metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
매핑_카테고리1 : (#M)생활/건강>반려동물>고양이 사료>건식사료 매핑_카테고리2 : MP > Naverstore > 반려동물용품
> 고양이용품 > 사료 > 건식 상품명 : 뉴질랜드 기능성 처방식 고양이 사료 HLD 간 기능 부전 장 개선 사료 5.21kg 옵션명
: 3kg
- text: >-
상품명 : [마이펫닥터] 시그니처 포 라이프 시니어 강아지 사료 종합 영양 심장 비뇨 관절 다이어트 장 노견 노령견, 2kg, 1ea
옵션명 : 포 라이프 시니어 2kg
- text: >-
매핑_카테고리1 : (#M)홈>🐶강아지 사료🍚>건식사료 매핑_카테고리2 : Naverstore > 반려동물용품 > 강아지용품 >
사료 > 건식 상품명 : 카나간 사료 프리런 치킨 포독 스몰 브리드 2kg 옵션명 : 🦃 하이랜드 피스트(칠면조
꿩)_6kg_5.포켄스 펫디저트 과일퓨레 105g
- text: >-
매핑_카테고리2 : Naverstore > 반려동물용품 > 강아지용품 > 간식 > 캔/파우치 옵션명 : 매핑_카테고리1 :
(#M)홈>생활/건강>반려동물>강아지 간식>캔/파우치 상품명 : 시저캔 강아지간식 캔 통조림 애견간식
- text: '옵션명 : 01=1_XS 상품명 : 원피스 공주 고양이 멜빵 민소매 활 애견 조끼 강아지 드레스 인스 스트리머 투투 스커트 '
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_item_top_ps_flat
model-index:
- name: SetFit with mini1013/master_item_top_ps_flat
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8774402903051344
name: Accuracy
SetFit with mini1013/master_item_top_ps_flat
This is a SetFit model that can be used for Text Classification. This SetFit model uses mini1013/master_item_top_ps_flat as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: mini1013/master_item_top_ps_flat
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 124 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| 86 |
|
| 93 |
|
| 85 |
|
| 28 |
|
| 95 |
|
| 120 |
|
| 65 |
|
| 77 |
|
| 106 |
|
| 37 |
|
| 2 |
|
| 69 |
|
| 81 |
|
| 63 |
|
| 34 |
|
| 107 |
|
| 18 |
|
| 30 |
|
| 87 |
|
| 48 |
|
| 49 |
|
| 7 |
|
| 45 |
|
| 44 |
|
| 61 |
|
| 31 |
|
| 111 |
|
| 101 |
|
| 83 |
|
| 51 |
|
| 25 |
|
| 73 |
|
| 78 |
|
| 52 |
|
| 24 |
|
| 41 |
|
| 70 |
|
| 55 |
|
| 57 |
|
| 60 |
|
| 19 |
|
| 94 |
|
| 108 |
|
| 119 |
|
| 8 |
|
| 105 |
|
| 21 |
|
| 10 |
|
| 6 |
|
| 91 |
|
| 114 |
|
| 15 |
|
| 89 |
|
| 27 |
|
| 46 |
|
| 122 |
|
| 4 |
|
| 23 |
|
| 104 |
|
| 109 |
|
| 38 |
|
| 79 |
|
| 82 |
|
| 1 |
|
| 59 |
|
| 71 |
|
| 97 |
|
| 80 |
|
| 35 |
|
| 116 |
|
| 121 |
|
| 88 |
|
| 118 |
|
| 3 |
|
| 113 |
|
| 26 |
|
| 110 |
|
| 102 |
|
| 39 |
|
| 5 |
|
| 100 |
|
| 72 |
|
| 84 |
|
| 9 |
|
| 29 |
|
| 76 |
|
| 56 |
|
| 62 |
|
| 17 |
|
| 66 |
|
| 90 |
|
| 43 |
|
| 33 |
|
| 14 |
|
| 64 |
|
| 98 |
|
| 58 |
|
| 16 |
|
| 75 |
|
| 112 |
|
| 0 |
|
| 54 |
|
| 47 |
|
| 115 |
|
| 92 |
|
| 117 |
|
| 74 |
|
| 36 |
|
| 99 |
|
| 67 |
|
| 53 |
|
| 12 |
|
| 22 |
|
| 13 |
|
| 96 |
|
| 50 |
|
| 42 |
|
| 11 |
|
| 103 |
|
| 40 |
|
| 32 |
|
| 20 |
|
| 68 |
|
| 123 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.8774 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_item_top_ps_flat")
# Run inference
preds = model("옵션명 : 01=1_XS 상품명 : 원피스 공주 고양이 멜빵 민소매 활 애견 조끼 강아지 드레스 인스 스트리머 투투 스커트 ")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 7 | 26.7504 | 57 |
| Label | Training Sample Count |
|---|---|
| 0 | 250 |
| 1 | 250 |
| 2 | 162 |
| 3 | 250 |
| 4 | 250 |
| 5 | 250 |
| 6 | 250 |
| 7 | 250 |
| 8 | 66 |
| 9 | 250 |
| 10 | 250 |
| 11 | 52 |
| 12 | 13 |
| 13 | 250 |
| 14 | 250 |
| 15 | 163 |
| 16 | 81 |
| 17 | 93 |
| 18 | 203 |
| 19 | 135 |
| 20 | 19 |
| 21 | 217 |
| 22 | 234 |
| 23 | 250 |
| 24 | 250 |
| 25 | 209 |
| 26 | 250 |
| 27 | 250 |
| 28 | 250 |
| 29 | 200 |
| 30 | 227 |
| 31 | 250 |
| 32 | 49 |
| 33 | 250 |
| 34 | 250 |
| 35 | 250 |
| 36 | 250 |
| 37 | 250 |
| 38 | 91 |
| 39 | 250 |
| 40 | 250 |
| 41 | 240 |
| 42 | 90 |
| 43 | 250 |
| 44 | 116 |
| 45 | 250 |
| 46 | 250 |
| 47 | 122 |
| 48 | 250 |
| 49 | 184 |
| 50 | 109 |
| 51 | 145 |
| 52 | 250 |
| 53 | 250 |
| 54 | 46 |
| 55 | 250 |
| 56 | 250 |
| 57 | 250 |
| 58 | 250 |
| 59 | 250 |
| 60 | 250 |
| 61 | 250 |
| 62 | 250 |
| 63 | 250 |
| 64 | 249 |
| 65 | 193 |
| 66 | 131 |
| 67 | 29 |
| 68 | 10 |
| 69 | 194 |
| 70 | 250 |
| 71 | 250 |
| 72 | 250 |
| 73 | 250 |
| 74 | 250 |
| 75 | 250 |
| 76 | 250 |
| 77 | 250 |
| 78 | 250 |
| 79 | 250 |
| 80 | 205 |
| 81 | 250 |
| 82 | 250 |
| 83 | 250 |
| 84 | 79 |
| 85 | 250 |
| 86 | 227 |
| 87 | 152 |
| 88 | 250 |
| 89 | 250 |
| 90 | 250 |
| 91 | 198 |
| 92 | 76 |
| 93 | 250 |
| 94 | 250 |
| 95 | 250 |
| 96 | 250 |
| 97 | 195 |
| 98 | 250 |
| 99 | 231 |
| 100 | 250 |
| 101 | 196 |
| 102 | 250 |
| 103 | 250 |
| 104 | 250 |
| 105 | 250 |
| 106 | 250 |
| 107 | 250 |
| 108 | 250 |
| 109 | 250 |
| 110 | 250 |
| 111 | 250 |
| 112 | 250 |
| 113 | 250 |
| 114 | 250 |
| 115 | 250 |
| 116 | 250 |
| 117 | 250 |
| 118 | 250 |
| 119 | 250 |
| 120 | 250 |
| 121 | 250 |
| 122 | 250 |
| 123 | 14 |
Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (100, 100)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: BatchAllTripletLoss
- 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 |
|---|---|---|---|
| 0.0024 | 1 | 0.1854 | - |
| 0.1211 | 50 | 0.1348 | - |
| 0.2421 | 100 | 0.1372 | - |
| 0.3632 | 150 | 0.1223 | - |
| 0.4843 | 200 | 0.1383 | - |
| 0.6053 | 250 | 0.1376 | - |
| 0.7264 | 300 | 0.1289 | - |
| 0.8475 | 350 | 0.1441 | - |
| 0.9685 | 400 | 0.1218 | - |
| 1.0896 | 450 | 0.1303 | - |
| 1.2107 | 500 | 0.1273 | - |
| 1.3317 | 550 | 0.1256 | - |
| 1.4528 | 600 | 0.1298 | - |
| 1.5738 | 650 | 0.1352 | - |
| 1.6949 | 700 | 0.1349 | - |
| 1.8160 | 750 | 0.1305 | - |
| 1.9370 | 800 | 0.1181 | - |
| 2.0581 | 850 | 0.1372 | - |
| 2.1792 | 900 | 0.1353 | - |
| 2.3002 | 950 | 0.1288 | - |
| 2.4213 | 1000 | 0.158 | - |
| 2.5424 | 1050 | 0.1332 | - |
| 2.6634 | 1100 | 0.1302 | - |
| 2.7845 | 1150 | 0.1392 | - |
| 2.9056 | 1200 | 0.1237 | - |
| 3.0266 | 1250 | 0.1477 | - |
| 3.1477 | 1300 | 0.1284 | - |
| 3.2688 | 1350 | 0.1214 | - |
| 3.3898 | 1400 | 0.1319 | - |
| 3.5109 | 1450 | 0.1201 | - |
| 3.6320 | 1500 | 0.1334 | - |
| 3.7530 | 1550 | 0.144 | - |
| 3.8741 | 1600 | 0.1343 | - |
| 3.9952 | 1650 | 0.1314 | - |
| 4.1162 | 1700 | 0.1268 | - |
| 4.2373 | 1750 | 0.1338 | - |
| 4.3584 | 1800 | 0.1339 | - |
| 4.4794 | 1850 | 0.1275 | - |
| 4.6005 | 1900 | 0.1225 | - |
| 4.7215 | 1950 | 0.1249 | - |
| 4.8426 | 2000 | 0.1289 | - |
| 4.9637 | 2050 | 0.1206 | - |
| 5.0847 | 2100 | 0.1309 | - |
| 5.2058 | 2150 | 0.1206 | - |
| 5.3269 | 2200 | 0.124 | - |
| 5.4479 | 2250 | 0.1303 | - |
| 5.5690 | 2300 | 0.1108 | - |
| 5.6901 | 2350 | 0.1108 | - |
| 5.8111 | 2400 | 0.1302 | - |
| 5.9322 | 2450 | 0.1199 | - |
| 6.0533 | 2500 | 0.1186 | - |
| 6.1743 | 2550 | 0.1247 | - |
| 6.2954 | 2600 | 0.1229 | - |
| 6.4165 | 2650 | 0.124 | - |
| 6.5375 | 2700 | 0.1282 | - |
| 6.6586 | 2750 | 0.1201 | - |
| 6.7797 | 2800 | 0.1196 | - |
| 6.9007 | 2850 | 0.1277 | - |
| 7.0218 | 2900 | 0.1256 | - |
| 7.1429 | 2950 | 0.1207 | - |
| 7.2639 | 3000 | 0.1155 | - |
| 7.3850 | 3050 | 0.1259 | - |
| 7.5061 | 3100 | 0.13 | - |
| 7.6271 | 3150 | 0.1366 | - |
| 7.7482 | 3200 | 0.131 | - |
| 7.8692 | 3250 | 0.1289 | - |
| 7.9903 | 3300 | 0.1321 | - |
| 8.1114 | 3350 | 0.1187 | - |
| 8.2324 | 3400 | 0.126 | - |
| 8.3535 | 3450 | 0.1277 | - |
| 8.4746 | 3500 | 0.1278 | - |
| 8.5956 | 3550 | 0.1273 | - |
| 8.7167 | 3600 | 0.118 | - |
| 8.8378 | 3650 | 0.1186 | - |
| 8.9588 | 3700 | 0.123 | - |
| 9.0799 | 3750 | 0.1166 | - |
| 9.2010 | 3800 | 0.1178 | - |
| 9.3220 | 3850 | 0.1205 | - |
| 9.4431 | 3900 | 0.1329 | - |
| 9.5642 | 3950 | 0.1154 | - |
| 9.6852 | 4000 | 0.1215 | - |
| 9.8063 | 4050 | 0.125 | - |
| 9.9274 | 4100 | 0.1198 | - |
| 10.0484 | 4150 | 0.1111 | - |
| 10.1695 | 4200 | 0.1221 | - |
| 10.2906 | 4250 | 0.1239 | - |
| 10.4116 | 4300 | 0.1248 | - |
| 10.5327 | 4350 | 0.1064 | - |
| 10.6538 | 4400 | 0.1202 | - |
| 10.7748 | 4450 | 0.125 | - |
| 10.8959 | 4500 | 0.1162 | - |
| 11.0169 | 4550 | 0.1072 | - |
| 11.1380 | 4600 | 0.1005 | - |
| 11.2591 | 4650 | 0.1143 | - |
| 11.3801 | 4700 | 0.1177 | - |
| 11.5012 | 4750 | 0.1244 | - |
| 11.6223 | 4800 | 0.1204 | - |
| 11.7433 | 4850 | 0.1131 | - |
| 11.8644 | 4900 | 0.1149 | - |
| 11.9855 | 4950 | 0.1247 | - |
| 12.1065 | 5000 | 0.1214 | - |
| 12.2276 | 5050 | 0.1199 | - |
| 12.3487 | 5100 | 0.1225 | - |
| 12.4697 | 5150 | 0.1149 | - |
| 12.5908 | 5200 | 0.1207 | - |
| 12.7119 | 5250 | 0.1241 | - |
| 12.8329 | 5300 | 0.1229 | - |
| 12.9540 | 5350 | 0.1134 | - |
| 13.0751 | 5400 | 0.1188 | - |
| 13.1961 | 5450 | 0.1105 | - |
| 13.3172 | 5500 | 0.1177 | - |
| 13.4383 | 5550 | 0.1123 | - |
| 13.5593 | 5600 | 0.1083 | - |
| 13.6804 | 5650 | 0.1189 | - |
| 13.8015 | 5700 | 0.1109 | - |
| 13.9225 | 5750 | 0.1078 | - |
| 14.0436 | 5800 | 0.1207 | - |
| 14.1646 | 5850 | 0.1022 | - |
| 14.2857 | 5900 | 0.1098 | - |
| 14.4068 | 5950 | 0.116 | - |
| 14.5278 | 6000 | 0.1157 | - |
| 14.6489 | 6050 | 0.1199 | - |
| 14.7700 | 6100 | 0.1118 | - |
| 14.8910 | 6150 | 0.1044 | - |
| 15.0121 | 6200 | 0.1096 | - |
| 15.1332 | 6250 | 0.0925 | - |
| 15.2542 | 6300 | 0.1015 | - |
| 15.3753 | 6350 | 0.1117 | - |
| 15.4964 | 6400 | 0.1116 | - |
| 15.6174 | 6450 | 0.1183 | - |
| 15.7385 | 6500 | 0.1206 | - |
| 15.8596 | 6550 | 0.1104 | - |
| 15.9806 | 6600 | 0.1148 | - |
| 16.1017 | 6650 | 0.1095 | - |
| 16.2228 | 6700 | 0.1168 | - |
| 16.3438 | 6750 | 0.1115 | - |
| 16.4649 | 6800 | 0.1042 | - |
| 16.5860 | 6850 | 0.1074 | - |
| 16.7070 | 6900 | 0.106 | - |
| 16.8281 | 6950 | 0.1058 | - |
| 16.9492 | 7000 | 0.1169 | - |
| 17.0702 | 7050 | 0.1043 | - |
| 17.1913 | 7100 | 0.0964 | - |
| 17.3123 | 7150 | 0.0945 | - |
| 17.4334 | 7200 | 0.1143 | - |
| 17.5545 | 7250 | 0.0938 | - |
| 17.6755 | 7300 | 0.0982 | - |
| 17.7966 | 7350 | 0.1055 | - |
| 17.9177 | 7400 | 0.1122 | - |
| 18.0387 | 7450 | 0.0897 | - |
| 18.1598 | 7500 | 0.1044 | - |
| 18.2809 | 7550 | 0.1052 | - |
| 18.4019 | 7600 | 0.0934 | - |
| 18.5230 | 7650 | 0.0978 | - |
| 18.6441 | 7700 | 0.0952 | - |
| 18.7651 | 7750 | 0.1081 | - |
| 18.8862 | 7800 | 0.1042 | - |
| 19.0073 | 7850 | 0.1099 | - |
| 19.1283 | 7900 | 0.1112 | - |
| 19.2494 | 7950 | 0.111 | - |
| 19.3705 | 8000 | 0.1016 | - |
| 19.4915 | 8050 | 0.1005 | - |
| 19.6126 | 8100 | 0.0838 | - |
| 19.7337 | 8150 | 0.095 | - |
| 19.8547 | 8200 | 0.1194 | - |
| 19.9758 | 8250 | 0.0993 | - |
| 20.0969 | 8300 | 0.0969 | - |
| 20.2179 | 8350 | 0.0954 | - |
| 20.3390 | 8400 | 0.1096 | - |
| 20.4600 | 8450 | 0.0979 | - |
| 20.5811 | 8500 | 0.0915 | - |
| 20.7022 | 8550 | 0.1042 | - |
| 20.8232 | 8600 | 0.1141 | - |
| 20.9443 | 8650 | 0.0894 | - |
| 21.0654 | 8700 | 0.0971 | - |
| 21.1864 | 8750 | 0.0835 | - |
| 21.3075 | 8800 | 0.0957 | - |
| 21.4286 | 8850 | 0.0866 | - |
| 21.5496 | 8900 | 0.0832 | - |
| 21.6707 | 8950 | 0.0786 | - |
| 21.7918 | 9000 | 0.0851 | - |
| 21.9128 | 9050 | 0.1033 | - |
| 22.0339 | 9100 | 0.1017 | - |
| 22.1550 | 9150 | 0.0878 | - |
| 22.2760 | 9200 | 0.0932 | - |
| 22.3971 | 9250 | 0.0905 | - |
| 22.5182 | 9300 | 0.0821 | - |
| 22.6392 | 9350 | 0.1042 | - |
| 22.7603 | 9400 | 0.0866 | - |
| 22.8814 | 9450 | 0.102 | - |
| 23.0024 | 9500 | 0.0929 | - |
| 23.1235 | 9550 | 0.0923 | - |
| 23.2446 | 9600 | 0.083 | - |
| 23.3656 | 9650 | 0.1073 | - |
| 23.4867 | 9700 | 0.0949 | - |
| 23.6077 | 9750 | 0.0938 | - |
| 23.7288 | 9800 | 0.0946 | - |
| 23.8499 | 9850 | 0.1085 | - |
| 23.9709 | 9900 | 0.0941 | - |
| 24.0920 | 9950 | 0.0842 | - |
| 24.2131 | 10000 | 0.0773 | - |
| 24.3341 | 10050 | 0.0883 | - |
| 24.4552 | 10100 | 0.1143 | - |
| 24.5763 | 10150 | 0.0972 | - |
| 24.6973 | 10200 | 0.077 | - |
| 24.8184 | 10250 | 0.0901 | - |
| 24.9395 | 10300 | 0.0899 | - |
| 25.0605 | 10350 | 0.0895 | - |
| 25.1816 | 10400 | 0.0775 | - |
| 25.3027 | 10450 | 0.0804 | - |
| 25.4237 | 10500 | 0.0901 | - |
| 25.5448 | 10550 | 0.0864 | - |
| 25.6659 | 10600 | 0.1167 | - |
| 25.7869 | 10650 | 0.0956 | - |
| 25.9080 | 10700 | 0.0882 | - |
| 26.0291 | 10750 | 0.0868 | - |
| 26.1501 | 10800 | 0.0798 | - |
| 26.2712 | 10850 | 0.0878 | - |
| 26.3923 | 10900 | 0.0944 | - |
| 26.5133 | 10950 | 0.0813 | - |
| 26.6344 | 11000 | 0.0858 | - |
| 26.7554 | 11050 | 0.0775 | - |
| 26.8765 | 11100 | 0.0882 | - |
| 26.9976 | 11150 | 0.0917 | - |
| 27.1186 | 11200 | 0.0838 | - |
| 27.2397 | 11250 | 0.1066 | - |
| 27.3608 | 11300 | 0.0899 | - |
| 27.4818 | 11350 | 0.095 | - |
| 27.6029 | 11400 | 0.0684 | - |
| 27.7240 | 11450 | 0.0975 | - |
| 27.8450 | 11500 | 0.0752 | - |
| 27.9661 | 11550 | 0.0694 | - |
| 28.0872 | 11600 | 0.0597 | - |
| 28.2082 | 11650 | 0.066 | - |
| 28.3293 | 11700 | 0.0545 | - |
| 28.4504 | 11750 | 0.0787 | - |
| 28.5714 | 11800 | 0.0877 | - |
| 28.6925 | 11850 | 0.0959 | - |
| 28.8136 | 11900 | 0.0748 | - |
| 28.9346 | 11950 | 0.0759 | - |
| 29.0557 | 12000 | 0.0722 | - |
| 29.1768 | 12050 | 0.0836 | - |
| 29.2978 | 12100 | 0.0965 | - |
| 29.4189 | 12150 | 0.0679 | - |
| 29.5400 | 12200 | 0.0699 | - |
| 29.6610 | 12250 | 0.0895 | - |
| 29.7821 | 12300 | 0.0593 | - |
| 29.9031 | 12350 | 0.0686 | - |
| 30.0242 | 12400 | 0.0694 | - |
| 30.1453 | 12450 | 0.0563 | - |
| 30.2663 | 12500 | 0.069 | - |
| 30.3874 | 12550 | 0.0662 | - |
| 30.5085 | 12600 | 0.0706 | - |
| 30.6295 | 12650 | 0.0671 | - |
| 30.7506 | 12700 | 0.051 | - |
| 30.8717 | 12750 | 0.0735 | - |
| 30.9927 | 12800 | 0.0712 | - |
| 31.1138 | 12850 | 0.0581 | - |
| 31.2349 | 12900 | 0.0758 | - |
| 31.3559 | 12950 | 0.0594 | - |
| 31.4770 | 13000 | 0.0607 | - |
| 31.5981 | 13050 | 0.0577 | - |
| 31.7191 | 13100 | 0.0878 | - |
| 31.8402 | 13150 | 0.0833 | - |
| 31.9613 | 13200 | 0.0776 | - |
| 32.0823 | 13250 | 0.0765 | - |
| 32.2034 | 13300 | 0.048 | - |
| 32.3245 | 13350 | 0.0622 | - |
| 32.4455 | 13400 | 0.0628 | - |
| 32.5666 | 13450 | 0.071 | - |
| 32.6877 | 13500 | 0.0707 | - |
| 32.8087 | 13550 | 0.0725 | - |
| 32.9298 | 13600 | 0.065 | - |
| 33.0508 | 13650 | 0.0726 | - |
| 33.1719 | 13700 | 0.0397 | - |
| 33.2930 | 13750 | 0.0571 | - |
| 33.4140 | 13800 | 0.0726 | - |
| 33.5351 | 13850 | 0.0711 | - |
| 33.6562 | 13900 | 0.0538 | - |
| 33.7772 | 13950 | 0.0469 | - |
| 33.8983 | 14000 | 0.0475 | - |
| 34.0194 | 14050 | 0.0654 | - |
| 34.1404 | 14100 | 0.0594 | - |
| 34.2615 | 14150 | 0.049 | - |
| 34.3826 | 14200 | 0.0713 | - |
| 34.5036 | 14250 | 0.0616 | - |
| 34.6247 | 14300 | 0.083 | - |
| 34.7458 | 14350 | 0.0689 | - |
| 34.8668 | 14400 | 0.0868 | - |
| 34.9879 | 14450 | 0.0649 | - |
| 35.1090 | 14500 | 0.0608 | - |
| 35.2300 | 14550 | 0.0788 | - |
| 35.3511 | 14600 | 0.0571 | - |
| 35.4722 | 14650 | 0.0364 | - |
| 35.5932 | 14700 | 0.0741 | - |
| 35.7143 | 14750 | 0.0372 | - |
| 35.8354 | 14800 | 0.0606 | - |
| 35.9564 | 14850 | 0.0679 | - |
| 36.0775 | 14900 | 0.0569 | - |
| 36.1985 | 14950 | 0.0526 | - |
| 36.3196 | 15000 | 0.046 | - |
| 36.4407 | 15050 | 0.056 | - |
| 36.5617 | 15100 | 0.052 | - |
| 36.6828 | 15150 | 0.0571 | - |
| 36.8039 | 15200 | 0.0668 | - |
| 36.9249 | 15250 | 0.0679 | - |
| 37.0460 | 15300 | 0.0495 | - |
| 37.1671 | 15350 | 0.0447 | - |
| 37.2881 | 15400 | 0.0592 | - |
| 37.4092 | 15450 | 0.0492 | - |
| 37.5303 | 15500 | 0.0573 | - |
| 37.6513 | 15550 | 0.042 | - |
| 37.7724 | 15600 | 0.052 | - |
| 37.8935 | 15650 | 0.0684 | - |
| 38.0145 | 15700 | 0.0394 | - |
| 38.1356 | 15750 | 0.0438 | - |
| 38.2567 | 15800 | 0.0296 | - |
| 38.3777 | 15850 | 0.0526 | - |
| 38.4988 | 15900 | 0.0456 | - |
| 38.6199 | 15950 | 0.0391 | - |
| 38.7409 | 16000 | 0.0867 | - |
| 38.8620 | 16050 | 0.0522 | - |
| 38.9831 | 16100 | 0.0414 | - |
| 39.1041 | 16150 | 0.0569 | - |
| 39.2252 | 16200 | 0.0696 | - |
| 39.3462 | 16250 | 0.0379 | - |
| 39.4673 | 16300 | 0.0562 | - |
| 39.5884 | 16350 | 0.0429 | - |
| 39.7094 | 16400 | 0.037 | - |
| 39.8305 | 16450 | 0.0533 | - |
| 39.9516 | 16500 | 0.0445 | - |
| 40.0726 | 16550 | 0.0487 | - |
| 40.1937 | 16600 | 0.0605 | - |
| 40.3148 | 16650 | 0.066 | - |
| 40.4358 | 16700 | 0.0534 | - |
| 40.5569 | 16750 | 0.0632 | - |
| 40.6780 | 16800 | 0.0712 | - |
| 40.7990 | 16850 | 0.0434 | - |
| 40.9201 | 16900 | 0.0464 | - |
| 41.0412 | 16950 | 0.0353 | - |
| 41.1622 | 17000 | 0.0323 | - |
| 41.2833 | 17050 | 0.0466 | - |
| 41.4044 | 17100 | 0.0608 | - |
| 41.5254 | 17150 | 0.0483 | - |
| 41.6465 | 17200 | 0.0377 | - |
| 41.7676 | 17250 | 0.0463 | - |
| 41.8886 | 17300 | 0.0375 | - |
| 42.0097 | 17350 | 0.0618 | - |
| 42.1308 | 17400 | 0.0455 | - |
| 42.2518 | 17450 | 0.0466 | - |
| 42.3729 | 17500 | 0.0342 | - |
| 42.4939 | 17550 | 0.0393 | - |
| 42.6150 | 17600 | 0.0425 | - |
| 42.7361 | 17650 | 0.0633 | - |
| 42.8571 | 17700 | 0.0379 | - |
| 42.9782 | 17750 | 0.0562 | - |
| 43.0993 | 17800 | 0.0226 | - |
| 43.2203 | 17850 | 0.0441 | - |
| 43.3414 | 17900 | 0.034 | - |
| 43.4625 | 17950 | 0.0342 | - |
| 43.5835 | 18000 | 0.0625 | - |
| 43.7046 | 18050 | 0.0443 | - |
| 43.8257 | 18100 | 0.0434 | - |
| 43.9467 | 18150 | 0.0349 | - |
| 44.0678 | 18200 | 0.0675 | - |
| 44.1889 | 18250 | 0.0397 | - |
| 44.3099 | 18300 | 0.0271 | - |
| 44.4310 | 18350 | 0.0284 | - |
| 44.5521 | 18400 | 0.0379 | - |
| 44.6731 | 18450 | 0.0351 | - |
| 44.7942 | 18500 | 0.0346 | - |
| 44.9153 | 18550 | 0.0381 | - |
| 45.0363 | 18600 | 0.0584 | - |
| 45.1574 | 18650 | 0.0444 | - |
| 45.2785 | 18700 | 0.0384 | - |
| 45.3995 | 18750 | 0.0468 | - |
| 45.5206 | 18800 | 0.0487 | - |
| 45.6416 | 18850 | 0.0303 | - |
| 45.7627 | 18900 | 0.0351 | - |
| 45.8838 | 18950 | 0.0214 | - |
| 46.0048 | 19000 | 0.0337 | - |
| 46.1259 | 19050 | 0.0478 | - |
| 46.2470 | 19100 | 0.045 | - |
| 46.3680 | 19150 | 0.0399 | - |
| 46.4891 | 19200 | 0.0324 | - |
| 46.6102 | 19250 | 0.0433 | - |
| 46.7312 | 19300 | 0.0524 | - |
| 46.8523 | 19350 | 0.0431 | - |
| 46.9734 | 19400 | 0.0308 | - |
| 47.0944 | 19450 | 0.0338 | - |
| 47.2155 | 19500 | 0.0395 | - |
| 47.3366 | 19550 | 0.0421 | - |
| 47.4576 | 19600 | 0.0404 | - |
| 47.5787 | 19650 | 0.021 | - |
| 47.6998 | 19700 | 0.0399 | - |
| 47.8208 | 19750 | 0.0397 | - |
| 47.9419 | 19800 | 0.0416 | - |
| 48.0630 | 19850 | 0.0371 | - |
| 48.1840 | 19900 | 0.027 | - |
| 48.3051 | 19950 | 0.0363 | - |
| 48.4262 | 20000 | 0.0262 | - |
| 48.5472 | 20050 | 0.0314 | - |
| 48.6683 | 20100 | 0.0398 | - |
| 48.7893 | 20150 | 0.0434 | - |
| 48.9104 | 20200 | 0.0455 | - |
| 49.0315 | 20250 | 0.0314 | - |
| 49.1525 | 20300 | 0.0322 | - |
| 49.2736 | 20350 | 0.0166 | - |
| 49.3947 | 20400 | 0.0295 | - |
| 49.5157 | 20450 | 0.0405 | - |
| 49.6368 | 20500 | 0.0349 | - |
| 49.7579 | 20550 | 0.0378 | - |
| 49.8789 | 20600 | 0.0566 | - |
| 50.0 | 20650 | 0.0542 | - |
| 50.1211 | 20700 | 0.0194 | - |
| 50.2421 | 20750 | 0.0462 | - |
| 50.3632 | 20800 | 0.0432 | - |
| 50.4843 | 20850 | 0.0295 | - |
| 50.6053 | 20900 | 0.0293 | - |
| 50.7264 | 20950 | 0.0226 | - |
| 50.8475 | 21000 | 0.0446 | - |
| 50.9685 | 21050 | 0.0181 | - |
| 51.0896 | 21100 | 0.0226 | - |
| 51.2107 | 21150 | 0.0141 | - |
| 51.3317 | 21200 | 0.0253 | - |
| 51.4528 | 21250 | 0.0479 | - |
| 51.5738 | 21300 | 0.0197 | - |
| 51.6949 | 21350 | 0.0387 | - |
| 51.8160 | 21400 | 0.0391 | - |
| 51.9370 | 21450 | 0.0289 | - |
| 52.0581 | 21500 | 0.0336 | - |
| 52.1792 | 21550 | 0.02 | - |
| 52.3002 | 21600 | 0.0203 | - |
| 52.4213 | 21650 | 0.034 | - |
| 52.5424 | 21700 | 0.0338 | - |
| 52.6634 | 21750 | 0.0253 | - |
| 52.7845 | 21800 | 0.0423 | - |
| 52.9056 | 21850 | 0.0427 | - |
| 53.0266 | 21900 | 0.0322 | - |
| 53.1477 | 21950 | 0.0169 | - |
| 53.2688 | 22000 | 0.0101 | - |
| 53.3898 | 22050 | 0.0349 | - |
| 53.5109 | 22100 | 0.0338 | - |
| 53.6320 | 22150 | 0.0573 | - |
| 53.7530 | 22200 | 0.0235 | - |
| 53.8741 | 22250 | 0.0357 | - |
| 53.9952 | 22300 | 0.0348 | - |
| 54.1162 | 22350 | 0.0289 | - |
| 54.2373 | 22400 | 0.0299 | - |
| 54.3584 | 22450 | 0.0339 | - |
| 54.4794 | 22500 | 0.0237 | - |
| 54.6005 | 22550 | 0.0261 | - |
| 54.7215 | 22600 | 0.0265 | - |
| 54.8426 | 22650 | 0.0192 | - |
| 54.9637 | 22700 | 0.0205 | - |
| 55.0847 | 22750 | 0.0302 | - |
| 55.2058 | 22800 | 0.0289 | - |
| 55.3269 | 22850 | 0.0188 | - |
| 55.4479 | 22900 | 0.0204 | - |
| 55.5690 | 22950 | 0.0404 | - |
| 55.6901 | 23000 | 0.0416 | - |
| 55.8111 | 23050 | 0.025 | - |
| 55.9322 | 23100 | 0.0427 | - |
| 56.0533 | 23150 | 0.0326 | - |
| 56.1743 | 23200 | 0.0299 | - |
| 56.2954 | 23250 | 0.0362 | - |
| 56.4165 | 23300 | 0.0413 | - |
| 56.5375 | 23350 | 0.029 | - |
| 56.6586 | 23400 | 0.0349 | - |
| 56.7797 | 23450 | 0.0372 | - |
| 56.9007 | 23500 | 0.0227 | - |
| 57.0218 | 23550 | 0.0195 | - |
| 57.1429 | 23600 | 0.0202 | - |
| 57.2639 | 23650 | 0.0223 | - |
| 57.3850 | 23700 | 0.0282 | - |
| 57.5061 | 23750 | 0.0168 | - |
| 57.6271 | 23800 | 0.0136 | - |
| 57.7482 | 23850 | 0.0276 | - |
| 57.8692 | 23900 | 0.0283 | - |
| 57.9903 | 23950 | 0.0344 | - |
| 58.1114 | 24000 | 0.0162 | - |
| 58.2324 | 24050 | 0.0241 | - |
| 58.3535 | 24100 | 0.0279 | - |
| 58.4746 | 24150 | 0.0346 | - |
| 58.5956 | 24200 | 0.0356 | - |
| 58.7167 | 24250 | 0.0322 | - |
| 58.8378 | 24300 | 0.0309 | - |
| 58.9588 | 24350 | 0.0224 | - |
| 59.0799 | 24400 | 0.0138 | - |
| 59.2010 | 24450 | 0.041 | - |
| 59.3220 | 24500 | 0.0248 | - |
| 59.4431 | 24550 | 0.0279 | - |
| 59.5642 | 24600 | 0.0242 | - |
| 59.6852 | 24650 | 0.0321 | - |
| 59.8063 | 24700 | 0.0188 | - |
| 59.9274 | 24750 | 0.0269 | - |
| 60.0484 | 24800 | 0.0215 | - |
| 60.1695 | 24850 | 0.0186 | - |
| 60.2906 | 24900 | 0.0229 | - |
| 60.4116 | 24950 | 0.0284 | - |
| 60.5327 | 25000 | 0.0263 | - |
| 60.6538 | 25050 | 0.0226 | - |
| 60.7748 | 25100 | 0.0182 | - |
| 60.8959 | 25150 | 0.0095 | - |
| 61.0169 | 25200 | 0.0199 | - |
| 61.1380 | 25250 | 0.0164 | - |
| 61.2591 | 25300 | 0.023 | - |
| 61.3801 | 25350 | 0.029 | - |
| 61.5012 | 25400 | 0.0229 | - |
| 61.6223 | 25450 | 0.023 | - |
| 61.7433 | 25500 | 0.0189 | - |
| 61.8644 | 25550 | 0.0268 | - |
| 61.9855 | 25600 | 0.0313 | - |
| 62.1065 | 25650 | 0.0121 | - |
| 62.2276 | 25700 | 0.0187 | - |
| 62.3487 | 25750 | 0.0126 | - |
| 62.4697 | 25800 | 0.0404 | - |
| 62.5908 | 25850 | 0.0238 | - |
| 62.7119 | 25900 | 0.026 | - |
| 62.8329 | 25950 | 0.0176 | - |
| 62.9540 | 26000 | 0.0153 | - |
| 63.0751 | 26050 | 0.0223 | - |
| 63.1961 | 26100 | 0.013 | - |
| 63.3172 | 26150 | 0.0177 | - |
| 63.4383 | 26200 | 0.0111 | - |
| 63.5593 | 26250 | 0.0185 | - |
| 63.6804 | 26300 | 0.0179 | - |
| 63.8015 | 26350 | 0.0111 | - |
| 63.9225 | 26400 | 0.0258 | - |
| 64.0436 | 26450 | 0.0199 | - |
| 64.1646 | 26500 | 0.0171 | - |
| 64.2857 | 26550 | 0.0169 | - |
| 64.4068 | 26600 | 0.0154 | - |
| 64.5278 | 26650 | 0.0106 | - |
| 64.6489 | 26700 | 0.0071 | - |
| 64.7700 | 26750 | 0.0151 | - |
| 64.8910 | 26800 | 0.0335 | - |
| 65.0121 | 26850 | 0.0184 | - |
| 65.1332 | 26900 | 0.0164 | - |
| 65.2542 | 26950 | 0.0178 | - |
| 65.3753 | 27000 | 0.0119 | - |
| 65.4964 | 27050 | 0.0113 | - |
| 65.6174 | 27100 | 0.0167 | - |
| 65.7385 | 27150 | 0.0163 | - |
| 65.8596 | 27200 | 0.0212 | - |
| 65.9806 | 27250 | 0.032 | - |
| 66.1017 | 27300 | 0.0264 | - |
| 66.2228 | 27350 | 0.0158 | - |
| 66.3438 | 27400 | 0.0269 | - |
| 66.4649 | 27450 | 0.0115 | - |
| 66.5860 | 27500 | 0.0135 | - |
| 66.7070 | 27550 | 0.0067 | - |
| 66.8281 | 27600 | 0.0135 | - |
| 66.9492 | 27650 | 0.0176 | - |
| 67.0702 | 27700 | 0.0236 | - |
| 67.1913 | 27750 | 0.0142 | - |
| 67.3123 | 27800 | 0.0262 | - |
| 67.4334 | 27850 | 0.0173 | - |
| 67.5545 | 27900 | 0.0133 | - |
| 67.6755 | 27950 | 0.011 | - |
| 67.7966 | 28000 | 0.0193 | - |
| 67.9177 | 28050 | 0.0251 | - |
| 68.0387 | 28100 | 0.0221 | - |
| 68.1598 | 28150 | 0.0176 | - |
| 68.2809 | 28200 | 0.01 | - |
| 68.4019 | 28250 | 0.0171 | - |
| 68.5230 | 28300 | 0.0209 | - |
| 68.6441 | 28350 | 0.0285 | - |
| 68.7651 | 28400 | 0.0316 | - |
| 68.8862 | 28450 | 0.0067 | - |
| 69.0073 | 28500 | 0.0103 | - |
| 69.1283 | 28550 | 0.0193 | - |
| 69.2494 | 28600 | 0.0172 | - |
| 69.3705 | 28650 | 0.0165 | - |
| 69.4915 | 28700 | 0.0079 | - |
| 69.6126 | 28750 | 0.0187 | - |
| 69.7337 | 28800 | 0.0269 | - |
| 69.8547 | 28850 | 0.0154 | - |
| 69.9758 | 28900 | 0.0059 | - |
| 70.0969 | 28950 | 0.0186 | - |
| 70.2179 | 29000 | 0.0144 | - |
| 70.3390 | 29050 | 0.0158 | - |
| 70.4600 | 29100 | 0.0329 | - |
| 70.5811 | 29150 | 0.0207 | - |
| 70.7022 | 29200 | 0.0192 | - |
| 70.8232 | 29250 | 0.0132 | - |
| 70.9443 | 29300 | 0.0297 | - |
| 71.0654 | 29350 | 0.0248 | - |
| 71.1864 | 29400 | 0.0191 | - |
| 71.3075 | 29450 | 0.0101 | - |
| 71.4286 | 29500 | 0.0027 | - |
| 71.5496 | 29550 | 0.0158 | - |
| 71.6707 | 29600 | 0.013 | - |
| 71.7918 | 29650 | 0.0061 | - |
| 71.9128 | 29700 | 0.0055 | - |
| 72.0339 | 29750 | 0.0219 | - |
| 72.1550 | 29800 | 0.0189 | - |
| 72.2760 | 29850 | 0.0227 | - |
| 72.3971 | 29900 | 0.0161 | - |
| 72.5182 | 29950 | 0.0168 | - |
| 72.6392 | 30000 | 0.018 | - |
| 72.7603 | 30050 | 0.0122 | - |
| 72.8814 | 30100 | 0.0152 | - |
| 73.0024 | 30150 | 0.0074 | - |
| 73.1235 | 30200 | 0.0024 | - |
| 73.2446 | 30250 | 0.0086 | - |
| 73.3656 | 30300 | 0.0028 | - |
| 73.4867 | 30350 | 0.0104 | - |
| 73.6077 | 30400 | 0.0144 | - |
| 73.7288 | 30450 | 0.0125 | - |
| 73.8499 | 30500 | 0.0248 | - |
| 73.9709 | 30550 | 0.0174 | - |
| 74.0920 | 30600 | 0.0063 | - |
| 74.2131 | 30650 | 0.0146 | - |
| 74.3341 | 30700 | 0.016 | - |
| 74.4552 | 30750 | 0.0145 | - |
| 74.5763 | 30800 | 0.0058 | - |
| 74.6973 | 30850 | 0.0114 | - |
| 74.8184 | 30900 | 0.0104 | - |
| 74.9395 | 30950 | 0.0277 | - |
| 75.0605 | 31000 | 0.0034 | - |
| 75.1816 | 31050 | 0.0111 | - |
| 75.3027 | 31100 | 0.0149 | - |
| 75.4237 | 31150 | 0.0053 | - |
| 75.5448 | 31200 | 0.008 | - |
| 75.6659 | 31250 | 0.013 | - |
| 75.7869 | 31300 | 0.0151 | - |
| 75.9080 | 31350 | 0.0198 | - |
| 76.0291 | 31400 | 0.013 | - |
| 76.1501 | 31450 | 0.0086 | - |
| 76.2712 | 31500 | 0.0028 | - |
| 76.3923 | 31550 | 0.0115 | - |
| 76.5133 | 31600 | 0.0295 | - |
| 76.6344 | 31650 | 0.0105 | - |
| 76.7554 | 31700 | 0.0098 | - |
| 76.8765 | 31750 | 0.0187 | - |
| 76.9976 | 31800 | 0.0133 | - |
| 77.1186 | 31850 | 0.0075 | - |
| 77.2397 | 31900 | 0.017 | - |
| 77.3608 | 31950 | 0.0112 | - |
| 77.4818 | 32000 | 0.006 | - |
| 77.6029 | 32050 | 0.0093 | - |
| 77.7240 | 32100 | 0.0166 | - |
| 77.8450 | 32150 | 0.0036 | - |
| 77.9661 | 32200 | 0.0109 | - |
| 78.0872 | 32250 | 0.0137 | - |
| 78.2082 | 32300 | 0.0051 | - |
| 78.3293 | 32350 | 0.0088 | - |
| 78.4504 | 32400 | 0.0127 | - |
| 78.5714 | 32450 | 0.021 | - |
| 78.6925 | 32500 | 0.011 | - |
| 78.8136 | 32550 | 0.0101 | - |
| 78.9346 | 32600 | 0.017 | - |
| 79.0557 | 32650 | 0.0042 | - |
| 79.1768 | 32700 | 0.0078 | - |
| 79.2978 | 32750 | 0.0 | - |
| 79.4189 | 32800 | 0.015 | - |
| 79.5400 | 32850 | 0.0023 | - |
| 79.6610 | 32900 | 0.0 | - |
| 79.7821 | 32950 | 0.0024 | - |
| 79.9031 | 33000 | 0.0087 | - |
| 80.0242 | 33050 | 0.0166 | - |
| 80.1453 | 33100 | 0.0007 | - |
| 80.2663 | 33150 | 0.0084 | - |
| 80.3874 | 33200 | 0.0086 | - |
| 80.5085 | 33250 | 0.0103 | - |
| 80.6295 | 33300 | 0.0121 | - |
| 80.7506 | 33350 | 0.0042 | - |
| 80.8717 | 33400 | 0.0042 | - |
| 80.9927 | 33450 | 0.0021 | - |
| 81.1138 | 33500 | 0.0041 | - |
| 81.2349 | 33550 | 0.0141 | - |
| 81.3559 | 33600 | 0.0144 | - |
| 81.4770 | 33650 | 0.0172 | - |
| 81.5981 | 33700 | 0.0077 | - |
| 81.7191 | 33750 | 0.0112 | - |
| 81.8402 | 33800 | 0.0109 | - |
| 81.9613 | 33850 | 0.009 | - |
| 82.0823 | 33900 | 0.004 | - |
| 82.2034 | 33950 | 0.0034 | - |
| 82.3245 | 34000 | 0.0019 | - |
| 82.4455 | 34050 | 0.011 | - |
| 82.5666 | 34100 | 0.0058 | - |
| 82.6877 | 34150 | 0.0091 | - |
| 82.8087 | 34200 | 0.0069 | - |
| 82.9298 | 34250 | 0.0047 | - |
| 83.0508 | 34300 | 0.015 | - |
| 83.1719 | 34350 | 0.0029 | - |
| 83.2930 | 34400 | 0.0197 | - |
| 83.4140 | 34450 | 0.0063 | - |
| 83.5351 | 34500 | 0.0121 | - |
| 83.6562 | 34550 | 0.0091 | - |
| 83.7772 | 34600 | 0.0096 | - |
| 83.8983 | 34650 | 0.0077 | - |
| 84.0194 | 34700 | 0.0097 | - |
| 84.1404 | 34750 | 0.0028 | - |
| 84.2615 | 34800 | 0.0006 | - |
| 84.3826 | 34850 | 0.0063 | - |
| 84.5036 | 34900 | 0.007 | - |
| 84.6247 | 34950 | 0.001 | - |
| 84.7458 | 35000 | 0.0069 | - |
| 84.8668 | 35050 | 0.0043 | - |
| 84.9879 | 35100 | 0.0068 | - |
| 85.1090 | 35150 | 0.0069 | - |
| 85.2300 | 35200 | 0.01 | - |
| 85.3511 | 35250 | 0.0067 | - |
| 85.4722 | 35300 | 0.0 | - |
| 85.5932 | 35350 | 0.0014 | - |
| 85.7143 | 35400 | 0.0038 | - |
| 85.8354 | 35450 | 0.0019 | - |
| 85.9564 | 35500 | 0.0057 | - |
| 86.0775 | 35550 | 0.0077 | - |
| 86.1985 | 35600 | 0.0067 | - |
| 86.3196 | 35650 | 0.0133 | - |
| 86.4407 | 35700 | 0.0152 | - |
| 86.5617 | 35750 | 0.0023 | - |
| 86.6828 | 35800 | 0.0155 | - |
| 86.8039 | 35850 | 0.011 | - |
| 86.9249 | 35900 | 0.0076 | - |
| 87.0460 | 35950 | 0.0153 | - |
| 87.1671 | 36000 | 0.0026 | - |
| 87.2881 | 36050 | 0.0115 | - |
| 87.4092 | 36100 | 0.0045 | - |
| 87.5303 | 36150 | 0.0016 | - |
| 87.6513 | 36200 | 0.0116 | - |
| 87.7724 | 36250 | 0.0018 | - |
| 87.8935 | 36300 | 0.0105 | - |
| 88.0145 | 36350 | 0.0119 | - |
| 88.1356 | 36400 | 0.0099 | - |
| 88.2567 | 36450 | 0.0076 | - |
| 88.3777 | 36500 | 0.0143 | - |
| 88.4988 | 36550 | 0.0067 | - |
| 88.6199 | 36600 | 0.0067 | - |
| 88.7409 | 36650 | 0.0097 | - |
| 88.8620 | 36700 | 0.0004 | - |
| 88.9831 | 36750 | 0.004 | - |
| 89.1041 | 36800 | 0.0 | - |
| 89.2252 | 36850 | 0.0132 | - |
| 89.3462 | 36900 | 0.0038 | - |
| 89.4673 | 36950 | 0.0042 | - |
| 89.5884 | 37000 | 0.0039 | - |
| 89.7094 | 37050 | 0.003 | - |
| 89.8305 | 37100 | 0.0013 | - |
| 89.9516 | 37150 | 0.0007 | - |
| 90.0726 | 37200 | 0.0002 | - |
| 90.1937 | 37250 | 0.004 | - |
| 90.3148 | 37300 | 0.0075 | - |
| 90.4358 | 37350 | 0.0 | - |
| 90.5569 | 37400 | 0.0071 | - |
| 90.6780 | 37450 | 0.0 | - |
| 90.7990 | 37500 | 0.0021 | - |
| 90.9201 | 37550 | 0.0064 | - |
| 91.0412 | 37600 | 0.0036 | - |
| 91.1622 | 37650 | 0.0049 | - |
| 91.2833 | 37700 | 0.0042 | - |
| 91.4044 | 37750 | 0.0072 | - |
| 91.5254 | 37800 | 0.0072 | - |
| 91.6465 | 37850 | 0.0126 | - |
| 91.7676 | 37900 | 0.0027 | - |
| 91.8886 | 37950 | 0.0074 | - |
| 92.0097 | 38000 | 0.0046 | - |
| 92.1308 | 38050 | 0.0115 | - |
| 92.2518 | 38100 | 0.0 | - |
| 92.3729 | 38150 | 0.0098 | - |
| 92.4939 | 38200 | 0.002 | - |
| 92.6150 | 38250 | 0.0018 | - |
| 92.7361 | 38300 | 0.0039 | - |
| 92.8571 | 38350 | 0.0069 | - |
| 92.9782 | 38400 | 0.0021 | - |
| 93.0993 | 38450 | 0.0053 | - |
| 93.2203 | 38500 | 0.0002 | - |
| 93.3414 | 38550 | 0.0079 | - |
| 93.4625 | 38600 | 0.0006 | - |
| 93.5835 | 38650 | 0.0054 | - |
| 93.7046 | 38700 | 0.0062 | - |
| 93.8257 | 38750 | 0.0006 | - |
| 93.9467 | 38800 | 0.0107 | - |
| 94.0678 | 38850 | 0.0059 | - |
| 94.1889 | 38900 | 0.0091 | - |
| 94.3099 | 38950 | 0.0 | - |
| 94.4310 | 39000 | 0.0018 | - |
| 94.5521 | 39050 | 0.0058 | - |
| 94.6731 | 39100 | 0.0031 | - |
| 94.7942 | 39150 | 0.0011 | - |
| 94.9153 | 39200 | 0.0003 | - |
| 95.0363 | 39250 | 0.012 | - |
| 95.1574 | 39300 | 0.0039 | - |
| 95.2785 | 39350 | 0.0025 | - |
| 95.3995 | 39400 | 0.0007 | - |
| 95.5206 | 39450 | 0.0029 | - |
| 95.6416 | 39500 | 0.0065 | - |
| 95.7627 | 39550 | 0.0 | - |
| 95.8838 | 39600 | 0.0034 | - |
| 96.0048 | 39650 | 0.0034 | - |
| 96.1259 | 39700 | 0.0015 | - |
| 96.2470 | 39750 | 0.0047 | - |
| 96.3680 | 39800 | 0.005 | - |
| 96.4891 | 39850 | 0.0032 | - |
| 96.6102 | 39900 | 0.0004 | - |
| 96.7312 | 39950 | 0.0015 | - |
| 96.8523 | 40000 | 0.0027 | - |
| 96.9734 | 40050 | 0.0059 | - |
| 97.0944 | 40100 | 0.0013 | - |
| 97.2155 | 40150 | 0.0003 | - |
| 97.3366 | 40200 | 0.0011 | - |
| 97.4576 | 40250 | 0.0 | - |
| 97.5787 | 40300 | 0.0003 | - |
| 97.6998 | 40350 | 0.0013 | - |
| 97.8208 | 40400 | 0.0112 | - |
| 97.9419 | 40450 | 0.0013 | - |
| 98.0630 | 40500 | 0.0096 | - |
| 98.1840 | 40550 | 0.0017 | - |
| 98.3051 | 40600 | 0.0116 | - |
| 98.4262 | 40650 | 0.0015 | - |
| 98.5472 | 40700 | 0.0007 | - |
| 98.6683 | 40750 | 0.0 | - |
| 98.7893 | 40800 | 0.0 | - |
| 98.9104 | 40850 | 0.0066 | - |
| 99.0315 | 40900 | 0.0001 | - |
| 99.1525 | 40950 | 0.0071 | - |
| 99.2736 | 41000 | 0.0001 | - |
| 99.3947 | 41050 | 0.0031 | - |
| 99.5157 | 41100 | 0.0056 | - |
| 99.6368 | 41150 | 0.0035 | - |
| 99.7579 | 41200 | 0.0048 | - |
| 99.8789 | 41250 | 0.0018 | - |
| 100.0 | 41300 | 0.0019 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- Transformers: 4.52.1
- PyTorch: 2.7.0+cu126
- Datasets: 3.6.0
- Tokenizers: 0.21.1
Citation
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}
}