li><li>'get rid of the egg allergy entry'</li><li>'delete the nickel allergy from my file'</li></ul> |
| settings.devices | <ul><li>'connect a health monitoring device'</li><li>'remove the old thermometer from my devices'</li><li>'is my blood pressure cuff connected'</li></ul> |
| profiles.personal_info.update | <ul><li>'update my last name to Johnson'</li><li>'nevermind, change my height'</li><li>'change my date of birth'</li></ul> |
| appointments.update | <ul><li>'reschedule my appointment from morning to afternoon'</li><li>'stop, can you change my appointment time instead'</li><li>'I need to change the time of my visit'</li></ul> |
| profiles.personal_info.verify_phone | <ul><li>'nevermind, verify my phone'</li><li>'I changed my number and need to verify it'</li><li>'trigger a new phone verification code'</li></ul> |
| profiles.allergies.create | <ul><li>'I found out I am allergic to something new'</li><li>'put down a soy allergy with mild stomach discomfort'</li><li>'add a penicillin allergy with severe severity I get hives and swelling and it started in childhood'</li></ul> |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
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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*List how someone could finetune this model on their own dataset.*
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 6.2825 | 21 |
| Label | Training Sample Count |
|:------------------------------------|:----------------------|
| appointments.create | 27 |
| appointments.delete | 37 |
| appointments.read | 63 |
| appointments.update | 24 |
| meal_time.read | 57 |
| meal_time.update | 26 |
| messages.read | 39 |
| messages.send | 109 |
| profiles.allergies.create | 22 |
| profiles.allergies.delete | 26 |
| profiles.allergies.read | 22 |
| profiles.allergies.update | 22 |
| profiles.care_team | 40 |
| profiles.family.create | 29 |
| profiles.family.delete | 21 |
| profiles.family.read | 35 |
| profiles.family.update | 21 |
| profiles.personal_info.picture | 21 |
| profiles.personal_info.read | 36 |
| profiles.personal_info.update | 28 |
| profiles.personal_info.verify_email | 22 |
| profiles.personal_info.verify_phone | 22 |
| profiles.read | 28 |
| profiles.update | 21 |
| routines.create | 42 |
| routines.delete | 24 |
| routines.read | 51 |
| routines.update | 24 |
| settings.calls | 21 |
| settings.devices | 22 |
| settings.display | 28 |
| settings.notifications | 22 |
| settings.wifi | 22 |
| summary.read | 32 |
| symptoms.report | 44 |
| system.cancel | 27 |
| system.oos | 180 |
| tests.take_test | 31 |
| tests.test_history | 76 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- evaluation_strategy: epoch
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0003 | 1 | 0.0006 | - |
| 0.0139 | 50 | 0.0042 | - |
| 0.0277 | 100 | 0.0011 | - |
| 0.0416 | 150 | 0.0009 | - |
| 0.0554 | 200 | 0.0004 | - |
| 0.0693 | 250 | 0.0005 | - |
| 0.0831 | 300 | 0.0004 | - |
| 0.0970 | 350 | 0.0005 | - |
| 0.1108 | 400 | 0.0004 | - |
| 0.1247 | 450 | 0.0004 | - |
| 0.1385 | 500 | 0.0005 | - |
| 0.1524 | 550 | 0.0004 | - |
| 0.1662 | 600 | 0.0004 | - |
| 0.1801 | 650 | 0.0004 | - |
| 0.1939 | 700 | 0.0004 | - |
| 0.2078 | 750 | 0.0004 | - |
| 0.2216 | 800 | 0.0004 | - |
| 0.2355 | 850 | 0.0005 | - |
| 0.2493 | 900 | 0.0005 | - |
| 0.2632 | 950 | 0.0005 | - |
| 0.2770 | 1000 | 0.0005 | - |
| 0.2909 | 1050 | 0.0005 | - |
| 0.3047 | 1100 | 0.0005 | - |
| 0.3186 | 1150 | 0.0005 | - |
| 0.3324 | 1200 | 0.0006 | - |
| 0.3463 | 1250 | 0.0005 | - |
| 0.3601 | 1300 | 0.0005 | - |
| 0.3740 | 1350 | 0.0005 | - |
| 0.3878 | 1400 | 0.0032 | - |
| 0.4017 | 1450 | 0.0006 | - |
| 0.4155 | 1500 | 0.0006 | - |
| 0.4294 | 1550 | 0.0005 | - |
| 0.4432 | 1600 | 0.0005 | - |
| 0.4571 | 1650 | 0.0005 | - |
| 0.4709 | 1700 | 0.0007 | - |
| 0.4848 | 1750 | 0.0005 | - |
| 0.4986 | 1800 | 0.0005 | - |
| 0.5125 | 1850 | 0.0005 | - |
| 0.5263 | 1900 | 0.0006 | - |
| 0.5402 | 1950 | 0.0005 | - |
| 0.5540 | 2000 | 0.0005 | - |
| 0.5679 | 2050 | 0.0005 | - |
| 0.5817 | 2100 | 0.0005 | - |
| 0.5956 | 2150 | 0.0006 | - |
| 0.6094 | 2200 | 0.0005 | - |
| 0.6233 | 2250 | 0.0004 | - |
| 0.6371 | 2300 | 0.0005 | - |
| 0.6510 | 2350 | 0.0005 | - |
| 0.6648 | 2400 | 0.0005 | - |
| 0.6787 | 2450 | 0.0005 | - |
| 0.6925 | 2500 | 0.0005 | - |
| 0.7064 | 2550 | 0.0005 | - |
| 0.7202 | 2600 | 0.0004 | - |
| 0.7341 | 2650 | 0.0013 | - |
| 0.7479 | 2700 | 0.0005 | - |
| 0.7618 | 2750 | 0.0005 | - |
| 0.7756 | 2800 | 0.0005 | - |
| 0.7895 | 2850 | 0.0004 | - |
| 0.8033 | 2900 | 0.0005 | - |
| 0.8172 | 2950 | 0.0005 | - |
| 0.8310 | 3000 | 0.0004 | - |
| 0.8449 | 3050 | 0.0005 | - |
| 0.8587 | 3100 | 0.0007 | - |
| 0.8726 | 3150 | 0.0004 | - |
| 0.8864 | 3200 | 0.0005 | - |
| 0.9003 | 3250 | 0.0005 | - |
| 0.9141 | 3300 | 0.0005 | - |
| 0.9280 | 3350 | 0.0004 | - |
| 0.9418 | 3400 | 0.0006 | - |
| 0.9557 | 3450 | 0.0004 | - |
| 0.9695 | 3500 | 0.0005 | - |
| 0.9834 | 3550 | 0.0004 | - |
| 0.9972 | 3600 | 0.0008 | - |
| 1.0 | 3610 | - | 0.0001 |
| 1.0111 | 3650 | 0.0004 | - |
| 1.0249 | 3700 | 0.0006 | - |
| 1.0388 | 3750 | 0.0005 | - |
| 1.0526 | 3800 | 0.0004 | - |
| 1.0665 | 3850 | 0.0007 | - |
| 1.0803 | 3900 | 0.0005 | - |
| 1.0942 | 3950 | 0.0004 | - |
| 1.1080 | 4000 | 0.0005 | - |
| 1.1219 | 4050 | 0.0004 | - |
| 1.1357 | 4100 | 0.0005 | - |
| 1.1496 | 4150 | 0.0004 | - |
| 1.1634 | 4200 | 0.0008 | - |
| 1.1773 | 4250 | 0.0004 | - |
| 1.1911 | 4300 | 0.0004 | - |
| 1.2050 | 4350 | 0.0004 | - |
| 1.2188 | 4400 | 0.0004 | - |
| 1.2327 | 4450 | 0.0004 | - |
| 1.2465 | 4500 | 0.0005 | - |
| 1.2604 | 4550 | 0.0004 | - |
| 1.2742 | 4600 | 0.0004 | - |
| 1.2881 | 4650 | 0.0005 | - |
| 1.3019 | 4700 | 0.0004 | - |
| 1.3158 | 4750 | 0.0005 | - |
| 1.3296 | 4800 | 0.0004 | - |
| 1.3435 | 4850 | 0.0004 | - |
| 1.3573 | 4900 | 0.0004 | - |
| 1.3712 | 4950 | 0.0004 | - |
| 1.3850 | 5000 | 0.0005 | - |
| 1.3989 | 5050 | 0.0004 | - |
| 1.4127 | 5100 | 0.0004 | - |
| 1.4266 | 5150 | 0.0004 | - |
| 1.4404 | 5200 | 0.0007 | - |
| 1.4543 | 5250 | 0.0004 | - |
| 1.4681 | 5300 | 0.0004 | - |
| 1.4820 | 5350 | 0.0004 | - |
| 1.4958 | 5400 | 0.0005 | - |
| 1.5097 | 5450 | 0.0004 | - |
| 1.5235 | 5500 | 0.0005 | - |
| 1.5374 | 5550 | 0.0004 | - |
| 1.5512 | 5600 | 0.0005 | - |
| 1.5651 | 5650 | 0.0004 | - |
| 1.5789 | 5700 | 0.0004 | - |
| 1.5928 | 5750 | 0.0004 | - |
| 1.6066 | 5800 | 0.0004 | - |
| 1.6205 | 5850 | 0.0004 | - |
| 1.6343 | 5900 | 0.0004 | - |
| 1.6482 | 5950 | 0.0004 | - |
| 1.6620 | 6000 | 0.0005 | - |
| 1.6759 | 6050 | 0.0005 | - |
| 1.6898 | 6100 | 0.0004 | - |
| 1.7036 | 6150 | 0.0012 | - |
| 1.7175 | 6200 | 0.0007 | - |
| 1.7313 | 6250 | 0.0006 | - |
| 1.7452 | 6300 | 0.0015 | - |
| 1.7590 | 6350 | 0.0005 | - |
| 1.7729 | 6400 | 0.0004 | - |
| 1.7867 | 6450 | 0.0005 | - |
| 1.8006 | 6500 | 0.0017 | - |
| 1.8144 | 6550 | 0.0004 | - |
| 1.8283 | 6600 | 0.0004 | - |
| 1.8421 | 6650 | 0.0004 | - |
| 1.8560 | 6700 | 0.0004 | - |
| 1.8698 | 6750 | 0.0004 | - |
| 1.8837 | 6800 | 0.0004 | - |
| 1.8975 | 6850 | 0.0004 | - |
| 1.9114 | 6900 | 0.0004 | - |
| 1.9252 | 6950 | 0.0004 | - |
| 1.9391 | 7000 | 0.0004 | - |
| 1.9529 | 7050 | 0.0004 | - |
| 1.9668 | 7100 | 0.0004 | - |
| 1.9806 | 7150 | 0.0004 | - |
| 1.9945 | 7200 | 0.0004 | - |
| 2.0 | 7220 | - | 0.0001 |
| 2.0083 | 7250 | 0.0005 | - |
| 2.0222 | 7300 | 0.0004 | - |
| 2.0360 | 7350 | 0.0006 | - |
| 2.0499 | 7400 | 0.0004 | - |
| 2.0637 | 7450 | 0.0004 | - |
| 2.0776 | 7500 | 0.0003 | - |
| 2.0914 | 7550 | 0.0004 | - |
| 2.1053 | 7600 | 0.0005 | - |
| 2.1191 | 7650 | 0.0004 | - |
| 2.1330 | 7700 | 0.0004 | - |
| 2.1468 | 7750 | 0.0004 | - |
| 2.1607 | 7800 | 0.0004 | - |
| 2.1745 | 7850 | 0.0004 | - |
| 2.1884 | 7900 | 0.0004 | - |
| 2.2022 | 7950 | 0.0004 | - |
| 2.2161 | 8000 | 0.0004 | - |
| 2.2299 | 8050 | 0.0005 | - |
| 2.2438 | 8100 | 0.0004 | - |
| 2.2576 | 8150 | 0.0004 | - |
| 2.2715 | 8200 | 0.0004 | - |
| 2.2853 | 8250 | 0.0004 | - |
| 2.2992 | 8300 | 0.0004 | - |
| 2.3130 | 8350 | 0.0004 | - |
| 2.3269 | 8400 | 0.0004 | - |
| 2.3407 | 8450 | 0.0004 | - |
| 2.3546 | 8500 | 0.0004 | - |
| 2.3684 | 8550 | 0.0004 | - |
| 2.3823 | 8600 | 0.0007 | - |
| 2.3961 | 8650 | 0.0004 | - |
| 2.4100 | 8700 | 0.0004 | - |
| 2.4238 | 8750 | 0.0004 | - |
| 2.4377 | 8800 | 0.0004 | - |
| 2.4515 | 8850 | 0.0003 | - |
| 2.4654 | 8900 | 0.0003 | - |
| 2.4792 | 8950 | 0.0004 | - |
| 2.4931 | 9000 | 0.0004 | - |
| 2.5069 | 9050 | 0.0004 | - |
| 2.5208 | 9100 | 0.0006 | - |
| 2.5346 | 9150 | 0.0004 | - |
| 2.5485 | 9200 | 0.0004 | - |
| 2.5623 | 9250 | 0.0004 | - |
| 2.5762 | 9300 | 0.0004 | - |
| 2.5900 | 9350 | 0.0003 | - |
| 2.6039 | 9400 | 0.0004 | - |
| 2.6177 | 9450 | 0.0003 | - |
| 2.6316 | 9500 | 0.0004 | - |
| 2.6454 | 9550 | 0.0003 | - |
| 2.6593 | 9600 | 0.0004 | - |
| 2.6731 | 9650 | 0.0004 | - |
| 2.6870 | 9700 | 0.0004 | - |
| 2.7008 | 9750 | 0.0003 | - |
| 2.7147 | 9800 | 0.0003 | - |
| 2.7285 | 9850 | 0.0003 | - |
| 2.7424 | 9900 | 0.0004 | - |
| 2.7562 | 9950 | 0.0003 | - |
| 2.7701 | 10000 | 0.0003 | - |
| 2.7839 | 10050 | 0.0005 | - |
| 2.7978 | 10100 | 0.0004 | - |
| 2.8116 | 10150 | 0.0003 | - |
| 2.8255 | 10200 | 0.0003 | - |
| 2.8393 | 10250 | 0.0003 | - |
| 2.8532 | 10300 | 0.0003 | - |
| 2.8670 | 10350 | 0.0004 | - |
| 2.8809 | 10400 | 0.0003 | - |
| 2.8947 | 10450 | 0.0004 | - |
| 2.9086 | 10500 | 0.0003 | - |
| 2.9224 | 10550 | 0.0004 | - |
| 2.9363 | 10600 | 0.0004 | - |
| 2.9501 | 10650 | 0.0004 | - |
| 2.9640 | 10700 | 0.0003 | - |
| 2.9778 | 10750 | 0.0003 | - |
| 2.9917 | 10800 | 0.0003 | - |
| 3.0 | 10830 | - | 0.0000 |
### 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
```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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