Push model using huggingface_hub.

6432d6e verified 28 days ago

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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: 엔프라니 옴므 선블록 썬크림 남성용 선크림 (#M)화장품/미용>남성화장품>선크림 Naverstore > 화장품/미용 > 남성화장품
	> 선크림
	- text: (시세이도)(시세이도)(특별한정) 파란자차 50ml 세트(+파란자차 정품 용량) NEW 파란자차 (정품) (#M)화장품/향수>선케어>선크림
	Gmarket > 뷰티 > 화장품/향수 > 선케어 > 선크림
	- text: 에스쁘아 워터스플래쉬 선크림 SPF50+ PA+++ 60ml × 5개 (#M)쿠팡 홈>뷰티>스킨케어>선케어/태닝>선케어>선블록/선크림/선로션
	Coupang > 뷰티 > 스킨케어 > 선케어/태닝 > 선케어 > 선블록/선크림/선로션
	- text: 이니스프리 인텐시브 롱래스팅 선스크린50ml 50ml × 6개 LotteOn > 뷰티 > 남성화장품 > 스킨 LotteOn > 뷰티
	> 남성화장품 > 스킨
	- text: 에스트라 리제덤 RX 듀얼 선크림 +BB 50ml 병원전용제품 (#M)SSG.COM/메이크업/베이스메이크업/BB/CC크림 ssg
	> 뷰티 > 메이크업 > 베이스메이크업 > BB/CC크림
	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: 0.4902962206332993
	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: 5 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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 2 \| <ul><li>'이니스프리 노세범 선쿠션 SPF50+ PA++++ 14g × 2개 (#M)위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 파우더/팩트 위메프 > 뷰티 > 메이크업 > 베이스 메이크업 > 파우더/팩트'</li><li>'스킨 세팅 톤업 선 쿠션(리필포함) + 추가구성품 톤업 선 쿠션 LotteOn > 백화점 > 뷰티 > 상단 배너 (Mobile) LotteOn > 뷰티 > 메이크업 > 베이스메이크업 > 쿠션/팩트'</li><li>'이니스프리 노세범 선쿠션 리필 14g 1 +1 (#M)쿠팡 홈>뷰티>스킨케어>선케어/태닝>선케어>선스틱 Coupang > 뷰티 > 로드샵 > 스킨케어 > 선케어/태닝'</li></ul> \|
	\| 1 \| <ul><li>'SUNDANCE 썬댄스 햇빛 차단+태닝 선스프레이 LSF 50, 200ml ssg > 뷰티 > 스킨케어 > 선케어 > 선스프레이 ssg > 뷰티 > 스킨케어 > 선케어 > 선스프레이'</li><li>'리더스 여름자외선 썬버디 올 오버 선 스프레이 180ml MinSellAmount (#M)화장품/향수>선케어>선스프레이 Gmarket > 뷰티 > 화장품/향수 > 선케어 > 선스프레이'</li><li>'온더바디 헬로키티 에코 썬 스프레이 120ml+120ml 기획세트 (#M)홈>화장품/미용>선케어>선케어세트 Naverstore > 화장품/미용 > 선케어 > 선케어세트'</li></ul> \|
	\| 0 \| <ul><li>'[피지오겔] [정가 85,000원] 레드 수딩 AI 에어리 썬스틱 1+1 특별기획 롯데홈쇼핑 > 뷰티 > 남성화장품 LotteOn > 뷰티 > 남성화장품 > 선크림'</li><li>'[빌리프][2106] 해피 보 이지워시 선스틱 18g 세트(타임스퀘어점패션관) (#M)11st>선케어>선밤>선밤 11st > 뷰티 > 선케어 > 선밤 > 선밤'</li><li>'피지오겔 레드 수딩 AI 에어리 썬스틱 7g 1+1(2개) (#M)홈>스킨케어>선케어 HMALL > 뷰티 > 스킨케어 > 선케어'</li></ul> \|
	\| 4 \| <ul><li>'오스트레일리안골드 헴프네이션 오리지널 탠 익스텐더 바디로션 535ml (#M)SSG.COM/스킨케어/선케어/태닝 ssg > 뷰티 > 스킨케어 > 선케어 > 태닝'</li><li>'수딩앤모이스처 알로에베라92%수딩젤300ml (#M)홈>화장품/미용>바디케어>바디로션 Naverstore > 화장품/미용 > 바디케어 > 바디로션'</li><li>'세인트 트로페즈 셀프 탠 익스프레스 어드밴스드 브론징 무스 200ml (#M)SSG.COM/스킨케어/선케어/태닝 ssg > 뷰티 > 스킨케어 > 선케어 > 태닝'</li></ul> \|
	\| 3 \| <ul><li>'[맥퀸뉴욕] 1+ 1 UV 데일리 모이스처(수분) 선크림 1+1 UV 데일리 모이스처 선크림 (#M)SSG.COM/메이크업/립메이크업/립글로스 ssg > 뷰티 > 메이크업 > 아이메이크업 > 아이라이너'</li><li>'[공식] 더마비 10주년 바디로션/기획세트/멀티오일/프레쉬/크림/워시 1+1 S11.(애브리데이) 대용량 선블록 200ml×2개_S1.튜브견본(랜덤) 쇼킹딜 홈;쇼킹딜 홈>뷰티>바디/향수>바디케어;11st>뷰티>바디/향수>바디케어;11st>바디케어>바디로션>바디로션;11st > 뷰티 > 바디케어 > 바디로션 11st Hour Event > 패션/뷰티 > 뷰티 > 바디/향수 > 바디케어'</li><li>'[20%찜+T11%+묶음+당일 ] 롬앤 11번가 런칭! 모든 취향 취급 중! 밀크 그로서리 외 BEST 1+1 옵션31. 제로 선 클린 단품_01 프레쉬 쇼킹딜 홈>뷰티>선케어/메이크업>립/치크메이크업;11st>메이크업>립메이크업>립틴트;11st>뷰티>선케어/메이크업>립/치크메이크업;11st>뷰티>선케어/메이크업>아이메이크업;11st>메이크업>아이메이크업>마스카라;11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 립/치크메이크업 11st Hour Event > 패션/뷰티 > 뷰티 > 선케어/메이크업 > 아이메이크업'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.4903 \|

	## 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_top8_test")
	# Run inference
	preds = model("엔프라니 옴므 선블록 썬크림 남성용 선크림 (#M)화장품/미용>남성화장품>선크림 Naverstore > 화장품/미용 > 남성화장품 > 선크림")
	```

	<!--
	### 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 \| 21.656 \| 72 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 50 \|
	\| 1 \| 50 \|
	\| 2 \| 50 \|
	\| 3 \| 50 \|
	\| 4 \| 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 \|
	\|:-------:\|:-----:\|:-------------:\|:---------------:\|
	\| 0.0026 \| 1 \| 0.4513 \| - \|
	\| 0.1279 \| 50 \| 0.4435 \| - \|
	\| 0.2558 \| 100 \| 0.4063 \| - \|
	\| 0.3836 \| 150 \| 0.3413 \| - \|
	\| 0.5115 \| 200 \| 0.2997 \| - \|
	\| 0.6394 \| 250 \| 0.2434 \| - \|
	\| 0.7673 \| 300 \| 0.1724 \| - \|
	\| 0.8951 \| 350 \| 0.1334 \| - \|
	\| 1.0230 \| 400 \| 0.1078 \| - \|
	\| 1.1509 \| 450 \| 0.0997 \| - \|
	\| 1.2788 \| 500 \| 0.0937 \| - \|
	\| 1.4066 \| 550 \| 0.0933 \| - \|
	\| 1.5345 \| 600 \| 0.0909 \| - \|
	\| 1.6624 \| 650 \| 0.0897 \| - \|
	\| 1.7903 \| 700 \| 0.0842 \| - \|
	\| 1.9182 \| 750 \| 0.0741 \| - \|
	\| 2.0460 \| 800 \| 0.0764 \| - \|
	\| 2.1739 \| 850 \| 0.0745 \| - \|
	\| 2.3018 \| 900 \| 0.0733 \| - \|
	\| 2.4297 \| 950 \| 0.0748 \| - \|
	\| 2.5575 \| 1000 \| 0.0718 \| - \|
	\| 2.6854 \| 1050 \| 0.0568 \| - \|
	\| 2.8133 \| 1100 \| 0.0415 \| - \|
	\| 2.9412 \| 1150 \| 0.0256 \| - \|
	\| 3.0691 \| 1200 \| 0.0233 \| - \|
	\| 3.1969 \| 1250 \| 0.0128 \| - \|
	\| 3.3248 \| 1300 \| 0.0088 \| - \|
	\| 3.4527 \| 1350 \| 0.0066 \| - \|
	\| 3.5806 \| 1400 \| 0.0058 \| - \|
	\| 3.7084 \| 1450 \| 0.006 \| - \|
	\| 3.8363 \| 1500 \| 0.0058 \| - \|
	\| 3.9642 \| 1550 \| 0.0039 \| - \|
	\| 4.0921 \| 1600 \| 0.0043 \| - \|
	\| 4.2199 \| 1650 \| 0.0033 \| - \|
	\| 4.3478 \| 1700 \| 0.0059 \| - \|
	\| 4.4757 \| 1750 \| 0.0065 \| - \|
	\| 4.6036 \| 1800 \| 0.0061 \| - \|
	\| 4.7315 \| 1850 \| 0.0052 \| - \|
	\| 4.8593 \| 1900 \| 0.0054 \| - \|
	\| 4.9872 \| 1950 \| 0.0043 \| - \|
	\| 5.1151 \| 2000 \| 0.0064 \| - \|
	\| 5.2430 \| 2050 \| 0.0042 \| - \|
	\| 5.3708 \| 2100 \| 0.0046 \| - \|
	\| 5.4987 \| 2150 \| 0.0038 \| - \|
	\| 5.6266 \| 2200 \| 0.0031 \| - \|
	\| 5.7545 \| 2250 \| 0.0021 \| - \|
	\| 5.8824 \| 2300 \| 0.0006 \| - \|
	\| 6.0102 \| 2350 \| 0.0003 \| - \|
	\| 6.1381 \| 2400 \| 0.0001 \| - \|
	\| 6.2660 \| 2450 \| 0.0002 \| - \|
	\| 6.3939 \| 2500 \| 0.0 \| - \|
	\| 6.5217 \| 2550 \| 0.0 \| - \|
	\| 6.6496 \| 2600 \| 0.0001 \| - \|
	\| 6.7775 \| 2650 \| 0.0 \| - \|
	\| 6.9054 \| 2700 \| 0.0 \| - \|
	\| 7.0332 \| 2750 \| 0.0 \| - \|
	\| 7.1611 \| 2800 \| 0.0 \| - \|
	\| 7.2890 \| 2850 \| 0.0 \| - \|
	\| 7.4169 \| 2900 \| 0.0 \| - \|
	\| 7.5448 \| 2950 \| 0.0 \| - \|
	\| 7.6726 \| 3000 \| 0.0 \| - \|
	\| 7.8005 \| 3050 \| 0.0 \| - \|
	\| 7.9284 \| 3100 \| 0.0 \| - \|
	\| 8.0563 \| 3150 \| 0.0 \| - \|
	\| 8.1841 \| 3200 \| 0.0 \| - \|
	\| 8.3120 \| 3250 \| 0.0 \| - \|
	\| 8.4399 \| 3300 \| 0.0 \| - \|
	\| 8.5678 \| 3350 \| 0.0 \| - \|
	\| 8.6957 \| 3400 \| 0.0 \| - \|
	\| 8.8235 \| 3450 \| 0.0 \| - \|
	\| 8.9514 \| 3500 \| 0.0 \| - \|
	\| 9.0793 \| 3550 \| 0.0 \| - \|
	\| 9.2072 \| 3600 \| 0.0 \| - \|
	\| 9.3350 \| 3650 \| 0.0 \| - \|
	\| 9.4629 \| 3700 \| 0.0 \| - \|
	\| 9.5908 \| 3750 \| 0.0 \| - \|
	\| 9.7187 \| 3800 \| 0.0 \| - \|
	\| 9.8465 \| 3850 \| 0.0 \| - \|
	\| 9.9744 \| 3900 \| 0.0 \| - \|
	\| 10.1023 \| 3950 \| 0.0 \| - \|
	\| 10.2302 \| 4000 \| 0.0 \| - \|
	\| 10.3581 \| 4050 \| 0.0 \| - \|
	\| 10.4859 \| 4100 \| 0.0 \| - \|
	\| 10.6138 \| 4150 \| 0.0 \| - \|
	\| 10.7417 \| 4200 \| 0.0 \| - \|
	\| 10.8696 \| 4250 \| 0.0 \| - \|
	\| 10.9974 \| 4300 \| 0.0 \| - \|
	\| 11.1253 \| 4350 \| 0.0 \| - \|
	\| 11.2532 \| 4400 \| 0.0 \| - \|
	\| 11.3811 \| 4450 \| 0.0 \| - \|
	\| 11.5090 \| 4500 \| 0.0 \| - \|
	\| 11.6368 \| 4550 \| 0.0 \| - \|
	\| 11.7647 \| 4600 \| 0.0 \| - \|
	\| 11.8926 \| 4650 \| 0.0 \| - \|
	\| 12.0205 \| 4700 \| 0.0 \| - \|
	\| 12.1483 \| 4750 \| 0.0 \| - \|
	\| 12.2762 \| 4800 \| 0.0 \| - \|
	\| 12.4041 \| 4850 \| 0.0 \| - \|
	\| 12.5320 \| 4900 \| 0.0 \| - \|
	\| 12.6598 \| 4950 \| 0.0 \| - \|
	\| 12.7877 \| 5000 \| 0.0 \| - \|
	\| 12.9156 \| 5050 \| 0.0 \| - \|
	\| 13.0435 \| 5100 \| 0.0 \| - \|
	\| 13.1714 \| 5150 \| 0.0 \| - \|
	\| 13.2992 \| 5200 \| 0.0 \| - \|
	\| 13.4271 \| 5250 \| 0.0 \| - \|
	\| 13.5550 \| 5300 \| 0.0 \| - \|
	\| 13.6829 \| 5350 \| 0.0 \| - \|
	\| 13.8107 \| 5400 \| 0.0 \| - \|
	\| 13.9386 \| 5450 \| 0.0 \| - \|
	\| 14.0665 \| 5500 \| 0.0 \| - \|
	\| 14.1944 \| 5550 \| 0.0 \| - \|
	\| 14.3223 \| 5600 \| 0.0 \| - \|
	\| 14.4501 \| 5650 \| 0.0 \| - \|
	\| 14.5780 \| 5700 \| 0.0 \| - \|
	\| 14.7059 \| 5750 \| 0.0 \| - \|
	\| 14.8338 \| 5800 \| 0.0 \| - \|
	\| 14.9616 \| 5850 \| 0.0 \| - \|
	\| 15.0895 \| 5900 \| 0.0 \| - \|
	\| 15.2174 \| 5950 \| 0.0 \| - \|
	\| 15.3453 \| 6000 \| 0.0 \| - \|
	\| 15.4731 \| 6050 \| 0.0 \| - \|
	\| 15.6010 \| 6100 \| 0.0 \| - \|
	\| 15.7289 \| 6150 \| 0.0 \| - \|
	\| 15.8568 \| 6200 \| 0.0 \| - \|
	\| 15.9847 \| 6250 \| 0.0 \| - \|
	\| 16.1125 \| 6300 \| 0.0 \| - \|
	\| 16.2404 \| 6350 \| 0.0 \| - \|
	\| 16.3683 \| 6400 \| 0.0 \| - \|
	\| 16.4962 \| 6450 \| 0.0 \| - \|
	\| 16.6240 \| 6500 \| 0.0 \| - \|
	\| 16.7519 \| 6550 \| 0.0 \| - \|
	\| 16.8798 \| 6600 \| 0.0 \| - \|
	\| 17.0077 \| 6650 \| 0.0 \| - \|
	\| 17.1355 \| 6700 \| 0.0 \| - \|
	\| 17.2634 \| 6750 \| 0.0 \| - \|
	\| 17.3913 \| 6800 \| 0.0 \| - \|
	\| 17.5192 \| 6850 \| 0.0 \| - \|
	\| 17.6471 \| 6900 \| 0.0 \| - \|
	\| 17.7749 \| 6950 \| 0.0 \| - \|
	\| 17.9028 \| 7000 \| 0.0 \| - \|
	\| 18.0307 \| 7050 \| 0.0 \| - \|
	\| 18.1586 \| 7100 \| 0.0 \| - \|
	\| 18.2864 \| 7150 \| 0.0 \| - \|
	\| 18.4143 \| 7200 \| 0.0 \| - \|
	\| 18.5422 \| 7250 \| 0.0 \| - \|
	\| 18.6701 \| 7300 \| 0.0 \| - \|
	\| 18.7980 \| 7350 \| 0.0 \| - \|
	\| 18.9258 \| 7400 \| 0.0 \| - \|
	\| 19.0537 \| 7450 \| 0.0 \| - \|
	\| 19.1816 \| 7500 \| 0.0 \| - \|
	\| 19.3095 \| 7550 \| 0.0 \| - \|
	\| 19.4373 \| 7600 \| 0.0 \| - \|
	\| 19.5652 \| 7650 \| 0.0 \| - \|
	\| 19.6931 \| 7700 \| 0.0 \| - \|
	\| 19.8210 \| 7750 \| 0.0 \| - \|
	\| 19.9488 \| 7800 \| 0.0 \| - \|
	\| 20.0767 \| 7850 \| 0.0 \| - \|
	\| 20.2046 \| 7900 \| 0.0 \| - \|
	\| 20.3325 \| 7950 \| 0.0 \| - \|
	\| 20.4604 \| 8000 \| 0.0 \| - \|
	\| 20.5882 \| 8050 \| 0.0 \| - \|
	\| 20.7161 \| 8100 \| 0.0 \| - \|
	\| 20.8440 \| 8150 \| 0.0 \| - \|
	\| 20.9719 \| 8200 \| 0.0 \| - \|
	\| 21.0997 \| 8250 \| 0.0 \| - \|
	\| 21.2276 \| 8300 \| 0.0 \| - \|
	\| 21.3555 \| 8350 \| 0.0 \| - \|
	\| 21.4834 \| 8400 \| 0.0 \| - \|
	\| 21.6113 \| 8450 \| 0.0 \| - \|
	\| 21.7391 \| 8500 \| 0.0 \| - \|
	\| 21.8670 \| 8550 \| 0.0 \| - \|
	\| 21.9949 \| 8600 \| 0.0 \| - \|
	\| 22.1228 \| 8650 \| 0.0 \| - \|
	\| 22.2506 \| 8700 \| 0.0 \| - \|
	\| 22.3785 \| 8750 \| 0.0 \| - \|
	\| 22.5064 \| 8800 \| 0.0 \| - \|
	\| 22.6343 \| 8850 \| 0.0 \| - \|
	\| 22.7621 \| 8900 \| 0.0 \| - \|
	\| 22.8900 \| 8950 \| 0.0 \| - \|
	\| 23.0179 \| 9000 \| 0.0 \| - \|
	\| 23.1458 \| 9050 \| 0.0 \| - \|
	\| 23.2737 \| 9100 \| 0.0 \| - \|
	\| 23.4015 \| 9150 \| 0.0 \| - \|
	\| 23.5294 \| 9200 \| 0.0 \| - \|
	\| 23.6573 \| 9250 \| 0.0 \| - \|
	\| 23.7852 \| 9300 \| 0.0 \| - \|
	\| 23.9130 \| 9350 \| 0.0 \| - \|
	\| 24.0409 \| 9400 \| 0.0 \| - \|
	\| 24.1688 \| 9450 \| 0.0 \| - \|
	\| 24.2967 \| 9500 \| 0.0 \| - \|
	\| 24.4246 \| 9550 \| 0.0 \| - \|
	\| 24.5524 \| 9600 \| 0.0 \| - \|
	\| 24.6803 \| 9650 \| 0.0 \| - \|
	\| 24.8082 \| 9700 \| 0.0 \| - \|
	\| 24.9361 \| 9750 \| 0.0 \| - \|
	\| 25.0639 \| 9800 \| 0.0 \| - \|
	\| 25.1918 \| 9850 \| 0.0 \| - \|
	\| 25.3197 \| 9900 \| 0.0 \| - \|
	\| 25.4476 \| 9950 \| 0.0 \| - \|
	\| 25.5754 \| 10000 \| 0.0 \| - \|
	\| 25.7033 \| 10050 \| 0.0 \| - \|
	\| 25.8312 \| 10100 \| 0.0 \| - \|
	\| 25.9591 \| 10150 \| 0.0 \| - \|
	\| 26.0870 \| 10200 \| 0.0 \| - \|
	\| 26.2148 \| 10250 \| 0.0 \| - \|
	\| 26.3427 \| 10300 \| 0.0 \| - \|
	\| 26.4706 \| 10350 \| 0.0 \| - \|
	\| 26.5985 \| 10400 \| 0.0 \| - \|
	\| 26.7263 \| 10450 \| 0.0 \| - \|
	\| 26.8542 \| 10500 \| 0.0 \| - \|
	\| 26.9821 \| 10550 \| 0.0 \| - \|
	\| 27.1100 \| 10600 \| 0.0 \| - \|
	\| 27.2379 \| 10650 \| 0.0 \| - \|
	\| 27.3657 \| 10700 \| 0.0 \| - \|
	\| 27.4936 \| 10750 \| 0.0 \| - \|
	\| 27.6215 \| 10800 \| 0.0 \| - \|
	\| 27.7494 \| 10850 \| 0.0 \| - \|
	\| 27.8772 \| 10900 \| 0.0 \| - \|
	\| 28.0051 \| 10950 \| 0.0 \| - \|
	\| 28.1330 \| 11000 \| 0.0 \| - \|
	\| 28.2609 \| 11050 \| 0.0 \| - \|
	\| 28.3887 \| 11100 \| 0.0 \| - \|
	\| 28.5166 \| 11150 \| 0.0 \| - \|
	\| 28.6445 \| 11200 \| 0.0 \| - \|
	\| 28.7724 \| 11250 \| 0.0 \| - \|
	\| 28.9003 \| 11300 \| 0.0 \| - \|
	\| 29.0281 \| 11350 \| 0.0 \| - \|
	\| 29.1560 \| 11400 \| 0.0 \| - \|
	\| 29.2839 \| 11450 \| 0.0 \| - \|
	\| 29.4118 \| 11500 \| 0.0 \| - \|
	\| 29.5396 \| 11550 \| 0.0 \| - \|
	\| 29.6675 \| 11600 \| 0.0 \| - \|
	\| 29.7954 \| 11650 \| 0.0 \| - \|
	\| 29.9233 \| 11700 \| 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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