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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 참존 디에이지 레드에디션 콘트롤크림 180ml (#M)홈>화장품/미용>마스크/팩>마사지크림/젤 Naverstore > 화장품/미용
	> 마스크/팩 > 마사지크림/젤
	- text: 바이오가 밀크 아미노산 크림 LotteOn > 뷰티 > 헤어/바디 > 바디케어 > 바디로션/크림 LotteOn > 뷰티 > 헤어/바디
	> 바디케어 > 바디로션/크림
	- text: 비오템 비오템 Life Plankton Sensitive Emulsion 50ml (#M)쿠팡 홈>뷰티>뷰티소품>용기/거울/기타소품>기타소품
	LOREAL > Coupang > 비오템 > Branded > 비오템
	- text: 푸드어홀릭 히알루론산 수분 젤 크림 300ml (#M)위메프 > 뷰티 > 스킨케어 > 크림 > 마사지크림 위메프 > 뷰티 > 스킨케어
	> 크림 > 마사지크림
	- text: 마몽드 로즈워터 토너 500ml × 1개 (#M)쿠팡 홈>뷰티>스킨케어>스킨 Coupang > 뷰티 > 스킨케어 > 스킨
	metrics:
	- accuracy
	pipeline_tag: text-classification
	library_name: setfit
	inference: true
	base_model: mini1013/master_domain
	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: 0.912736827548057
	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: 11 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 \|
	\|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 0 \| <ul><li>'휘또뷔스뜨 플러스 데콜테 50ml (가슴 에센스) ssg > 뷰티 > 헤어/바디 > 바디케어 > 바디로션/크림;SSG.COM/바디케어/바디로션/크림/오일/바디로션/크림;(#M)SSG.COM>바디케어>바디로션/크림/오일>바디로션/크림 ssg > 뷰티 > 바디케어 > 바디로션/크림/오일'</li><li>'메디필 나이테 실 넥크림 100ml (#M)11st>스킨케어>탄력크림>탄력크림 11st > 뷰티 > 스킨케어 > 탄력크림 > 탄력크림'</li><li>'[1만원 상품권][4][단독] 기적의 크림 60ml 세트 (+18만 5천원 상당 넥/데콜테 크림) 모이스춰라이징 소프트 크림 ssg > 뷰티 > 스킨케어 > 크림;ssg > 뷰티 > 명품화장품 > 스킨케어 세트;ssg > 뷰티 > 스킨케어 > 스킨케어세트 ssg > 뷰티 > 스킨케어 > 크림'</li></ul> \|
	\| 4 \| <ul><li>'[랑콤] 토닉 꽁포르 400ml 세트 (+이드라젠 크림 30ml 용량 추가 증정) 없음 (#M)홈>스킨케어>스킨/토너 HMALL > 현대백화점 > 화장품 > 스킨케어 > 스킨로션/미스트'</li><li>'아이오페 라이브 리프트 소프너 스킨 인텐시브 150ml MinSellAmount (#M)화장품/향수>스킨케어>스킨/토너 Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 스킨/토너'</li><li>'설린수 150ml 150~300ml LotteOn > 뷰티 > 스킨케어 > 스킨/토너 LotteOn > 뷰티 > 스킨케어 > 스킨/토너'</li></ul> \|
	\| 8 \| <ul><li>'그린티 히알루론산 로션 170mL 레티놀 시카 흔적 앰플 30mL + 레티놀 앰플 7mL (#M)위메프 > 뷰티 > 남성화장품 > 남성 스킨케어 > 남성스킨 위메프 > 뷰티 > 남성화장품 > 남성 스킨케어 > 남성스킨'</li><li>'아이오페 바이오 컨디셔닝 에센스 168ml 아이오페 바이오 컨디셔닝 에센스 216ml (#M)홈>화장품/미용>스킨케어>에센스 Naverstore > 화장품/미용 > 스킨케어 > 에센스'</li><li>'퓨어샷 나이트 리부트 세럼 50ml LotteOn > 백화점 TAP > 명품화장품 > 메인 배너 (PC) LotteOn > 뷰티 > 럭셔리 스킨케어 > 에센스/세럼'</li></ul> \|
	\| 9 \| <ul><li>'더테라피 로얄메이드 오일블렌딩 크림 50ml/JL MinSellAmount (#M)화장품/향수>스킨케어>크림/젤 Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 크림/젤'</li><li>'닥터지 레드 블레미쉬 클리어 수딩크림 70ml × 3개 Coupang > 뷰티 > 선물세트/키트 > 선물세트 > 스킨케어;(#M)쿠팡 홈>뷰티>선물세트/키트>선물세트>스킨케어 Coupang > 뷰티 > 선물세트/키트 > 선물세트 > 스킨케어'</li><li>'유리아쥬 제모스 세라뜨 200ml /HY MinSellAmount (#M)화장품/향수>스킨케어>크림/젤 Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 크림/젤'</li></ul> \|
	\| 6 \| <ul><li>'워터뱅크 크림 아이젤 라네즈 아이케어 보습 (#M)홈>화장품/미용>스킨케어>로션 Naverstore > 화장품/미용 > 스킨케어 > 로션'</li><li>'엑스트라 아이 리페어 인텐스 1+1 LotteOn > 백화점 > 뷰티 > 상단 배너 (Mobile) LotteOn > 뷰티 > 럭셔리 스킨케어 > 아이케어/넥케어'</li><li>'골드마스크 구매시 설화수샘플 자음생아이크림 7장증정 (#M)위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 마스크시트팩 위메프 > 뷰티 > 스킨케어 > 팩/마스크 > 마스크시트팩'</li></ul> \|
	\| 1 \| <ul><li>'[최신제조] 설화수 자음유액 125ml LotteOn > 뷰티 > 스킨케어 > 아이케어/넥케어 LotteOn > 뷰티 > 스킨케어 > 아이케어/넥케어'</li><li>'바비 브라운 인텐시브 스킨 세럼 레디언스 에멀전 바비 브라운 인텐시브 스킨 세럼 레디언스 에멀전 홈>스킨케어>스킨/로션/올인원>스킨/토너;(#M)홈>스킨케어>토너/로션/올인원>스킨/토너 OLIVEYOUNG > 스킨케어 > 토너/로션/올인원 > 스킨/토너'</li><li>'한율 빨간쌀 진액에멀젼 125ml/로션+보습+피부방어력+피부장벽 LotteOn > 뷰티 > 스킨케어 > 아이케어/넥케어 LotteOn > 뷰티 > 스킨케어 > 아이케어/넥케어'</li></ul> \|
	\| 3 \| <ul><li>'은율 모이스처 글로우 멀티밤 10g 12개 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트 LotteOn > 뷰티 > 남성화장품 > 남성화장품세트'</li><li>'KAHI 가히 1+1 링클바운스 멀티밤 수분 주름 스틱 보습 주름케어 LotteOn > 뷰티 > 스킨케어 > 미스트 LotteOn > 뷰티 > 스킨케어 > 미스트'</li><li>'가히 서울 링클 바운스 멀티밤 9g LotteOn > 뷰티 > 스킨케어 > 크림 LotteOn > 뷰티 > 스킨케어 > 크림'</li></ul> \|
	\| 7 \| <ul><li>'NEW 모이스춰 써지 아이 96-아워 하이드로 컨센트레이트 15ml LotteOn > 뷰티 > 스킨케어 > 아이케어 LotteOn > 뷰티 > 럭셔리 스킨케어 > 아이케어/넥케어'</li><li>'SNP 골드 콜라겐 니들 패치 3박스 (24매) (#M)11st>스킨케어>팩/마스크>고무팩 11st > 뷰티 > 스킨케어 > 팩/마스크 > 고무팩'</li><li>'프럼네이처 골드 하이드로겔 아이패치 60매입 × 1개 쿠팡 홈>뷰티>스킨케어>마스크/팩>코팩/기타패치>아이 패치;쿠팡 홈>뷰티>스킨케어>마스크/팩>패치/코팩>기타패치;(#M)쿠팡 홈>뷰티>스킨케어>마스크/팩>패치/코팩>아이 패치 Coupang > 뷰티 > 스킨케어 > 마스크/팩 > 패치/코팩 > 아이 패치'</li></ul> \|
	\| 10 \| <ul><li>'블랙티 유스 인핸싱 오일 30mL LotteOn > 뷰티 > 스킨케어 > 오일 LotteOn > 뷰티 > 스킨케어 > 오일'</li><li>'[3월][한정] 아베이 로얄 유쓰 워터리 오일 50ml 세트 아베이 로얄 유쓰 워터리 오일 LotteOn > 뷰티 > 명품화장품 > 스킨케어 > 오일 LotteOn > 뷰티 > 명품화장품 > 스킨케어 > 오일'</li><li>'청미정 비타민나무 페이스오일 LotteOn > 뷰티 > 스킨케어 > 오일 LotteOn > 뷰티 > 스킨케어 > 오일'</li></ul> \|
	\| 2 \| <ul><li>'[본사직영] 글로우 스킨밤 투 고 미스트 80 ml 위메프 > 뷰티 > 메이크업 > 립 메이크업;위메프 > 뷰티 > 메이크업 > 립 메이크업 > 립글로즈;위메프 > 뷰티 > 스킨케어 > 앰플/에센스/세럼 > 에센스;위메프 > 뷰티 > 스킨케어 > 미스트;(#M)위메프 > 뷰티 > 스킨케어 > 미스트 > 미스트 위메프 > 뷰티 > 스킨케어 > 미스트'</li><li>'메이블린 뉴욕 래스팅 픽스 스프레이 60ml × 1개 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머 LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 베이스/프라이머'</li><li>'(아벤느 공식판매) 오떼르말 300ml(1+1)_AN08-2 A 화장품\|미용>헤어케어\|염색>샴푸린스>샴푸;(#M)홈>화장품/미용>헤어케어\|염색>샴푸린스>샴푸 HMALL > 뷰티 > 화장품/미용 > 헤어케어 > 샴푸린스 > 샴푸'</li></ul> \|
	\| 5 \| <ul><li>'아이오페 여성화장품 라이브 리프트 스페셜 2종세트 MinSellAmount (#M)화장품/향수>스킨케어>페이스오일 Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 페이스오일'</li><li>'설화수 탄력 에센셜 3종기획세트 탄력크림 기초화장품 30대여자화장품 추천 LotteOn > 뷰티 > 스킨케어 > 화장품세트 LotteOn > 뷰티 > 스킨케어 > 화장품세트'</li><li>'그린티 스킨케어세트 이니스프리 그린티세트 밸런싱 (#M)위메프 > 생활·주방용품 > 바디/헤어 > 바디로션/핸드/풋 > 생활선물세트 위메프 > 뷰티 > 바디/헤어 > 바디로션/핸드/풋 > 생활선물세트'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.9127 \|

	## 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_item_top_bt9")
	# Run inference
	preds = model("마몽드 로즈워터 토너 500ml × 1개 (#M)쿠팡 홈>뷰티>스킨케어>스킨 Coupang > 뷰티 > 스킨케어 > 스킨")
	```

	<!--
	### 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 \| 10 \| 20.6873 \| 55 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 50 \|
	\| 1 \| 50 \|
	\| 2 \| 50 \|
	\| 3 \| 50 \|
	\| 4 \| 50 \|
	\| 5 \| 50 \|
	\| 6 \| 50 \|
	\| 7 \| 50 \|
	\| 8 \| 50 \|
	\| 9 \| 50 \|
	\| 10 \| 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 \|
	\|:-------:\|:-----:\|:-------------:\|:---------------:\|
	\| 0.0012 \| 1 \| 0.3079 \| - \|
	\| 0.0581 \| 50 \| 0.3024 \| - \|
	\| 0.1163 \| 100 \| 0.288 \| - \|
	\| 0.1744 \| 150 \| 0.2621 \| - \|
	\| 0.2326 \| 200 \| 0.2186 \| - \|
	\| 0.2907 \| 250 \| 0.191 \| - \|
	\| 0.3488 \| 300 \| 0.1552 \| - \|
	\| 0.4070 \| 350 \| 0.1255 \| - \|
	\| 0.4651 \| 400 \| 0.1053 \| - \|
	\| 0.5233 \| 450 \| 0.0908 \| - \|
	\| 0.5814 \| 500 \| 0.0691 \| - \|
	\| 0.6395 \| 550 \| 0.0665 \| - \|
	\| 0.6977 \| 600 \| 0.053 \| - \|
	\| 0.7558 \| 650 \| 0.0438 \| - \|
	\| 0.8140 \| 700 \| 0.0407 \| - \|
	\| 0.8721 \| 750 \| 0.0325 \| - \|
	\| 0.9302 \| 800 \| 0.0277 \| - \|
	\| 0.9884 \| 850 \| 0.0232 \| - \|
	\| 1.0465 \| 900 \| 0.0197 \| - \|
	\| 1.1047 \| 950 \| 0.0171 \| - \|
	\| 1.1628 \| 1000 \| 0.0137 \| - \|
	\| 1.2209 \| 1050 \| 0.0113 \| - \|
	\| 1.2791 \| 1100 \| 0.0104 \| - \|
	\| 1.3372 \| 1150 \| 0.0109 \| - \|
	\| 1.3953 \| 1200 \| 0.0086 \| - \|
	\| 1.4535 \| 1250 \| 0.0075 \| - \|
	\| 1.5116 \| 1300 \| 0.0065 \| - \|
	\| 1.5698 \| 1350 \| 0.0075 \| - \|
	\| 1.6279 \| 1400 \| 0.0071 \| - \|
	\| 1.6860 \| 1450 \| 0.0072 \| - \|
	\| 1.7442 \| 1500 \| 0.0081 \| - \|
	\| 1.8023 \| 1550 \| 0.006 \| - \|
	\| 1.8605 \| 1600 \| 0.0062 \| - \|
	\| 1.9186 \| 1650 \| 0.0034 \| - \|
	\| 1.9767 \| 1700 \| 0.0019 \| - \|
	\| 2.0349 \| 1750 \| 0.0023 \| - \|
	\| 2.0930 \| 1800 \| 0.0019 \| - \|
	\| 2.1512 \| 1850 \| 0.0012 \| - \|
	\| 2.2093 \| 1900 \| 0.0009 \| - \|
	\| 2.2674 \| 1950 \| 0.0007 \| - \|
	\| 2.3256 \| 2000 \| 0.0001 \| - \|
	\| 2.3837 \| 2050 \| 0.0004 \| - \|
	\| 2.4419 \| 2100 \| 0.0008 \| - \|
	\| 2.5 \| 2150 \| 0.0011 \| - \|
	\| 2.5581 \| 2200 \| 0.0012 \| - \|
	\| 2.6163 \| 2250 \| 0.0009 \| - \|
	\| 2.6744 \| 2300 \| 0.0008 \| - \|
	\| 2.7326 \| 2350 \| 0.0006 \| - \|
	\| 2.7907 \| 2400 \| 0.0001 \| - \|
	\| 2.8488 \| 2450 \| 0.0002 \| - \|
	\| 2.9070 \| 2500 \| 0.0002 \| - \|
	\| 2.9651 \| 2550 \| 0.0004 \| - \|
	\| 3.0233 \| 2600 \| 0.0 \| - \|
	\| 3.0814 \| 2650 \| 0.0 \| - \|
	\| 3.1395 \| 2700 \| 0.0 \| - \|
	\| 3.1977 \| 2750 \| 0.0 \| - \|
	\| 3.2558 \| 2800 \| 0.0 \| - \|
	\| 3.3140 \| 2850 \| 0.0 \| - \|
	\| 3.3721 \| 2900 \| 0.0 \| - \|
	\| 3.4302 \| 2950 \| 0.0008 \| - \|
	\| 3.4884 \| 3000 \| 0.0008 \| - \|
	\| 3.5465 \| 3050 \| 0.0 \| - \|
	\| 3.6047 \| 3100 \| 0.0004 \| - \|
	\| 3.6628 \| 3150 \| 0.0026 \| - \|
	\| 3.7209 \| 3200 \| 0.0033 \| - \|
	\| 3.7791 \| 3250 \| 0.0013 \| - \|
	\| 3.8372 \| 3300 \| 0.0002 \| - \|
	\| 3.8953 \| 3350 \| 0.001 \| - \|
	\| 3.9535 \| 3400 \| 0.0006 \| - \|
	\| 4.0116 \| 3450 \| 0.0 \| - \|
	\| 4.0698 \| 3500 \| 0.0 \| - \|
	\| 4.1279 \| 3550 \| 0.0 \| - \|
	\| 4.1860 \| 3600 \| 0.0 \| - \|
	\| 4.2442 \| 3650 \| 0.0002 \| - \|
	\| 4.3023 \| 3700 \| 0.0002 \| - \|
	\| 4.3605 \| 3750 \| 0.0002 \| - \|
	\| 4.4186 \| 3800 \| 0.0 \| - \|
	\| 4.4767 \| 3850 \| 0.0 \| - \|
	\| 4.5349 \| 3900 \| 0.0 \| - \|
	\| 4.5930 \| 3950 \| 0.0 \| - \|
	\| 4.6512 \| 4000 \| 0.0 \| - \|
	\| 4.7093 \| 4050 \| 0.0 \| - \|
	\| 4.7674 \| 4100 \| 0.0 \| - \|
	\| 4.8256 \| 4150 \| 0.0 \| - \|
	\| 4.8837 \| 4200 \| 0.0 \| - \|
	\| 4.9419 \| 4250 \| 0.0 \| - \|
	\| 5.0 \| 4300 \| 0.0 \| - \|
	\| 5.0581 \| 4350 \| 0.0 \| - \|
	\| 5.1163 \| 4400 \| 0.0 \| - \|
	\| 5.1744 \| 4450 \| 0.0 \| - \|
	\| 5.2326 \| 4500 \| 0.0 \| - \|
	\| 5.2907 \| 4550 \| 0.0 \| - \|
	\| 5.3488 \| 4600 \| 0.0 \| - \|
	\| 5.4070 \| 4650 \| 0.0 \| - \|
	\| 5.4651 \| 4700 \| 0.0 \| - \|
	\| 5.5233 \| 4750 \| 0.0 \| - \|
	\| 5.5814 \| 4800 \| 0.0 \| - \|
	\| 5.6395 \| 4850 \| 0.0 \| - \|
	\| 5.6977 \| 4900 \| 0.0 \| - \|
	\| 5.7558 \| 4950 \| 0.0 \| - \|
	\| 5.8140 \| 5000 \| 0.0 \| - \|
	\| 5.8721 \| 5050 \| 0.0 \| - \|
	\| 5.9302 \| 5100 \| 0.0 \| - \|
	\| 5.9884 \| 5150 \| 0.0 \| - \|
	\| 6.0465 \| 5200 \| 0.0 \| - \|
	\| 6.1047 \| 5250 \| 0.0 \| - \|
	\| 6.1628 \| 5300 \| 0.0 \| - \|
	\| 6.2209 \| 5350 \| 0.0 \| - \|
	\| 6.2791 \| 5400 \| 0.0 \| - \|
	\| 6.3372 \| 5450 \| 0.0 \| - \|
	\| 6.3953 \| 5500 \| 0.0 \| - \|
	\| 6.4535 \| 5550 \| 0.0 \| - \|
	\| 6.5116 \| 5600 \| 0.0 \| - \|
	\| 6.5698 \| 5650 \| 0.0 \| - \|
	\| 6.6279 \| 5700 \| 0.0 \| - \|
	\| 6.6860 \| 5750 \| 0.0 \| - \|
	\| 6.7442 \| 5800 \| 0.0 \| - \|
	\| 6.8023 \| 5850 \| 0.0 \| - \|
	\| 6.8605 \| 5900 \| 0.0 \| - \|
	\| 6.9186 \| 5950 \| 0.0 \| - \|
	\| 6.9767 \| 6000 \| 0.0 \| - \|
	\| 7.0349 \| 6050 \| 0.0 \| - \|
	\| 7.0930 \| 6100 \| 0.0 \| - \|
	\| 7.1512 \| 6150 \| 0.0 \| - \|
	\| 7.2093 \| 6200 \| 0.0 \| - \|
	\| 7.2674 \| 6250 \| 0.0 \| - \|
	\| 7.3256 \| 6300 \| 0.0 \| - \|
	\| 7.3837 \| 6350 \| 0.0 \| - \|
	\| 7.4419 \| 6400 \| 0.0 \| - \|
	\| 7.5 \| 6450 \| 0.0 \| - \|
	\| 7.5581 \| 6500 \| 0.0 \| - \|
	\| 7.6163 \| 6550 \| 0.0 \| - \|
	\| 7.6744 \| 6600 \| 0.0 \| - \|
	\| 7.7326 \| 6650 \| 0.0 \| - \|
	\| 7.7907 \| 6700 \| 0.0 \| - \|
	\| 7.8488 \| 6750 \| 0.0 \| - \|
	\| 7.9070 \| 6800 \| 0.0 \| - \|
	\| 7.9651 \| 6850 \| 0.0 \| - \|
	\| 8.0233 \| 6900 \| 0.0 \| - \|
	\| 8.0814 \| 6950 \| 0.0005 \| - \|
	\| 8.1395 \| 7000 \| 0.0089 \| - \|
	\| 8.1977 \| 7050 \| 0.0043 \| - \|
	\| 8.2558 \| 7100 \| 0.0012 \| - \|
	\| 8.3140 \| 7150 \| 0.0003 \| - \|
	\| 8.3721 \| 7200 \| 0.0003 \| - \|
	\| 8.4302 \| 7250 \| 0.0003 \| - \|
	\| 8.4884 \| 7300 \| 0.0001 \| - \|
	\| 8.5465 \| 7350 \| 0.0 \| - \|
	\| 8.6047 \| 7400 \| 0.0 \| - \|
	\| 8.6628 \| 7450 \| 0.0 \| - \|
	\| 8.7209 \| 7500 \| 0.0 \| - \|
	\| 8.7791 \| 7550 \| 0.0 \| - \|
	\| 8.8372 \| 7600 \| 0.0 \| - \|
	\| 8.8953 \| 7650 \| 0.0 \| - \|
	\| 8.9535 \| 7700 \| 0.0 \| - \|
	\| 9.0116 \| 7750 \| 0.0 \| - \|
	\| 9.0698 \| 7800 \| 0.0 \| - \|
	\| 9.1279 \| 7850 \| 0.0 \| - \|
	\| 9.1860 \| 7900 \| 0.0 \| - \|
	\| 9.2442 \| 7950 \| 0.0 \| - \|
	\| 9.3023 \| 8000 \| 0.0 \| - \|
	\| 9.3605 \| 8050 \| 0.0 \| - \|
	\| 9.4186 \| 8100 \| 0.0 \| - \|
	\| 9.4767 \| 8150 \| 0.0 \| - \|
	\| 9.5349 \| 8200 \| 0.0 \| - \|
	\| 9.5930 \| 8250 \| 0.0 \| - \|
	\| 9.6512 \| 8300 \| 0.0 \| - \|
	\| 9.7093 \| 8350 \| 0.0 \| - \|
	\| 9.7674 \| 8400 \| 0.0 \| - \|
	\| 9.8256 \| 8450 \| 0.0 \| - \|
	\| 9.8837 \| 8500 \| 0.0 \| - \|
	\| 9.9419 \| 8550 \| 0.0 \| - \|
	\| 10.0 \| 8600 \| 0.0 \| - \|
	\| 10.0581 \| 8650 \| 0.0 \| - \|
	\| 10.1163 \| 8700 \| 0.0 \| - \|
	\| 10.1744 \| 8750 \| 0.0 \| - \|
	\| 10.2326 \| 8800 \| 0.0 \| - \|
	\| 10.2907 \| 8850 \| 0.0 \| - \|
	\| 10.3488 \| 8900 \| 0.0 \| - \|
	\| 10.4070 \| 8950 \| 0.0 \| - \|
	\| 10.4651 \| 9000 \| 0.0 \| - \|
	\| 10.5233 \| 9050 \| 0.0 \| - \|
	\| 10.5814 \| 9100 \| 0.0 \| - \|
	\| 10.6395 \| 9150 \| 0.0 \| - \|
	\| 10.6977 \| 9200 \| 0.0 \| - \|
	\| 10.7558 \| 9250 \| 0.0 \| - \|
	\| 10.8140 \| 9300 \| 0.0 \| - \|
	\| 10.8721 \| 9350 \| 0.0 \| - \|
	\| 10.9302 \| 9400 \| 0.0 \| - \|
	\| 10.9884 \| 9450 \| 0.0 \| - \|
	\| 11.0465 \| 9500 \| 0.0 \| - \|
	\| 11.1047 \| 9550 \| 0.0 \| - \|
	\| 11.1628 \| 9600 \| 0.0 \| - \|
	\| 11.2209 \| 9650 \| 0.0 \| - \|
	\| 11.2791 \| 9700 \| 0.0 \| - \|
	\| 11.3372 \| 9750 \| 0.0 \| - \|
	\| 11.3953 \| 9800 \| 0.0 \| - \|
	\| 11.4535 \| 9850 \| 0.0 \| - \|
	\| 11.5116 \| 9900 \| 0.0 \| - \|
	\| 11.5698 \| 9950 \| 0.0 \| - \|
	\| 11.6279 \| 10000 \| 0.0 \| - \|
	\| 11.6860 \| 10050 \| 0.0 \| - \|
	\| 11.7442 \| 10100 \| 0.0 \| - \|
	\| 11.8023 \| 10150 \| 0.0 \| - \|
	\| 11.8605 \| 10200 \| 0.0 \| - \|
	\| 11.9186 \| 10250 \| 0.0 \| - \|
	\| 11.9767 \| 10300 \| 0.0 \| - \|
	\| 12.0349 \| 10350 \| 0.0 \| - \|
	\| 12.0930 \| 10400 \| 0.0 \| - \|
	\| 12.1512 \| 10450 \| 0.0 \| - \|
	\| 12.2093 \| 10500 \| 0.0 \| - \|
	\| 12.2674 \| 10550 \| 0.0 \| - \|
	\| 12.3256 \| 10600 \| 0.0 \| - \|
	\| 12.3837 \| 10650 \| 0.0 \| - \|
	\| 12.4419 \| 10700 \| 0.0 \| - \|
	\| 12.5 \| 10750 \| 0.0 \| - \|
	\| 12.5581 \| 10800 \| 0.0 \| - \|
	\| 12.6163 \| 10850 \| 0.0 \| - \|
	\| 12.6744 \| 10900 \| 0.0 \| - \|
	\| 12.7326 \| 10950 \| 0.0 \| - \|
	\| 12.7907 \| 11000 \| 0.0 \| - \|
	\| 12.8488 \| 11050 \| 0.0 \| - \|
	\| 12.9070 \| 11100 \| 0.0 \| - \|
	\| 12.9651 \| 11150 \| 0.0 \| - \|
	\| 13.0233 \| 11200 \| 0.0 \| - \|
	\| 13.0814 \| 11250 \| 0.0 \| - \|
	\| 13.1395 \| 11300 \| 0.0 \| - \|
	\| 13.1977 \| 11350 \| 0.0 \| - \|
	\| 13.2558 \| 11400 \| 0.0 \| - \|
	\| 13.3140 \| 11450 \| 0.0 \| - \|
	\| 13.3721 \| 11500 \| 0.0 \| - \|
	\| 13.4302 \| 11550 \| 0.0 \| - \|
	\| 13.4884 \| 11600 \| 0.0 \| - \|
	\| 13.5465 \| 11650 \| 0.0 \| - \|
	\| 13.6047 \| 11700 \| 0.0 \| - \|
	\| 13.6628 \| 11750 \| 0.0 \| - \|
	\| 13.7209 \| 11800 \| 0.0 \| - \|
	\| 13.7791 \| 11850 \| 0.0 \| - \|
	\| 13.8372 \| 11900 \| 0.0 \| - \|
	\| 13.8953 \| 11950 \| 0.0 \| - \|
	\| 13.9535 \| 12000 \| 0.0 \| - \|
	\| 14.0116 \| 12050 \| 0.0 \| - \|
	\| 14.0698 \| 12100 \| 0.0 \| - \|
	\| 14.1279 \| 12150 \| 0.0 \| - \|
	\| 14.1860 \| 12200 \| 0.0 \| - \|
	\| 14.2442 \| 12250 \| 0.0 \| - \|
	\| 14.3023 \| 12300 \| 0.0 \| - \|
	\| 14.3605 \| 12350 \| 0.0 \| - \|
	\| 14.4186 \| 12400 \| 0.0 \| - \|
	\| 14.4767 \| 12450 \| 0.0 \| - \|
	\| 14.5349 \| 12500 \| 0.0 \| - \|
	\| 14.5930 \| 12550 \| 0.0 \| - \|
	\| 14.6512 \| 12600 \| 0.0 \| - \|
	\| 14.7093 \| 12650 \| 0.0 \| - \|
	\| 14.7674 \| 12700 \| 0.0 \| - \|
	\| 14.8256 \| 12750 \| 0.0 \| - \|
	\| 14.8837 \| 12800 \| 0.0 \| - \|
	\| 14.9419 \| 12850 \| 0.0 \| - \|
	\| 15.0 \| 12900 \| 0.0 \| - \|
	\| 15.0581 \| 12950 \| 0.0 \| - \|
	\| 15.1163 \| 13000 \| 0.0 \| - \|
	\| 15.1744 \| 13050 \| 0.0 \| - \|
	\| 15.2326 \| 13100 \| 0.0 \| - \|
	\| 15.2907 \| 13150 \| 0.0 \| - \|
	\| 15.3488 \| 13200 \| 0.0 \| - \|
	\| 15.4070 \| 13250 \| 0.0 \| - \|
	\| 15.4651 \| 13300 \| 0.0 \| - \|
	\| 15.5233 \| 13350 \| 0.0 \| - \|
	\| 15.5814 \| 13400 \| 0.0 \| - \|
	\| 15.6395 \| 13450 \| 0.0 \| - \|
	\| 15.6977 \| 13500 \| 0.0 \| - \|
	\| 15.7558 \| 13550 \| 0.0 \| - \|
	\| 15.8140 \| 13600 \| 0.0 \| - \|
	\| 15.8721 \| 13650 \| 0.0 \| - \|
	\| 15.9302 \| 13700 \| 0.0 \| - \|
	\| 15.9884 \| 13750 \| 0.0 \| - \|
	\| 16.0465 \| 13800 \| 0.0 \| - \|
	\| 16.1047 \| 13850 \| 0.0 \| - \|
	\| 16.1628 \| 13900 \| 0.0 \| - \|
	\| 16.2209 \| 13950 \| 0.0 \| - \|
	\| 16.2791 \| 14000 \| 0.0 \| - \|
	\| 16.3372 \| 14050 \| 0.0 \| - \|
	\| 16.3953 \| 14100 \| 0.0 \| - \|
	\| 16.4535 \| 14150 \| 0.0 \| - \|
	\| 16.5116 \| 14200 \| 0.0 \| - \|
	\| 16.5698 \| 14250 \| 0.0 \| - \|
	\| 16.6279 \| 14300 \| 0.0 \| - \|
	\| 16.6860 \| 14350 \| 0.0 \| - \|
	\| 16.7442 \| 14400 \| 0.0 \| - \|
	\| 16.8023 \| 14450 \| 0.0 \| - \|
	\| 16.8605 \| 14500 \| 0.0 \| - \|
	\| 16.9186 \| 14550 \| 0.0 \| - \|
	\| 16.9767 \| 14600 \| 0.0 \| - \|
	\| 17.0349 \| 14650 \| 0.0 \| - \|
	\| 17.0930 \| 14700 \| 0.0 \| - \|
	\| 17.1512 \| 14750 \| 0.0 \| - \|
	\| 17.2093 \| 14800 \| 0.0 \| - \|
	\| 17.2674 \| 14850 \| 0.0 \| - \|
	\| 17.3256 \| 14900 \| 0.0 \| - \|
	\| 17.3837 \| 14950 \| 0.0 \| - \|
	\| 17.4419 \| 15000 \| 0.0 \| - \|
	\| 17.5 \| 15050 \| 0.0 \| - \|
	\| 17.5581 \| 15100 \| 0.0 \| - \|
	\| 17.6163 \| 15150 \| 0.0 \| - \|
	\| 17.6744 \| 15200 \| 0.0 \| - \|
	\| 17.7326 \| 15250 \| 0.0 \| - \|
	\| 17.7907 \| 15300 \| 0.0 \| - \|
	\| 17.8488 \| 15350 \| 0.0 \| - \|
	\| 17.9070 \| 15400 \| 0.0 \| - \|
	\| 17.9651 \| 15450 \| 0.0 \| - \|
	\| 18.0233 \| 15500 \| 0.0 \| - \|
	\| 18.0814 \| 15550 \| 0.0 \| - \|
	\| 18.1395 \| 15600 \| 0.0 \| - \|
	\| 18.1977 \| 15650 \| 0.0 \| - \|
	\| 18.2558 \| 15700 \| 0.0 \| - \|
	\| 18.3140 \| 15750 \| 0.0 \| - \|
	\| 18.3721 \| 15800 \| 0.0 \| - \|
	\| 18.4302 \| 15850 \| 0.0 \| - \|
	\| 18.4884 \| 15900 \| 0.0 \| - \|
	\| 18.5465 \| 15950 \| 0.0 \| - \|
	\| 18.6047 \| 16000 \| 0.0 \| - \|
	\| 18.6628 \| 16050 \| 0.0 \| - \|
	\| 18.7209 \| 16100 \| 0.0 \| - \|
	\| 18.7791 \| 16150 \| 0.0 \| - \|
	\| 18.8372 \| 16200 \| 0.0 \| - \|
	\| 18.8953 \| 16250 \| 0.0 \| - \|
	\| 18.9535 \| 16300 \| 0.0 \| - \|
	\| 19.0116 \| 16350 \| 0.0 \| - \|
	\| 19.0698 \| 16400 \| 0.0 \| - \|
	\| 19.1279 \| 16450 \| 0.0 \| - \|
	\| 19.1860 \| 16500 \| 0.0 \| - \|
	\| 19.2442 \| 16550 \| 0.0 \| - \|
	\| 19.3023 \| 16600 \| 0.0 \| - \|
	\| 19.3605 \| 16650 \| 0.0 \| - \|
	\| 19.4186 \| 16700 \| 0.0 \| - \|
	\| 19.4767 \| 16750 \| 0.0 \| - \|
	\| 19.5349 \| 16800 \| 0.0 \| - \|
	\| 19.5930 \| 16850 \| 0.0 \| - \|
	\| 19.6512 \| 16900 \| 0.0 \| - \|
	\| 19.7093 \| 16950 \| 0.0 \| - \|
	\| 19.7674 \| 17000 \| 0.0 \| - \|
	\| 19.8256 \| 17050 \| 0.0 \| - \|
	\| 19.8837 \| 17100 \| 0.0 \| - \|
	\| 19.9419 \| 17150 \| 0.0 \| - \|
	\| 20.0 \| 17200 \| 0.0 \| - \|
	\| 20.0581 \| 17250 \| 0.0 \| - \|
	\| 20.1163 \| 17300 \| 0.0 \| - \|
	\| 20.1744 \| 17350 \| 0.0 \| - \|
	\| 20.2326 \| 17400 \| 0.0 \| - \|
	\| 20.2907 \| 17450 \| 0.0 \| - \|
	\| 20.3488 \| 17500 \| 0.0 \| - \|
	\| 20.4070 \| 17550 \| 0.0 \| - \|
	\| 20.4651 \| 17600 \| 0.0 \| - \|
	\| 20.5233 \| 17650 \| 0.0 \| - \|
	\| 20.5814 \| 17700 \| 0.0 \| - \|
	\| 20.6395 \| 17750 \| 0.0 \| - \|
	\| 20.6977 \| 17800 \| 0.0 \| - \|
	\| 20.7558 \| 17850 \| 0.0 \| - \|
	\| 20.8140 \| 17900 \| 0.0 \| - \|
	\| 20.8721 \| 17950 \| 0.0 \| - \|
	\| 20.9302 \| 18000 \| 0.0 \| - \|
	\| 20.9884 \| 18050 \| 0.0 \| - \|
	\| 21.0465 \| 18100 \| 0.0 \| - \|
	\| 21.1047 \| 18150 \| 0.0 \| - \|
	\| 21.1628 \| 18200 \| 0.0 \| - \|
	\| 21.2209 \| 18250 \| 0.0 \| - \|
	\| 21.2791 \| 18300 \| 0.0 \| - \|
	\| 21.3372 \| 18350 \| 0.0 \| - \|
	\| 21.3953 \| 18400 \| 0.0 \| - \|
	\| 21.4535 \| 18450 \| 0.0 \| - \|
	\| 21.5116 \| 18500 \| 0.0 \| - \|
	\| 21.5698 \| 18550 \| 0.0 \| - \|
	\| 21.6279 \| 18600 \| 0.0 \| - \|
	\| 21.6860 \| 18650 \| 0.0 \| - \|
	\| 21.7442 \| 18700 \| 0.0 \| - \|
	\| 21.8023 \| 18750 \| 0.0 \| - \|
	\| 21.8605 \| 18800 \| 0.0 \| - \|
	\| 21.9186 \| 18850 \| 0.0 \| - \|
	\| 21.9767 \| 18900 \| 0.0 \| - \|
	\| 22.0349 \| 18950 \| 0.0 \| - \|
	\| 22.0930 \| 19000 \| 0.0 \| - \|
	\| 22.1512 \| 19050 \| 0.0 \| - \|
	\| 22.2093 \| 19100 \| 0.0 \| - \|
	\| 22.2674 \| 19150 \| 0.0 \| - \|
	\| 22.3256 \| 19200 \| 0.0 \| - \|
	\| 22.3837 \| 19250 \| 0.0 \| - \|
	\| 22.4419 \| 19300 \| 0.0 \| - \|
	\| 22.5 \| 19350 \| 0.0 \| - \|
	\| 22.5581 \| 19400 \| 0.0 \| - \|
	\| 22.6163 \| 19450 \| 0.0 \| - \|
	\| 22.6744 \| 19500 \| 0.0 \| - \|
	\| 22.7326 \| 19550 \| 0.0 \| - \|
	\| 22.7907 \| 19600 \| 0.0 \| - \|
	\| 22.8488 \| 19650 \| 0.0 \| - \|
	\| 22.9070 \| 19700 \| 0.0 \| - \|
	\| 22.9651 \| 19750 \| 0.0 \| - \|
	\| 23.0233 \| 19800 \| 0.0 \| - \|
	\| 23.0814 \| 19850 \| 0.0 \| - \|
	\| 23.1395 \| 19900 \| 0.0 \| - \|
	\| 23.1977 \| 19950 \| 0.0 \| - \|
	\| 23.2558 \| 20000 \| 0.0 \| - \|
	\| 23.3140 \| 20050 \| 0.0 \| - \|
	\| 23.3721 \| 20100 \| 0.0 \| - \|
	\| 23.4302 \| 20150 \| 0.0 \| - \|
	\| 23.4884 \| 20200 \| 0.0 \| - \|
	\| 23.5465 \| 20250 \| 0.0 \| - \|
	\| 23.6047 \| 20300 \| 0.0 \| - \|
	\| 23.6628 \| 20350 \| 0.0 \| - \|
	\| 23.7209 \| 20400 \| 0.0 \| - \|
	\| 23.7791 \| 20450 \| 0.0 \| - \|
	\| 23.8372 \| 20500 \| 0.0 \| - \|
	\| 23.8953 \| 20550 \| 0.0 \| - \|
	\| 23.9535 \| 20600 \| 0.0 \| - \|
	\| 24.0116 \| 20650 \| 0.0 \| - \|
	\| 24.0698 \| 20700 \| 0.0 \| - \|
	\| 24.1279 \| 20750 \| 0.0 \| - \|
	\| 24.1860 \| 20800 \| 0.0 \| - \|
	\| 24.2442 \| 20850 \| 0.0 \| - \|
	\| 24.3023 \| 20900 \| 0.0 \| - \|
	\| 24.3605 \| 20950 \| 0.0 \| - \|
	\| 24.4186 \| 21000 \| 0.0 \| - \|
	\| 24.4767 \| 21050 \| 0.0 \| - \|
	\| 24.5349 \| 21100 \| 0.0 \| - \|
	\| 24.5930 \| 21150 \| 0.0 \| - \|
	\| 24.6512 \| 21200 \| 0.0003 \| - \|
	\| 24.7093 \| 21250 \| 0.0001 \| - \|
	\| 24.7674 \| 21300 \| 0.0001 \| - \|
	\| 24.8256 \| 21350 \| 0.0 \| - \|
	\| 24.8837 \| 21400 \| 0.0 \| - \|
	\| 24.9419 \| 21450 \| 0.0 \| - \|
	\| 25.0 \| 21500 \| 0.0 \| - \|
	\| 25.0581 \| 21550 \| 0.0 \| - \|
	\| 25.1163 \| 21600 \| 0.0 \| - \|
	\| 25.1744 \| 21650 \| 0.0 \| - \|
	\| 25.2326 \| 21700 \| 0.0 \| - \|
	\| 25.2907 \| 21750 \| 0.0 \| - \|
	\| 25.3488 \| 21800 \| 0.0 \| - \|
	\| 25.4070 \| 21850 \| 0.0 \| - \|
	\| 25.4651 \| 21900 \| 0.0 \| - \|
	\| 25.5233 \| 21950 \| 0.0 \| - \|
	\| 25.5814 \| 22000 \| 0.0 \| - \|
	\| 25.6395 \| 22050 \| 0.0 \| - \|
	\| 25.6977 \| 22100 \| 0.0 \| - \|
	\| 25.7558 \| 22150 \| 0.0 \| - \|
	\| 25.8140 \| 22200 \| 0.0 \| - \|
	\| 25.8721 \| 22250 \| 0.0 \| - \|
	\| 25.9302 \| 22300 \| 0.0 \| - \|
	\| 25.9884 \| 22350 \| 0.0 \| - \|
	\| 26.0465 \| 22400 \| 0.0 \| - \|
	\| 26.1047 \| 22450 \| 0.0 \| - \|
	\| 26.1628 \| 22500 \| 0.0 \| - \|
	\| 26.2209 \| 22550 \| 0.0 \| - \|
	\| 26.2791 \| 22600 \| 0.0 \| - \|
	\| 26.3372 \| 22650 \| 0.0 \| - \|
	\| 26.3953 \| 22700 \| 0.0 \| - \|
	\| 26.4535 \| 22750 \| 0.0 \| - \|
	\| 26.5116 \| 22800 \| 0.0 \| - \|
	\| 26.5698 \| 22850 \| 0.0 \| - \|
	\| 26.6279 \| 22900 \| 0.0 \| - \|
	\| 26.6860 \| 22950 \| 0.0 \| - \|
	\| 26.7442 \| 23000 \| 0.0 \| - \|
	\| 26.8023 \| 23050 \| 0.0 \| - \|
	\| 26.8605 \| 23100 \| 0.0 \| - \|
	\| 26.9186 \| 23150 \| 0.0 \| - \|
	\| 26.9767 \| 23200 \| 0.0 \| - \|
	\| 27.0349 \| 23250 \| 0.0 \| - \|
	\| 27.0930 \| 23300 \| 0.0 \| - \|
	\| 27.1512 \| 23350 \| 0.0 \| - \|
	\| 27.2093 \| 23400 \| 0.0 \| - \|
	\| 27.2674 \| 23450 \| 0.0 \| - \|
	\| 27.3256 \| 23500 \| 0.0 \| - \|
	\| 27.3837 \| 23550 \| 0.0 \| - \|
	\| 27.4419 \| 23600 \| 0.0 \| - \|
	\| 27.5 \| 23650 \| 0.0 \| - \|
	\| 27.5581 \| 23700 \| 0.0 \| - \|
	\| 27.6163 \| 23750 \| 0.0 \| - \|
	\| 27.6744 \| 23800 \| 0.0 \| - \|
	\| 27.7326 \| 23850 \| 0.0 \| - \|
	\| 27.7907 \| 23900 \| 0.0 \| - \|
	\| 27.8488 \| 23950 \| 0.0 \| - \|
	\| 27.9070 \| 24000 \| 0.0 \| - \|
	\| 27.9651 \| 24050 \| 0.0 \| - \|
	\| 28.0233 \| 24100 \| 0.0 \| - \|
	\| 28.0814 \| 24150 \| 0.0 \| - \|
	\| 28.1395 \| 24200 \| 0.0 \| - \|
	\| 28.1977 \| 24250 \| 0.0 \| - \|
	\| 28.2558 \| 24300 \| 0.0 \| - \|
	\| 28.3140 \| 24350 \| 0.0 \| - \|
	\| 28.3721 \| 24400 \| 0.0 \| - \|
	\| 28.4302 \| 24450 \| 0.0 \| - \|
	\| 28.4884 \| 24500 \| 0.0 \| - \|
	\| 28.5465 \| 24550 \| 0.0 \| - \|
	\| 28.6047 \| 24600 \| 0.0 \| - \|
	\| 28.6628 \| 24650 \| 0.0 \| - \|
	\| 28.7209 \| 24700 \| 0.0 \| - \|
	\| 28.7791 \| 24750 \| 0.0 \| - \|
	\| 28.8372 \| 24800 \| 0.0 \| - \|
	\| 28.8953 \| 24850 \| 0.0 \| - \|
	\| 28.9535 \| 24900 \| 0.0 \| - \|
	\| 29.0116 \| 24950 \| 0.0 \| - \|
	\| 29.0698 \| 25000 \| 0.0 \| - \|
	\| 29.1279 \| 25050 \| 0.0 \| - \|
	\| 29.1860 \| 25100 \| 0.0 \| - \|
	\| 29.2442 \| 25150 \| 0.0 \| - \|
	\| 29.3023 \| 25200 \| 0.0 \| - \|
	\| 29.3605 \| 25250 \| 0.0 \| - \|
	\| 29.4186 \| 25300 \| 0.0 \| - \|
	\| 29.4767 \| 25350 \| 0.0 \| - \|
	\| 29.5349 \| 25400 \| 0.0 \| - \|
	\| 29.5930 \| 25450 \| 0.0 \| - \|
	\| 29.6512 \| 25500 \| 0.0 \| - \|
	\| 29.7093 \| 25550 \| 0.0 \| - \|
	\| 29.7674 \| 25600 \| 0.0 \| - \|
	\| 29.8256 \| 25650 \| 0.0 \| - \|
	\| 29.8837 \| 25700 \| 0.0 \| - \|
	\| 29.9419 \| 25750 \| 0.0 \| - \|
	\| 30.0 \| 25800 \| 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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