master_item_fd / README.md

Push model using huggingface_hub.

6b346d6 verified 14 days ago

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	---
	base_model: klue/roberta-base
	library_name: setfit
	metrics:
	- metric
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 🍯국산쌀 조청🍯 수제오란다 선물세트 답례품 18P 9월 3일 출고(4일~5일 도착예정)_견과스페셜(오리지널8p+스페셜10p)_감사의
	마음을 전합니다 생프룻(st.fruit)
	- text: 포카리스웨트 245ml 1개 중패트_뽀로로(밀크맛) 235ML X 24병 주식회사 송민
	- text: 포카리스웨트 245ml 1개 미니캔_티오피 스위트 아메리카노 200ml 30개 대영상사
	- text: 쉐이크베이비 다이어트 단백질 쉐이크 맛있는 식사대용 식단 음식 딸기맛 750g 3+폴딩박스 구성_초코+스윗콘+마시멜로초코_폴딩박스+핑크보틀
	1개+화이트보틀 1개 주식회사 슬로우로켓
	- text: 포뉴 초임계 알티지 XV 오메가3 미니 카제로템 포뉴 초임계 식물성 알티지 오메가3△ (주)포터블뉴트리션
	inference: true
	model-index:
	- name: SetFit with klue/roberta-base
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: metric
	value: 0.9180474602529828
	name: Metric
	---

	# SetFit with klue/roberta-base

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) 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: [klue/roberta-base](https://huggingface.co/klue/roberta-base)
	- 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: 22 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 \|
	\|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 9.0 \| <ul><li>'동보식품 깐마늘 4kg 잘고려'</li><li>'[산들해반찬]콩자반 200g 주식회사 산들해'</li><li>'쌀게 무침 4kg 대용량 업소용 식당 반찬 방게 조림 (유) 협동맛사랑식품'</li></ul> \|
	\| 0.0 \| <ul><li>'뚜레반 엿기름가루 1kg (복합) 주식회사 삼부'</li><li>'프리미엄 아몬드가루 1kg 95% 아몬드분말 아몬드파우더 프리미엄 아몬드분말(95%) 1kg 비타민플러스'</li><li>'오뚜기 튀김가루 2kg 리얼유통컴퍼니'</li></ul> \|
	\| 2.0 \| <ul><li>'시로이코이비토 이시야 일본과자 쿠크다스 하얀연인 9개입 도톤보리의 연인 12매입 INCAPE CO.,LTD'</li><li>'자일리톨 오리지날 리필 115gx3봉 외 5종 02.자일리톨 알파 리필 102g_01.자일리톨 오리지날 리필 115g_06.졸음번쩍껌 톡톡!87g (주)푸드조이'</li><li>'70g 추억의 도나스 1개 에스제이(SJ)조아쇼핑'</li></ul> \|
	\| 12.0 \| <ul><li>'바쏘 엑스트라버진 올리브오일 2L 제3자의 배송관련 개인정보 이용에 대해 동의함 버니버즈'</li><li>'CJ제일제당 백설 콩기름 1.5L 사조 해표 식용유 1.8L 삼영유통'</li><li>'CJ [만능]백설 건강을 생각한 요리유 900ml 가을식재료 엄마 맛집 믿고먹는 우리집 건강한 식재료 봄날스토어'</li></ul> \|
	\| 6.0 \| <ul><li>'한국정품 허벌라이프 허벌티 허벌베버리지 망구 에이'</li><li>'스키니랩 가벼워지는 시서스 다이어트 14포 cissus 보조제 씨서스 15포 올바른영양 주식회사'</li><li>'프롬바이오 아프리카망고 워터믹스 레몬맛 2주 14포x1박스 아프리카 망고 워터믹스 레몬 2주 주식회사 프롬바이오'</li></ul> \|
	\| 18.0 \| <ul><li>'인산죽염 9회 자죽염 고체 60g 비타코어'</li><li>'[청정원] 순후추 50g (경산점) 주식회사 에스에스지닷컴'</li><li>'브래그 유기농 초모 사과식초 946ml 천연발효 식초트릭 데니그리스 애플사이다비니거 드니그리스 사과식초 1000ml 인영이네'</li></ul> \|
	\| 4.0 \| <ul><li>'동원 양반 영양닭죽 285g 태성유통'</li><li>'푸디버디 숫자치킨너겟500g 미니까스 2종 480g 1+1 미니돈까스 1개+미니치킨까스 1개 다솜플랜잇'</li><li>'[삼진어묵] 100사각 1봉 320g (10개입) (주)동방유래'</li></ul> \|
	\| 21.0 \| <ul><li>'[리오산토] 할라페뇨 슬라이스 540g (경산점) 주식회사 에스에스지닷컴'</li><li>'사조 안심 닭가슴살 90g 주식회사 킴벌리마스타'</li><li>'샘표 꽁치 400g 지영산업'</li></ul> \|
	\| 17.0 \| <ul><li>'곰표 통밀 호떡믹스 450g x 4 코스트컷몰'</li><li>'CJ 핫케익믹스 1kg 홈파티 엄마손맛 생일파티 홈메이드 바삭한 밀키트 마이컴퍼니'</li><li>'곰표 통밀 호떡믹스 450g x 4개 코스트코 홈베이킹 호떡 재료 이지코스'</li></ul> \|
	\| 20.0 \| <ul><li>'돼지 머릿고기 슬라이스 업소용 머리고기 1kg 돼지국밥용 절단 순대국 창업 재료 -선지없는 매콤 백토종순대 500g X 2팩 푸드씨'</li><li>'흑염소 캠핑고기 구이 300g 국내산 암컷전문 보양식 염소고기 수육 전골 탕 요리 음식 ●수육용 뒷다리살 500g (껍데기+고기) 흑염소연구소'</li><li>'파티큐 귀족 통돼지바베큐 (5-10인분) 만화고기 캠핑음식 집들이 출장 경주시외버스터미널_1/6하체 주식회사 파티큐'</li></ul> \|
	\| 15.0 \| <ul><li>'카카오닙스 500g 오구오구(5959)'</li><li>'제주콩생낫또 특허기술로 만든 냄새없고 맛있는 낫또 53 g x 7 개 제주콩 생낫또 7 개 (주)으뜸엘엔에스'</li><li>'[2+2] 해찬들 사계절쌈장 500G 메가글로벌001'</li></ul> \|
	\| 14.0 \| <ul><li>'몬스터 에너지 울트라 시트라 355ml 1개 슬림캔_게토레이 레몬 240ml 30개 주식회사 송민'</li><li>'미떼 핫초코 오리지날 미니스틱 40T+볼펜 미떼 마일드10T x2개+볼펜증정 유니콘마트'</li><li>'일화 초정탄산수 1.5L 6개 중패트_쟈뎅 아워티 오렌지자몽블랙티 500ml 12개 주식회사 송민'</li></ul> \|
	\| 10.0 \| <ul><li>'하인즈 노슈가 케찹 (하인즈 리듀스드슈가 케찹) 369g (주)아이미에프에스'</li><li>'대상 청정원 우리아이 케찹 620g / 806kcal 수리ZIP'</li><li>'스테이크소스 ACE 260g 스테이크소스 식자재 마트 다라조명'</li></ul> \|
	\| 16.0 \| <ul><li>'록키릿지 메이플시럽 340g 세컨드 베이스'</li><li>'복음자리 딸기잼 500g 3개 딸기잼(3개) 딸기잼+선물용 종이가방 더써드도어'</li><li>'Chocolate Hazelnut Spread 선한상점'</li></ul> \|
	\| 1.0 \| <ul><li>'[공식] 닥터겟잇 키즈이뮨 20g x 14포 (4+4혜택) 8박스 [47%할인+무료배송] 주식회사 그릿인피스'</li><li>'러시아 차가버섯 차 원물 300g 차가버섯 300g 농업회사법인 주식회사 두손애약초'</li><li>'커클랜드시그니춰 마누카 허니 2.27kg 와일드플라워 웰니스 플래닛'</li></ul> \|
	\| 13.0 \| <ul><li>'서울우유 동물성 생크림 500ml 커피숍 제빵 생크림 1개[포장 미선택시 배송지연] 더드림'</li><li>'서울우유 바리스타즈 휘핑크림 500g 스프레이형 바리스타 휘핑크림 500g_아이스박스 올굿유통'</li><li>'[아이스박스무료] 선인 DB 휘핑크림 1L 무가당 혼합 생크림 재이F&B'</li></ul> \|
	\| 3.0 \| <ul><li>'평창애 강원도 평창 절임배추 20kg 고랭지 통배추 절임배추10kg_12-22금요일 배송출발일 주식회사 올마루(Allmaru)'</li><li>'김권태 전라도 포기 배추 김치 김장김치 2kg 옥과 맛있는 [2-3] 저온숙성 묵은지 5kg 주식회사 우아한끼'</li><li>'보리김치 3kg 5kg 제주산보리 [100%국내산] 한백김치 송도점'</li></ul> \|
	\| 8.0 \| <ul><li>'[신세계 규격](신세계 본점)띠굴리오올리브앤케이퍼소스 주식회사 에스에스지닷컴'</li><li>'프레시밀 베이컨크림파스타 630g 1팩 630g × 2팩 엠투커머스'</li><li>'[쿠캣][쿠캣메이드] 렌지쿠캣 마라로제 찜닭 230g X 3팩 디휴니'</li></ul> \|
	\| 11.0 \| <ul><li>'정미소 쭈꾸미 볶음 450g 2인 집들이 음식 캠핑 요리 주식회사 믹스앤맥스(MIXNMAX CO.,LTD.)'</li><li>'새벽장어 국산 자포니카 풍천 민물장어 1kg 손질 후 750g이내 초벌장어 1kg(자포니카 순살 500g내외) 새벽컴퍼니 주식회사'</li><li>'엄마애바다 새우섞인 지리멸치 1.5kg 팔이일삼일오'</li></ul> \|
	\| 7.0 \| <ul><li>'삼양 큰컵까르보불닭볶음면 105g x 4개 까르보불닭볶음면 130g x 4 플랜트더퓨처'</li><li>'[1+1 냉면 골라담기] CJ 동치미 물냉면 비빔냉면 외 20종 [5+5]함흥비빔냉면소스85g 씨제이제일제당 (주)'</li><li>'청수 포장국수 3.75KG / 37인분 잔치 옛날 멸치 비빔국수 소면 (주)정이비에스'</li></ul> \|
	\| 5.0 \| <ul><li>'청도 아이스홍시 탈피 3kg 40과내외 05_대봉 탈피 3kg 15-20과 감미인영농조합법인'</li><li>'국산 쥐눈이콩 콩나물콩 1kg 약콩 서목태 검은콩 8. 쪄서볶은 서리태가루 500g 주식회사 태극인 농업회사법인'</li><li>'베트남선물 반건조 망고 베트남건망고 말랭이 100G X 10개입 유월의바다'</li></ul> \|
	\| 19.0 \| <ul><li>'박재서명인 안동소주 양반탈 800ml 명인안동소주'</li><li>'해창주조 해창막걸리 9도 프리미엄 막걸리 캠핑 위스키 차박 와인 필수템 글램핑 감성사진 농업회사법인 술담화 주식회사 직매장지점'</li><li>'다농바이오 가무치소주 43도 375ml 항아리숙성 농업법인 우리도가 (주) 서울지점'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.9180 \|

	## 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_fd")
	# Run inference
	preds = model("포카리스웨트 245ml 1개 중패트_뽀로로(밀크맛) 235ML X 24병 주식회사 송민")
	```

	<!--
	### 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 \| 3 \| 9.1979 \| 30 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 448 \|
	\| 1.0 \| 579 \|
	\| 2.0 \| 800 \|
	\| 3.0 \| 552 \|
	\| 4.0 \| 1049 \|
	\| 5.0 \| 350 \|
	\| 6.0 \| 800 \|
	\| 7.0 \| 100 \|
	\| 8.0 \| 400 \|
	\| 9.0 \| 414 \|
	\| 10.0 \| 581 \|
	\| 11.0 \| 275 \|
	\| 12.0 \| 450 \|
	\| 13.0 \| 300 \|
	\| 14.0 \| 600 \|
	\| 15.0 \| 422 \|
	\| 16.0 \| 400 \|
	\| 17.0 \| 200 \|
	\| 18.0 \| 571 \|
	\| 19.0 \| 50 \|
	\| 20.0 \| 346 \|
	\| 21.0 \| 450 \|

	### Training Hyperparameters
	- batch_size: (512, 512)
	- num_epochs: (20, 20)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 40
	- body_learning_rate: (2e-05, 2e-05)
	- head_learning_rate: 2e-05
	- loss: CosineSimilarityLoss
	- distance_metric: cosine_distance
	- margin: 0.25
	- end_to_end: False
	- use_amp: False
	- warmup_proportion: 0.1
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:-------:\|:-----:\|:-------------:\|:---------------:\|
	\| 0.0006 \| 1 \| 0.4086 \| - \|
	\| 0.0316 \| 50 \| 0.3967 \| - \|
	\| 0.0631 \| 100 \| 0.3705 \| - \|
	\| 0.0947 \| 150 \| 0.3541 \| - \|
	\| 0.1263 \| 200 \| 0.2971 \| - \|
	\| 0.1578 \| 250 \| 0.2651 \| - \|
	\| 0.1894 \| 300 \| 0.2404 \| - \|
	\| 0.2210 \| 350 \| 0.1946 \| - \|
	\| 0.2525 \| 400 \| 0.1848 \| - \|
	\| 0.2841 \| 450 \| 0.1706 \| - \|
	\| 0.3157 \| 500 \| 0.1394 \| - \|
	\| 0.3472 \| 550 \| 0.1364 \| - \|
	\| 0.3788 \| 600 \| 0.1178 \| - \|
	\| 0.4104 \| 650 \| 0.0926 \| - \|
	\| 0.4419 \| 700 \| 0.0949 \| - \|
	\| 0.4735 \| 750 \| 0.0732 \| - \|
	\| 0.5051 \| 800 \| 0.0806 \| - \|
	\| 0.5366 \| 850 \| 0.0648 \| - \|
	\| 0.5682 \| 900 \| 0.0707 \| - \|
	\| 0.5997 \| 950 \| 0.0523 \| - \|
	\| 0.6313 \| 1000 \| 0.0529 \| - \|
	\| 0.6629 \| 1050 \| 0.0491 \| - \|
	\| 0.6944 \| 1100 \| 0.0486 \| - \|
	\| 0.7260 \| 1150 \| 0.0369 \| - \|
	\| 0.7576 \| 1200 \| 0.0296 \| - \|
	\| 0.7891 \| 1250 \| 0.0303 \| - \|
	\| 0.8207 \| 1300 \| 0.0232 \| - \|
	\| 0.8523 \| 1350 \| 0.0281 \| - \|
	\| 0.8838 \| 1400 \| 0.0178 \| - \|
	\| 0.9154 \| 1450 \| 0.0346 \| - \|
	\| 0.9470 \| 1500 \| 0.025 \| - \|
	\| 0.9785 \| 1550 \| 0.0218 \| - \|
	\| 1.0101 \| 1600 \| 0.0335 \| - \|
	\| 1.0417 \| 1650 \| 0.0206 \| - \|
	\| 1.0732 \| 1700 \| 0.0168 \| - \|
	\| 1.1048 \| 1750 \| 0.0294 \| - \|
	\| 1.1364 \| 1800 \| 0.0219 \| - \|
	\| 1.1679 \| 1850 \| 0.0176 \| - \|
	\| 1.1995 \| 1900 \| 0.0196 \| - \|
	\| 1.2311 \| 1950 \| 0.0141 \| - \|
	\| 1.2626 \| 2000 \| 0.0031 \| - \|
	\| 1.2942 \| 2050 \| 0.0131 \| - \|
	\| 1.3258 \| 2100 \| 0.0158 \| - \|
	\| 1.3573 \| 2150 \| 0.0121 \| - \|
	\| 1.3889 \| 2200 \| 0.0088 \| - \|
	\| 1.4205 \| 2250 \| 0.0047 \| - \|
	\| 1.4520 \| 2300 \| 0.0138 \| - \|
	\| 1.4836 \| 2350 \| 0.0029 \| - \|
	\| 1.5152 \| 2400 \| 0.0063 \| - \|
	\| 1.5467 \| 2450 \| 0.0042 \| - \|
	\| 1.5783 \| 2500 \| 0.0015 \| - \|
	\| 1.6098 \| 2550 \| 0.0078 \| - \|
	\| 1.6414 \| 2600 \| 0.0014 \| - \|
	\| 1.6730 \| 2650 \| 0.0055 \| - \|
	\| 1.7045 \| 2700 \| 0.0011 \| - \|
	\| 1.7361 \| 2750 \| 0.0052 \| - \|
	\| 1.7677 \| 2800 \| 0.0018 \| - \|
	\| 1.7992 \| 2850 \| 0.003 \| - \|
	\| 1.8308 \| 2900 \| 0.004 \| - \|
	\| 1.8624 \| 2950 \| 0.0006 \| - \|
	\| 1.8939 \| 3000 \| 0.0058 \| - \|
	\| 1.9255 \| 3050 \| 0.0004 \| - \|
	\| 1.9571 \| 3100 \| 0.0028 \| - \|
	\| 1.9886 \| 3150 \| 0.0005 \| - \|
	\| 2.0202 \| 3200 \| 0.0006 \| - \|
	\| 2.0518 \| 3250 \| 0.0016 \| - \|
	\| 2.0833 \| 3300 \| 0.0036 \| - \|
	\| 2.1149 \| 3350 \| 0.0009 \| - \|
	\| 2.1465 \| 3400 \| 0.001 \| - \|
	\| 2.1780 \| 3450 \| 0.0007 \| - \|
	\| 2.2096 \| 3500 \| 0.0003 \| - \|
	\| 2.2412 \| 3550 \| 0.0003 \| - \|
	\| 2.2727 \| 3600 \| 0.0004 \| - \|
	\| 2.3043 \| 3650 \| 0.0002 \| - \|
	\| 2.3359 \| 3700 \| 0.0002 \| - \|
	\| 2.3674 \| 3750 \| 0.0002 \| - \|
	\| 2.3990 \| 3800 \| 0.003 \| - \|
	\| 2.4306 \| 3850 \| 0.0004 \| - \|
	\| 2.4621 \| 3900 \| 0.0013 \| - \|
	\| 2.4937 \| 3950 \| 0.0003 \| - \|
	\| 2.5253 \| 4000 \| 0.0002 \| - \|
	\| 2.5568 \| 4050 \| 0.0001 \| - \|
	\| 2.5884 \| 4100 \| 0.0002 \| - \|
	\| 2.6199 \| 4150 \| 0.0001 \| - \|
	\| 2.6515 \| 4200 \| 0.0003 \| - \|
	\| 2.6831 \| 4250 \| 0.0003 \| - \|
	\| 2.7146 \| 4300 \| 0.0002 \| - \|
	\| 2.7462 \| 4350 \| 0.0001 \| - \|
	\| 2.7778 \| 4400 \| 0.0018 \| - \|
	\| 2.8093 \| 4450 \| 0.0005 \| - \|
	\| 2.8409 \| 4500 \| 0.0001 \| - \|
	\| 2.8725 \| 4550 \| 0.0003 \| - \|
	\| 2.9040 \| 4600 \| 0.0001 \| - \|
	\| 2.9356 \| 4650 \| 0.0002 \| - \|
	\| 2.9672 \| 4700 \| 0.0002 \| - \|
	\| 2.9987 \| 4750 \| 0.0002 \| - \|
	\| 3.0303 \| 4800 \| 0.0018 \| - \|
	\| 3.0619 \| 4850 \| 0.0001 \| - \|
	\| 3.0934 \| 4900 \| 0.0002 \| - \|
	\| 3.125 \| 4950 \| 0.0001 \| - \|
	\| 3.1566 \| 5000 \| 0.0002 \| - \|
	\| 3.1881 \| 5050 \| 0.0004 \| - \|
	\| 3.2197 \| 5100 \| 0.0001 \| - \|
	\| 3.2513 \| 5150 \| 0.0001 \| - \|
	\| 3.2828 \| 5200 \| 0.0002 \| - \|
	\| 3.3144 \| 5250 \| 0.0003 \| - \|
	\| 3.3460 \| 5300 \| 0.0001 \| - \|
	\| 3.3775 \| 5350 \| 0.0003 \| - \|
	\| 3.4091 \| 5400 \| 0.0001 \| - \|
	\| 3.4407 \| 5450 \| 0.0001 \| - \|
	\| 3.4722 \| 5500 \| 0.0001 \| - \|
	\| 3.5038 \| 5550 \| 0.0003 \| - \|
	\| 3.5354 \| 5600 \| 0.0002 \| - \|
	\| 3.5669 \| 5650 \| 0.0001 \| - \|
	\| 3.5985 \| 5700 \| 0.0005 \| - \|
	\| 3.6301 \| 5750 \| 0.0003 \| - \|
	\| 3.6616 \| 5800 \| 0.0001 \| - \|
	\| 3.6932 \| 5850 \| 0.0003 \| - \|
	\| 3.7247 \| 5900 \| 0.0001 \| - \|
	\| 3.7563 \| 5950 \| 0.0001 \| - \|
	\| 3.7879 \| 6000 \| 0.0001 \| - \|
	\| 3.8194 \| 6050 \| 0.0006 \| - \|
	\| 3.8510 \| 6100 \| 0.0002 \| - \|
	\| 3.8826 \| 6150 \| 0.0004 \| - \|
	\| 3.9141 \| 6200 \| 0.0001 \| - \|
	\| 3.9457 \| 6250 \| 0.0001 \| - \|
	\| 3.9773 \| 6300 \| 0.0001 \| - \|
	\| 4.0088 \| 6350 \| 0.0002 \| - \|
	\| 4.0404 \| 6400 \| 0.0001 \| - \|
	\| 4.0720 \| 6450 \| 0.0 \| - \|
	\| 4.1035 \| 6500 \| 0.0001 \| - \|
	\| 4.1351 \| 6550 \| 0.0001 \| - \|
	\| 4.1667 \| 6600 \| 0.0 \| - \|
	\| 4.1982 \| 6650 \| 0.0 \| - \|
	\| 4.2298 \| 6700 \| 0.0 \| - \|
	\| 4.2614 \| 6750 \| 0.0 \| - \|
	\| 4.2929 \| 6800 \| 0.0001 \| - \|
	\| 4.3245 \| 6850 \| 0.0 \| - \|
	\| 4.3561 \| 6900 \| 0.0 \| - \|
	\| 4.3876 \| 6950 \| 0.0002 \| - \|
	\| 4.4192 \| 7000 \| 0.0007 \| - \|
	\| 4.4508 \| 7050 \| 0.0018 \| - \|
	\| 4.4823 \| 7100 \| 0.0001 \| - \|
	\| 4.5139 \| 7150 \| 0.0001 \| - \|
	\| 4.5455 \| 7200 \| 0.0001 \| - \|
	\| 4.5770 \| 7250 \| 0.0003 \| - \|
	\| 4.6086 \| 7300 \| 0.0001 \| - \|
	\| 4.6402 \| 7350 \| 0.0008 \| - \|
	\| 4.6717 \| 7400 \| 0.0001 \| - \|
	\| 4.7033 \| 7450 \| 0.0 \| - \|
	\| 4.7348 \| 7500 \| 0.0001 \| - \|
	\| 4.7664 \| 7550 \| 0.0001 \| - \|
	\| 4.7980 \| 7600 \| 0.0 \| - \|
	\| 4.8295 \| 7650 \| 0.0 \| - \|
	\| 4.8611 \| 7700 \| 0.0 \| - \|
	\| 4.8927 \| 7750 \| 0.0019 \| - \|
	\| 4.9242 \| 7800 \| 0.0 \| - \|
	\| 4.9558 \| 7850 \| 0.0 \| - \|
	\| 4.9874 \| 7900 \| 0.001 \| - \|
	\| 5.0189 \| 7950 \| 0.0 \| - \|
	\| 5.0505 \| 8000 \| 0.0011 \| - \|
	\| 5.0821 \| 8050 \| 0.0002 \| - \|
	\| 5.1136 \| 8100 \| 0.0004 \| - \|
	\| 5.1452 \| 8150 \| 0.0 \| - \|
	\| 5.1768 \| 8200 \| 0.0018 \| - \|
	\| 5.2083 \| 8250 \| 0.0001 \| - \|
	\| 5.2399 \| 8300 \| 0.0 \| - \|
	\| 5.2715 \| 8350 \| 0.0018 \| - \|
	\| 5.3030 \| 8400 \| 0.0 \| - \|
	\| 5.3346 \| 8450 \| 0.0005 \| - \|
	\| 5.3662 \| 8500 \| 0.0001 \| - \|
	\| 5.3977 \| 8550 \| 0.0 \| - \|
	\| 5.4293 \| 8600 \| 0.0 \| - \|
	\| 5.4609 \| 8650 \| 0.0001 \| - \|
	\| 5.4924 \| 8700 \| 0.0 \| - \|
	\| 5.5240 \| 8750 \| 0.0001 \| - \|
	\| 5.5556 \| 8800 \| 0.0 \| - \|
	\| 5.5871 \| 8850 \| 0.0001 \| - \|
	\| 5.6187 \| 8900 \| 0.0001 \| - \|
	\| 5.6503 \| 8950 \| 0.0 \| - \|
	\| 5.6818 \| 9000 \| 0.0001 \| - \|
	\| 5.7134 \| 9050 \| 0.0008 \| - \|
	\| 5.7449 \| 9100 \| 0.0001 \| - \|
	\| 5.7765 \| 9150 \| 0.0 \| - \|
	\| 5.8081 \| 9200 \| 0.0008 \| - \|
	\| 5.8396 \| 9250 \| 0.0001 \| - \|
	\| 5.8712 \| 9300 \| 0.0 \| - \|
	\| 5.9028 \| 9350 \| 0.0001 \| - \|
	\| 5.9343 \| 9400 \| 0.0 \| - \|
	\| 5.9659 \| 9450 \| 0.0 \| - \|
	\| 5.9975 \| 9500 \| 0.0001 \| - \|
	\| 6.0290 \| 9550 \| 0.0 \| - \|
	\| 6.0606 \| 9600 \| 0.0 \| - \|
	\| 6.0922 \| 9650 \| 0.0 \| - \|
	\| 6.1237 \| 9700 \| 0.0 \| - \|
	\| 6.1553 \| 9750 \| 0.0 \| - \|
	\| 6.1869 \| 9800 \| 0.0 \| - \|
	\| 6.2184 \| 9850 \| 0.0 \| - \|
	\| 6.25 \| 9900 \| 0.0 \| - \|
	\| 6.2816 \| 9950 \| 0.0 \| - \|
	\| 6.3131 \| 10000 \| 0.0 \| - \|
	\| 6.3447 \| 10050 \| 0.0 \| - \|
	\| 6.3763 \| 10100 \| 0.0 \| - \|
	\| 6.4078 \| 10150 \| 0.0 \| - \|
	\| 6.4394 \| 10200 \| 0.0 \| - \|
	\| 6.4710 \| 10250 \| 0.0 \| - \|
	\| 6.5025 \| 10300 \| 0.0001 \| - \|
	\| 6.5341 \| 10350 \| 0.0 \| - \|
	\| 6.5657 \| 10400 \| 0.0001 \| - \|
	\| 6.5972 \| 10450 \| 0.0 \| - \|
	\| 6.6288 \| 10500 \| 0.0 \| - \|
	\| 6.6604 \| 10550 \| 0.0 \| - \|
	\| 6.6919 \| 10600 \| 0.0 \| - \|
	\| 6.7235 \| 10650 \| 0.0 \| - \|
	\| 6.7551 \| 10700 \| 0.0 \| - \|
	\| 6.7866 \| 10750 \| 0.0 \| - \|
	\| 6.8182 \| 10800 \| 0.0 \| - \|
	\| 6.8497 \| 10850 \| 0.0 \| - \|
	\| 6.8813 \| 10900 \| 0.0002 \| - \|
	\| 6.9129 \| 10950 \| 0.0016 \| - \|
	\| 6.9444 \| 11000 \| 0.0 \| - \|
	\| 6.9760 \| 11050 \| 0.0002 \| - \|
	\| 7.0076 \| 11100 \| 0.0 \| - \|
	\| 7.0391 \| 11150 \| 0.0006 \| - \|
	\| 7.0707 \| 11200 \| 0.0 \| - \|
	\| 7.1023 \| 11250 \| 0.0 \| - \|
	\| 7.1338 \| 11300 \| 0.0 \| - \|
	\| 7.1654 \| 11350 \| 0.0 \| - \|
	\| 7.1970 \| 11400 \| 0.0 \| - \|
	\| 7.2285 \| 11450 \| 0.0 \| - \|
	\| 7.2601 \| 11500 \| 0.0 \| - \|
	\| 7.2917 \| 11550 \| 0.0 \| - \|
	\| 7.3232 \| 11600 \| 0.0 \| - \|
	\| 7.3548 \| 11650 \| 0.0 \| - \|
	\| 7.3864 \| 11700 \| 0.0 \| - \|
	\| 7.4179 \| 11750 \| 0.0 \| - \|
	\| 7.4495 \| 11800 \| 0.0 \| - \|
	\| 7.4811 \| 11850 \| 0.0 \| - \|
	\| 7.5126 \| 11900 \| 0.0 \| - \|
	\| 7.5442 \| 11950 \| 0.0 \| - \|
	\| 7.5758 \| 12000 \| 0.0 \| - \|
	\| 7.6073 \| 12050 \| 0.0 \| - \|
	\| 7.6389 \| 12100 \| 0.0 \| - \|
	\| 7.6705 \| 12150 \| 0.0 \| - \|
	\| 7.7020 \| 12200 \| 0.0 \| - \|
	\| 7.7336 \| 12250 \| 0.0 \| - \|
	\| 7.7652 \| 12300 \| 0.0 \| - \|
	\| 7.7967 \| 12350 \| 0.0003 \| - \|
	\| 7.8283 \| 12400 \| 0.0001 \| - \|
	\| 7.8598 \| 12450 \| 0.0 \| - \|
	\| 7.8914 \| 12500 \| 0.0 \| - \|
	\| 7.9230 \| 12550 \| 0.0 \| - \|
	\| 7.9545 \| 12600 \| 0.0 \| - \|
	\| 7.9861 \| 12650 \| 0.0 \| - \|
	\| 8.0177 \| 12700 \| 0.0001 \| - \|
	\| 8.0492 \| 12750 \| 0.0 \| - \|
	\| 8.0808 \| 12800 \| 0.0 \| - \|
	\| 8.1124 \| 12850 \| 0.0 \| - \|
	\| 8.1439 \| 12900 \| 0.0 \| - \|
	\| 8.1755 \| 12950 \| 0.0 \| - \|
	\| 8.2071 \| 13000 \| 0.0 \| - \|
	\| 8.2386 \| 13050 \| 0.0 \| - \|
	\| 8.2702 \| 13100 \| 0.0 \| - \|
	\| 8.3018 \| 13150 \| 0.0 \| - \|
	\| 8.3333 \| 13200 \| 0.0 \| - \|
	\| 8.3649 \| 13250 \| 0.0 \| - \|
	\| 8.3965 \| 13300 \| 0.0 \| - \|
	\| 8.4280 \| 13350 \| 0.0 \| - \|
	\| 8.4596 \| 13400 \| 0.0 \| - \|
	\| 8.4912 \| 13450 \| 0.0 \| - \|
	\| 8.5227 \| 13500 \| 0.0 \| - \|
	\| 8.5543 \| 13550 \| 0.0 \| - \|
	\| 8.5859 \| 13600 \| 0.0 \| - \|
	\| 8.6174 \| 13650 \| 0.0 \| - \|
	\| 8.6490 \| 13700 \| 0.0021 \| - \|
	\| 8.6806 \| 13750 \| 0.0006 \| - \|
	\| 8.7121 \| 13800 \| 0.0002 \| - \|
	\| 8.7437 \| 13850 \| 0.0013 \| - \|
	\| 8.7753 \| 13900 \| 0.0 \| - \|
	\| 8.8068 \| 13950 \| 0.0 \| - \|
	\| 8.8384 \| 14000 \| 0.0 \| - \|
	\| 8.8699 \| 14050 \| 0.0 \| - \|
	\| 8.9015 \| 14100 \| 0.0 \| - \|
	\| 8.9331 \| 14150 \| 0.0 \| - \|
	\| 8.9646 \| 14200 \| 0.0 \| - \|
	\| 8.9962 \| 14250 \| 0.0 \| - \|
	\| 9.0278 \| 14300 \| 0.0 \| - \|
	\| 9.0593 \| 14350 \| 0.0 \| - \|
	\| 9.0909 \| 14400 \| 0.0 \| - \|
	\| 9.1225 \| 14450 \| 0.0 \| - \|
	\| 9.1540 \| 14500 \| 0.0 \| - \|
	\| 9.1856 \| 14550 \| 0.0002 \| - \|
	\| 9.2172 \| 14600 \| 0.0 \| - \|
	\| 9.2487 \| 14650 \| 0.0 \| - \|
	\| 9.2803 \| 14700 \| 0.0 \| - \|
	\| 9.3119 \| 14750 \| 0.0 \| - \|
	\| 9.3434 \| 14800 \| 0.0 \| - \|
	\| 9.375 \| 14850 \| 0.0 \| - \|
	\| 9.4066 \| 14900 \| 0.0 \| - \|
	\| 9.4381 \| 14950 \| 0.0 \| - \|
	\| 9.4697 \| 15000 \| 0.0 \| - \|
	\| 9.5013 \| 15050 \| 0.0 \| - \|
	\| 9.5328 \| 15100 \| 0.0 \| - \|
	\| 9.5644 \| 15150 \| 0.0 \| - \|
	\| 9.5960 \| 15200 \| 0.0 \| - \|
	\| 9.6275 \| 15250 \| 0.0 \| - \|
	\| 9.6591 \| 15300 \| 0.0 \| - \|
	\| 9.6907 \| 15350 \| 0.0 \| - \|
	\| 9.7222 \| 15400 \| 0.0002 \| - \|
	\| 9.7538 \| 15450 \| 0.0 \| - \|
	\| 9.7854 \| 15500 \| 0.0 \| - \|
	\| 9.8169 \| 15550 \| 0.0 \| - \|
	\| 9.8485 \| 15600 \| 0.0 \| - \|
	\| 9.8801 \| 15650 \| 0.0 \| - \|
	\| 9.9116 \| 15700 \| 0.0001 \| - \|
	\| 9.9432 \| 15750 \| 0.0 \| - \|
	\| 9.9747 \| 15800 \| 0.0003 \| - \|
	\| 10.0063 \| 15850 \| 0.0 \| - \|
	\| 10.0379 \| 15900 \| 0.0 \| - \|
	\| 10.0694 \| 15950 \| 0.0001 \| - \|
	\| 10.1010 \| 16000 \| 0.0 \| - \|
	\| 10.1326 \| 16050 \| 0.0 \| - \|
	\| 10.1641 \| 16100 \| 0.0 \| - \|
	\| 10.1957 \| 16150 \| 0.0 \| - \|
	\| 10.2273 \| 16200 \| 0.0 \| - \|
	\| 10.2588 \| 16250 \| 0.0 \| - \|
	\| 10.2904 \| 16300 \| 0.0 \| - \|
	\| 10.3220 \| 16350 \| 0.0 \| - \|
	\| 10.3535 \| 16400 \| 0.0008 \| - \|
	\| 10.3851 \| 16450 \| 0.0 \| - \|
	\| 10.4167 \| 16500 \| 0.0 \| - \|
	\| 10.4482 \| 16550 \| 0.0 \| - \|
	\| 10.4798 \| 16600 \| 0.0 \| - \|
	\| 10.5114 \| 16650 \| 0.0 \| - \|
	\| 10.5429 \| 16700 \| 0.0 \| - \|
	\| 10.5745 \| 16750 \| 0.0 \| - \|
	\| 10.6061 \| 16800 \| 0.0 \| - \|
	\| 10.6376 \| 16850 \| 0.0 \| - \|
	\| 10.6692 \| 16900 \| 0.0009 \| - \|
	\| 10.7008 \| 16950 \| 0.0 \| - \|
	\| 10.7323 \| 17000 \| 0.0 \| - \|
	\| 10.7639 \| 17050 \| 0.0 \| - \|
	\| 10.7955 \| 17100 \| 0.0 \| - \|
	\| 10.8270 \| 17150 \| 0.0 \| - \|
	\| 10.8586 \| 17200 \| 0.0 \| - \|
	\| 10.8902 \| 17250 \| 0.0 \| - \|
	\| 10.9217 \| 17300 \| 0.0 \| - \|
	\| 10.9533 \| 17350 \| 0.0 \| - \|
	\| 10.9848 \| 17400 \| 0.0 \| - \|
	\| 11.0164 \| 17450 \| 0.0001 \| - \|
	\| 11.0480 \| 17500 \| 0.0 \| - \|
	\| 11.0795 \| 17550 \| 0.0 \| - \|
	\| 11.1111 \| 17600 \| 0.0 \| - \|
	\| 11.1427 \| 17650 \| 0.0 \| - \|
	\| 11.1742 \| 17700 \| 0.0 \| - \|
	\| 11.2058 \| 17750 \| 0.0 \| - \|
	\| 11.2374 \| 17800 \| 0.0 \| - \|
	\| 11.2689 \| 17850 \| 0.0 \| - \|
	\| 11.3005 \| 17900 \| 0.0 \| - \|
	\| 11.3321 \| 17950 \| 0.0 \| - \|
	\| 11.3636 \| 18000 \| 0.0 \| - \|
	\| 11.3952 \| 18050 \| 0.0 \| - \|
	\| 11.4268 \| 18100 \| 0.0 \| - \|
	\| 11.4583 \| 18150 \| 0.0 \| - \|
	\| 11.4899 \| 18200 \| 0.0003 \| - \|
	\| 11.5215 \| 18250 \| 0.0 \| - \|
	\| 11.5530 \| 18300 \| 0.0005 \| - \|
	\| 11.5846 \| 18350 \| 0.0 \| - \|
	\| 11.6162 \| 18400 \| 0.0 \| - \|
	\| 11.6477 \| 18450 \| 0.0 \| - \|
	\| 11.6793 \| 18500 \| 0.0 \| - \|
	\| 11.7109 \| 18550 \| 0.0 \| - \|
	\| 11.7424 \| 18600 \| 0.0 \| - \|
	\| 11.7740 \| 18650 \| 0.0 \| - \|
	\| 11.8056 \| 18700 \| 0.0 \| - \|
	\| 11.8371 \| 18750 \| 0.0 \| - \|
	\| 11.8687 \| 18800 \| 0.0 \| - \|
	\| 11.9003 \| 18850 \| 0.0 \| - \|
	\| 11.9318 \| 18900 \| 0.0 \| - \|
	\| 11.9634 \| 18950 \| 0.0 \| - \|
	\| 11.9949 \| 19000 \| 0.0 \| - \|
	\| 12.0265 \| 19050 \| 0.0 \| - \|
	\| 12.0581 \| 19100 \| 0.0 \| - \|
	\| 12.0896 \| 19150 \| 0.0 \| - \|
	\| 12.1212 \| 19200 \| 0.0 \| - \|
	\| 12.1528 \| 19250 \| 0.0 \| - \|
	\| 12.1843 \| 19300 \| 0.0 \| - \|
	\| 12.2159 \| 19350 \| 0.0 \| - \|
	\| 12.2475 \| 19400 \| 0.0 \| - \|
	\| 12.2790 \| 19450 \| 0.0 \| - \|
	\| 12.3106 \| 19500 \| 0.0 \| - \|
	\| 12.3422 \| 19550 \| 0.0 \| - \|
	\| 12.3737 \| 19600 \| 0.0 \| - \|
	\| 12.4053 \| 19650 \| 0.0 \| - \|
	\| 12.4369 \| 19700 \| 0.0 \| - \|
	\| 12.4684 \| 19750 \| 0.0 \| - \|
	\| 12.5 \| 19800 \| 0.0 \| - \|
	\| 12.5316 \| 19850 \| 0.0 \| - \|
	\| 12.5631 \| 19900 \| 0.0 \| - \|
	\| 12.5947 \| 19950 \| 0.0 \| - \|
	\| 12.6263 \| 20000 \| 0.0 \| - \|
	\| 12.6578 \| 20050 \| 0.0 \| - \|
	\| 12.6894 \| 20100 \| 0.0 \| - \|
	\| 12.7210 \| 20150 \| 0.0 \| - \|
	\| 12.7525 \| 20200 \| 0.0 \| - \|
	\| 12.7841 \| 20250 \| 0.0 \| - \|
	\| 12.8157 \| 20300 \| 0.0 \| - \|
	\| 12.8472 \| 20350 \| 0.0 \| - \|
	\| 12.8788 \| 20400 \| 0.0 \| - \|
	\| 12.9104 \| 20450 \| 0.0 \| - \|
	\| 12.9419 \| 20500 \| 0.0 \| - \|
	\| 12.9735 \| 20550 \| 0.0 \| - \|
	\| 13.0051 \| 20600 \| 0.0 \| - \|
	\| 13.0366 \| 20650 \| 0.0002 \| - \|
	\| 13.0682 \| 20700 \| 0.0 \| - \|
	\| 13.0997 \| 20750 \| 0.0 \| - \|
	\| 13.1313 \| 20800 \| 0.0 \| - \|
	\| 13.1629 \| 20850 \| 0.0 \| - \|
	\| 13.1944 \| 20900 \| 0.0 \| - \|
	\| 13.2260 \| 20950 \| 0.0 \| - \|
	\| 13.2576 \| 21000 \| 0.0 \| - \|
	\| 13.2891 \| 21050 \| 0.0015 \| - \|
	\| 13.3207 \| 21100 \| 0.0 \| - \|
	\| 13.3523 \| 21150 \| 0.0 \| - \|
	\| 13.3838 \| 21200 \| 0.0 \| - \|
	\| 13.4154 \| 21250 \| 0.0 \| - \|
	\| 13.4470 \| 21300 \| 0.0 \| - \|
	\| 13.4785 \| 21350 \| 0.0 \| - \|
	\| 13.5101 \| 21400 \| 0.0 \| - \|
	\| 13.5417 \| 21450 \| 0.0 \| - \|
	\| 13.5732 \| 21500 \| 0.0 \| - \|
	\| 13.6048 \| 21550 \| 0.0 \| - \|
	\| 13.6364 \| 21600 \| 0.0 \| - \|
	\| 13.6679 \| 21650 \| 0.0 \| - \|
	\| 13.6995 \| 21700 \| 0.0 \| - \|
	\| 13.7311 \| 21750 \| 0.0 \| - \|
	\| 13.7626 \| 21800 \| 0.0 \| - \|
	\| 13.7942 \| 21850 \| 0.0 \| - \|
	\| 13.8258 \| 21900 \| 0.0 \| - \|
	\| 13.8573 \| 21950 \| 0.0 \| - \|
	\| 13.8889 \| 22000 \| 0.0 \| - \|
	\| 13.9205 \| 22050 \| 0.0 \| - \|
	\| 13.9520 \| 22100 \| 0.0 \| - \|
	\| 13.9836 \| 22150 \| 0.0 \| - \|
	\| 14.0152 \| 22200 \| 0.0 \| - \|
	\| 14.0467 \| 22250 \| 0.0 \| - \|
	\| 14.0783 \| 22300 \| 0.0 \| - \|
	\| 14.1098 \| 22350 \| 0.0 \| - \|
	\| 14.1414 \| 22400 \| 0.0 \| - \|
	\| 14.1730 \| 22450 \| 0.0 \| - \|
	\| 14.2045 \| 22500 \| 0.0002 \| - \|
	\| 14.2361 \| 22550 \| 0.0 \| - \|
	\| 14.2677 \| 22600 \| 0.002 \| - \|
	\| 14.2992 \| 22650 \| 0.0 \| - \|
	\| 14.3308 \| 22700 \| 0.0 \| - \|
	\| 14.3624 \| 22750 \| 0.0 \| - \|
	\| 14.3939 \| 22800 \| 0.0 \| - \|
	\| 14.4255 \| 22850 \| 0.0 \| - \|
	\| 14.4571 \| 22900 \| 0.0 \| - \|
	\| 14.4886 \| 22950 \| 0.0 \| - \|
	\| 14.5202 \| 23000 \| 0.0 \| - \|
	\| 14.5518 \| 23050 \| 0.0 \| - \|
	\| 14.5833 \| 23100 \| 0.0 \| - \|
	\| 14.6149 \| 23150 \| 0.0 \| - \|
	\| 14.6465 \| 23200 \| 0.0 \| - \|
	\| 14.6780 \| 23250 \| 0.0 \| - \|
	\| 14.7096 \| 23300 \| 0.0 \| - \|
	\| 14.7412 \| 23350 \| 0.0 \| - \|
	\| 14.7727 \| 23400 \| 0.0 \| - \|
	\| 14.8043 \| 23450 \| 0.0 \| - \|
	\| 14.8359 \| 23500 \| 0.0 \| - \|
	\| 14.8674 \| 23550 \| 0.0 \| - \|
	\| 14.8990 \| 23600 \| 0.0 \| - \|
	\| 14.9306 \| 23650 \| 0.0 \| - \|
	\| 14.9621 \| 23700 \| 0.0 \| - \|
	\| 14.9937 \| 23750 \| 0.0 \| - \|
	\| 15.0253 \| 23800 \| 0.0 \| - \|
	\| 15.0568 \| 23850 \| 0.0 \| - \|
	\| 15.0884 \| 23900 \| 0.0 \| - \|
	\| 15.1199 \| 23950 \| 0.0 \| - \|
	\| 15.1515 \| 24000 \| 0.0 \| - \|
	\| 15.1831 \| 24050 \| 0.0 \| - \|
	\| 15.2146 \| 24100 \| 0.0 \| - \|
	\| 15.2462 \| 24150 \| 0.0 \| - \|
	\| 15.2778 \| 24200 \| 0.0 \| - \|
	\| 15.3093 \| 24250 \| 0.0 \| - \|
	\| 15.3409 \| 24300 \| 0.0 \| - \|
	\| 15.3725 \| 24350 \| 0.0 \| - \|
	\| 15.4040 \| 24400 \| 0.0 \| - \|
	\| 15.4356 \| 24450 \| 0.0 \| - \|
	\| 15.4672 \| 24500 \| 0.0 \| - \|
	\| 15.4987 \| 24550 \| 0.0 \| - \|
	\| 15.5303 \| 24600 \| 0.0 \| - \|
	\| 15.5619 \| 24650 \| 0.0 \| - \|
	\| 15.5934 \| 24700 \| 0.0 \| - \|
	\| 15.625 \| 24750 \| 0.0 \| - \|
	\| 15.6566 \| 24800 \| 0.0 \| - \|
	\| 15.6881 \| 24850 \| 0.0 \| - \|
	\| 15.7197 \| 24900 \| 0.0 \| - \|
	\| 15.7513 \| 24950 \| 0.0 \| - \|
	\| 15.7828 \| 25000 \| 0.0 \| - \|
	\| 15.8144 \| 25050 \| 0.0 \| - \|
	\| 15.8460 \| 25100 \| 0.0 \| - \|
	\| 15.8775 \| 25150 \| 0.0 \| - \|
	\| 15.9091 \| 25200 \| 0.0 \| - \|
	\| 15.9407 \| 25250 \| 0.0 \| - \|
	\| 15.9722 \| 25300 \| 0.0 \| - \|
	\| 16.0038 \| 25350 \| 0.0 \| - \|
	\| 16.0354 \| 25400 \| 0.0 \| - \|
	\| 16.0669 \| 25450 \| 0.0 \| - \|
	\| 16.0985 \| 25500 \| 0.0 \| - \|
	\| 16.1301 \| 25550 \| 0.0 \| - \|
	\| 16.1616 \| 25600 \| 0.0 \| - \|
	\| 16.1932 \| 25650 \| 0.0 \| - \|
	\| 16.2247 \| 25700 \| 0.0 \| - \|
	\| 16.2563 \| 25750 \| 0.0 \| - \|
	\| 16.2879 \| 25800 \| 0.0 \| - \|
	\| 16.3194 \| 25850 \| 0.0 \| - \|
	\| 16.3510 \| 25900 \| 0.0 \| - \|
	\| 16.3826 \| 25950 \| 0.0 \| - \|
	\| 16.4141 \| 26000 \| 0.0 \| - \|
	\| 16.4457 \| 26050 \| 0.0 \| - \|
	\| 16.4773 \| 26100 \| 0.0 \| - \|
	\| 16.5088 \| 26150 \| 0.0 \| - \|
	\| 16.5404 \| 26200 \| 0.0 \| - \|
	\| 16.5720 \| 26250 \| 0.0 \| - \|
	\| 16.6035 \| 26300 \| 0.0 \| - \|
	\| 16.6351 \| 26350 \| 0.0 \| - \|
	\| 16.6667 \| 26400 \| 0.0 \| - \|
	\| 16.6982 \| 26450 \| 0.0 \| - \|
	\| 16.7298 \| 26500 \| 0.0 \| - \|
	\| 16.7614 \| 26550 \| 0.0 \| - \|
	\| 16.7929 \| 26600 \| 0.0 \| - \|
	\| 16.8245 \| 26650 \| 0.0 \| - \|
	\| 16.8561 \| 26700 \| 0.0 \| - \|
	\| 16.8876 \| 26750 \| 0.0 \| - \|
	\| 16.9192 \| 26800 \| 0.0 \| - \|
	\| 16.9508 \| 26850 \| 0.0 \| - \|
	\| 16.9823 \| 26900 \| 0.0 \| - \|
	\| 17.0139 \| 26950 \| 0.0 \| - \|
	\| 17.0455 \| 27000 \| 0.0 \| - \|
	\| 17.0770 \| 27050 \| 0.0 \| - \|
	\| 17.1086 \| 27100 \| 0.0 \| - \|
	\| 17.1402 \| 27150 \| 0.0 \| - \|
	\| 17.1717 \| 27200 \| 0.0 \| - \|
	\| 17.2033 \| 27250 \| 0.0 \| - \|
	\| 17.2348 \| 27300 \| 0.0 \| - \|
	\| 17.2664 \| 27350 \| 0.0 \| - \|
	\| 17.2980 \| 27400 \| 0.0 \| - \|
	\| 17.3295 \| 27450 \| 0.0 \| - \|
	\| 17.3611 \| 27500 \| 0.0 \| - \|
	\| 17.3927 \| 27550 \| 0.0 \| - \|
	\| 17.4242 \| 27600 \| 0.0 \| - \|
	\| 17.4558 \| 27650 \| 0.0 \| - \|
	\| 17.4874 \| 27700 \| 0.0 \| - \|
	\| 17.5189 \| 27750 \| 0.0 \| - \|
	\| 17.5505 \| 27800 \| 0.0 \| - \|
	\| 17.5821 \| 27850 \| 0.0 \| - \|
	\| 17.6136 \| 27900 \| 0.0 \| - \|
	\| 17.6452 \| 27950 \| 0.0 \| - \|
	\| 17.6768 \| 28000 \| 0.0 \| - \|
	\| 17.7083 \| 28050 \| 0.0 \| - \|
	\| 17.7399 \| 28100 \| 0.0 \| - \|
	\| 17.7715 \| 28150 \| 0.0 \| - \|
	\| 17.8030 \| 28200 \| 0.0 \| - \|
	\| 17.8346 \| 28250 \| 0.0 \| - \|
	\| 17.8662 \| 28300 \| 0.0 \| - \|
	\| 17.8977 \| 28350 \| 0.0 \| - \|
	\| 17.9293 \| 28400 \| 0.0 \| - \|
	\| 17.9609 \| 28450 \| 0.0 \| - \|
	\| 17.9924 \| 28500 \| 0.0 \| - \|
	\| 18.0240 \| 28550 \| 0.0 \| - \|
	\| 18.0556 \| 28600 \| 0.0 \| - \|
	\| 18.0871 \| 28650 \| 0.0 \| - \|
	\| 18.1187 \| 28700 \| 0.0 \| - \|
	\| 18.1503 \| 28750 \| 0.0 \| - \|
	\| 18.1818 \| 28800 \| 0.0 \| - \|
	\| 18.2134 \| 28850 \| 0.0 \| - \|
	\| 18.2449 \| 28900 \| 0.0 \| - \|
	\| 18.2765 \| 28950 \| 0.0 \| - \|
	\| 18.3081 \| 29000 \| 0.0 \| - \|
	\| 18.3396 \| 29050 \| 0.0 \| - \|
	\| 18.3712 \| 29100 \| 0.0 \| - \|
	\| 18.4028 \| 29150 \| 0.0 \| - \|
	\| 18.4343 \| 29200 \| 0.0 \| - \|
	\| 18.4659 \| 29250 \| 0.0 \| - \|
	\| 18.4975 \| 29300 \| 0.0 \| - \|
	\| 18.5290 \| 29350 \| 0.0 \| - \|
	\| 18.5606 \| 29400 \| 0.0 \| - \|
	\| 18.5922 \| 29450 \| 0.0 \| - \|
	\| 18.6237 \| 29500 \| 0.0 \| - \|
	\| 18.6553 \| 29550 \| 0.0 \| - \|
	\| 18.6869 \| 29600 \| 0.0 \| - \|
	\| 18.7184 \| 29650 \| 0.0 \| - \|
	\| 18.75 \| 29700 \| 0.0 \| - \|
	\| 18.7816 \| 29750 \| 0.0 \| - \|
	\| 18.8131 \| 29800 \| 0.0 \| - \|
	\| 18.8447 \| 29850 \| 0.0 \| - \|
	\| 18.8763 \| 29900 \| 0.0 \| - \|
	\| 18.9078 \| 29950 \| 0.0 \| - \|
	\| 18.9394 \| 30000 \| 0.0 \| - \|
	\| 18.9710 \| 30050 \| 0.0 \| - \|
	\| 19.0025 \| 30100 \| 0.0 \| - \|
	\| 19.0341 \| 30150 \| 0.0 \| - \|
	\| 19.0657 \| 30200 \| 0.0 \| - \|
	\| 19.0972 \| 30250 \| 0.0 \| - \|
	\| 19.1288 \| 30300 \| 0.0 \| - \|
	\| 19.1604 \| 30350 \| 0.0 \| - \|
	\| 19.1919 \| 30400 \| 0.0 \| - \|
	\| 19.2235 \| 30450 \| 0.0 \| - \|
	\| 19.2551 \| 30500 \| 0.0 \| - \|
	\| 19.2866 \| 30550 \| 0.0 \| - \|
	\| 19.3182 \| 30600 \| 0.0 \| - \|
	\| 19.3497 \| 30650 \| 0.0 \| - \|
	\| 19.3813 \| 30700 \| 0.0 \| - \|
	\| 19.4129 \| 30750 \| 0.0 \| - \|
	\| 19.4444 \| 30800 \| 0.0 \| - \|
	\| 19.4760 \| 30850 \| 0.0 \| - \|
	\| 19.5076 \| 30900 \| 0.0 \| - \|
	\| 19.5391 \| 30950 \| 0.0 \| - \|
	\| 19.5707 \| 31000 \| 0.0 \| - \|
	\| 19.6023 \| 31050 \| 0.0 \| - \|
	\| 19.6338 \| 31100 \| 0.0 \| - \|
	\| 19.6654 \| 31150 \| 0.0 \| - \|
	\| 19.6970 \| 31200 \| 0.0 \| - \|
	\| 19.7285 \| 31250 \| 0.0 \| - \|
	\| 19.7601 \| 31300 \| 0.0 \| - \|
	\| 19.7917 \| 31350 \| 0.0 \| - \|
	\| 19.8232 \| 31400 \| 0.0 \| - \|
	\| 19.8548 \| 31450 \| 0.0 \| - \|
	\| 19.8864 \| 31500 \| 0.0 \| - \|
	\| 19.9179 \| 31550 \| 0.0 \| - \|
	\| 19.9495 \| 31600 \| 0.0 \| - \|
	\| 19.9811 \| 31650 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.1.0.dev0
	- Sentence Transformers: 3.1.1
	- Transformers: 4.46.1
	- PyTorch: 2.4.0+cu121
	- Datasets: 2.20.0
	- Tokenizers: 0.20.0

	## 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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