kepri-embedding / README.md

Add SetFit model

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	---
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 인공지능 챗봇 기술 향상에 대한 아이디어가 있는데, 관련된 역시 자세하 정보가 담긴 논문이나 보고서를 찾아줄래요?
	- text: 연구 자료의 서론 부분을 한 줄로 요약해 줄 수 있나요?
	- text: 우리 회사의 HR 정책 개선 방안에 대한 과제를 진행 중이야. 같은 주제의 이전 과제와 어떤 부분에서 왜 중복되었는지 궁금해
	- text: 초전도체의 임계 온도에 관한 연구 자료를 모으고 있어요. 여기에 관련된 유사한 연구나 보고서를 추천받고 싶어요
	- text: 공장에서 발생하는 가스 누출 문제 해결을 위한 시스템을 개발하려고 하는데, 이와 같은 측면에서 진행된 기존 연구나 비슷한 프로젝트가
	있는지 알려주세요
	inference: true
	model-index:
	- name: SetFit
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.9891304347826086
	name: Accuracy
	---

	# SetFit

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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: [Unknown](https://huggingface.co/unknown) -->
	- 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 \|
	\|:-----------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 오탈자 탐지 \| <ul><li>'건축 프로젝트 설명 문장에서 오타나 잘못된 맞춤법을 찾아줘.'</li><li>'경영 보고서 내용에 대한 오탈자를 검토하고 수정해 드릴 수 있을까요?'</li><li>'경쟁사 분석 항목 내 문장 구성의 오류를 지적해주겠습니까?'</li></ul> \|
	\| 요약 \| <ul><li>'(특정 논문 제목)의 결론 및 향후 연구 방향에 대해 요점을 정리해 주세요.'</li><li>'(특정 특허번호)를 기반으로 한 발명의 전체적인 개념을 짧게 설명 부탁드립니다.'</li><li>'1장의 데이터 수집 기술에 대해 요약해주세요'</li></ul> \|
	\| 유사문서 \| <ul><li>'5G 통신 모듈 최적화에 관련된 프로젝트를 하고 있는데, 비슷한 내용의 프로젝트나 논문이 있는지 연결해서 말해줄래?'</li><li>'AI 기반 헬스케어 솔루션 개발에 관한 문헌 조사를 하고 있습니다. 와 같은 주제를 다룬 문서를 찾아줄 수 있을까요?'</li><li>'AI 연산 속도를 최적화하기 위한 반도체 설계 방식을 연구하고 있어. 관련된 유사한 논문이나 보고서를 찾고 싶어'</li></ul> \|
	\| 중복성 검토 \| <ul><li>'5G 통신망을 기반으로 스마트 시티 구축에 관한 연구를 시작했어. 이와 동일하거나 겹치는 연구 과제나 프로젝트가 있는지 알아봐주고, 이유도 명확하게 밝혀줘'</li><li>'건물의 내진 설계 강화 방안을 조사하고 있는데 이에 연관된 기존 프로젝트가 무엇이 있는지 그리고 왜 겹치는지 말해줄래?'</li><li>'고성능 메모리 소자의 내구성을 향상시키는 기술을 개발하고 있어. 이와 비슷한 과제가 이전에 있었는지, 그리고 어떻게 유사하거나 중복되는지 말해줘'</li></ul> \|
	\| 특화 지식정보 제공 \| <ul><li>'3D 금속 배선 기술(HBM, TSV)의 도입으로 인한 전력 소비 감소 방안에는 어떤 것이 있는가요?'</li><li>'AI 워크로드를 처리하기 위한 반도체 아키텍처 설계에서는 어떤 전략들이 사용되나요?'</li><li>'LEED 인증의 기준과 획득 과정에 대해 알고 싶습니다.'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.9891 \|

	## 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("NTIS/kepri-embedding")
	# Run inference
	preds = model("연구 자료의 서론 부분을 한 줄로 요약해 줄 수 있나요?")
	```

	<!--
	### 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 \| 6 \| 12.4709 \| 27 \|

	\| Label \| Training Sample Count \|
	\|:-----------\|:----------------------\|
	\| 요약 \| 105 \|
	\| 중복성 검토 \| 78 \|
	\| 특화 지식정보 제공 \| 106 \|
	\| 유사문서 \| 115 \|
	\| 오탈자 탐지 \| 95 \|

	### Training Hyperparameters
	- batch_size: (64, 64)
	- num_epochs: (10, 10)
	- max_steps: -1
	- sampling_strategy: oversampling
	- 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
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: True

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:-------:\|:--------:\|:-------------:\|:---------------:\|
	\| 0.0003 \| 1 \| 0.2062 \| - \|
	\| 0.0161 \| 50 \| 0.2314 \| - \|
	\| 0.0322 \| 100 \| 0.2008 \| - \|
	\| 0.0484 \| 150 \| 0.1395 \| - \|
	\| 0.0645 \| 200 \| 0.11 \| - \|
	\| 0.0806 \| 250 \| 0.0872 \| - \|
	\| 0.0967 \| 300 \| 0.0462 \| - \|
	\| 0.1129 \| 350 \| 0.0188 \| - \|
	\| 0.1290 \| 400 \| 0.0201 \| - \|
	\| 0.1451 \| 450 \| 0.025 \| - \|
	\| 0.1612 \| 500 \| 0.004 \| - \|
	\| 0.1774 \| 550 \| 0.002 \| - \|
	\| 0.1935 \| 600 \| 0.0153 \| - \|
	\| 0.2096 \| 650 \| 0.0011 \| - \|
	\| 0.2257 \| 700 \| 0.0007 \| - \|
	\| 0.2419 \| 750 \| 0.0006 \| - \|
	\| 0.2580 \| 800 \| 0.0006 \| - \|
	\| 0.2741 \| 850 \| 0.0005 \| - \|
	\| 0.2902 \| 900 \| 0.0004 \| - \|
	\| 0.3064 \| 950 \| 0.0005 \| - \|
	\| 0.3225 \| 1000 \| 0.0002 \| - \|
	\| 0.3386 \| 1050 \| 0.0002 \| - \|
	\| 0.3547 \| 1100 \| 0.0003 \| - \|
	\| 0.3708 \| 1150 \| 0.0002 \| - \|
	\| 0.3870 \| 1200 \| 0.0002 \| - \|
	\| 0.4031 \| 1250 \| 0.0002 \| - \|
	\| 0.4192 \| 1300 \| 0.0001 \| - \|
	\| 0.4353 \| 1350 \| 0.0002 \| - \|
	\| 0.4515 \| 1400 \| 0.0001 \| - \|
	\| 0.4676 \| 1450 \| 0.0001 \| - \|
	\| 0.4837 \| 1500 \| 0.0001 \| - \|
	\| 0.4998 \| 1550 \| 0.0001 \| - \|
	\| 0.5160 \| 1600 \| 0.0001 \| - \|
	\| 0.5321 \| 1650 \| 0.0001 \| - \|
	\| 0.5482 \| 1700 \| 0.0001 \| - \|
	\| 0.5643 \| 1750 \| 0.0001 \| - \|
	\| 0.5805 \| 1800 \| 0.0001 \| - \|
	\| 0.5966 \| 1850 \| 0.0001 \| - \|
	\| 0.6127 \| 1900 \| 0.0001 \| - \|
	\| 0.6288 \| 1950 \| 0.0001 \| - \|
	\| 0.6450 \| 2000 \| 0.0001 \| - \|
	\| 0.6611 \| 2050 \| 0.0001 \| - \|
	\| 0.6772 \| 2100 \| 0.0001 \| - \|
	\| 0.6933 \| 2150 \| 0.0001 \| - \|
	\| 0.7094 \| 2200 \| 0.0001 \| - \|
	\| 0.7256 \| 2250 \| 0.0001 \| - \|
	\| 0.7417 \| 2300 \| 0.0001 \| - \|
	\| 0.7578 \| 2350 \| 0.0001 \| - \|
	\| 0.7739 \| 2400 \| 0.0001 \| - \|
	\| 0.7901 \| 2450 \| 0.0001 \| - \|
	\| 0.8062 \| 2500 \| 0.0001 \| - \|
	\| 0.8223 \| 2550 \| 0.0001 \| - \|
	\| 0.8384 \| 2600 \| 0.0 \| - \|
	\| 0.8546 \| 2650 \| 0.0 \| - \|
	\| 0.8707 \| 2700 \| 0.0 \| - \|
	\| 0.8868 \| 2750 \| 0.0001 \| - \|
	\| 0.9029 \| 2800 \| 0.0 \| - \|
	\| 0.9191 \| 2850 \| 0.0001 \| - \|
	\| 0.9352 \| 2900 \| 0.0 \| - \|
	\| 0.9513 \| 2950 \| 0.0 \| - \|
	\| 0.9674 \| 3000 \| 0.0 \| - \|
	\| 0.9836 \| 3050 \| 0.0 \| - \|
	\| 0.9997 \| 3100 \| 0.0 \| - \|
	\| 1.0 \| 3101 \| - \| 0.0247 \|
	\| 1.0158 \| 3150 \| 0.0 \| - \|
	\| 1.0319 \| 3200 \| 0.0 \| - \|
	\| 1.0480 \| 3250 \| 0.0 \| - \|
	\| 1.0642 \| 3300 \| 0.0001 \| - \|
	\| 1.0803 \| 3350 \| 0.0 \| - \|
	\| 1.0964 \| 3400 \| 0.0 \| - \|
	\| 1.1125 \| 3450 \| 0.0 \| - \|
	\| 1.1287 \| 3500 \| 0.0 \| - \|
	\| 1.1448 \| 3550 \| 0.0 \| - \|
	\| 1.1609 \| 3600 \| 0.0 \| - \|
	\| 1.1770 \| 3650 \| 0.0 \| - \|
	\| 1.1932 \| 3700 \| 0.0 \| - \|
	\| 1.2093 \| 3750 \| 0.0 \| - \|
	\| 1.2254 \| 3800 \| 0.0 \| - \|
	\| 1.2415 \| 3850 \| 0.0 \| - \|
	\| 1.2577 \| 3900 \| 0.0 \| - \|
	\| 1.2738 \| 3950 \| 0.0 \| - \|
	\| 1.2899 \| 4000 \| 0.0 \| - \|
	\| 1.3060 \| 4050 \| 0.0 \| - \|
	\| 1.3222 \| 4100 \| 0.0 \| - \|
	\| 1.3383 \| 4150 \| 0.0 \| - \|
	\| 1.3544 \| 4200 \| 0.0 \| - \|
	\| 1.3705 \| 4250 \| 0.0 \| - \|
	\| 1.3866 \| 4300 \| 0.0 \| - \|
	\| 1.4028 \| 4350 \| 0.0 \| - \|
	\| 1.4189 \| 4400 \| 0.0 \| - \|
	\| 1.4350 \| 4450 \| 0.0 \| - \|
	\| 1.4511 \| 4500 \| 0.0 \| - \|
	\| 1.4673 \| 4550 \| 0.0 \| - \|
	\| 1.4834 \| 4600 \| 0.0 \| - \|
	\| 1.4995 \| 4650 \| 0.0 \| - \|
	\| 1.5156 \| 4700 \| 0.0 \| - \|
	\| 1.5318 \| 4750 \| 0.0 \| - \|
	\| 1.5479 \| 4800 \| 0.0 \| - \|
	\| 1.5640 \| 4850 \| 0.0 \| - \|
	\| 1.5801 \| 4900 \| 0.0 \| - \|
	\| 1.5963 \| 4950 \| 0.0 \| - \|
	\| 1.6124 \| 5000 \| 0.0 \| - \|
	\| 1.6285 \| 5050 \| 0.0 \| - \|
	\| 1.6446 \| 5100 \| 0.0 \| - \|
	\| 1.6608 \| 5150 \| 0.0 \| - \|
	\| 1.6769 \| 5200 \| 0.0 \| - \|
	\| 1.6930 \| 5250 \| 0.0 \| - \|
	\| 1.7091 \| 5300 \| 0.0 \| - \|
	\| 1.7252 \| 5350 \| 0.0 \| - \|
	\| 1.7414 \| 5400 \| 0.0 \| - \|
	\| 1.7575 \| 5450 \| 0.0 \| - \|
	\| 1.7736 \| 5500 \| 0.0 \| - \|
	\| 1.7897 \| 5550 \| 0.0 \| - \|
	\| 1.8059 \| 5600 \| 0.0 \| - \|
	\| 1.8220 \| 5650 \| 0.0 \| - \|
	\| 1.8381 \| 5700 \| 0.0 \| - \|
	\| 1.8542 \| 5750 \| 0.0 \| - \|
	\| 1.8704 \| 5800 \| 0.0 \| - \|
	\| 1.8865 \| 5850 \| 0.0 \| - \|
	\| 1.9026 \| 5900 \| 0.0 \| - \|
	\| 1.9187 \| 5950 \| 0.0 \| - \|
	\| 1.9349 \| 6000 \| 0.0 \| - \|
	\| 1.9510 \| 6050 \| 0.0 \| - \|
	\| 1.9671 \| 6100 \| 0.0 \| - \|
	\| 1.9832 \| 6150 \| 0.0 \| - \|
	\| 1.9994 \| 6200 \| 0.0 \| - \|
	\| 2.0 \| 6202 \| - \| 0.0262 \|
	\| 2.0155 \| 6250 \| 0.0 \| - \|
	\| 2.0316 \| 6300 \| 0.0 \| - \|
	\| 2.0477 \| 6350 \| 0.0 \| - \|
	\| 2.0639 \| 6400 \| 0.0 \| - \|
	\| 2.0800 \| 6450 \| 0.0 \| - \|
	\| 2.0961 \| 6500 \| 0.0 \| - \|
	\| 2.1122 \| 6550 \| 0.0 \| - \|
	\| 2.1283 \| 6600 \| 0.0 \| - \|
	\| 2.1445 \| 6650 \| 0.0 \| - \|
	\| 2.1606 \| 6700 \| 0.0 \| - \|
	\| 2.1767 \| 6750 \| 0.0 \| - \|
	\| 2.1928 \| 6800 \| 0.0 \| - \|
	\| 2.2090 \| 6850 \| 0.0 \| - \|
	\| 2.2251 \| 6900 \| 0.0 \| - \|
	\| 2.2412 \| 6950 \| 0.0 \| - \|
	\| 2.2573 \| 7000 \| 0.0 \| - \|
	\| 2.2735 \| 7050 \| 0.0 \| - \|
	\| 2.2896 \| 7100 \| 0.0 \| - \|
	\| 2.3057 \| 7150 \| 0.0 \| - \|
	\| 2.3218 \| 7200 \| 0.0 \| - \|
	\| 2.3380 \| 7250 \| 0.0 \| - \|
	\| 2.3541 \| 7300 \| 0.0 \| - \|
	\| 2.3702 \| 7350 \| 0.0 \| - \|
	\| 2.3863 \| 7400 \| 0.0 \| - \|
	\| 2.4025 \| 7450 \| 0.0 \| - \|
	\| 2.4186 \| 7500 \| 0.0 \| - \|
	\| 2.4347 \| 7550 \| 0.0 \| - \|
	\| 2.4508 \| 7600 \| 0.0 \| - \|
	\| 2.4669 \| 7650 \| 0.0 \| - \|
	\| 2.4831 \| 7700 \| 0.0 \| - \|
	\| 2.4992 \| 7750 \| 0.0 \| - \|
	\| 2.5153 \| 7800 \| 0.0 \| - \|
	\| 2.5314 \| 7850 \| 0.0 \| - \|
	\| 2.5476 \| 7900 \| 0.0 \| - \|
	\| 2.5637 \| 7950 \| 0.0 \| - \|
	\| 2.5798 \| 8000 \| 0.0 \| - \|
	\| 2.5959 \| 8050 \| 0.0 \| - \|
	\| 2.6121 \| 8100 \| 0.0 \| - \|
	\| 2.6282 \| 8150 \| 0.0 \| - \|
	\| 2.6443 \| 8200 \| 0.0 \| - \|
	\| 2.6604 \| 8250 \| 0.0 \| - \|
	\| 2.6766 \| 8300 \| 0.0 \| - \|
	\| 2.6927 \| 8350 \| 0.0 \| - \|
	\| 2.7088 \| 8400 \| 0.0 \| - \|
	\| 2.7249 \| 8450 \| 0.0 \| - \|
	\| 2.7411 \| 8500 \| 0.0 \| - \|
	\| 2.7572 \| 8550 \| 0.0 \| - \|
	\| 2.7733 \| 8600 \| 0.0 \| - \|
	\| 2.7894 \| 8650 \| 0.0 \| - \|
	\| 2.8055 \| 8700 \| 0.0 \| - \|
	\| 2.8217 \| 8750 \| 0.0 \| - \|
	\| 2.8378 \| 8800 \| 0.0 \| - \|
	\| 2.8539 \| 8850 \| 0.0 \| - \|
	\| 2.8700 \| 8900 \| 0.0 \| - \|
	\| 2.8862 \| 8950 \| 0.0 \| - \|
	\| 2.9023 \| 9000 \| 0.0 \| - \|
	\| 2.9184 \| 9050 \| 0.0 \| - \|
	\| 2.9345 \| 9100 \| 0.0 \| - \|
	\| 2.9507 \| 9150 \| 0.0 \| - \|
	\| 2.9668 \| 9200 \| 0.0 \| - \|
	\| 2.9829 \| 9250 \| 0.0 \| - \|
	\| 2.9990 \| 9300 \| 0.0 \| - \|
	\| 3.0 \| 9303 \| - \| 0.025 \|
	\| 3.0152 \| 9350 \| 0.0 \| - \|
	\| 3.0313 \| 9400 \| 0.0 \| - \|
	\| 3.0474 \| 9450 \| 0.0 \| - \|
	\| 3.0635 \| 9500 \| 0.0 \| - \|
	\| 3.0797 \| 9550 \| 0.0 \| - \|
	\| 3.0958 \| 9600 \| 0.0 \| - \|
	\| 3.1119 \| 9650 \| 0.0 \| - \|
	\| 3.1280 \| 9700 \| 0.0 \| - \|
	\| 3.1441 \| 9750 \| 0.0 \| - \|
	\| 3.1603 \| 9800 \| 0.0 \| - \|
	\| 3.1764 \| 9850 \| 0.0 \| - \|
	\| 3.1925 \| 9900 \| 0.0 \| - \|
	\| 3.2086 \| 9950 \| 0.0 \| - \|
	\| 3.2248 \| 10000 \| 0.0 \| - \|
	\| 3.2409 \| 10050 \| 0.0 \| - \|
	\| 3.2570 \| 10100 \| 0.0 \| - \|
	\| 3.2731 \| 10150 \| 0.0 \| - \|
	\| 3.2893 \| 10200 \| 0.0 \| - \|
	\| 3.3054 \| 10250 \| 0.0 \| - \|
	\| 3.3215 \| 10300 \| 0.0 \| - \|
	\| 3.3376 \| 10350 \| 0.0 \| - \|
	\| 3.3538 \| 10400 \| 0.0 \| - \|
	\| 3.3699 \| 10450 \| 0.0 \| - \|
	\| 3.3860 \| 10500 \| 0.0 \| - \|
	\| 3.4021 \| 10550 \| 0.0 \| - \|
	\| 3.4183 \| 10600 \| 0.0 \| - \|
	\| 3.4344 \| 10650 \| 0.0 \| - \|
	\| 3.4505 \| 10700 \| 0.0 \| - \|
	\| 3.4666 \| 10750 \| 0.0083 \| - \|
	\| 3.4827 \| 10800 \| 0.0019 \| - \|
	\| 3.4989 \| 10850 \| 0.0001 \| - \|
	\| 3.5150 \| 10900 \| 0.0 \| - \|
	\| 3.5311 \| 10950 \| 0.001 \| - \|
	\| 3.5472 \| 11000 \| 0.0 \| - \|
	\| 3.5634 \| 11050 \| 0.0 \| - \|
	\| 3.5795 \| 11100 \| 0.0 \| - \|
	\| 3.5956 \| 11150 \| 0.0 \| - \|
	\| 3.6117 \| 11200 \| 0.0 \| - \|
	\| 3.6279 \| 11250 \| 0.0 \| - \|
	\| 3.6440 \| 11300 \| 0.0 \| - \|
	\| 3.6601 \| 11350 \| 0.0 \| - \|
	\| 3.6762 \| 11400 \| 0.0 \| - \|
	\| 3.6924 \| 11450 \| 0.0 \| - \|
	\| 3.7085 \| 11500 \| 0.0 \| - \|
	\| 3.7246 \| 11550 \| 0.0 \| - \|
	\| 3.7407 \| 11600 \| 0.0 \| - \|
	\| 3.7569 \| 11650 \| 0.0 \| - \|
	\| 3.7730 \| 11700 \| 0.0 \| - \|
	\| 3.7891 \| 11750 \| 0.0 \| - \|
	\| 3.8052 \| 11800 \| 0.0 \| - \|
	\| 3.8213 \| 11850 \| 0.0 \| - \|
	\| 3.8375 \| 11900 \| 0.0 \| - \|
	\| 3.8536 \| 11950 \| 0.0 \| - \|
	\| 3.8697 \| 12000 \| 0.0 \| - \|
	\| 3.8858 \| 12050 \| 0.0 \| - \|
	\| 3.9020 \| 12100 \| 0.0 \| - \|
	\| 3.9181 \| 12150 \| 0.0 \| - \|
	\| 3.9342 \| 12200 \| 0.0 \| - \|
	\| 3.9503 \| 12250 \| 0.0 \| - \|
	\| 3.9665 \| 12300 \| 0.0 \| - \|
	\| 3.9826 \| 12350 \| 0.0 \| - \|
	\| 3.9987 \| 12400 \| 0.0 \| - \|
	\| 4.0 \| 12404 \| - \| 0.0253 \|
	\| 4.0148 \| 12450 \| 0.0 \| - \|
	\| 4.0310 \| 12500 \| 0.0 \| - \|
	\| 4.0471 \| 12550 \| 0.0 \| - \|
	\| 4.0632 \| 12600 \| 0.0 \| - \|
	\| 4.0793 \| 12650 \| 0.0 \| - \|
	\| 4.0955 \| 12700 \| 0.0 \| - \|
	\| 4.1116 \| 12750 \| 0.0 \| - \|
	\| 4.1277 \| 12800 \| 0.0 \| - \|
	\| 4.1438 \| 12850 \| 0.0 \| - \|
	\| 4.1599 \| 12900 \| 0.0 \| - \|
	\| 4.1761 \| 12950 \| 0.0 \| - \|
	\| 4.1922 \| 13000 \| 0.0 \| - \|
	\| 4.2083 \| 13050 \| 0.0 \| - \|
	\| 4.2244 \| 13100 \| 0.0 \| - \|
	\| 4.2406 \| 13150 \| 0.0 \| - \|
	\| 4.2567 \| 13200 \| 0.0 \| - \|
	\| 4.2728 \| 13250 \| 0.0 \| - \|
	\| 4.2889 \| 13300 \| 0.0 \| - \|
	\| 4.3051 \| 13350 \| 0.0 \| - \|
	\| 4.3212 \| 13400 \| 0.0 \| - \|
	\| 4.3373 \| 13450 \| 0.0 \| - \|
	\| 4.3534 \| 13500 \| 0.0 \| - \|
	\| 4.3696 \| 13550 \| 0.0 \| - \|
	\| 4.3857 \| 13600 \| 0.0 \| - \|
	\| 4.4018 \| 13650 \| 0.0 \| - \|
	\| 4.4179 \| 13700 \| 0.0 \| - \|
	\| 4.4341 \| 13750 \| 0.0 \| - \|
	\| 4.4502 \| 13800 \| 0.0 \| - \|
	\| 4.4663 \| 13850 \| 0.0 \| - \|
	\| 4.4824 \| 13900 \| 0.0 \| - \|
	\| 4.4985 \| 13950 \| 0.0 \| - \|
	\| 4.5147 \| 14000 \| 0.0 \| - \|
	\| 4.5308 \| 14050 \| 0.0 \| - \|
	\| 4.5469 \| 14100 \| 0.0 \| - \|
	\| 4.5630 \| 14150 \| 0.0 \| - \|
	\| 4.5792 \| 14200 \| 0.0 \| - \|
	\| 4.5953 \| 14250 \| 0.0 \| - \|
	\| 4.6114 \| 14300 \| 0.0 \| - \|
	\| 4.6275 \| 14350 \| 0.0 \| - \|
	\| 4.6437 \| 14400 \| 0.0 \| - \|
	\| 4.6598 \| 14450 \| 0.0 \| - \|
	\| 4.6759 \| 14500 \| 0.0 \| - \|
	\| 4.6920 \| 14550 \| 0.0 \| - \|
	\| 4.7082 \| 14600 \| 0.0 \| - \|
	\| 4.7243 \| 14650 \| 0.0 \| - \|
	\| 4.7404 \| 14700 \| 0.0 \| - \|
	\| 4.7565 \| 14750 \| 0.0 \| - \|
	\| 4.7727 \| 14800 \| 0.0 \| - \|
	\| 4.7888 \| 14850 \| 0.0 \| - \|
	\| 4.8049 \| 14900 \| 0.0 \| - \|
	\| 4.8210 \| 14950 \| 0.0 \| - \|
	\| 4.8371 \| 15000 \| 0.0 \| - \|
	\| 4.8533 \| 15050 \| 0.0 \| - \|
	\| 4.8694 \| 15100 \| 0.0 \| - \|
	\| 4.8855 \| 15150 \| 0.0 \| - \|
	\| 4.9016 \| 15200 \| 0.0 \| - \|
	\| 4.9178 \| 15250 \| 0.0 \| - \|
	\| 4.9339 \| 15300 \| 0.0 \| - \|
	\| 4.9500 \| 15350 \| 0.0 \| - \|
	\| 4.9661 \| 15400 \| 0.0 \| - \|
	\| 4.9823 \| 15450 \| 0.0 \| - \|
	\| 4.9984 \| 15500 \| 0.0 \| - \|
	\| 5.0 \| 15505 \| - \| 0.0259 \|
	\| 5.0145 \| 15550 \| 0.0 \| - \|
	\| 5.0306 \| 15600 \| 0.0 \| - \|
	\| 5.0468 \| 15650 \| 0.0 \| - \|
	\| 5.0629 \| 15700 \| 0.0 \| - \|
	\| 5.0790 \| 15750 \| 0.0 \| - \|
	\| 5.0951 \| 15800 \| 0.0 \| - \|
	\| 5.1113 \| 15850 \| 0.0 \| - \|
	\| 5.1274 \| 15900 \| 0.0 \| - \|
	\| 5.1435 \| 15950 \| 0.0 \| - \|
	\| 5.1596 \| 16000 \| 0.0 \| - \|
	\| 5.1757 \| 16050 \| 0.0 \| - \|
	\| 5.1919 \| 16100 \| 0.0 \| - \|
	\| 5.2080 \| 16150 \| 0.0 \| - \|
	\| 5.2241 \| 16200 \| 0.0 \| - \|
	\| 5.2402 \| 16250 \| 0.0 \| - \|
	\| 5.2564 \| 16300 \| 0.0 \| - \|
	\| 5.2725 \| 16350 \| 0.0 \| - \|
	\| 5.2886 \| 16400 \| 0.0 \| - \|
	\| 5.3047 \| 16450 \| 0.0 \| - \|
	\| 5.3209 \| 16500 \| 0.0 \| - \|
	\| 5.3370 \| 16550 \| 0.0 \| - \|
	\| 5.3531 \| 16600 \| 0.0 \| - \|
	\| 5.3692 \| 16650 \| 0.0 \| - \|
	\| 5.3854 \| 16700 \| 0.0 \| - \|
	\| 5.4015 \| 16750 \| 0.0 \| - \|
	\| 5.4176 \| 16800 \| 0.0 \| - \|
	\| 5.4337 \| 16850 \| 0.0 \| - \|
	\| 5.4499 \| 16900 \| 0.0 \| - \|
	\| 5.4660 \| 16950 \| 0.0 \| - \|
	\| 5.4821 \| 17000 \| 0.0 \| - \|
	\| 5.4982 \| 17050 \| 0.0 \| - \|
	\| 5.5144 \| 17100 \| 0.0 \| - \|
	\| 5.5305 \| 17150 \| 0.0 \| - \|
	\| 5.5466 \| 17200 \| 0.0 \| - \|
	\| 5.5627 \| 17250 \| 0.0 \| - \|
	\| 5.5788 \| 17300 \| 0.0 \| - \|
	\| 5.5950 \| 17350 \| 0.0 \| - \|
	\| 5.6111 \| 17400 \| 0.0 \| - \|
	\| 5.6272 \| 17450 \| 0.0 \| - \|
	\| 5.6433 \| 17500 \| 0.0 \| - \|
	\| 5.6595 \| 17550 \| 0.0 \| - \|
	\| 5.6756 \| 17600 \| 0.0 \| - \|
	\| 5.6917 \| 17650 \| 0.0 \| - \|
	\| 5.7078 \| 17700 \| 0.0 \| - \|
	\| 5.7240 \| 17750 \| 0.0 \| - \|
	\| 5.7401 \| 17800 \| 0.0 \| - \|
	\| 5.7562 \| 17850 \| 0.0 \| - \|
	\| 5.7723 \| 17900 \| 0.0 \| - \|
	\| 5.7885 \| 17950 \| 0.0 \| - \|
	\| 5.8046 \| 18000 \| 0.0 \| - \|
	\| 5.8207 \| 18050 \| 0.0 \| - \|
	\| 5.8368 \| 18100 \| 0.0 \| - \|
	\| 5.8530 \| 18150 \| 0.0 \| - \|
	\| 5.8691 \| 18200 \| 0.0 \| - \|
	\| 5.8852 \| 18250 \| 0.0 \| - \|
	\| 5.9013 \| 18300 \| 0.0 \| - \|
	\| 5.9174 \| 18350 \| 0.0 \| - \|
	\| 5.9336 \| 18400 \| 0.0 \| - \|
	\| 5.9497 \| 18450 \| 0.0 \| - \|
	\| 5.9658 \| 18500 \| 0.0 \| - \|
	\| 5.9819 \| 18550 \| 0.0 \| - \|
	\| 5.9981 \| 18600 \| 0.0 \| - \|
	\| 6.0 \| 18606 \| - \| 0.0255 \|
	\| 6.0142 \| 18650 \| 0.0 \| - \|
	\| 6.0303 \| 18700 \| 0.0 \| - \|
	\| 6.0464 \| 18750 \| 0.0 \| - \|
	\| 6.0626 \| 18800 \| 0.0 \| - \|
	\| 6.0787 \| 18850 \| 0.0 \| - \|
	\| 6.0948 \| 18900 \| 0.0 \| - \|
	\| 6.1109 \| 18950 \| 0.0 \| - \|
	\| 6.1271 \| 19000 \| 0.0 \| - \|
	\| 6.1432 \| 19050 \| 0.0 \| - \|
	\| 6.1593 \| 19100 \| 0.0 \| - \|
	\| 6.1754 \| 19150 \| 0.0 \| - \|
	\| 6.1916 \| 19200 \| 0.0 \| - \|
	\| 6.2077 \| 19250 \| 0.0 \| - \|
	\| 6.2238 \| 19300 \| 0.0 \| - \|
	\| 6.2399 \| 19350 \| 0.0 \| - \|
	\| 6.2560 \| 19400 \| 0.0 \| - \|
	\| 6.2722 \| 19450 \| 0.0 \| - \|
	\| 6.2883 \| 19500 \| 0.0 \| - \|
	\| 6.3044 \| 19550 \| 0.0 \| - \|
	\| 6.3205 \| 19600 \| 0.0 \| - \|
	\| 6.3367 \| 19650 \| 0.0 \| - \|
	\| 6.3528 \| 19700 \| 0.0 \| - \|
	\| 6.3689 \| 19750 \| 0.0 \| - \|
	\| 6.3850 \| 19800 \| 0.0 \| - \|
	\| 6.4012 \| 19850 \| 0.0 \| - \|
	\| 6.4173 \| 19900 \| 0.0 \| - \|
	\| 6.4334 \| 19950 \| 0.0 \| - \|
	\| 6.4495 \| 20000 \| 0.0 \| - \|
	\| 6.4657 \| 20050 \| 0.0 \| - \|
	\| 6.4818 \| 20100 \| 0.0 \| - \|
	\| 6.4979 \| 20150 \| 0.0 \| - \|
	\| 6.5140 \| 20200 \| 0.0 \| - \|
	\| 6.5302 \| 20250 \| 0.0 \| - \|
	\| 6.5463 \| 20300 \| 0.0 \| - \|
	\| 6.5624 \| 20350 \| 0.0 \| - \|
	\| 6.5785 \| 20400 \| 0.0 \| - \|
	\| 6.5946 \| 20450 \| 0.0 \| - \|
	\| 6.6108 \| 20500 \| 0.0 \| - \|
	\| 6.6269 \| 20550 \| 0.0 \| - \|
	\| 6.6430 \| 20600 \| 0.0 \| - \|
	\| 6.6591 \| 20650 \| 0.0 \| - \|
	\| 6.6753 \| 20700 \| 0.0 \| - \|
	\| 6.6914 \| 20750 \| 0.0 \| - \|
	\| 6.7075 \| 20800 \| 0.0 \| - \|
	\| 6.7236 \| 20850 \| 0.0 \| - \|
	\| 6.7398 \| 20900 \| 0.0 \| - \|
	\| 6.7559 \| 20950 \| 0.0 \| - \|
	\| 6.7720 \| 21000 \| 0.0 \| - \|
	\| 6.7881 \| 21050 \| 0.0 \| - \|
	\| 6.8043 \| 21100 \| 0.0 \| - \|
	\| 6.8204 \| 21150 \| 0.0 \| - \|
	\| 6.8365 \| 21200 \| 0.0 \| - \|
	\| 6.8526 \| 21250 \| 0.0 \| - \|
	\| 6.8688 \| 21300 \| 0.0 \| - \|
	\| 6.8849 \| 21350 \| 0.0 \| - \|
	\| 6.9010 \| 21400 \| 0.0 \| - \|
	\| 6.9171 \| 21450 \| 0.0 \| - \|
	\| 6.9332 \| 21500 \| 0.0 \| - \|
	\| 6.9494 \| 21550 \| 0.0 \| - \|
	\| 6.9655 \| 21600 \| 0.0 \| - \|
	\| 6.9816 \| 21650 \| 0.0 \| - \|
	\| 6.9977 \| 21700 \| 0.0 \| - \|
	\| 7.0 \| 21707 \| - \| 0.0264 \|
	\| 7.0139 \| 21750 \| 0.0 \| - \|
	\| 7.0300 \| 21800 \| 0.0 \| - \|
	\| 7.0461 \| 21850 \| 0.0 \| - \|
	\| 7.0622 \| 21900 \| 0.0 \| - \|
	\| 7.0784 \| 21950 \| 0.0 \| - \|
	\| 7.0945 \| 22000 \| 0.0 \| - \|
	\| 7.1106 \| 22050 \| 0.0 \| - \|
	\| 7.1267 \| 22100 \| 0.0 \| - \|
	\| 7.1429 \| 22150 \| 0.0 \| - \|
	\| 7.1590 \| 22200 \| 0.0 \| - \|
	\| 7.1751 \| 22250 \| 0.0 \| - \|
	\| 7.1912 \| 22300 \| 0.0 \| - \|
	\| 7.2074 \| 22350 \| 0.0 \| - \|
	\| 7.2235 \| 22400 \| 0.0 \| - \|
	\| 7.2396 \| 22450 \| 0.0 \| - \|
	\| 7.2557 \| 22500 \| 0.0 \| - \|
	\| 7.2718 \| 22550 \| 0.0 \| - \|
	\| 7.2880 \| 22600 \| 0.0 \| - \|
	\| 7.3041 \| 22650 \| 0.0 \| - \|
	\| 7.3202 \| 22700 \| 0.0 \| - \|
	\| 7.3363 \| 22750 \| 0.0 \| - \|
	\| 7.3525 \| 22800 \| 0.0 \| - \|
	\| 7.3686 \| 22850 \| 0.0 \| - \|
	\| 7.3847 \| 22900 \| 0.0 \| - \|
	\| 7.4008 \| 22950 \| 0.0 \| - \|
	\| 7.4170 \| 23000 \| 0.0 \| - \|
	\| 7.4331 \| 23050 \| 0.0 \| - \|
	\| 7.4492 \| 23100 \| 0.0 \| - \|
	\| 7.4653 \| 23150 \| 0.0 \| - \|
	\| 7.4815 \| 23200 \| 0.0 \| - \|
	\| 7.4976 \| 23250 \| 0.0 \| - \|
	\| 7.5137 \| 23300 \| 0.0 \| - \|
	\| 7.5298 \| 23350 \| 0.0 \| - \|
	\| 7.5460 \| 23400 \| 0.0 \| - \|
	\| 7.5621 \| 23450 \| 0.0 \| - \|
	\| 7.5782 \| 23500 \| 0.0 \| - \|
	\| 7.5943 \| 23550 \| 0.0 \| - \|
	\| 7.6104 \| 23600 \| 0.0 \| - \|
	\| 7.6266 \| 23650 \| 0.0 \| - \|
	\| 7.6427 \| 23700 \| 0.0 \| - \|
	\| 7.6588 \| 23750 \| 0.0 \| - \|
	\| 7.6749 \| 23800 \| 0.0 \| - \|
	\| 7.6911 \| 23850 \| 0.0 \| - \|
	\| 7.7072 \| 23900 \| 0.0 \| - \|
	\| 7.7233 \| 23950 \| 0.0 \| - \|
	\| 7.7394 \| 24000 \| 0.0 \| - \|
	\| 7.7556 \| 24050 \| 0.0 \| - \|
	\| 7.7717 \| 24100 \| 0.0 \| - \|
	\| 7.7878 \| 24150 \| 0.0 \| - \|
	\| 7.8039 \| 24200 \| 0.0 \| - \|
	\| 7.8201 \| 24250 \| 0.0 \| - \|
	\| 7.8362 \| 24300 \| 0.0 \| - \|
	\| 7.8523 \| 24350 \| 0.0 \| - \|
	\| 7.8684 \| 24400 \| 0.0 \| - \|
	\| 7.8846 \| 24450 \| 0.0 \| - \|
	\| 7.9007 \| 24500 \| 0.0 \| - \|
	\| 7.9168 \| 24550 \| 0.0 \| - \|
	\| 7.9329 \| 24600 \| 0.0 \| - \|
	\| 7.9490 \| 24650 \| 0.0 \| - \|
	\| 7.9652 \| 24700 \| 0.0 \| - \|
	\| 7.9813 \| 24750 \| 0.0 \| - \|
	\| 7.9974 \| 24800 \| 0.0 \| - \|
	\| 8.0 \| 24808 \| - \| 0.0252 \|
	\| 8.0135 \| 24850 \| 0.0 \| - \|
	\| 8.0297 \| 24900 \| 0.0 \| - \|
	\| 8.0458 \| 24950 \| 0.0 \| - \|
	\| 8.0619 \| 25000 \| 0.0 \| - \|
	\| 8.0780 \| 25050 \| 0.0 \| - \|
	\| 8.0942 \| 25100 \| 0.0 \| - \|
	\| 8.1103 \| 25150 \| 0.0 \| - \|
	\| 8.1264 \| 25200 \| 0.0 \| - \|
	\| 8.1425 \| 25250 \| 0.0 \| - \|
	\| 8.1587 \| 25300 \| 0.0 \| - \|
	\| 8.1748 \| 25350 \| 0.0 \| - \|
	\| 8.1909 \| 25400 \| 0.0 \| - \|
	\| 8.2070 \| 25450 \| 0.0 \| - \|
	\| 8.2232 \| 25500 \| 0.0 \| - \|
	\| 8.2393 \| 25550 \| 0.0 \| - \|
	\| 8.2554 \| 25600 \| 0.0 \| - \|
	\| 8.2715 \| 25650 \| 0.0 \| - \|
	\| 8.2876 \| 25700 \| 0.0 \| - \|
	\| 8.3038 \| 25750 \| 0.0 \| - \|
	\| 8.3199 \| 25800 \| 0.0 \| - \|
	\| 8.3360 \| 25850 \| 0.0 \| - \|
	\| 8.3521 \| 25900 \| 0.0 \| - \|
	\| 8.3683 \| 25950 \| 0.0 \| - \|
	\| 8.3844 \| 26000 \| 0.0 \| - \|
	\| 8.4005 \| 26050 \| 0.0 \| - \|
	\| 8.4166 \| 26100 \| 0.0 \| - \|
	\| 8.4328 \| 26150 \| 0.0 \| - \|
	\| 8.4489 \| 26200 \| 0.0 \| - \|
	\| 8.4650 \| 26250 \| 0.0 \| - \|
	\| 8.4811 \| 26300 \| 0.0 \| - \|
	\| 8.4973 \| 26350 \| 0.0 \| - \|
	\| 8.5134 \| 26400 \| 0.0 \| - \|
	\| 8.5295 \| 26450 \| 0.0 \| - \|
	\| 8.5456 \| 26500 \| 0.0 \| - \|
	\| 8.5618 \| 26550 \| 0.0 \| - \|
	\| 8.5779 \| 26600 \| 0.0 \| - \|
	\| 8.5940 \| 26650 \| 0.0 \| - \|
	\| 8.6101 \| 26700 \| 0.0 \| - \|
	\| 8.6262 \| 26750 \| 0.0 \| - \|
	\| 8.6424 \| 26800 \| 0.0 \| - \|
	\| 8.6585 \| 26850 \| 0.0 \| - \|
	\| 8.6746 \| 26900 \| 0.0 \| - \|
	\| 8.6907 \| 26950 \| 0.0 \| - \|
	\| 8.7069 \| 27000 \| 0.0 \| - \|
	\| 8.7230 \| 27050 \| 0.0 \| - \|
	\| 8.7391 \| 27100 \| 0.0 \| - \|
	\| 8.7552 \| 27150 \| 0.0 \| - \|
	\| 8.7714 \| 27200 \| 0.0 \| - \|
	\| 8.7875 \| 27250 \| 0.0 \| - \|
	\| 8.8036 \| 27300 \| 0.0 \| - \|
	\| 8.8197 \| 27350 \| 0.0 \| - \|
	\| 8.8359 \| 27400 \| 0.0 \| - \|
	\| 8.8520 \| 27450 \| 0.0 \| - \|
	\| 8.8681 \| 27500 \| 0.0 \| - \|
	\| 8.8842 \| 27550 \| 0.0 \| - \|
	\| 8.9004 \| 27600 \| 0.0 \| - \|
	\| 8.9165 \| 27650 \| 0.0 \| - \|
	\| 8.9326 \| 27700 \| 0.0 \| - \|
	\| 8.9487 \| 27750 \| 0.0 \| - \|
	\| 8.9649 \| 27800 \| 0.0 \| - \|
	\| 8.9810 \| 27850 \| 0.0 \| - \|
	\| 8.9971 \| 27900 \| 0.0 \| - \|
	\| 9.0 \| 27909 \| - \| 0.0255 \|
	\| 9.0132 \| 27950 \| 0.0 \| - \|
	\| 9.0293 \| 28000 \| 0.0 \| - \|
	\| 9.0455 \| 28050 \| 0.0 \| - \|
	\| 9.0616 \| 28100 \| 0.0 \| - \|
	\| 9.0777 \| 28150 \| 0.0 \| - \|
	\| 9.0938 \| 28200 \| 0.0 \| - \|
	\| 9.1100 \| 28250 \| 0.0 \| - \|
	\| 9.1261 \| 28300 \| 0.0 \| - \|
	\| 9.1422 \| 28350 \| 0.0 \| - \|
	\| 9.1583 \| 28400 \| 0.0 \| - \|
	\| 9.1745 \| 28450 \| 0.0 \| - \|
	\| 9.1906 \| 28500 \| 0.0 \| - \|
	\| 9.2067 \| 28550 \| 0.0 \| - \|
	\| 9.2228 \| 28600 \| 0.0 \| - \|
	\| 9.2390 \| 28650 \| 0.0 \| - \|
	\| 9.2551 \| 28700 \| 0.0 \| - \|
	\| 9.2712 \| 28750 \| 0.0 \| - \|
	\| 9.2873 \| 28800 \| 0.0 \| - \|
	\| 9.3035 \| 28850 \| 0.0 \| - \|
	\| 9.3196 \| 28900 \| 0.0 \| - \|
	\| 9.3357 \| 28950 \| 0.0 \| - \|
	\| 9.3518 \| 29000 \| 0.0 \| - \|
	\| 9.3679 \| 29050 \| 0.0 \| - \|
	\| 9.3841 \| 29100 \| 0.0 \| - \|
	\| 9.4002 \| 29150 \| 0.0 \| - \|
	\| 9.4163 \| 29200 \| 0.0 \| - \|
	\| 9.4324 \| 29250 \| 0.0 \| - \|
	\| 9.4486 \| 29300 \| 0.0 \| - \|
	\| 9.4647 \| 29350 \| 0.0 \| - \|
	\| 9.4808 \| 29400 \| 0.0 \| - \|
	\| 9.4969 \| 29450 \| 0.0 \| - \|
	\| 9.5131 \| 29500 \| 0.0 \| - \|
	\| 9.5292 \| 29550 \| 0.0 \| - \|
	\| 9.5453 \| 29600 \| 0.0 \| - \|
	\| 9.5614 \| 29650 \| 0.0 \| - \|
	\| 9.5776 \| 29700 \| 0.0 \| - \|
	\| 9.5937 \| 29750 \| 0.0 \| - \|
	\| 9.6098 \| 29800 \| 0.0 \| - \|
	\| 9.6259 \| 29850 \| 0.0 \| - \|
	\| 9.6421 \| 29900 \| 0.0 \| - \|
	\| 9.6582 \| 29950 \| 0.0 \| - \|
	\| 9.6743 \| 30000 \| 0.0 \| - \|
	\| 9.6904 \| 30050 \| 0.0 \| - \|
	\| 9.7065 \| 30100 \| 0.0 \| - \|
	\| 9.7227 \| 30150 \| 0.0 \| - \|
	\| 9.7388 \| 30200 \| 0.0 \| - \|
	\| 9.7549 \| 30250 \| 0.0 \| - \|
	\| 9.7710 \| 30300 \| 0.0 \| - \|
	\| 9.7872 \| 30350 \| 0.0 \| - \|
	\| 9.8033 \| 30400 \| 0.0 \| - \|
	\| 9.8194 \| 30450 \| 0.0 \| - \|
	\| 9.8355 \| 30500 \| 0.0 \| - \|
	\| 9.8517 \| 30550 \| 0.0 \| - \|
	\| 9.8678 \| 30600 \| 0.0 \| - \|
	\| 9.8839 \| 30650 \| 0.0 \| - \|
	\| 9.9000 \| 30700 \| 0.0 \| - \|
	\| 9.9162 \| 30750 \| 0.0 \| - \|
	\| 9.9323 \| 30800 \| 0.0 \| - \|
	\| 9.9484 \| 30850 \| 0.0 \| - \|
	\| 9.9645 \| 30900 \| 0.0 \| - \|
	\| 9.9807 \| 30950 \| 0.0 \| - \|
	\| 9.9968 \| 31000 \| 0.0 \| - \|
	\| 10.0 \| 31010 \| - \| 0.0264 \|

	* The bold row denotes the saved checkpoint.
	### Framework Versions
	- Python: 3.9.18
	- SetFit: 1.0.3
	- Sentence Transformers: 2.2.1
	- Transformers: 4.32.1
	- PyTorch: 1.10.0
	- Datasets: 2.20.0
	- Tokenizers: 0.13.3

	## 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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