---
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: 노트북 > msi > 블루라이트차단
- text: 해외직구 > 건강식품 > 칼슘
- text: 출산 / 육아용품 > 침구 / 수면용품 > 이불 / 담요
- text: 생활가전 > 청소기 > 핸디청소기
- text: 생활 > 건강 / 안마용품 > 온열 / 찜질용품 > 냉온주머니 / 핫팩
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: 0.9797794117647058
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:** 18 classes
### 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 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 10 | - '자동차용품 > 차량용전자기기 > 차량용가전 > 기타가전'
- '금호타이어 > 마제스티9ta91 > 19인치'
- '타이어 > 금호타이어 > 마제스티9ta91'
|
| 7 | - '전동레저 / 인라인 / 킥보드 > 인라인용품 > 인라인바퀴'
- '프리모리 > 캠핑가방'
- 'ssgcom > 자전거 > 스케이트 > 롤러 > 자전거잡화 > 기타자전거잡화'
|
| 4 | - '강아지사료 > 건식사료수입산'
- '사료샘플'
- '펫상품 > 펫9단'
|
| 3 | - '문구 / 오피스 > 사무용품전문관 > 사무용가구 / 수납 > 데스크정리소품 > 모니터받침대'
- '완구취미 > 보드게임 > 학습카드게임'
- 'ssgcom > 문구 > 미술용품 > 피규어 > 미술 > 제도용품 > 미술 > 화방 > 조소용품 > 구성 > 디자인'
|
| 11 | - '세탁기건조기세트 > 건조기키트'
- '가전컴퓨터 > 모니터 > 모니터 > 일반모니터'
- 'ssgcom > 세탁기 > 생활가전 > 청소기 > 청소기필터 > 액세서리'
|
| 12 | - '그립톡젤리'
- 'xbox액세서리 > 기타'
- '카메라렌즈조명악세서리 > zhiyun지윤텍'
|
| 8 | - '건강식품 > 혈행 / 눈건강 / 간건강 > 밀크씨슬'
- 'jardin1984스마트스토어 > 브랜드관'
- '식품 > 면 / 통조림 / 가공식품 > 즉석밥 / 간편조리 > 기타즉석식품'
|
| 5 | - '바디케어 > 바디워시 > 바디클렌저'
- '스킨케어 > 팩 / 마스크 > 슬리핑팩'
- 'ssgcom > 메이크업 > 치크메이크업 > 하이라이터'
|
| 6 | - 'ssgcom > 주방용품 > 냄비 / 솥 / 주전자 > 돌솥 / 가마솥'
- '생활 / 건강 > 생활용품 > 주방 / 청소세제 > 유리세정제'
- '생활용품 > 공구 / 철물 / diy > 전동 / 정밀공구 > 전기톱 / 직소 > 리벤토'
|
| 15 | - '남성패션 > 맨투맨 / 후드 / 티셔츠 > 반팔티셔츠'
- '여성커리어 > 팬츠 > 데님'
- '남성패션 > 팬츠 > 데님'
|
| 16 | - '브랜드패션 > 여성신발'
- 'ssgcom > 가방 > 지갑 > 캐주얼가방 > 토트백'
- '남성패션 > 브랜드신발'
|
| 1 | - '헬스 / 건강식품 > 건강 / 의료용품 > 자세교정 / 보호대 > 바른자세용품'
- '헬스 / 건강식품 > 건강 / 의료용품 > 보호대 / 교정용품 > 건강보호대'
- '헬스 / 건강식품 > 건강 / 의료용품 > 눈건강 / 렌즈관리 > 렌즈관리용품'
|
| 14 | - 'ssgcom > 유모차 > 실내용품 > 침구 > 수면용품 > 방수요 > 패드 > 매트'
- 'ssgcom > 유아동신발 / 잡화 > 신발 > 샌들'
- '유아동 > 출산 / 육아용품 > 유아전용세제 > 유아세탁세제'
|
| 2 | - 'ssgcom > 도서 > 국내도서 > 여행 > 취미 > 레저 > 악기 > 레저 > 스포츠'
- '도서 / 음반 / dvd > 해외도서 > 취미 / 실용 / 스포츠 > 스포츠 / 아웃도어 > 개인스포츠'
- 'ssgcom > 도서 > 국내도서 > 잡지 > 잡지기타'
|
| 17 | - 'tv쇼핑 > 가구 / 인테리어'
- '생활잡화패션 > 인테리어소품'
- '책상desk'
|
| 13 | - '전자담배기기 > 가변모드기기'
- '전자담배기기 > 입호흡mtl'
- 'lilstore스마트스토어'
|
| 9 | - 'ssgcom > 여행 > 해외패키지 > 중국 / 홍콩 / 하이난'
- 'ssgcom > 여행 > 호텔 / 리조트 / 펜션 > 국내호텔 / 리조트'
- 'ssgcom > 여행 > 해외패키지 > 유럽'
|
| 0 | - '여행 / 렌탈 / 금융 > 여행 / 숙박 / 항공권'
- '여행 / 렌탈 / 금융 > 상품권 / 이용권'
- 'ssgcom > 여행 > 내륙여행 / 입장권 > 워터파크 / 스키'
|
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.9798 |
## 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("setfit_model_id")
# Run inference
preds = model("해외직구 > 건강식품 > 칼슘")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 7.8919 | 45 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 52 |
| 1 | 422 |
| 2 | 377 |
| 3 | 535 |
| 4 | 4826 |
| 5 | 4085 |
| 6 | 3868 |
| 7 | 3223 |
| 8 | 3998 |
| 9 | 19 |
| 10 | 887 |
| 11 | 22087 |
| 12 | 2307 |
| 13 | 113 |
| 14 | 1409 |
| 15 | 2267 |
| 16 | 2404 |
| 17 | 929 |
### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (10, 10)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0002 | 1 | 0.2773 | - |
| 0.0119 | 50 | 0.2679 | - |
| 0.0238 | 100 | 0.2132 | - |
| 0.0357 | 150 | 0.1508 | - |
| 0.0476 | 200 | 0.1032 | - |
| 0.0595 | 250 | 0.0765 | - |
| 0.0714 | 300 | 0.0692 | - |
| 0.0833 | 350 | 0.0675 | - |
| 0.0951 | 400 | 0.05 | - |
| 0.1070 | 450 | 0.0564 | - |
| 0.1189 | 500 | 0.0408 | - |
| 0.1308 | 550 | 0.0309 | - |
| 0.1427 | 600 | 0.029 | - |
| 0.1546 | 650 | 0.0268 | - |
| 0.1665 | 700 | 0.0357 | - |
| 0.1784 | 750 | 0.0295 | - |
| 0.1903 | 800 | 0.0242 | - |
| 0.2022 | 850 | 0.026 | - |
| 0.2141 | 900 | 0.0225 | - |
| 0.2260 | 950 | 0.0266 | - |
| 0.2379 | 1000 | 0.0193 | - |
| 0.2498 | 1050 | 0.0179 | - |
| 0.2617 | 1100 | 0.0208 | - |
| 0.2735 | 1150 | 0.0238 | - |
| 0.2854 | 1200 | 0.0196 | - |
| 0.2973 | 1250 | 0.0126 | - |
| 0.3092 | 1300 | 0.0194 | - |
| 0.3211 | 1350 | 0.0124 | - |
| 0.3330 | 1400 | 0.0175 | - |
| 0.3449 | 1450 | 0.0163 | - |
| 0.3568 | 1500 | 0.0097 | - |
| 0.3687 | 1550 | 0.0083 | - |
| 0.3806 | 1600 | 0.0192 | - |
| 0.3925 | 1650 | 0.0078 | - |
| 0.4044 | 1700 | 0.012 | - |
| 0.4163 | 1750 | 0.0087 | - |
| 0.4282 | 1800 | 0.0123 | - |
| 0.4401 | 1850 | 0.0149 | - |
| 0.4520 | 1900 | 0.0113 | - |
| 0.4638 | 1950 | 0.0102 | - |
| 0.4757 | 2000 | 0.0075 | - |
| 0.4876 | 2050 | 0.0049 | - |
| 0.4995 | 2100 | 0.0132 | - |
| 0.5114 | 2150 | 0.0044 | - |
| 0.5233 | 2200 | 0.0061 | - |
| 0.5352 | 2250 | 0.0088 | - |
| 0.5471 | 2300 | 0.0103 | - |
| 0.5590 | 2350 | 0.0107 | - |
| 0.5709 | 2400 | 0.0111 | - |
| 0.5828 | 2450 | 0.0119 | - |
| 0.5947 | 2500 | 0.0044 | - |
| 0.6066 | 2550 | 0.0105 | - |
| 0.6185 | 2600 | 0.0056 | - |
| 0.6304 | 2650 | 0.0089 | - |
| 0.6422 | 2700 | 0.0062 | - |
| 0.6541 | 2750 | 0.0099 | - |
| 0.6660 | 2800 | 0.0047 | - |
| 0.6779 | 2850 | 0.015 | - |
| 0.6898 | 2900 | 0.0034 | - |
| 0.7017 | 2950 | 0.0061 | - |
| 0.7136 | 3000 | 0.0077 | - |
| 0.7255 | 3050 | 0.0097 | - |
| 0.7374 | 3100 | 0.0071 | - |
| 0.7493 | 3150 | 0.0062 | - |
| 0.7612 | 3200 | 0.0157 | - |
| 0.7731 | 3250 | 0.0026 | - |
| 0.7850 | 3300 | 0.0048 | - |
| 0.7969 | 3350 | 0.0039 | - |
| 0.8088 | 3400 | 0.0088 | - |
| 0.8206 | 3450 | 0.0011 | - |
| 0.8325 | 3500 | 0.0034 | - |
| 0.8444 | 3550 | 0.0031 | - |
| 0.8563 | 3600 | 0.0033 | - |
| 0.8682 | 3650 | 0.0117 | - |
| 0.8801 | 3700 | 0.0073 | - |
| 0.8920 | 3750 | 0.0047 | - |
| 0.9039 | 3800 | 0.0008 | - |
| 0.9158 | 3850 | 0.0062 | - |
| 0.9277 | 3900 | 0.0032 | - |
| 0.9396 | 3950 | 0.0033 | - |
| 0.9515 | 4000 | 0.0081 | - |
| 0.9634 | 4050 | 0.0123 | - |
| 0.9753 | 4100 | 0.0025 | - |
| 0.9872 | 4150 | 0.0078 | - |
| 0.9990 | 4200 | 0.0047 | - |
| 1.0109 | 4250 | 0.0027 | - |
| 1.0228 | 4300 | 0.0052 | - |
| 1.0347 | 4350 | 0.0064 | - |
| 1.0466 | 4400 | 0.0092 | - |
| 1.0585 | 4450 | 0.0034 | - |
| 1.0704 | 4500 | 0.0046 | - |
| 1.0823 | 4550 | 0.0071 | - |
| 1.0942 | 4600 | 0.0061 | - |
| 1.1061 | 4650 | 0.0043 | - |
| 1.1180 | 4700 | 0.0052 | - |
| 1.1299 | 4750 | 0.0029 | - |
| 1.1418 | 4800 | 0.001 | - |
| 1.1537 | 4850 | 0.0053 | - |
| 1.1656 | 4900 | 0.0029 | - |
| 1.1775 | 4950 | 0.0003 | - |
| 1.1893 | 5000 | 0.0012 | - |
| 1.2012 | 5050 | 0.0014 | - |
| 1.2131 | 5100 | 0.0021 | - |
| 1.2250 | 5150 | 0.0024 | - |
| 1.2369 | 5200 | 0.0015 | - |
| 1.2488 | 5250 | 0.0057 | - |
| 1.2607 | 5300 | 0.0037 | - |
| 1.2726 | 5350 | 0.0088 | - |
| 1.2845 | 5400 | 0.01 | - |
| 1.2964 | 5450 | 0.0059 | - |
| 1.3083 | 5500 | 0.0016 | - |
| 1.3202 | 5550 | 0.004 | - |
| 1.3321 | 5600 | 0.0022 | - |
| 1.3440 | 5650 | 0.0044 | - |
| 1.3559 | 5700 | 0.0084 | - |
| 1.3677 | 5750 | 0.0046 | - |
| 1.3796 | 5800 | 0.0043 | - |
| 1.3915 | 5850 | 0.0044 | - |
| 1.4034 | 5900 | 0.0051 | - |
| 1.4153 | 5950 | 0.0051 | - |
| 1.4272 | 6000 | 0.0048 | - |
| 1.4391 | 6050 | 0.0021 | - |
| 1.4510 | 6100 | 0.0041 | - |
| 1.4629 | 6150 | 0.0047 | - |
| 1.4748 | 6200 | 0.0048 | - |
| 1.4867 | 6250 | 0.0019 | - |
| 1.4986 | 6300 | 0.005 | - |
| 1.5105 | 6350 | 0.0001 | - |
| 1.5224 | 6400 | 0.0004 | - |
| 1.5343 | 6450 | 0.0012 | - |
| 1.5461 | 6500 | 0.0003 | - |
| 1.5580 | 6550 | 0.0042 | - |
| 1.5699 | 6600 | 0.0022 | - |
| 1.5818 | 6650 | 0.0021 | - |
| 1.5937 | 6700 | 0.0014 | - |
| 1.6056 | 6750 | 0.0002 | - |
| 1.6175 | 6800 | 0.0014 | - |
| 1.6294 | 6850 | 0.0057 | - |
| 1.6413 | 6900 | 0.0023 | - |
| 1.6532 | 6950 | 0.0024 | - |
| 1.6651 | 7000 | 0.0028 | - |
| 1.6770 | 7050 | 0.0017 | - |
| 1.6889 | 7100 | 0.0056 | - |
| 1.7008 | 7150 | 0.0003 | - |
| 1.7127 | 7200 | 0.0006 | - |
| 1.7245 | 7250 | 0.0055 | - |
| 1.7364 | 7300 | 0.0001 | - |
| 1.7483 | 7350 | 0.0071 | - |
| 1.7602 | 7400 | 0.0013 | - |
| 1.7721 | 7450 | 0.0021 | - |
| 1.7840 | 7500 | 0.0022 | - |
| 1.7959 | 7550 | 0.001 | - |
| 1.8078 | 7600 | 0.0075 | - |
| 1.8197 | 7650 | 0.0003 | - |
| 1.8316 | 7700 | 0.0004 | - |
| 1.8435 | 7750 | 0.0004 | - |
| 1.8554 | 7800 | 0.0023 | - |
| 1.8673 | 7850 | 0.0032 | - |
| 1.8792 | 7900 | 0.0021 | - |
| 1.8911 | 7950 | 0.0028 | - |
| 1.9029 | 8000 | 0.0031 | - |
| 1.9148 | 8050 | 0.002 | - |
| 1.9267 | 8100 | 0.0041 | - |
| 1.9386 | 8150 | 0.0027 | - |
| 1.9505 | 8200 | 0.0003 | - |
| 1.9624 | 8250 | 0.0062 | - |
| 1.9743 | 8300 | 0.0005 | - |
| 1.9862 | 8350 | 0.0044 | - |
| 1.9981 | 8400 | 0.0016 | - |
| 2.0100 | 8450 | 0.0002 | - |
| 2.0219 | 8500 | 0.0003 | - |
| 2.0338 | 8550 | 0.0021 | - |
| 2.0457 | 8600 | 0.0027 | - |
| 2.0576 | 8650 | 0.001 | - |
| 2.0695 | 8700 | 0.0004 | - |
| 2.0814 | 8750 | 0.0027 | - |
| 2.0932 | 8800 | 0.0003 | - |
| 2.1051 | 8850 | 0.0015 | - |
| 2.1170 | 8900 | 0.002 | - |
| 2.1289 | 8950 | 0.0005 | - |
| 2.1408 | 9000 | 0.0067 | - |
| 2.1527 | 9050 | 0.001 | - |
| 2.1646 | 9100 | 0.0024 | - |
| 2.1765 | 9150 | 0.0004 | - |
| 2.1884 | 9200 | 0.0038 | - |
| 2.2003 | 9250 | 0.0001 | - |
| 2.2122 | 9300 | 0.0048 | - |
| 2.2241 | 9350 | 0.0021 | - |
| 2.2360 | 9400 | 0.0031 | - |
| 2.2479 | 9450 | 0.0024 | - |
| 2.2598 | 9500 | 0.0006 | - |
| 2.2716 | 9550 | 0.007 | - |
| 2.2835 | 9600 | 0.0001 | - |
| 2.2954 | 9650 | 0.0018 | - |
| 2.3073 | 9700 | 0.0013 | - |
| 2.3192 | 9750 | 0.0059 | - |
| 2.3311 | 9800 | 0.0012 | - |
| 2.3430 | 9850 | 0.0028 | - |
| 2.3549 | 9900 | 0.0025 | - |
| 2.3668 | 9950 | 0.0006 | - |
| 2.3787 | 10000 | 0.0005 | - |
| 2.3906 | 10050 | 0.0001 | - |
| 2.4025 | 10100 | 0.0002 | - |
| 2.4144 | 10150 | 0.0009 | - |
| 2.4263 | 10200 | 0.0004 | - |
| 2.4382 | 10250 | 0.001 | - |
| 2.4500 | 10300 | 0.0003 | - |
| 2.4619 | 10350 | 0.0003 | - |
| 2.4738 | 10400 | 0.0026 | - |
| 2.4857 | 10450 | 0.0002 | - |
| 2.4976 | 10500 | 0.0045 | - |
| 2.5095 | 10550 | 0.0017 | - |
| 2.5214 | 10600 | 0.0002 | - |
| 2.5333 | 10650 | 0.0018 | - |
| 2.5452 | 10700 | 0.0001 | - |
| 2.5571 | 10750 | 0.0023 | - |
| 2.5690 | 10800 | 0.0013 | - |
| 2.5809 | 10850 | 0.0022 | - |
| 2.5928 | 10900 | 0.0036 | - |
| 2.6047 | 10950 | 0.0012 | - |
| 2.6166 | 11000 | 0.0028 | - |
| 2.6284 | 11050 | 0.0019 | - |
| 2.6403 | 11100 | 0.0001 | - |
| 2.6522 | 11150 | 0.0044 | - |
| 2.6641 | 11200 | 0.0012 | - |
| 2.6760 | 11250 | 0.0013 | - |
| 2.6879 | 11300 | 0.0001 | - |
| 2.6998 | 11350 | 0.0016 | - |
| 2.7117 | 11400 | 0.0037 | - |
| 2.7236 | 11450 | 0.0003 | - |
| 2.7355 | 11500 | 0.0004 | - |
| 2.7474 | 11550 | 0.0055 | - |
| 2.7593 | 11600 | 0.0002 | - |
| 2.7712 | 11650 | 0.0001 | - |
| 2.7831 | 11700 | 0.0006 | - |
| 2.7950 | 11750 | 0.0061 | - |
| 2.8069 | 11800 | 0.0007 | - |
| 2.8187 | 11850 | 0.0027 | - |
| 2.8306 | 11900 | 0.0022 | - |
| 2.8425 | 11950 | 0.0002 | - |
| 2.8544 | 12000 | 0.0022 | - |
| 2.8663 | 12050 | 0.0015 | - |
| 2.8782 | 12100 | 0.0003 | - |
| 2.8901 | 12150 | 0.001 | - |
| 2.9020 | 12200 | 0.0014 | - |
| 2.9139 | 12250 | 0.0001 | - |
| 2.9258 | 12300 | 0.0009 | - |
| 2.9377 | 12350 | 0.0007 | - |
| 2.9496 | 12400 | 0.0005 | - |
| 2.9615 | 12450 | 0.0004 | - |
| 2.9734 | 12500 | 0.0004 | - |
| 2.9853 | 12550 | 0.0026 | - |
| 2.9971 | 12600 | 0.0011 | - |
| 3.0090 | 12650 | 0.0019 | - |
| 3.0209 | 12700 | 0.0 | - |
| 3.0328 | 12750 | 0.0004 | - |
| 3.0447 | 12800 | 0.0004 | - |
| 3.0566 | 12850 | 0.0001 | - |
| 3.0685 | 12900 | 0.0003 | - |
| 3.0804 | 12950 | 0.0003 | - |
| 3.0923 | 13000 | 0.0015 | - |
| 3.1042 | 13050 | 0.0018 | - |
| 3.1161 | 13100 | 0.002 | - |
| 3.1280 | 13150 | 0.0018 | - |
| 3.1399 | 13200 | 0.0002 | - |
| 3.1518 | 13250 | 0.0003 | - |
| 3.1637 | 13300 | 0.0007 | - |
| 3.1755 | 13350 | 0.0002 | - |
| 3.1874 | 13400 | 0.0014 | - |
| 3.1993 | 13450 | 0.0026 | - |
| 3.2112 | 13500 | 0.0005 | - |
| 3.2231 | 13550 | 0.0015 | - |
| 3.2350 | 13600 | 0.0012 | - |
| 3.2469 | 13650 | 0.0029 | - |
| 3.2588 | 13700 | 0.0001 | - |
| 3.2707 | 13750 | 0.0001 | - |
| 3.2826 | 13800 | 0.0013 | - |
| 3.2945 | 13850 | 0.0021 | - |
| 3.3064 | 13900 | 0.0002 | - |
| 3.3183 | 13950 | 0.0014 | - |
| 3.3302 | 14000 | 0.0021 | - |
| 3.3421 | 14050 | 0.0011 | - |
| 3.3539 | 14100 | 0.0007 | - |
| 3.3658 | 14150 | 0.0015 | - |
| 3.3777 | 14200 | 0.0022 | - |
| 3.3896 | 14250 | 0.0 | - |
| 3.4015 | 14300 | 0.0008 | - |
| 3.4134 | 14350 | 0.0002 | - |
| 3.4253 | 14400 | 0.0002 | - |
| 3.4372 | 14450 | 0.002 | - |
| 3.4491 | 14500 | 0.0019 | - |
| 3.4610 | 14550 | 0.0018 | - |
| 3.4729 | 14600 | 0.0001 | - |
| 3.4848 | 14650 | 0.002 | - |
| 3.4967 | 14700 | 0.0003 | - |
| 3.5086 | 14750 | 0.0004 | - |
| 3.5205 | 14800 | 0.0003 | - |
| 3.5324 | 14850 | 0.0019 | - |
| 3.5442 | 14900 | 0.0005 | - |
| 3.5561 | 14950 | 0.0007 | - |
| 3.5680 | 15000 | 0.0023 | - |
| 3.5799 | 15050 | 0.0019 | - |
| 3.5918 | 15100 | 0.0002 | - |
| 3.6037 | 15150 | 0.002 | - |
| 3.6156 | 15200 | 0.0023 | - |
| 3.6275 | 15250 | 0.0019 | - |
| 3.6394 | 15300 | 0.0005 | - |
| 3.6513 | 15350 | 0.0001 | - |
| 3.6632 | 15400 | 0.0009 | - |
| 3.6751 | 15450 | 0.0003 | - |
| 3.6870 | 15500 | 0.0052 | - |
| 3.6989 | 15550 | 0.0058 | - |
| 3.7108 | 15600 | 0.0003 | - |
| 3.7226 | 15650 | 0.0011 | - |
| 3.7345 | 15700 | 0.003 | - |
| 3.7464 | 15750 | 0.0003 | - |
| 3.7583 | 15800 | 0.0001 | - |
| 3.7702 | 15850 | 0.0004 | - |
| 3.7821 | 15900 | 0.0004 | - |
| 3.7940 | 15950 | 0.0001 | - |
| 3.8059 | 16000 | 0.0009 | - |
| 3.8178 | 16050 | 0.002 | - |
| 3.8297 | 16100 | 0.0004 | - |
| 3.8416 | 16150 | 0.0001 | - |
| 3.8535 | 16200 | 0.0004 | - |
| 3.8654 | 16250 | 0.0001 | - |
| 3.8773 | 16300 | 0.0014 | - |
| 3.8892 | 16350 | 0.002 | - |
| 3.9010 | 16400 | 0.0023 | - |
| 3.9129 | 16450 | 0.002 | - |
| 3.9248 | 16500 | 0.0004 | - |
| 3.9367 | 16550 | 0.0002 | - |
| 3.9486 | 16600 | 0.0001 | - |
| 3.9605 | 16650 | 0.0007 | - |
| 3.9724 | 16700 | 0.0009 | - |
| 3.9843 | 16750 | 0.0002 | - |
| 3.9962 | 16800 | 0.0006 | - |
| 4.0081 | 16850 | 0.0001 | - |
| 4.0200 | 16900 | 0.0004 | - |
| 4.0319 | 16950 | 0.0014 | - |
| 4.0438 | 17000 | 0.0001 | - |
| 4.0557 | 17050 | 0.001 | - |
| 4.0676 | 17100 | 0.0003 | - |
| 4.0794 | 17150 | 0.0045 | - |
| 4.0913 | 17200 | 0.0039 | - |
| 4.1032 | 17250 | 0.0005 | - |
| 4.1151 | 17300 | 0.001 | - |
| 4.1270 | 17350 | 0.0019 | - |
| 4.1389 | 17400 | 0.0 | - |
| 4.1508 | 17450 | 0.0003 | - |
| 4.1627 | 17500 | 0.0007 | - |
| 4.1746 | 17550 | 0.0052 | - |
| 4.1865 | 17600 | 0.0002 | - |
| 4.1984 | 17650 | 0.0006 | - |
| 4.2103 | 17700 | 0.0001 | - |
| 4.2222 | 17750 | 0.0 | - |
| 4.2341 | 17800 | 0.0002 | - |
| 4.2460 | 17850 | 0.0003 | - |
| 4.2578 | 17900 | 0.0012 | - |
| 4.2697 | 17950 | 0.0005 | - |
| 4.2816 | 18000 | 0.0003 | - |
| 4.2935 | 18050 | 0.0031 | - |
| 4.3054 | 18100 | 0.0026 | - |
| 4.3173 | 18150 | 0.001 | - |
| 4.3292 | 18200 | 0.0 | - |
| 4.3411 | 18250 | 0.0002 | - |
| 4.3530 | 18300 | 0.0006 | - |
| 4.3649 | 18350 | 0.0018 | - |
| 4.3768 | 18400 | 0.0003 | - |
| 4.3887 | 18450 | 0.0012 | - |
| 4.4006 | 18500 | 0.0 | - |
| 4.4125 | 18550 | 0.0001 | - |
| 4.4244 | 18600 | 0.002 | - |
| 4.4363 | 18650 | 0.0012 | - |
| 4.4481 | 18700 | 0.0021 | - |
| 4.4600 | 18750 | 0.0002 | - |
| 4.4719 | 18800 | 0.0015 | - |
| 4.4838 | 18850 | 0.0002 | - |
| 4.4957 | 18900 | 0.0 | - |
| 4.5076 | 18950 | 0.0003 | - |
| 4.5195 | 19000 | 0.0001 | - |
| 4.5314 | 19050 | 0.001 | - |
| 4.5433 | 19100 | 0.0001 | - |
| 4.5552 | 19150 | 0.0 | - |
| 4.5671 | 19200 | 0.0017 | - |
| 4.5790 | 19250 | 0.0003 | - |
| 4.5909 | 19300 | 0.001 | - |
| 4.6028 | 19350 | 0.0015 | - |
| 4.6147 | 19400 | 0.0001 | - |
| 4.6265 | 19450 | 0.0001 | - |
| 4.6384 | 19500 | 0.0022 | - |
| 4.6503 | 19550 | 0.0005 | - |
| 4.6622 | 19600 | 0.0003 | - |
| 4.6741 | 19650 | 0.0009 | - |
| 4.6860 | 19700 | 0.0001 | - |
| 4.6979 | 19750 | 0.0018 | - |
| 4.7098 | 19800 | 0.0001 | - |
| 4.7217 | 19850 | 0.0012 | - |
| 4.7336 | 19900 | 0.0002 | - |
| 4.7455 | 19950 | 0.0003 | - |
| 4.7574 | 20000 | 0.0006 | - |
| 4.7693 | 20050 | 0.0011 | - |
| 4.7812 | 20100 | 0.0033 | - |
| 4.7931 | 20150 | 0.0003 | - |
| 4.8049 | 20200 | 0.001 | - |
| 4.8168 | 20250 | 0.003 | - |
| 4.8287 | 20300 | 0.0035 | - |
| 4.8406 | 20350 | 0.0001 | - |
| 4.8525 | 20400 | 0.0002 | - |
| 4.8644 | 20450 | 0.0006 | - |
| 4.8763 | 20500 | 0.0 | - |
| 4.8882 | 20550 | 0.003 | - |
| 4.9001 | 20600 | 0.0001 | - |
| 4.9120 | 20650 | 0.0001 | - |
| 4.9239 | 20700 | 0.0002 | - |
| 4.9358 | 20750 | 0.0007 | - |
| 4.9477 | 20800 | 0.0002 | - |
| 4.9596 | 20850 | 0.0007 | - |
| 4.9715 | 20900 | 0.0032 | - |
| 4.9833 | 20950 | 0.0002 | - |
| 4.9952 | 21000 | 0.0 | - |
| 5.0071 | 21050 | 0.0018 | - |
| 5.0190 | 21100 | 0.0002 | - |
| 5.0309 | 21150 | 0.0017 | - |
| 5.0428 | 21200 | 0.0013 | - |
| 5.0547 | 21250 | 0.0014 | - |
| 5.0666 | 21300 | 0.0 | - |
| 5.0785 | 21350 | 0.0001 | - |
| 5.0904 | 21400 | 0.0001 | - |
| 5.1023 | 21450 | 0.0001 | - |
| 5.1142 | 21500 | 0.0022 | - |
| 5.1261 | 21550 | 0.0004 | - |
| 5.1380 | 21600 | 0.0002 | - |
| 5.1499 | 21650 | 0.0016 | - |
| 5.1618 | 21700 | 0.0036 | - |
| 5.1736 | 21750 | 0.0021 | - |
| 5.1855 | 21800 | 0.0018 | - |
| 5.1974 | 21850 | 0.0005 | - |
| 5.2093 | 21900 | 0.0024 | - |
| 5.2212 | 21950 | 0.0004 | - |
| 5.2331 | 22000 | 0.0002 | - |
| 5.2450 | 22050 | 0.0 | - |
| 5.2569 | 22100 | 0.0019 | - |
| 5.2688 | 22150 | 0.0001 | - |
| 5.2807 | 22200 | 0.0001 | - |
| 5.2926 | 22250 | 0.0014 | - |
| 5.3045 | 22300 | 0.0001 | - |
| 5.3164 | 22350 | 0.0018 | - |
| 5.3283 | 22400 | 0.0006 | - |
| 5.3402 | 22450 | 0.0004 | - |
| 5.3520 | 22500 | 0.0003 | - |
| 5.3639 | 22550 | 0.0008 | - |
| 5.3758 | 22600 | 0.0002 | - |
| 5.3877 | 22650 | 0.0002 | - |
| 5.3996 | 22700 | 0.0002 | - |
| 5.4115 | 22750 | 0.0009 | - |
| 5.4234 | 22800 | 0.0008 | - |
| 5.4353 | 22850 | 0.0002 | - |
| 5.4472 | 22900 | 0.0 | - |
| 5.4591 | 22950 | 0.0018 | - |
| 5.4710 | 23000 | 0.0015 | - |
| 5.4829 | 23050 | 0.002 | - |
| 5.4948 | 23100 | 0.0002 | - |
| 5.5067 | 23150 | 0.0 | - |
| 5.5186 | 23200 | 0.0002 | - |
| 5.5304 | 23250 | 0.0001 | - |
| 5.5423 | 23300 | 0.0 | - |
| 5.5542 | 23350 | 0.0007 | - |
| 5.5661 | 23400 | 0.002 | - |
| 5.5780 | 23450 | 0.0019 | - |
| 5.5899 | 23500 | 0.0 | - |
| 5.6018 | 23550 | 0.0029 | - |
| 5.6137 | 23600 | 0.0 | - |
| 5.6256 | 23650 | 0.0016 | - |
| 5.6375 | 23700 | 0.0013 | - |
| 5.6494 | 23750 | 0.002 | - |
| 5.6613 | 23800 | 0.0001 | - |
| 5.6732 | 23850 | 0.0001 | - |
| 5.6851 | 23900 | 0.0004 | - |
| 5.6970 | 23950 | 0.0005 | - |
| 5.7088 | 24000 | 0.0012 | - |
| 5.7207 | 24050 | 0.0001 | - |
| 5.7326 | 24100 | 0.0002 | - |
| 5.7445 | 24150 | 0.0011 | - |
| 5.7564 | 24200 | 0.0001 | - |
| 5.7683 | 24250 | 0.0012 | - |
| 5.7802 | 24300 | 0.0002 | - |
| 5.7921 | 24350 | 0.0002 | - |
| 5.8040 | 24400 | 0.0015 | - |
| 5.8159 | 24450 | 0.0 | - |
| 5.8278 | 24500 | 0.0001 | - |
| 5.8397 | 24550 | 0.0 | - |
| 5.8516 | 24600 | 0.0001 | - |
| 5.8635 | 24650 | 0.0029 | - |
| 5.8754 | 24700 | 0.0001 | - |
| 5.8873 | 24750 | 0.0016 | - |
| 5.8991 | 24800 | 0.0011 | - |
| 5.9110 | 24850 | 0.0006 | - |
| 5.9229 | 24900 | 0.0 | - |
| 5.9348 | 24950 | 0.0001 | - |
| 5.9467 | 25000 | 0.0003 | - |
| 5.9586 | 25050 | 0.0001 | - |
| 5.9705 | 25100 | 0.0 | - |
| 5.9824 | 25150 | 0.0003 | - |
| 5.9943 | 25200 | 0.0022 | - |
| 6.0062 | 25250 | 0.0 | - |
| 6.0181 | 25300 | 0.0002 | - |
| 6.0300 | 25350 | 0.0001 | - |
| 6.0419 | 25400 | 0.0 | - |
| 6.0538 | 25450 | 0.0009 | - |
| 6.0657 | 25500 | 0.0031 | - |
| 6.0775 | 25550 | 0.0 | - |
| 6.0894 | 25600 | 0.0005 | - |
| 6.1013 | 25650 | 0.0011 | - |
| 6.1132 | 25700 | 0.0012 | - |
| 6.1251 | 25750 | 0.0018 | - |
| 6.1370 | 25800 | 0.0001 | - |
| 6.1489 | 25850 | 0.0 | - |
| 6.1608 | 25900 | 0.0002 | - |
| 6.1727 | 25950 | 0.0014 | - |
| 6.1846 | 26000 | 0.0004 | - |
| 6.1965 | 26050 | 0.0003 | - |
| 6.2084 | 26100 | 0.0015 | - |
| 6.2203 | 26150 | 0.0011 | - |
| 6.2322 | 26200 | 0.0 | - |
| 6.2441 | 26250 | 0.0028 | - |
| 6.2559 | 26300 | 0.0002 | - |
| 6.2678 | 26350 | 0.0013 | - |
| 6.2797 | 26400 | 0.0001 | - |
| 6.2916 | 26450 | 0.0024 | - |
| 6.3035 | 26500 | 0.004 | - |
| 6.3154 | 26550 | 0.0 | - |
| 6.3273 | 26600 | 0.0029 | - |
| 6.3392 | 26650 | 0.0001 | - |
| 6.3511 | 26700 | 0.0001 | - |
| 6.3630 | 26750 | 0.0002 | - |
| 6.3749 | 26800 | 0.0 | - |
| 6.3868 | 26850 | 0.0016 | - |
| 6.3987 | 26900 | 0.0002 | - |
| 6.4106 | 26950 | 0.0002 | - |
| 6.4225 | 27000 | 0.0001 | - |
| 6.4343 | 27050 | 0.0 | - |
| 6.4462 | 27100 | 0.0015 | - |
| 6.4581 | 27150 | 0.0027 | - |
| 6.4700 | 27200 | 0.0007 | - |
| 6.4819 | 27250 | 0.0033 | - |
| 6.4938 | 27300 | 0.0024 | - |
| 6.5057 | 27350 | 0.0001 | - |
| 6.5176 | 27400 | 0.0004 | - |
| 6.5295 | 27450 | 0.0002 | - |
| 6.5414 | 27500 | 0.0001 | - |
| 6.5533 | 27550 | 0.0004 | - |
| 6.5652 | 27600 | 0.0003 | - |
| 6.5771 | 27650 | 0.0023 | - |
| 6.5890 | 27700 | 0.0013 | - |
| 6.6009 | 27750 | 0.0035 | - |
| 6.6127 | 27800 | 0.0003 | - |
| 6.6246 | 27850 | 0.0019 | - |
| 6.6365 | 27900 | 0.0 | - |
| 6.6484 | 27950 | 0.0015 | - |
| 6.6603 | 28000 | 0.0 | - |
| 6.6722 | 28050 | 0.0004 | - |
| 6.6841 | 28100 | 0.0012 | - |
| 6.6960 | 28150 | 0.0007 | - |
| 6.7079 | 28200 | 0.0 | - |
| 6.7198 | 28250 | 0.0001 | - |
| 6.7317 | 28300 | 0.0 | - |
| 6.7436 | 28350 | 0.0002 | - |
| 6.7555 | 28400 | 0.0 | - |
| 6.7674 | 28450 | 0.0001 | - |
| 6.7793 | 28500 | 0.0031 | - |
| 6.7912 | 28550 | 0.0016 | - |
| 6.8030 | 28600 | 0.0 | - |
| 6.8149 | 28650 | 0.0 | - |
| 6.8268 | 28700 | 0.0004 | - |
| 6.8387 | 28750 | 0.0005 | - |
| 6.8506 | 28800 | 0.0012 | - |
| 6.8625 | 28850 | 0.0 | - |
| 6.8744 | 28900 | 0.0002 | - |
| 6.8863 | 28950 | 0.0004 | - |
| 6.8982 | 29000 | 0.0001 | - |
| 6.9101 | 29050 | 0.0002 | - |
| 6.9220 | 29100 | 0.0034 | - |
| 6.9339 | 29150 | 0.0004 | - |
| 6.9458 | 29200 | 0.0002 | - |
| 6.9577 | 29250 | 0.0001 | - |
| 6.9696 | 29300 | 0.0011 | - |
| 6.9814 | 29350 | 0.0022 | - |
| 6.9933 | 29400 | 0.0006 | - |
| 7.0052 | 29450 | 0.0002 | - |
| 7.0171 | 29500 | 0.0003 | - |
| 7.0290 | 29550 | 0.0001 | - |
| 7.0409 | 29600 | 0.0 | - |
| 7.0528 | 29650 | 0.0001 | - |
| 7.0647 | 29700 | 0.0017 | - |
| 7.0766 | 29750 | 0.0002 | - |
| 7.0885 | 29800 | 0.0001 | - |
| 7.1004 | 29850 | 0.0003 | - |
| 7.1123 | 29900 | 0.0021 | - |
| 7.1242 | 29950 | 0.0 | - |
| 7.1361 | 30000 | 0.0002 | - |
| 7.1480 | 30050 | 0.0003 | - |
| 7.1598 | 30100 | 0.0012 | - |
| 7.1717 | 30150 | 0.0022 | - |
| 7.1836 | 30200 | 0.0001 | - |
| 7.1955 | 30250 | 0.0003 | - |
| 7.2074 | 30300 | 0.0023 | - |
| 7.2193 | 30350 | 0.0 | - |
| 7.2312 | 30400 | 0.0001 | - |
| 7.2431 | 30450 | 0.0001 | - |
| 7.2550 | 30500 | 0.0003 | - |
| 7.2669 | 30550 | 0.0001 | - |
| 7.2788 | 30600 | 0.0012 | - |
| 7.2907 | 30650 | 0.0 | - |
| 7.3026 | 30700 | 0.0027 | - |
| 7.3145 | 30750 | 0.0 | - |
| 7.3264 | 30800 | 0.0001 | - |
| 7.3382 | 30850 | 0.0001 | - |
| 7.3501 | 30900 | 0.0019 | - |
| 7.3620 | 30950 | 0.0001 | - |
| 7.3739 | 31000 | 0.001 | - |
| 7.3858 | 31050 | 0.0013 | - |
| 7.3977 | 31100 | 0.0026 | - |
| 7.4096 | 31150 | 0.0017 | - |
| 7.4215 | 31200 | 0.0016 | - |
| 7.4334 | 31250 | 0.0012 | - |
| 7.4453 | 31300 | 0.0 | - |
| 7.4572 | 31350 | 0.0032 | - |
| 7.4691 | 31400 | 0.0 | - |
| 7.4810 | 31450 | 0.0035 | - |
| 7.4929 | 31500 | 0.0036 | - |
| 7.5048 | 31550 | 0.0 | - |
| 7.5167 | 31600 | 0.0013 | - |
| 7.5285 | 31650 | 0.0011 | - |
| 7.5404 | 31700 | 0.0023 | - |
| 7.5523 | 31750 | 0.0002 | - |
| 7.5642 | 31800 | 0.0004 | - |
| 7.5761 | 31850 | 0.0002 | - |
| 7.5880 | 31900 | 0.0002 | - |
| 7.5999 | 31950 | 0.0018 | - |
| 7.6118 | 32000 | 0.0001 | - |
| 7.6237 | 32050 | 0.0004 | - |
| 7.6356 | 32100 | 0.0002 | - |
| 7.6475 | 32150 | 0.0 | - |
| 7.6594 | 32200 | 0.0017 | - |
| 7.6713 | 32250 | 0.0021 | - |
| 7.6832 | 32300 | 0.001 | - |
| 7.6951 | 32350 | 0.0002 | - |
| 7.7069 | 32400 | 0.0027 | - |
| 7.7188 | 32450 | 0.0032 | - |
| 7.7307 | 32500 | 0.0018 | - |
| 7.7426 | 32550 | 0.0013 | - |
| 7.7545 | 32600 | 0.0001 | - |
| 7.7664 | 32650 | 0.0 | - |
| 7.7783 | 32700 | 0.0025 | - |
| 7.7902 | 32750 | 0.0016 | - |
| 7.8021 | 32800 | 0.0012 | - |
| 7.8140 | 32850 | 0.0 | - |
| 7.8259 | 32900 | 0.0007 | - |
| 7.8378 | 32950 | 0.0 | - |
| 7.8497 | 33000 | 0.0004 | - |
| 7.8616 | 33050 | 0.0004 | - |
| 7.8735 | 33100 | 0.0001 | - |
| 7.8853 | 33150 | 0.0 | - |
| 7.8972 | 33200 | 0.0023 | - |
| 7.9091 | 33250 | 0.0002 | - |
| 7.9210 | 33300 | 0.0 | - |
| 7.9329 | 33350 | 0.0 | - |
| 7.9448 | 33400 | 0.0 | - |
| 7.9567 | 33450 | 0.0021 | - |
| 7.9686 | 33500 | 0.0021 | - |
| 7.9805 | 33550 | 0.0002 | - |
| 7.9924 | 33600 | 0.0003 | - |
| 8.0043 | 33650 | 0.0003 | - |
| 8.0162 | 33700 | 0.0 | - |
| 8.0281 | 33750 | 0.0 | - |
| 8.0400 | 33800 | 0.0001 | - |
| 8.0519 | 33850 | 0.0003 | - |
| 8.0637 | 33900 | 0.0001 | - |
| 8.0756 | 33950 | 0.0002 | - |
| 8.0875 | 34000 | 0.0007 | - |
| 8.0994 | 34050 | 0.0007 | - |
| 8.1113 | 34100 | 0.0025 | - |
| 8.1232 | 34150 | 0.0002 | - |
| 8.1351 | 34200 | 0.0 | - |
| 8.1470 | 34250 | 0.0001 | - |
| 8.1589 | 34300 | 0.0026 | - |
| 8.1708 | 34350 | 0.0002 | - |
| 8.1827 | 34400 | 0.0004 | - |
| 8.1946 | 34450 | 0.0 | - |
| 8.2065 | 34500 | 0.0001 | - |
| 8.2184 | 34550 | 0.0021 | - |
| 8.2303 | 34600 | 0.0001 | - |
| 8.2422 | 34650 | 0.0001 | - |
| 8.2540 | 34700 | 0.0009 | - |
| 8.2659 | 34750 | 0.0014 | - |
| 8.2778 | 34800 | 0.0026 | - |
| 8.2897 | 34850 | 0.0002 | - |
| 8.3016 | 34900 | 0.0 | - |
| 8.3135 | 34950 | 0.0002 | - |
| 8.3254 | 35000 | 0.0 | - |
| 8.3373 | 35050 | 0.0021 | - |
| 8.3492 | 35100 | 0.0001 | - |
| 8.3611 | 35150 | 0.0002 | - |
| 8.3730 | 35200 | 0.0 | - |
| 8.3849 | 35250 | 0.0 | - |
| 8.3968 | 35300 | 0.0001 | - |
| 8.4087 | 35350 | 0.0004 | - |
| 8.4206 | 35400 | 0.0001 | - |
| 8.4324 | 35450 | 0.0 | - |
| 8.4443 | 35500 | 0.0003 | - |
| 8.4562 | 35550 | 0.0011 | - |
| 8.4681 | 35600 | 0.0003 | - |
| 8.4800 | 35650 | 0.0 | - |
| 8.4919 | 35700 | 0.0002 | - |
| 8.5038 | 35750 | 0.0014 | - |
| 8.5157 | 35800 | 0.0016 | - |
| 8.5276 | 35850 | 0.0012 | - |
| 8.5395 | 35900 | 0.0002 | - |
| 8.5514 | 35950 | 0.0036 | - |
| 8.5633 | 36000 | 0.0 | - |
| 8.5752 | 36050 | 0.0 | - |
| 8.5871 | 36100 | 0.0 | - |
| 8.5990 | 36150 | 0.0 | - |
| 8.6108 | 36200 | 0.0015 | - |
| 8.6227 | 36250 | 0.003 | - |
| 8.6346 | 36300 | 0.0002 | - |
| 8.6465 | 36350 | 0.0016 | - |
| 8.6584 | 36400 | 0.0001 | - |
| 8.6703 | 36450 | 0.0 | - |
| 8.6822 | 36500 | 0.001 | - |
| 8.6941 | 36550 | 0.0008 | - |
| 8.7060 | 36600 | 0.002 | - |
| 8.7179 | 36650 | 0.0012 | - |
| 8.7298 | 36700 | 0.0002 | - |
| 8.7417 | 36750 | 0.0015 | - |
| 8.7536 | 36800 | 0.0 | - |
| 8.7655 | 36850 | 0.0024 | - |
| 8.7774 | 36900 | 0.0002 | - |
| 8.7892 | 36950 | 0.0 | - |
| 8.8011 | 37000 | 0.0 | - |
| 8.8130 | 37050 | 0.0001 | - |
| 8.8249 | 37100 | 0.0003 | - |
| 8.8368 | 37150 | 0.0014 | - |
| 8.8487 | 37200 | 0.0 | - |
| 8.8606 | 37250 | 0.0013 | - |
| 8.8725 | 37300 | 0.0001 | - |
| 8.8844 | 37350 | 0.0001 | - |
| 8.8963 | 37400 | 0.0033 | - |
| 8.9082 | 37450 | 0.0 | - |
| 8.9201 | 37500 | 0.0001 | - |
| 8.9320 | 37550 | 0.0022 | - |
| 8.9439 | 37600 | 0.0 | - |
| 8.9558 | 37650 | 0.0 | - |
| 8.9676 | 37700 | 0.0002 | - |
| 8.9795 | 37750 | 0.0003 | - |
| 8.9914 | 37800 | 0.0003 | - |
| 9.0033 | 37850 | 0.0017 | - |
| 9.0152 | 37900 | 0.0014 | - |
| 9.0271 | 37950 | 0.0002 | - |
| 9.0390 | 38000 | 0.0006 | - |
| 9.0509 | 38050 | 0.0006 | - |
| 9.0628 | 38100 | 0.0 | - |
| 9.0747 | 38150 | 0.0002 | - |
| 9.0866 | 38200 | 0.0 | - |
| 9.0985 | 38250 | 0.0001 | - |
| 9.1104 | 38300 | 0.0006 | - |
| 9.1223 | 38350 | 0.0014 | - |
| 9.1342 | 38400 | 0.0001 | - |
| 9.1461 | 38450 | 0.0 | - |
| 9.1579 | 38500 | 0.0002 | - |
| 9.1698 | 38550 | 0.0003 | - |
| 9.1817 | 38600 | 0.0004 | - |
| 9.1936 | 38650 | 0.0001 | - |
| 9.2055 | 38700 | 0.0001 | - |
| 9.2174 | 38750 | 0.002 | - |
| 9.2293 | 38800 | 0.0002 | - |
| 9.2412 | 38850 | 0.0016 | - |
| 9.2531 | 38900 | 0.0001 | - |
| 9.2650 | 38950 | 0.0 | - |
| 9.2769 | 39000 | 0.0002 | - |
| 9.2888 | 39050 | 0.0017 | - |
| 9.3007 | 39100 | 0.0015 | - |
| 9.3126 | 39150 | 0.0003 | - |
| 9.3245 | 39200 | 0.0 | - |
| 9.3363 | 39250 | 0.0 | - |
| 9.3482 | 39300 | 0.0004 | - |
| 9.3601 | 39350 | 0.002 | - |
| 9.3720 | 39400 | 0.0003 | - |
| 9.3839 | 39450 | 0.0 | - |
| 9.3958 | 39500 | 0.0 | - |
| 9.4077 | 39550 | 0.0014 | - |
| 9.4196 | 39600 | 0.0024 | - |
| 9.4315 | 39650 | 0.0015 | - |
| 9.4434 | 39700 | 0.0007 | - |
| 9.4553 | 39750 | 0.0002 | - |
| 9.4672 | 39800 | 0.0017 | - |
| 9.4791 | 39850 | 0.0002 | - |
| 9.4910 | 39900 | 0.0013 | - |
| 9.5029 | 39950 | 0.0013 | - |
| 9.5147 | 40000 | 0.002 | - |
| 9.5266 | 40050 | 0.0003 | - |
| 9.5385 | 40100 | 0.0013 | - |
| 9.5504 | 40150 | 0.0002 | - |
| 9.5623 | 40200 | 0.0016 | - |
| 9.5742 | 40250 | 0.0007 | - |
| 9.5861 | 40300 | 0.0013 | - |
| 9.5980 | 40350 | 0.0 | - |
| 9.6099 | 40400 | 0.0003 | - |
| 9.6218 | 40450 | 0.0002 | - |
| 9.6337 | 40500 | 0.0002 | - |
| 9.6456 | 40550 | 0.0001 | - |
| 9.6575 | 40600 | 0.0002 | - |
| 9.6694 | 40650 | 0.0013 | - |
| 9.6813 | 40700 | 0.0015 | - |
| 9.6931 | 40750 | 0.0 | - |
| 9.7050 | 40800 | 0.0001 | - |
| 9.7169 | 40850 | 0.0002 | - |
| 9.7288 | 40900 | 0.0 | - |
| 9.7407 | 40950 | 0.0 | - |
| 9.7526 | 41000 | 0.0 | - |
| 9.7645 | 41050 | 0.0002 | - |
| 9.7764 | 41100 | 0.0002 | - |
| 9.7883 | 41150 | 0.0001 | - |
| 9.8002 | 41200 | 0.0007 | - |
| 9.8121 | 41250 | 0.0001 | - |
| 9.8240 | 41300 | 0.002 | - |
| 9.8359 | 41350 | 0.0017 | - |
| 9.8478 | 41400 | 0.0019 | - |
| 9.8597 | 41450 | 0.0039 | - |
| 9.8716 | 41500 | 0.0001 | - |
| 9.8834 | 41550 | 0.0002 | - |
| 9.8953 | 41600 | 0.0007 | - |
| 9.9072 | 41650 | 0.0 | - |
| 9.9191 | 41700 | 0.0003 | - |
| 9.9310 | 41750 | 0.0012 | - |
| 9.9429 | 41800 | 0.0001 | - |
| 9.9548 | 41850 | 0.0001 | - |
| 9.9667 | 41900 | 0.0002 | - |
| 9.9786 | 41950 | 0.0002 | - |
| 9.9905 | 42000 | 0.0023 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- 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}
}
```