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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: 센카 퍼펙트 휩 클렌징 폼 리뉴얼 120g × 10개 (#M)쿠팡 홈>싱글라이프>샤워/세안>클렌징>폼/젤/비누 Coupang > 뷰티
	> 클렌징/필링 > 클렌징 폼
	- text: 프로필링 소프트젤 100ml 피부 세안제 클렌징 필링 (#M)홈>화장품/미용>클렌징>스크럽/필링 Naverstore > 화장품/미용
	> 클렌징 > 스크럽/필링
	- text: 센카 퍼펙트 휩 페이셜 워시 대용량 클렌징 폼 150g × 3개 (#M)쿠팡 홈>싱글라이프>샤워/세안>클렌징>폼/젤/비누 Coupang
	> 뷰티 > 클렌징/필링 > 클렌징 폼
	- text: 센카 퍼펙트휩 2개+아크네케어 2개 센카 퍼펙트휩 2개+아크네케어 2개 LotteOn > 뷰티 > 남성화장품 > 클렌징 LotteOn
	> 뷰티 > 남성화장품 > 클렌징
	- text: '[20% ]한스킨 모공앰플체험딜 2500원 91% 外 비비크림/컨실러/클렌징오일/선크림/기초 전품목 32.블랙헤드 클렌징 티슈_블랙헤드
	클렌징 티슈 100매 [GH990850] 쇼킹딜 홈>뷰티>선케어/메이크업>페이스메이크업;11st>뷰티>선케어/메이크업>페이스메이크업;11st>메이크업>페이스메이크업>BB크림;11st
	> 뷰티 > 메이크업 > 페이스메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 페이스메이크업'
	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: 0.9232323232323232
	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>'남자클렌징폼 알로에성분 수분밸런스 세면도구 미셀라 클클 워터 미셀라워터100ml (#M)위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저 위메프 > 뷰티 > 이미용소품/기기 > 클렌징소품 > 브러쉬/진동클렌저'</li><li>'차앤박 CNP 에이클린 퓨리파잉 포밍 클렌저 145mL LotteOn > 뷰티 > 남성화장품 > 남성화장품세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트'</li><li>'[클린앤드클리어] 딥 액션 블랙헤드 데일리 클렌저 100gx2 CC딥액션블랙헤드클렌저100gx2 (#M)뷰티>화장품/향수>스킨케어>로션/에멀전 CJmall > 뷰티 > 화장품/향수 > 스킨케어 > 에센스/세럼/오일'</li></ul> \|
	\| 2 \| <ul><li>'(키엘) 미드나잇 리커버리 보태니컬 클렌징 오일 - 모든 피부용 --85ml/2.8oz ssg > 뷰티 > 스킨케어 > 스킨/토너/미스트 > 스킨/토너 LOREAL > Ssg > 키엘 > Branded > 키엘'</li><li>'마녀공장 퓨어 클렌징 오일 141238 200ml x 3개 (#M)11st>바디케어>바디미스트>바디미스트 11st > 뷰티 > 바디케어 > 바디미스트'</li><li>'[정품 세럼쿠션 & 비타민 크림 샘플 증정] 인텐시브 세럼 파운데이션 세트 쿨 아이보리 ssg > 뷰티 > 메이크업 > 립메이크업;ssg > 뷰티 > 메이크업 > 베이스메이크업;ssg > 뷰티 > 스킨케어 > 스킨/토너;ssg > 뷰티 > 메이크업 > 베이스메이크업 > 파운데이션;SSG.COM/메이크업/베이스메이크업/리퀴드파운데이션;ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이섀도우;ssg > 뷰티 > 명품화장품 > 메이크업 ssg > 뷰티 > 메이크업 > 아이메이크업'</li></ul> \|
	\| 5 \| <ul><li>'[라끄베르] 딥 앤 모이스트 클렌징 티슈 05_클렌징 티슈 80매 홈>5월 행사;홈>6월 행사!;홈>전체상품;(#M)홈>라끄베르 Naverstore > 화장품/미용 > 클렌징 > 클렌징티슈'</li><li>'토니모리 프로클린 소프트 클렌징 티슈 1+1 (#M)홈>화장품/미용>클렌징>클렌징티슈 Naverstore > 화장품/미용 > 클렌징 > 클렌징티슈'</li><li>'[소미Pick! 코스알엑스] 원스텝 스킨패드 3종 / NEW 더 비타민C 세럼 外 포어리스 패드 11st>뷰티>스킨케어>스킨/로션;11st>스킨케어>스킨/토너>스킨/토너;11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 스킨/로션 11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 스킨/로션'</li></ul> \|
	\| 0 \| <ul><li>'[일리윤] 프레쉬 모이스춰 립앤아이리무버 100ml 3개 단일상품 (#M)위메프 > 뷰티 > 네일케어 > 네일리무버 > 네일리무버 위메프 > 뷰티 > 네일케어 > 네일리무버 > 네일리무버'</li><li>'키스미 히로인메이크 스피디 마스카라 리무버 6.마스카라 리무버(K407A) (#M)화장품/향수>색조메이크업>마스카라 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 > 마스카라'</li><li>'랑콤 비파실 200ml ssg > 뷰티 > 스킨케어 > 스킨/토너 ssg > 뷰티 > 스킨케어 > 스킨/토너'</li></ul> \|
	\| 4 \| <ul><li>'라떼 다 토일레테 250ml 화이트_Free (#M)뷰티>헤어/바디/미용기기>바디케어>바디로션/크림 CJmall > 뷰티 > 화장품/향수 > 향수/홈프래그런스 > 디퓨저/방향제'</li><li>'마몽드 트리플 멀티 클렌징 크림 190ml (#M)GSSHOP>뷰티>스킨케어>스킨케어세트 GSSHOP > 뷰티 > 스킨케어 > 스킨케어세트'</li><li>'마몽드 트리플 멀티 클렌징 크림 190ml MinSellAmount (#M)화장품/향수>클렌징/필링>클렌징크림 Gmarket > 뷰티 > 화장품/향수 > 클렌징/필링 > 클렌징크림'</li></ul> \|
	\| 1 \| <ul><li>'클라란스 컴포트 스크럽 - 너리싱 오일 스크럽50ml/1.7oz (#M)홈>스트로베리넷>향수\|디퓨저>향수\|디퓨저 전체보기 HMALL > 뷰티 > 스킨케어 > 스크럽/필링'</li><li>'닥터지 레드 블레미쉬 수딩 크림 토너 폼 에멀전 필링 젤 03.브라이트닝 필링 젤 120g (#M)11st>스킨케어>앰플>앰플 11st > 뷰티 > 스킨케어 > 앰플'</li><li>'데쌍브르 올인원 각질 트러블 흔적 시카 미백 아하 바하 스피큘 해초 약초 니들필링50g 30데이즈(필링크림30g+앰플30ea) (#M)화장품/미용>클렌징>스크럽/필링 Naverstore > 화장품/미용 > 클렌징 > 스크럽/필링'</li></ul> \|
	\| 3 \| <ul><li>'산타마리아노벨라 아쿠아 디 로즈 미셀라 워터 200ml 투명_F (#M)화장품/미용>클렌징>클렌징워터 Naverstore > 화장품/미용 > 클렌징 > 클렌징워터'</li><li>'[쿠폰+T11%] 라네즈 퍼펙트리뉴 유스 레티놀 프로 꿀잠 잠옷 증정!/1밤1레티놀/라네즈레티놀 35. 라네즈 워터뱅크 아이젤 25ml_선택완료 쇼킹딜 홈>뷰티>스킨케어>스킨/로션;11st>스킨케어>스킨/토너>스킨/토너;쇼킹딜 홈>뷰티>선케어/메이크업>선블록;11st>뷰티>선케어/메이크업>선블록;11st > 뷰티 > 스킨케어 > 스킨/토너;(#M)11st>뷰티>스킨케어>스킨/로션 11st Hour Event > 패션/뷰티 > 뷰티 > 스킨케어 > 스킨/로션'</li><li>'[클린앤클리어] 미셀라 워터 100ml x2 (#M)GSSHOP>뷰티>클렌징>클렌징폼 GSSHOP > 뷰티 > 클렌징 > 클렌징폼'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.9232 \|

	## 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_top10_test")
	# Run inference
	preds = model("프로필링 소프트젤 100ml 피부 세안제 클렌징 필링 (#M)홈>화장품/미용>클렌징>스크럽/필링 Naverstore > 화장품/미용 > 클렌징 > 스크럽/필링")
	```

	<!--
	### 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 \| 11 \| 21.9571 \| 61 \|

	\| 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 \| 0.4528 \| - \|
	\| 0.0914 \| 50 \| 0.4525 \| - \|
	\| 0.1828 \| 100 \| 0.4612 \| - \|
	\| 0.2742 \| 150 \| 0.4424 \| - \|
	\| 0.3656 \| 200 \| 0.4291 \| - \|
	\| 0.4570 \| 250 \| 0.3832 \| - \|
	\| 0.5484 \| 300 \| 0.3246 \| - \|
	\| 0.6399 \| 350 \| 0.2943 \| - \|
	\| 0.7313 \| 400 \| 0.2745 \| - \|
	\| 0.8227 \| 450 \| 0.2655 \| - \|
	\| 0.9141 \| 500 \| 0.2604 \| - \|
	\| 1.0055 \| 550 \| 0.253 \| - \|
	\| 1.0969 \| 600 \| 0.2367 \| - \|
	\| 1.1883 \| 650 \| 0.228 \| - \|
	\| 1.2797 \| 700 \| 0.2115 \| - \|
	\| 1.3711 \| 750 \| 0.1976 \| - \|
	\| 1.4625 \| 800 \| 0.1786 \| - \|
	\| 1.5539 \| 850 \| 0.1609 \| - \|
	\| 1.6453 \| 900 \| 0.1472 \| - \|
	\| 1.7367 \| 950 \| 0.13 \| - \|
	\| 1.8282 \| 1000 \| 0.1213 \| - \|
	\| 1.9196 \| 1050 \| 0.1079 \| - \|
	\| 2.0110 \| 1100 \| 0.1058 \| - \|
	\| 2.1024 \| 1150 \| 0.0985 \| - \|
	\| 2.1938 \| 1200 \| 0.0824 \| - \|
	\| 2.2852 \| 1250 \| 0.0546 \| - \|
	\| 2.3766 \| 1300 \| 0.039 \| - \|
	\| 2.4680 \| 1350 \| 0.0202 \| - \|
	\| 2.5594 \| 1400 \| 0.0089 \| - \|
	\| 2.6508 \| 1450 \| 0.0044 \| - \|
	\| 2.7422 \| 1500 \| 0.004 \| - \|
	\| 2.8336 \| 1550 \| 0.0045 \| - \|
	\| 2.9250 \| 1600 \| 0.0016 \| - \|
	\| 3.0165 \| 1650 \| 0.0005 \| - \|
	\| 3.1079 \| 1700 \| 0.0004 \| - \|
	\| 3.1993 \| 1750 \| 0.0003 \| - \|
	\| 3.2907 \| 1800 \| 0.0002 \| - \|
	\| 3.3821 \| 1850 \| 0.0002 \| - \|
	\| 3.4735 \| 1900 \| 0.0001 \| - \|
	\| 3.5649 \| 1950 \| 0.0002 \| - \|
	\| 3.6563 \| 2000 \| 0.0003 \| - \|
	\| 3.7477 \| 2050 \| 0.0002 \| - \|
	\| 3.8391 \| 2100 \| 0.0001 \| - \|
	\| 3.9305 \| 2150 \| 0.0001 \| - \|
	\| 4.0219 \| 2200 \| 0.0002 \| - \|
	\| 4.1133 \| 2250 \| 0.0002 \| - \|
	\| 4.2048 \| 2300 \| 0.0003 \| - \|
	\| 4.2962 \| 2350 \| 0.0001 \| - \|
	\| 4.3876 \| 2400 \| 0.0003 \| - \|
	\| 4.4790 \| 2450 \| 0.0001 \| - \|
	\| 4.5704 \| 2500 \| 0.0001 \| - \|
	\| 4.6618 \| 2550 \| 0.0006 \| - \|
	\| 4.7532 \| 2600 \| 0.0002 \| - \|
	\| 4.8446 \| 2650 \| 0.0001 \| - \|
	\| 4.9360 \| 2700 \| 0.0022 \| - \|
	\| 5.0274 \| 2750 \| 0.0046 \| - \|
	\| 5.1188 \| 2800 \| 0.0028 \| - \|
	\| 5.2102 \| 2850 \| 0.0033 \| - \|
	\| 5.3016 \| 2900 \| 0.0025 \| - \|
	\| 5.3931 \| 2950 \| 0.0023 \| - \|
	\| 5.4845 \| 3000 \| 0.002 \| - \|
	\| 5.5759 \| 3050 \| 0.004 \| - \|
	\| 5.6673 \| 3100 \| 0.0044 \| - \|
	\| 5.7587 \| 3150 \| 0.004 \| - \|
	\| 5.8501 \| 3200 \| 0.0027 \| - \|
	\| 5.9415 \| 3250 \| 0.0032 \| - \|
	\| 6.0329 \| 3300 \| 0.0002 \| - \|
	\| 6.1243 \| 3350 \| 0.0003 \| - \|
	\| 6.2157 \| 3400 \| 0.0 \| - \|
	\| 6.3071 \| 3450 \| 0.0006 \| - \|
	\| 6.3985 \| 3500 \| 0.0005 \| - \|
	\| 6.4899 \| 3550 \| 0.0035 \| - \|
	\| 6.5814 \| 3600 \| 0.0053 \| - \|
	\| 6.6728 \| 3650 \| 0.004 \| - \|
	\| 6.7642 \| 3700 \| 0.0042 \| - \|
	\| 6.8556 \| 3750 \| 0.0046 \| - \|
	\| 6.9470 \| 3800 \| 0.0038 \| - \|
	\| 7.0384 \| 3850 \| 0.0017 \| - \|
	\| 7.1298 \| 3900 \| 0.0015 \| - \|
	\| 7.2212 \| 3950 \| 0.0001 \| - \|
	\| 7.3126 \| 4000 \| 0.0 \| - \|
	\| 7.4040 \| 4050 \| 0.0 \| - \|
	\| 7.4954 \| 4100 \| 0.0001 \| - \|
	\| 7.5868 \| 4150 \| 0.0 \| - \|
	\| 7.6782 \| 4200 \| 0.0002 \| - \|
	\| 7.7697 \| 4250 \| 0.0001 \| - \|
	\| 7.8611 \| 4300 \| 0.0005 \| - \|
	\| 7.9525 \| 4350 \| 0.0 \| - \|
	\| 8.0439 \| 4400 \| 0.0002 \| - \|
	\| 8.1353 \| 4450 \| 0.0 \| - \|
	\| 8.2267 \| 4500 \| 0.0008 \| - \|
	\| 8.3181 \| 4550 \| 0.0001 \| - \|
	\| 8.4095 \| 4600 \| 0.0002 \| - \|
	\| 8.5009 \| 4650 \| 0.0 \| - \|
	\| 8.5923 \| 4700 \| 0.0 \| - \|
	\| 8.6837 \| 4750 \| 0.0 \| - \|
	\| 8.7751 \| 4800 \| 0.0 \| - \|
	\| 8.8665 \| 4850 \| 0.0002 \| - \|
	\| 8.9580 \| 4900 \| 0.0008 \| - \|
	\| 9.0494 \| 4950 \| 0.0009 \| - \|
	\| 9.1408 \| 5000 \| 0.0004 \| - \|
	\| 9.2322 \| 5050 \| 0.0 \| - \|
	\| 9.3236 \| 5100 \| 0.0 \| - \|
	\| 9.4150 \| 5150 \| 0.0002 \| - \|
	\| 9.5064 \| 5200 \| 0.0006 \| - \|
	\| 9.5978 \| 5250 \| 0.0021 \| - \|
	\| 9.6892 \| 5300 \| 0.0004 \| - \|
	\| 9.7806 \| 5350 \| 0.0016 \| - \|
	\| 9.8720 \| 5400 \| 0.0003 \| - \|
	\| 9.9634 \| 5450 \| 0.0 \| - \|
	\| 10.0548 \| 5500 \| 0.0002 \| - \|
	\| 10.1463 \| 5550 \| 0.0 \| - \|
	\| 10.2377 \| 5600 \| 0.0 \| - \|
	\| 10.3291 \| 5650 \| 0.0001 \| - \|
	\| 10.4205 \| 5700 \| 0.0008 \| - \|
	\| 10.5119 \| 5750 \| 0.0002 \| - \|
	\| 10.6033 \| 5800 \| 0.0003 \| - \|
	\| 10.6947 \| 5850 \| 0.0 \| - \|
	\| 10.7861 \| 5900 \| 0.0 \| - \|
	\| 10.8775 \| 5950 \| 0.0 \| - \|
	\| 10.9689 \| 6000 \| 0.0 \| - \|
	\| 11.0603 \| 6050 \| 0.0 \| - \|
	\| 11.1517 \| 6100 \| 0.0 \| - \|
	\| 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 \| 0.0 \| - \|
	\| 11.7916 \| 6450 \| 0.0 \| - \|
	\| 11.8830 \| 6500 \| 0.0 \| - \|
	\| 11.9744 \| 6550 \| 0.0 \| - \|
	\| 12.0658 \| 6600 \| 0.0 \| - \|
	\| 12.1572 \| 6650 \| 0.0 \| - \|
	\| 12.2486 \| 6700 \| 0.0 \| - \|
	\| 12.3400 \| 6750 \| 0.0001 \| - \|
	\| 12.4314 \| 6800 \| 0.0001 \| - \|
	\| 12.5229 \| 6850 \| 0.0002 \| - \|
	\| 12.6143 \| 6900 \| 0.0 \| - \|
	\| 12.7057 \| 6950 \| 0.0003 \| - \|
	\| 12.7971 \| 7000 \| 0.0001 \| - \|
	\| 12.8885 \| 7050 \| 0.0004 \| - \|
	\| 12.9799 \| 7100 \| 0.0018 \| - \|
	\| 13.0713 \| 7150 \| 0.0002 \| - \|
	\| 13.1627 \| 7200 \| 0.0 \| - \|
	\| 13.2541 \| 7250 \| 0.0001 \| - \|
	\| 13.3455 \| 7300 \| 0.0 \| - \|
	\| 13.4369 \| 7350 \| 0.0 \| - \|
	\| 13.5283 \| 7400 \| 0.0001 \| - \|
	\| 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 \| 0.0 \| - \|
	\| 14.2596 \| 7800 \| 0.0 \| - \|
	\| 14.3510 \| 7850 \| 0.0 \| - \|
	\| 14.4424 \| 7900 \| 0.0 \| - \|
	\| 14.5338 \| 7950 \| 0.0 \| - \|
	\| 14.6252 \| 8000 \| 0.0 \| - \|
	\| 14.7166 \| 8050 \| 0.0 \| - \|
	\| 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 \| 0.0002 \| - \|
	\| 17.5503 \| 9600 \| 0.0011 \| - \|
	\| 17.6417 \| 9650 \| 0.0003 \| - \|
	\| 17.7331 \| 9700 \| 0.0 \| - \|
	\| 17.8245 \| 9750 \| 0.0001 \| - \|
	\| 17.9159 \| 9800 \| 0.0001 \| - \|
	\| 18.0073 \| 9850 \| 0.0002 \| - \|
	\| 18.0987 \| 9900 \| 0.0003 \| - \|
	\| 18.1901 \| 9950 \| 0.0 \| - \|
	\| 18.2815 \| 10000 \| 0.0 \| - \|
	\| 18.3729 \| 10050 \| 0.0 \| - \|
	\| 18.4644 \| 10100 \| 0.0 \| - \|
	\| 18.5558 \| 10150 \| 0.0002 \| - \|
	\| 18.6472 \| 10200 \| 0.0002 \| - \|
	\| 18.7386 \| 10250 \| 0.0 \| - \|
	\| 18.8300 \| 10300 \| 0.0004 \| - \|
	\| 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 \| 0.0002 \| - \|
	\| 19.7441 \| 10800 \| 0.0003 \| - \|
	\| 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 \| 0.0004 \| - \|
	\| 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 \| 0.0001 \| - \|
	\| 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 \| 0.0001 \| - \|
	\| 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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