Push model using huggingface_hub.

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	---
	base_model: mini1013/master_domain
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: '[7월/롯데단독] 엉크르 드 뽀 쿠션 리필 듀오 세트(+립 미니어처+파데5ml) 20호_35호 LotteOn > 백화점 > 뷰티 >
	상단 배너 (Mobile) LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트'
	- text: '[기획]블랙쿠션 리뉴얼 리필 듀오 21N1_23N1 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn
	> 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머'
	- text: 랑콤 비비크림 spf50 50ml 0.1kg 1팩 솔에일 브론저 선 비비 선 (#M)SSG.COM/헤어/바디/세정/입욕용품/비누 ssg
	> 뷰티 > 헤어/바디 > 세정/입욕용품 > 비누
	- text: (1+1) 더샘 커버 퍼펙션 팟 컨실러 4g (당일발송) MinSellAmount (#M)화장품/향수>베이스메이크업>컨실러 Gmarket
	> 뷰티 > 화장품/향수 > 베이스메이크업 > 컨실러
	- text: 헤라 메이크업픽서 110ml × 4개 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>메이크업픽서 Coupang > 뷰티 > 메이크업
	> 베이스 메이크업 > 메이크업픽서
	inference: true
	model-index:
	- name: SetFit with mini1013/master_domain
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.6730190571715146
	name: Accuracy
	---

	# SetFit with mini1013/master_domain

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. 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 body: [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
	- 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: 7 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 \|
	\|:------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 6 \| <ul><li>'글램 업 메이크업 픽서 100ml 글램 업 메이크업 하이라이터 7g LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머'</li><li>'달바 블랑 드 런웨이 올데이 세럼 메이크업 픽서 80ml × 1개 LotteOn > 뷰티 > 메이크업 > 하이라이터 LotteOn > 뷰티 > 메이크업 > 하이라이터'</li><li>'메이블린 마스터 픽서 메이크업 리무버 펜 3ml ssg > 뷰티 > 스킨케어 > 클렌징 > 클렌징워터 ssg > 뷰티 > 스킨케어 > 클렌징 > 클렌징워터'</li></ul> \|
	\| 2 \| <ul><li>'데이지크 프로 컨실러 팔레트 9g 01 커버 (#M)위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > BB크림 위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > BB크림'</li><li>'블레미쉬커버 퍼프 7매입 세트 (#M)뷰티>화장품/향수>미용소품>퍼프/스폰지/브러쉬 CJmall > 뷰티 > 화장품/향수 > 미용소품 > 퍼프/스폰지/브러쉬'</li><li>'동성제약 메디커버 DHA 펜 10퍼센트(보통 피부색) (#M)11st>메이크업>페이스메이크업>메이크업베이스 11st > 뷰티 > 메이크업 > 페이스메이크업 > 메이크업베이스'</li></ul> \|
	\| 5 \| <ul><li>'헤라 하이드레이팅 래디언스 프라이머 35ml (#M)11st>남성화장품>남성크림>남성크림 11st > 뷰티 > 남성화장품 > 남성크림 > 남성크림'</li><li>'맥 라이트풀 C+ 코랄 그라스 틴티드 프라이머 (#M)11st>메이크업>페이스메이크업>프라이머 11st > 뷰티 > 메이크업 > 페이스메이크업 > 프라이머'</li><li>'코드글로컬러 엠.하이드로 프라이머 기획 LotteOn > 뷰티 > 색조메이크업 > 아이메이크업 > 아이섀도우 LotteOn > 뷰티 > 색조메이크업 > 아이메이크업 > 아이섀도우'</li></ul> \|
	\| 0 \| <ul><li>'스웨거 페이스 터미네이터 올인원 비비 크림 SPF 50+ PA+++ 스웨거 페이스 터미네이터 × 1개 (#M)쿠팡 홈>뷰티>남성화장품>남성메이크업>베이스메이크업 Coupang > 뷰티 > 남성화장품 > 남성메이크업 > 베이스메이크업'</li><li>'인셀덤 원빈화장품 엑티브크림EX 외 전제품 선택 데일리 아쿠아 비비크림30g (#M)홈>화장품/미용>스킨케어>화장품세트 Naverstore > 화장품/미용 > 스킨케어 > 화장품세트'</li><li>'AHC 프리미엄 인텐스 컨튜어밤 10ml 2개 MinSellAmount (#M)스마일배송 홈>뷰티>메이크업/선케어>베이스메이크업 Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > BB크림/톤업크림'</li></ul> \|
	\| 4 \| <ul><li>'[SSG-단독]블랙쿠션 리뉴얼 리필 듀오 17N1_17C1 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션;ssg > 뷰티 > 메이크업 > 립메이크업 > 립스틱;ssg > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션파운데이션;신세계백화점/메이크업/베이스메이크업/쿠션파운데이션;(#M)SSG.COM/메이크업/베이스메이크업/쿠션파운데이션 ssg > 뷰티 > 메이크업 > 립메이크업'</li><li>'라네즈 맨즈 그루밍 네오 쿠션 매트 SPF42 PA++ 15g (옵션)+남성크림스킨 25ml 4개 증정 23C 쿨 샌드 (#M)화장품/향수>베이스메이크업>파운데이션 Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > 파운데이션'</li><li>'아이오페 에어쿠션 스킨핏 톤업 15g x 2 단일상품 (#M)화장품/향수>베이스메이크업>파운데이션 Gmarket > 뷰티 > 화장품/향수 > 베이스메이크업 > 파운데이션'</li></ul> \|
	\| 1 \| <ul><li>'[특별] 톤업 프라이머 쿠션 세트 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 프라이머 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 프라이머'</li><li>'[한스킨] 핑크물밤(톤업팩트) 2개 세트 본품*2_[B0008449] (#M)11st>메이크업>페이스메이크업>파운데이션 11st > 뷰티 > 메이크업 > 페이스메이크업 > 파운데이션'</li><li>'[2특별] NEW 래디언트 파운데이션 세트(+컨실러 정품+키트 2종) 110 알라바스터 LotteOn > 뷰티 > 명품화장품 > 메이크업 > 파운데이션 LotteOn > 뷰티 > 베이스메이크업 > 파운데이션'</li></ul> \|
	\| 3 \| <ul><li>'[본사직영] 래디언스 팩트 SPF27/PA++ (바닐라) 위메프 > 뷰티 > 스킨케어 > 스킨/토너;위메프 > 뷰티 > 메이크업 > 립 메이크업;위메프 > 뷰티 > 메이크업 > 립 메이크업 > 립글로즈;위메프 > 뷰티 > 선케어 > 선크림/선블록 > 선크림/선블록;위메프 > 뷰티 > 메이크업 > 베이스 메이크업;(#M)위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 쿠션팩트 위메프 > 뷰티 > 선케어 > 선크림/선블록'</li><li>'[AK PLAZA][DIOR] 캡춰 토탈 퍼펙션 앤 유쓰 래디언스 루스 파우더 단일상품 (#M)홈>화장품/미용>향수>향수세트 Naverstore > 화장품/미용 > 향수 > 향수세트'</li><li>'설화수 NEW 진설파우더팩트 리필 23N1 (#M)화장품/향수>스킨케어>에센스/세럼 Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 에센스/세럼'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.6730 \|

	## 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("mini1013/master_cate_bt_top5_test")
	# Run inference
	preds = model("헤라 메이크업픽서 110ml × 4개 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>메이크업픽서 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 메이크업픽서")
	```

	<!--
	### Downstream Use

	List how someone could finetune this model on their own dataset.
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### 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 \| 12 \| 24.3657 \| 87 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 50 \|
	\| 1 \| 50 \|
	\| 2 \| 50 \|
	\| 3 \| 50 \|
	\| 4 \| 50 \|
	\| 5 \| 50 \|
	\| 6 \| 50 \|

	### Training Hyperparameters
	- batch_size: (64, 64)
	- num_epochs: (30, 30)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 100
	- 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
	- eval_max_steps: -1
	- load_best_model_at_end: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:-------:\|:-----:\|:-------------:\|:---------------:\|
	\| 0.0018 \| 1 \| 0.4623 \| - \|
	\| 0.0914 \| 50 \| 0.4618 \| - \|
	\| 0.1828 \| 100 \| 0.4384 \| - \|
	\| 0.2742 \| 150 \| 0.4275 \| - \|
	\| 0.3656 \| 200 \| 0.3889 \| - \|
	\| 0.4570 \| 250 \| 0.3422 \| - \|
	\| 0.5484 \| 300 \| 0.3055 \| - \|
	\| 0.6399 \| 350 \| 0.2795 \| - \|
	\| 0.7313 \| 400 \| 0.2616 \| - \|
	\| 0.8227 \| 450 \| 0.252 \| - \|
	\| 0.9141 \| 500 \| 0.2394 \| - \|
	\| 1.0055 \| 550 \| 0.2274 \| - \|
	\| 1.0969 \| 600 \| 0.2154 \| - \|
	\| 1.1883 \| 650 \| 0.2031 \| - \|
	\| 1.2797 \| 700 \| 0.197 \| - \|
	\| 1.3711 \| 750 \| 0.1768 \| - \|
	\| 1.4625 \| 800 \| 0.1752 \| - \|
	\| 1.5539 \| 850 \| 0.1631 \| - \|
	\| 1.6453 \| 900 \| 0.1513 \| - \|
	\| 1.7367 \| 950 \| 0.1368 \| - \|
	\| 1.8282 \| 1000 \| 0.1354 \| - \|
	\| 1.9196 \| 1050 \| 0.1235 \| - \|
	\| 2.0110 \| 1100 \| 0.1113 \| - \|
	\| 2.1024 \| 1150 \| 0.1015 \| - \|
	\| 2.1938 \| 1200 \| 0.084 \| - \|
	\| 2.2852 \| 1250 \| 0.0598 \| - \|
	\| 2.3766 \| 1300 \| 0.0472 \| - \|
	\| 2.4680 \| 1350 \| 0.0382 \| - \|
	\| 2.5594 \| 1400 \| 0.032 \| - \|
	\| 2.6508 \| 1450 \| 0.0212 \| - \|
	\| 2.7422 \| 1500 \| 0.0082 \| - \|
	\| 2.8336 \| 1550 \| 0.0046 \| - \|
	\| 2.9250 \| 1600 \| 0.0025 \| - \|
	\| 3.0165 \| 1650 \| 0.0014 \| - \|
	\| 3.1079 \| 1700 \| 0.0007 \| - \|
	\| 3.1993 \| 1750 \| 0.0003 \| - \|
	\| 3.2907 \| 1800 \| 0.0002 \| - \|
	\| 3.3821 \| 1850 \| 0.0008 \| - \|
	\| 3.4735 \| 1900 \| 0.0011 \| - \|
	\| 3.5649 \| 1950 \| 0.0011 \| - \|
	\| 3.6563 \| 2000 \| 0.0003 \| - \|
	\| 3.7477 \| 2050 \| 0.0001 \| - \|
	\| 3.8391 \| 2100 \| 0.0001 \| - \|
	\| 3.9305 \| 2150 \| 0.0001 \| - \|
	\| 4.0219 \| 2200 \| 0.0002 \| - \|
	\| 4.1133 \| 2250 \| 0.0001 \| - \|
	\| 4.2048 \| 2300 \| 0.0001 \| - \|
	\| 4.2962 \| 2350 \| 0.0002 \| - \|
	\| 4.3876 \| 2400 \| 0.0001 \| - \|
	\| 4.4790 \| 2450 \| 0.0 \| - \|
	\| 4.5704 \| 2500 \| 0.0002 \| - \|
	\| 4.6618 \| 2550 \| 0.0001 \| - \|
	\| 4.7532 \| 2600 \| 0.0 \| - \|
	\| 4.8446 \| 2650 \| 0.0 \| - \|
	\| 4.9360 \| 2700 \| 0.0028 \| - \|
	\| 5.0274 \| 2750 \| 0.0031 \| - \|
	\| 5.1188 \| 2800 \| 0.0023 \| - \|
	\| 5.2102 \| 2850 \| 0.0002 \| - \|
	\| 5.3016 \| 2900 \| 0.0002 \| - \|
	\| 5.3931 \| 2950 \| 0.0001 \| - \|
	\| 5.4845 \| 3000 \| 0.0 \| - \|
	\| 5.5759 \| 3050 \| 0.0001 \| - \|
	\| 5.6673 \| 3100 \| 0.0002 \| - \|
	\| 5.7587 \| 3150 \| 0.0001 \| - \|
	\| 5.8501 \| 3200 \| 0.0 \| - \|
	\| 5.9415 \| 3250 \| 0.0001 \| - \|
	\| 6.0329 \| 3300 \| 0.0002 \| - \|
	\| 6.1243 \| 3350 \| 0.0 \| - \|
	\| 6.2157 \| 3400 \| 0.0001 \| - \|
	\| 6.3071 \| 3450 \| 0.0003 \| - \|
	\| 6.3985 \| 3500 \| 0.0009 \| - \|
	\| 6.4899 \| 3550 \| 0.0009 \| - \|
	\| 6.5814 \| 3600 \| 0.0009 \| - \|
	\| 6.6728 \| 3650 \| 0.0003 \| - \|
	\| 6.7642 \| 3700 \| 0.0002 \| - \|
	\| 6.8556 \| 3750 \| 0.0 \| - \|
	\| 6.9470 \| 3800 \| 0.0 \| - \|
	\| 7.0384 \| 3850 \| 0.0 \| - \|
	\| 7.1298 \| 3900 \| 0.0 \| - \|
	\| 7.2212 \| 3950 \| 0.0 \| - \|
	\| 7.3126 \| 4000 \| 0.0 \| - \|
	\| 7.4040 \| 4050 \| 0.0 \| - \|
	\| 7.4954 \| 4100 \| 0.0 \| - \|
	\| 7.5868 \| 4150 \| 0.0 \| - \|
	\| 7.6782 \| 4200 \| 0.0 \| - \|
	\| 7.7697 \| 4250 \| 0.0003 \| - \|
	\| 7.8611 \| 4300 \| 0.0 \| - \|
	\| 7.9525 \| 4350 \| 0.0 \| - \|
	\| 8.0439 \| 4400 \| 0.0 \| - \|
	\| 8.1353 \| 4450 \| 0.0 \| - \|
	\| 8.2267 \| 4500 \| 0.0 \| - \|
	\| 8.3181 \| 4550 \| 0.0 \| - \|
	\| 8.4095 \| 4600 \| 0.0 \| - \|
	\| 8.5009 \| 4650 \| 0.0 \| - \|
	\| 8.5923 \| 4700 \| 0.0 \| - \|
	\| 8.6837 \| 4750 \| 0.0 \| - \|
	\| 8.7751 \| 4800 \| 0.0 \| - \|
	\| 8.8665 \| 4850 \| 0.0 \| - \|
	\| 8.9580 \| 4900 \| 0.0 \| - \|
	\| 9.0494 \| 4950 \| 0.0 \| - \|
	\| 9.1408 \| 5000 \| 0.0 \| - \|
	\| 9.2322 \| 5050 \| 0.0 \| - \|
	\| 9.3236 \| 5100 \| 0.0 \| - \|
	\| 9.4150 \| 5150 \| 0.0 \| - \|
	\| 9.5064 \| 5200 \| 0.0 \| - \|
	\| 9.5978 \| 5250 \| 0.0 \| - \|
	\| 9.6892 \| 5300 \| 0.0 \| - \|
	\| 9.7806 \| 5350 \| 0.0 \| - \|
	\| 9.8720 \| 5400 \| 0.0 \| - \|
	\| 9.9634 \| 5450 \| 0.0 \| - \|
	\| 10.0548 \| 5500 \| 0.0 \| - \|
	\| 10.1463 \| 5550 \| 0.0011 \| - \|
	\| 10.2377 \| 5600 \| 0.0066 \| - \|
	\| 10.3291 \| 5650 \| 0.0048 \| - \|
	\| 10.4205 \| 5700 \| 0.0088 \| - \|
	\| 10.5119 \| 5750 \| 0.0071 \| - \|
	\| 10.6033 \| 5800 \| 0.0054 \| - \|
	\| 10.6947 \| 5850 \| 0.0029 \| - \|
	\| 10.7861 \| 5900 \| 0.0028 \| - \|
	\| 10.8775 \| 5950 \| 0.0014 \| - \|
	\| 10.9689 \| 6000 \| 0.0008 \| - \|
	\| 11.0603 \| 6050 \| 0.0001 \| - \|
	\| 11.1517 \| 6100 \| 0.0001 \| - \|
	\| 11.2431 \| 6150 \| 0.0 \| - \|
	\| 11.3346 \| 6200 \| 0.0 \| - \|
	\| 11.4260 \| 6250 \| 0.0 \| - \|
	\| 11.5174 \| 6300 \| 0.0 \| - \|
	\| 11.6088 \| 6350 \| 0.0 \| - \|
	\| 11.7002 \| 6400 \| 0.0007 \| - \|
	\| 11.7916 \| 6450 \| 0.0 \| - \|
	\| 11.8830 \| 6500 \| 0.0002 \| - \|
	\| 11.9744 \| 6550 \| 0.0 \| - \|
	\| 12.0658 \| 6600 \| 0.0 \| - \|
	\| 12.1572 \| 6650 \| 0.0 \| - \|
	\| 12.2486 \| 6700 \| 0.0 \| - \|
	\| 12.3400 \| 6750 \| 0.0 \| - \|
	\| 12.4314 \| 6800 \| 0.0 \| - \|
	\| 12.5229 \| 6850 \| 0.0 \| - \|
	\| 12.6143 \| 6900 \| 0.0 \| - \|
	\| 12.7057 \| 6950 \| 0.0 \| - \|
	\| 12.7971 \| 7000 \| 0.0 \| - \|
	\| 12.8885 \| 7050 \| 0.0 \| - \|
	\| 12.9799 \| 7100 \| 0.0 \| - \|
	\| 13.0713 \| 7150 \| 0.0 \| - \|
	\| 13.1627 \| 7200 \| 0.0 \| - \|
	\| 13.2541 \| 7250 \| 0.0 \| - \|
	\| 13.3455 \| 7300 \| 0.0 \| - \|
	\| 13.4369 \| 7350 \| 0.0 \| - \|
	\| 13.5283 \| 7400 \| 0.0 \| - \|
	\| 13.6197 \| 7450 \| 0.0 \| - \|
	\| 13.7112 \| 7500 \| 0.0 \| - \|
	\| 13.8026 \| 7550 \| 0.0 \| - \|
	\| 13.8940 \| 7600 \| 0.0 \| - \|
	\| 13.9854 \| 7650 \| 0.0 \| - \|
	\| 14.0768 \| 7700 \| 0.0 \| - \|
	\| 14.1682 \| 7750 \| 0.0024 \| - \|
	\| 14.2596 \| 7800 \| 0.0026 \| - \|
	\| 14.3510 \| 7850 \| 0.0039 \| - \|
	\| 14.4424 \| 7900 \| 0.0022 \| - \|
	\| 14.5338 \| 7950 \| 0.0008 \| - \|
	\| 14.6252 \| 8000 \| 0.0002 \| - \|
	\| 14.7166 \| 8050 \| 0.0003 \| - \|
	\| 14.8080 \| 8100 \| 0.0 \| - \|
	\| 14.8995 \| 8150 \| 0.0 \| - \|
	\| 14.9909 \| 8200 \| 0.0 \| - \|
	\| 15.0823 \| 8250 \| 0.0 \| - \|
	\| 15.1737 \| 8300 \| 0.0 \| - \|
	\| 15.2651 \| 8350 \| 0.0 \| - \|
	\| 15.3565 \| 8400 \| 0.0 \| - \|
	\| 15.4479 \| 8450 \| 0.0 \| - \|
	\| 15.5393 \| 8500 \| 0.0 \| - \|
	\| 15.6307 \| 8550 \| 0.0 \| - \|
	\| 15.7221 \| 8600 \| 0.0 \| - \|
	\| 15.8135 \| 8650 \| 0.0 \| - \|
	\| 15.9049 \| 8700 \| 0.0 \| - \|
	\| 15.9963 \| 8750 \| 0.0 \| - \|
	\| 16.0878 \| 8800 \| 0.0 \| - \|
	\| 16.1792 \| 8850 \| 0.0 \| - \|
	\| 16.2706 \| 8900 \| 0.0 \| - \|
	\| 16.3620 \| 8950 \| 0.0 \| - \|
	\| 16.4534 \| 9000 \| 0.0 \| - \|
	\| 16.5448 \| 9050 \| 0.0 \| - \|
	\| 16.6362 \| 9100 \| 0.0 \| - \|
	\| 16.7276 \| 9150 \| 0.0 \| - \|
	\| 16.8190 \| 9200 \| 0.0 \| - \|
	\| 16.9104 \| 9250 \| 0.0 \| - \|
	\| 17.0018 \| 9300 \| 0.0 \| - \|
	\| 17.0932 \| 9350 \| 0.0 \| - \|
	\| 17.1846 \| 9400 \| 0.0 \| - \|
	\| 17.2761 \| 9450 \| 0.0 \| - \|
	\| 17.3675 \| 9500 \| 0.0 \| - \|
	\| 17.4589 \| 9550 \| 0.0 \| - \|
	\| 17.5503 \| 9600 \| 0.0 \| - \|
	\| 17.6417 \| 9650 \| 0.0 \| - \|
	\| 17.7331 \| 9700 \| 0.0 \| - \|
	\| 17.8245 \| 9750 \| 0.0 \| - \|
	\| 17.9159 \| 9800 \| 0.0 \| - \|
	\| 18.0073 \| 9850 \| 0.0 \| - \|
	\| 18.0987 \| 9900 \| 0.0 \| - \|
	\| 18.1901 \| 9950 \| 0.0 \| - \|
	\| 18.2815 \| 10000 \| 0.0 \| - \|
	\| 18.3729 \| 10050 \| 0.0 \| - \|
	\| 18.4644 \| 10100 \| 0.0 \| - \|
	\| 18.5558 \| 10150 \| 0.0 \| - \|
	\| 18.6472 \| 10200 \| 0.0 \| - \|
	\| 18.7386 \| 10250 \| 0.0 \| - \|
	\| 18.8300 \| 10300 \| 0.0 \| - \|
	\| 18.9214 \| 10350 \| 0.0 \| - \|
	\| 19.0128 \| 10400 \| 0.0 \| - \|
	\| 19.1042 \| 10450 \| 0.0 \| - \|
	\| 19.1956 \| 10500 \| 0.0 \| - \|
	\| 19.2870 \| 10550 \| 0.0 \| - \|
	\| 19.3784 \| 10600 \| 0.0 \| - \|
	\| 19.4698 \| 10650 \| 0.0 \| - \|
	\| 19.5612 \| 10700 \| 0.0 \| - \|
	\| 19.6527 \| 10750 \| 0.0 \| - \|
	\| 19.7441 \| 10800 \| 0.0 \| - \|
	\| 19.8355 \| 10850 \| 0.0 \| - \|
	\| 19.9269 \| 10900 \| 0.0 \| - \|
	\| 20.0183 \| 10950 \| 0.0 \| - \|
	\| 20.1097 \| 11000 \| 0.0 \| - \|
	\| 20.2011 \| 11050 \| 0.0 \| - \|
	\| 20.2925 \| 11100 \| 0.0 \| - \|
	\| 20.3839 \| 11150 \| 0.0 \| - \|
	\| 20.4753 \| 11200 \| 0.0 \| - \|
	\| 20.5667 \| 11250 \| 0.0 \| - \|
	\| 20.6581 \| 11300 \| 0.0 \| - \|
	\| 20.7495 \| 11350 \| 0.0 \| - \|
	\| 20.8410 \| 11400 \| 0.0 \| - \|
	\| 20.9324 \| 11450 \| 0.0 \| - \|
	\| 21.0238 \| 11500 \| 0.0 \| - \|
	\| 21.1152 \| 11550 \| 0.0 \| - \|
	\| 21.2066 \| 11600 \| 0.0 \| - \|
	\| 21.2980 \| 11650 \| 0.0 \| - \|
	\| 21.3894 \| 11700 \| 0.0 \| - \|
	\| 21.4808 \| 11750 \| 0.0 \| - \|
	\| 21.5722 \| 11800 \| 0.0 \| - \|
	\| 21.6636 \| 11850 \| 0.0 \| - \|
	\| 21.7550 \| 11900 \| 0.0 \| - \|
	\| 21.8464 \| 11950 \| 0.0 \| - \|
	\| 21.9378 \| 12000 \| 0.0 \| - \|
	\| 22.0293 \| 12050 \| 0.0 \| - \|
	\| 22.1207 \| 12100 \| 0.0 \| - \|
	\| 22.2121 \| 12150 \| 0.0 \| - \|
	\| 22.3035 \| 12200 \| 0.0 \| - \|
	\| 22.3949 \| 12250 \| 0.0 \| - \|
	\| 22.4863 \| 12300 \| 0.0 \| - \|
	\| 22.5777 \| 12350 \| 0.0 \| - \|
	\| 22.6691 \| 12400 \| 0.0 \| - \|
	\| 22.7605 \| 12450 \| 0.0 \| - \|
	\| 22.8519 \| 12500 \| 0.0 \| - \|
	\| 22.9433 \| 12550 \| 0.0 \| - \|
	\| 23.0347 \| 12600 \| 0.0 \| - \|
	\| 23.1261 \| 12650 \| 0.0 \| - \|
	\| 23.2176 \| 12700 \| 0.0 \| - \|
	\| 23.3090 \| 12750 \| 0.0 \| - \|
	\| 23.4004 \| 12800 \| 0.0 \| - \|
	\| 23.4918 \| 12850 \| 0.0 \| - \|
	\| 23.5832 \| 12900 \| 0.0 \| - \|
	\| 23.6746 \| 12950 \| 0.0 \| - \|
	\| 23.7660 \| 13000 \| 0.0 \| - \|
	\| 23.8574 \| 13050 \| 0.0 \| - \|
	\| 23.9488 \| 13100 \| 0.0 \| - \|
	\| 24.0402 \| 13150 \| 0.0 \| - \|
	\| 24.1316 \| 13200 \| 0.0 \| - \|
	\| 24.2230 \| 13250 \| 0.0 \| - \|
	\| 24.3144 \| 13300 \| 0.0 \| - \|
	\| 24.4059 \| 13350 \| 0.0 \| - \|
	\| 24.4973 \| 13400 \| 0.0 \| - \|
	\| 24.5887 \| 13450 \| 0.0 \| - \|
	\| 24.6801 \| 13500 \| 0.0 \| - \|
	\| 24.7715 \| 13550 \| 0.0 \| - \|
	\| 24.8629 \| 13600 \| 0.0 \| - \|
	\| 24.9543 \| 13650 \| 0.0 \| - \|
	\| 25.0457 \| 13700 \| 0.0 \| - \|
	\| 25.1371 \| 13750 \| 0.0 \| - \|
	\| 25.2285 \| 13800 \| 0.0 \| - \|
	\| 25.3199 \| 13850 \| 0.0 \| - \|
	\| 25.4113 \| 13900 \| 0.0 \| - \|
	\| 25.5027 \| 13950 \| 0.0 \| - \|
	\| 25.5941 \| 14000 \| 0.0 \| - \|
	\| 25.6856 \| 14050 \| 0.0 \| - \|
	\| 25.7770 \| 14100 \| 0.0 \| - \|
	\| 25.8684 \| 14150 \| 0.0 \| - \|
	\| 25.9598 \| 14200 \| 0.0 \| - \|
	\| 26.0512 \| 14250 \| 0.0 \| - \|
	\| 26.1426 \| 14300 \| 0.0 \| - \|
	\| 26.2340 \| 14350 \| 0.0 \| - \|
	\| 26.3254 \| 14400 \| 0.0 \| - \|
	\| 26.4168 \| 14450 \| 0.0 \| - \|
	\| 26.5082 \| 14500 \| 0.0 \| - \|
	\| 26.5996 \| 14550 \| 0.0 \| - \|
	\| 26.6910 \| 14600 \| 0.0 \| - \|
	\| 26.7824 \| 14650 \| 0.0 \| - \|
	\| 26.8739 \| 14700 \| 0.0 \| - \|
	\| 26.9653 \| 14750 \| 0.0 \| - \|
	\| 27.0567 \| 14800 \| 0.0 \| - \|
	\| 27.1481 \| 14850 \| 0.0 \| - \|
	\| 27.2395 \| 14900 \| 0.0 \| - \|
	\| 27.3309 \| 14950 \| 0.0 \| - \|
	\| 27.4223 \| 15000 \| 0.0 \| - \|
	\| 27.5137 \| 15050 \| 0.0 \| - \|
	\| 27.6051 \| 15100 \| 0.0 \| - \|
	\| 27.6965 \| 15150 \| 0.0 \| - \|
	\| 27.7879 \| 15200 \| 0.0 \| - \|
	\| 27.8793 \| 15250 \| 0.0 \| - \|
	\| 27.9707 \| 15300 \| 0.0 \| - \|
	\| 28.0622 \| 15350 \| 0.0 \| - \|
	\| 28.1536 \| 15400 \| 0.0 \| - \|
	\| 28.2450 \| 15450 \| 0.0 \| - \|
	\| 28.3364 \| 15500 \| 0.0 \| - \|
	\| 28.4278 \| 15550 \| 0.0 \| - \|
	\| 28.5192 \| 15600 \| 0.0 \| - \|
	\| 28.6106 \| 15650 \| 0.0 \| - \|
	\| 28.7020 \| 15700 \| 0.0 \| - \|
	\| 28.7934 \| 15750 \| 0.0 \| - \|
	\| 28.8848 \| 15800 \| 0.0 \| - \|
	\| 28.9762 \| 15850 \| 0.0 \| - \|
	\| 29.0676 \| 15900 \| 0.0 \| - \|
	\| 29.1590 \| 15950 \| 0.0 \| - \|
	\| 29.2505 \| 16000 \| 0.0 \| - \|
	\| 29.3419 \| 16050 \| 0.0 \| - \|
	\| 29.4333 \| 16100 \| 0.0 \| - \|
	\| 29.5247 \| 16150 \| 0.0 \| - \|
	\| 29.6161 \| 16200 \| 0.0 \| - \|
	\| 29.7075 \| 16250 \| 0.0 \| - \|
	\| 29.7989 \| 16300 \| 0.0 \| - \|
	\| 29.8903 \| 16350 \| 0.0 \| - \|
	\| 29.9817 \| 16400 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.1.0
	- Sentence Transformers: 3.3.1
	- Transformers: 4.44.2
	- PyTorch: 2.2.0a0+81ea7a4
	- Datasets: 3.2.0
	- Tokenizers: 0.19.1

	## 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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