--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 에이치엘사이언스 닥터 슈퍼칸 30캡슐 3박스 MinSellAmount (#M)건강식품>영양제>밀크씨슬 Gmarket > 식품 > 건강식품 > 영양제 > 밀크씨슬 - text: 풍기도깨비 금쪽같은 고려홍삼 키즈홍삼 90p 90포 × 900g × 1박스 (#M)식품>건강식품>홍삼>혼합세트 T200 > Naverstore > 식품 > 건강식품 > 홍삼/인삼 > 홍삼세트 - text: 대웅생명과학 저온 초임계 알티지 오메가3 60캡슐 6박스6개월분 233219 (#M)식품>건강식품>영양제>스쿠알렌 T200 > Naverstore > 식품 > 건강식품 > 영양제 > 스쿠알렌 - text: '[1 ]락피도 프로바이오틱스,비타민D,코코몽 키즈 외 13_알티지 오메가3 츄어블 키즈 X2개 11st>건강식품>영양제>칼슘;11st Hour Event > 마트/유아동 > 식품 > 건강/다이어트식품 > 건강식품 11st Hour Event > 마트/유아동 > 식품 > 건강/다이어트식품 > 건강식품' - text: 굿헬스 스쿠알렌 1000mg 상어간유 스쿠알란 300정 4개 Good Health Squalene (#M)식품>건강식품>영양제>스쿠알렌 T200 > Naverstore > 식품 > 건강식품 > 영양제 > 스쿠알렌 metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true base_model: klue/roberta-base 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: accuracy value: 0.9025131816957241 name: Accuracy --- # 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:** 101 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 | |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 9.0 | | | 13.0 | | | 16.0 | | | 65.0 | | | 54.0 | | | 59.0 | | | 24.0 | | | 88.0 | | | 87.0 | | | 30.0 | | | 38.0 | | | 80.0 | | | 25.0 | | | 7.0 | | | 98.0 | | | 11.0 | | | 10.0 | | | 58.0 | | | 34.0 | | | 99.0 | | | 56.0 | | | 39.0 | | | 91.0 | | | 96.0 | | | 69.0 | | | 68.0 | | | 75.0 | | | 100.0 | | | 20.0 | | | 47.0 | | | 79.0 | | | 37.0 | | | 66.0 | | | 0.0 | | | 46.0 | | | 74.0 | | | 4.0 | | | 86.0 | | | 73.0 | | | 85.0 | | | 55.0 | | | 8.0 | | | 31.0 | | | 45.0 | | | 48.0 | | | 49.0 | | | 2.0 | | | 60.0 | | | 29.0 | | | 61.0 | | | 62.0 | | | 81.0 | | | 78.0 | | | 23.0 | | | 89.0 | | | 35.0 | | | 93.0 | | | 43.0 | | | 51.0 | | | 28.0 | | | 95.0 | | | 71.0 | | | 76.0 | | | 90.0 | | | 27.0 | | | 42.0 | | | 14.0 | | | 6.0 | | | 77.0 | | | 17.0 | | | 70.0 | | | 57.0 | | | 92.0 | | | 40.0 | | | 36.0 | | | 63.0 | | | 52.0 | | | 97.0 | | | 32.0 | | | 41.0 | | | 21.0 | | | 1.0 | | | 84.0 | | | 67.0 | | | 26.0 | | | 5.0 | | | 33.0 | | | 18.0 | | | 72.0 | | | 15.0 | | | 12.0 | | | 83.0 | | | 22.0 | | | 44.0 | | | 64.0 | | | 53.0 | | | 3.0 | | | 19.0 | | | 82.0 | | | 94.0 | | | 50.0 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.9025 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_top_fd1") # Run inference preds = model("에이치엘사이언스 닥터 슈퍼칸 30캡슐 3박스 MinSellAmount (#M)건강식품>영양제>밀크씨슬 Gmarket > 식품 > 건강식품 > 영양제 > 밀크씨슬") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 6 | 21.7359 | 60 | | Label | Training Sample Count | |:------|:----------------------| | 0.0 | 50 | | 1.0 | 50 | | 2.0 | 50 | | 3.0 | 2 | | 4.0 | 50 | | 5.0 | 50 | | 6.0 | 50 | | 7.0 | 50 | | 8.0 | 50 | | 9.0 | 50 | | 10.0 | 50 | | 11.0 | 50 | | 12.0 | 50 | | 13.0 | 50 | | 14.0 | 50 | | 15.0 | 31 | | 16.0 | 50 | | 17.0 | 27 | | 18.0 | 50 | | 19.0 | 3 | | 20.0 | 50 | | 21.0 | 15 | | 22.0 | 26 | | 23.0 | 50 | | 24.0 | 49 | | 25.0 | 50 | | 26.0 | 50 | | 27.0 | 50 | | 28.0 | 50 | | 29.0 | 50 | | 30.0 | 50 | | 31.0 | 50 | | 32.0 | 50 | | 33.0 | 39 | | 34.0 | 50 | | 35.0 | 50 | | 36.0 | 50 | | 37.0 | 50 | | 38.0 | 50 | | 39.0 | 50 | | 40.0 | 50 | | 41.0 | 50 | | 42.0 | 50 | | 43.0 | 50 | | 44.0 | 10 | | 45.0 | 50 | | 46.0 | 50 | | 47.0 | 50 | | 48.0 | 50 | | 49.0 | 50 | | 50.0 | 1 | | 51.0 | 50 | | 52.0 | 11 | | 53.0 | 12 | | 54.0 | 26 | | 55.0 | 50 | | 56.0 | 50 | | 57.0 | 50 | | 58.0 | 50 | | 59.0 | 50 | | 60.0 | 50 | | 61.0 | 50 | | 62.0 | 44 | | 63.0 | 50 | | 64.0 | 16 | | 65.0 | 50 | | 66.0 | 50 | | 67.0 | 50 | | 68.0 | 50 | | 69.0 | 50 | | 70.0 | 50 | | 71.0 | 4 | | 72.0 | 50 | | 73.0 | 50 | | 74.0 | 50 | | 75.0 | 50 | | 76.0 | 50 | | 77.0 | 50 | | 78.0 | 50 | | 79.0 | 47 | | 80.0 | 24 | | 81.0 | 50 | | 82.0 | 1 | | 83.0 | 50 | | 84.0 | 50 | | 85.0 | 50 | | 86.0 | 50 | | 87.0 | 50 | | 88.0 | 50 | | 89.0 | 50 | | 90.0 | 50 | | 91.0 | 50 | | 92.0 | 50 | | 93.0 | 13 | | 94.0 | 2 | | 95.0 | 50 | | 96.0 | 50 | | 97.0 | 50 | | 98.0 | 36 | | 99.0 | 50 | | 100.0 | 50 | ### Training Hyperparameters - batch_size: (64, 64) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 30 - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:-----:|:-------------:|:---------------:| | 0.0005 | 1 | 0.5992 | - | | 0.0243 | 50 | 0.4884 | - | | 0.0486 | 100 | 0.4851 | - | | 0.0729 | 150 | 0.481 | - | | 0.0972 | 200 | 0.3988 | - | | 0.1215 | 250 | 0.2751 | - | | 0.1458 | 300 | 0.2462 | - | | 0.1701 | 350 | 0.2105 | - | | 0.1944 | 400 | 0.186 | - | | 0.2187 | 450 | 0.171 | - | | 0.2430 | 500 | 0.1598 | - | | 0.2672 | 550 | 0.1399 | - | | 0.2915 | 600 | 0.1307 | - | | 0.3158 | 650 | 0.1118 | - | | 0.3401 | 700 | 0.1066 | - | | 0.3644 | 750 | 0.0928 | - | | 0.3887 | 800 | 0.0829 | - | | 0.4130 | 850 | 0.0774 | - | | 0.4373 | 900 | 0.0736 | - | | 0.4616 | 950 | 0.0645 | - | | 0.4859 | 1000 | 0.0605 | - | | 0.5102 | 1050 | 0.0552 | - | | 0.5345 | 1100 | 0.0518 | - | | 0.5588 | 1150 | 0.0488 | - | | 0.5831 | 1200 | 0.0449 | - | | 0.6074 | 1250 | 0.0419 | - | | 0.6317 | 1300 | 0.0407 | - | | 0.6560 | 1350 | 0.0367 | - | | 0.6803 | 1400 | 0.0356 | - | | 0.7046 | 1450 | 0.0353 | - | | 0.7289 | 1500 | 0.0331 | - | | 0.7532 | 1550 | 0.0299 | - | | 0.7775 | 1600 | 0.0273 | - | | 0.8017 | 1650 | 0.0275 | - | | 0.8260 | 1700 | 0.028 | - | | 0.8503 | 1750 | 0.0262 | - | | 0.8746 | 1800 | 0.0243 | - | | 0.8989 | 1850 | 0.0235 | - | | 0.9232 | 1900 | 0.0217 | - | | 0.9475 | 1950 | 0.0207 | - | | 0.9718 | 2000 | 0.0223 | - | | 0.9961 | 2050 | 0.0208 | - | | 1.0204 | 2100 | 0.0181 | - | | 1.0447 | 2150 | 0.0186 | - | | 1.0690 | 2200 | 0.0197 | - | | 1.0933 | 2250 | 0.0154 | - | | 1.1176 | 2300 | 0.0157 | - | | 1.1419 | 2350 | 0.017 | - | | 1.1662 | 2400 | 0.0159 | - | | 1.1905 | 2450 | 0.0151 | - | | 1.2148 | 2500 | 0.0152 | - | | 1.2391 | 2550 | 0.0157 | - | | 1.2634 | 2600 | 0.0136 | - | | 1.2877 | 2650 | 0.0136 | - | | 1.3120 | 2700 | 0.0142 | - | | 1.3362 | 2750 | 0.0131 | - | | 1.3605 | 2800 | 0.0118 | - | | 1.3848 | 2850 | 0.0133 | - | | 1.4091 | 2900 | 0.01 | - | | 1.4334 | 2950 | 0.0108 | - | | 1.4577 | 3000 | 0.013 | - | | 1.4820 | 3050 | 0.0106 | - | | 1.5063 | 3100 | 0.0107 | - | | 1.5306 | 3150 | 0.0126 | - | | 1.5549 | 3200 | 0.0103 | - | | 1.5792 | 3250 | 0.0092 | - | | 1.6035 | 3300 | 0.0112 | - | | 1.6278 | 3350 | 0.009 | - | | 1.6521 | 3400 | 0.009 | - | | 1.6764 | 3450 | 0.0086 | - | | 1.7007 | 3500 | 0.0075 | - | | 1.7250 | 3550 | 0.0089 | - | | 1.7493 | 3600 | 0.0099 | - | | 1.7736 | 3650 | 0.0085 | - | | 1.7979 | 3700 | 0.0089 | - | | 1.8222 | 3750 | 0.009 | - | | 1.8465 | 3800 | 0.008 | - | | 1.8707 | 3850 | 0.009 | - | | 1.8950 | 3900 | 0.0083 | - | | 1.9193 | 3950 | 0.0087 | - | | 1.9436 | 4000 | 0.0098 | - | | 1.9679 | 4050 | 0.0099 | - | | 1.9922 | 4100 | 0.0098 | - | | 2.0165 | 4150 | 0.0075 | - | | 2.0408 | 4200 | 0.0067 | - | | 2.0651 | 4250 | 0.0078 | - | | 2.0894 | 4300 | 0.0084 | - | | 2.1137 | 4350 | 0.0079 | - | | 2.1380 | 4400 | 0.0054 | - | | 2.1623 | 4450 | 0.005 | - | | 2.1866 | 4500 | 0.0047 | - | | 2.2109 | 4550 | 0.0062 | - | | 2.2352 | 4600 | 0.0063 | - | | 2.2595 | 4650 | 0.0066 | - | | 2.2838 | 4700 | 0.0066 | - | | 2.3081 | 4750 | 0.0059 | - | | 2.3324 | 4800 | 0.005 | - | | 2.3567 | 4850 | 0.0049 | - | | 2.3810 | 4900 | 0.0044 | - | | 2.4052 | 4950 | 0.0037 | - | | 2.4295 | 5000 | 0.0046 | - | | 2.4538 | 5050 | 0.007 | - | | 2.4781 | 5100 | 0.006 | - | | 2.5024 | 5150 | 0.005 | - | | 2.5267 | 5200 | 0.0058 | - | | 2.5510 | 5250 | 0.0054 | - | | 2.5753 | 5300 | 0.0067 | - | | 2.5996 | 5350 | 0.0059 | - | | 2.6239 | 5400 | 0.0081 | - | | 2.6482 | 5450 | 0.0068 | - | | 2.6725 | 5500 | 0.006 | - | | 2.6968 | 5550 | 0.0062 | - | | 2.7211 | 5600 | 0.0059 | - | | 2.7454 | 5650 | 0.0061 | - | | 2.7697 | 5700 | 0.0045 | - | | 2.7940 | 5750 | 0.0051 | - | | 2.8183 | 5800 | 0.0048 | - | | 2.8426 | 5850 | 0.0052 | - | | 2.8669 | 5900 | 0.0053 | - | | 2.8912 | 5950 | 0.0061 | - | | 2.9155 | 6000 | 0.0058 | - | | 2.9397 | 6050 | 0.0051 | - | | 2.9640 | 6100 | 0.0054 | - | | 2.9883 | 6150 | 0.0053 | - | | 3.0126 | 6200 | 0.0045 | - | | 3.0369 | 6250 | 0.0048 | - | | 3.0612 | 6300 | 0.003 | - | | 3.0855 | 6350 | 0.0033 | - | | 3.1098 | 6400 | 0.0038 | - | | 3.1341 | 6450 | 0.0038 | - | | 3.1584 | 6500 | 0.0047 | - | | 3.1827 | 6550 | 0.0036 | - | | 3.2070 | 6600 | 0.0036 | - | | 3.2313 | 6650 | 0.0036 | - | | 3.2556 | 6700 | 0.0045 | - | | 3.2799 | 6750 | 0.0029 | - | | 3.3042 | 6800 | 0.0032 | - | | 3.3285 | 6850 | 0.0037 | - | | 3.3528 | 6900 | 0.0026 | - | | 3.3771 | 6950 | 0.0026 | - | | 3.4014 | 7000 | 0.005 | - | | 3.4257 | 7050 | 0.0028 | - | | 3.4500 | 7100 | 0.0025 | - | | 3.4742 | 7150 | 0.0035 | - | | 3.4985 | 7200 | 0.0045 | - | | 3.5228 | 7250 | 0.005 | - | | 3.5471 | 7300 | 0.005 | - | | 3.5714 | 7350 | 0.0044 | - | | 3.5957 | 7400 | 0.0037 | - | | 3.6200 | 7450 | 0.0034 | - | | 3.6443 | 7500 | 0.0027 | - | | 3.6686 | 7550 | 0.0018 | - | | 3.6929 | 7600 | 0.0029 | - | | 3.7172 | 7650 | 0.0035 | - | | 3.7415 | 7700 | 0.0029 | - | | 3.7658 | 7750 | 0.0045 | - | | 3.7901 | 7800 | 0.0043 | - | | 3.8144 | 7850 | 0.0028 | - | | 3.8387 | 7900 | 0.0036 | - | | 3.8630 | 7950 | 0.0039 | - | | 3.8873 | 8000 | 0.0033 | - | | 3.9116 | 8050 | 0.0031 | - | | 3.9359 | 8100 | 0.0029 | - | | 3.9602 | 8150 | 0.002 | - | | 3.9845 | 8200 | 0.0024 | - | | 4.0087 | 8250 | 0.0026 | - | | 4.0330 | 8300 | 0.0016 | - | | 4.0573 | 8350 | 0.0036 | - | | 4.0816 | 8400 | 0.0035 | - | | 4.1059 | 8450 | 0.0039 | - | | 4.1302 | 8500 | 0.0034 | - | | 4.1545 | 8550 | 0.0024 | - | | 4.1788 | 8600 | 0.0025 | - | | 4.2031 | 8650 | 0.003 | - | | 4.2274 | 8700 | 0.0017 | - | | 4.2517 | 8750 | 0.0021 | - | | 4.2760 | 8800 | 0.0021 | - | | 4.3003 | 8850 | 0.0012 | - | | 4.3246 | 8900 | 0.0013 | - | | 4.3489 | 8950 | 0.0026 | - | | 4.3732 | 9000 | 0.0014 | - | | 4.3975 | 9050 | 0.0023 | - | | 4.4218 | 9100 | 0.0021 | - | | 4.4461 | 9150 | 0.0024 | - | | 4.4704 | 9200 | 0.0025 | - | | 4.4947 | 9250 | 0.002 | - | | 4.5190 | 9300 | 0.0019 | - | | 4.5432 | 9350 | 0.002 | - | | 4.5675 | 9400 | 0.0012 | - | | 4.5918 | 9450 | 0.0022 | - | | 4.6161 | 9500 | 0.0015 | - | | 4.6404 | 9550 | 0.0013 | - | | 4.6647 | 9600 | 0.0014 | - | | 4.6890 | 9650 | 0.0025 | - | | 4.7133 | 9700 | 0.0016 | - | | 4.7376 | 9750 | 0.0014 | - | | 4.7619 | 9800 | 0.0019 | - | | 4.7862 | 9850 | 0.0016 | - | | 4.8105 | 9900 | 0.002 | - | | 4.8348 | 9950 | 0.0019 | - | | 4.8591 | 10000 | 0.0019 | - | | 4.8834 | 10050 | 0.0022 | - | | 4.9077 | 10100 | 0.0027 | - | | 4.9320 | 10150 | 0.0029 | - | | 4.9563 | 10200 | 0.0037 | - | | 4.9806 | 10250 | 0.003 | - | | 5.0049 | 10300 | 0.0016 | - | | 5.0292 | 10350 | 0.0021 | - | | 5.0534 | 10400 | 0.0023 | - | | 5.0777 | 10450 | 0.0015 | - | | 5.1020 | 10500 | 0.0014 | - | | 5.1263 | 10550 | 0.0013 | - | | 5.1506 | 10600 | 0.0015 | - | | 5.1749 | 10650 | 0.0018 | - | | 5.1992 | 10700 | 0.0017 | - | | 5.2235 | 10750 | 0.0019 | - | | 5.2478 | 10800 | 0.0025 | - | | 5.2721 | 10850 | 0.002 | - | | 5.2964 | 10900 | 0.0026 | - | | 5.3207 | 10950 | 0.0014 | - | | 5.3450 | 11000 | 0.0015 | - | | 5.3693 | 11050 | 0.0033 | - | | 5.3936 | 11100 | 0.0015 | - | | 5.4179 | 11150 | 0.0022 | - | | 5.4422 | 11200 | 0.0023 | - | | 5.4665 | 11250 | 0.0025 | - | | 5.4908 | 11300 | 0.0011 | - | | 5.5151 | 11350 | 0.0019 | - | | 5.5394 | 11400 | 0.0017 | - | | 5.5637 | 11450 | 0.0014 | - | | 5.5879 | 11500 | 0.0009 | - | | 5.6122 | 11550 | 0.0016 | - | | 5.6365 | 11600 | 0.0025 | - | | 5.6608 | 11650 | 0.0012 | - | | 5.6851 | 11700 | 0.0014 | - | | 5.7094 | 11750 | 0.0015 | - | | 5.7337 | 11800 | 0.0017 | - | | 5.7580 | 11850 | 0.0009 | - | | 5.7823 | 11900 | 0.0018 | - | | 5.8066 | 11950 | 0.0005 | - | | 5.8309 | 12000 | 0.0014 | - | | 5.8552 | 12050 | 0.0006 | - | | 5.8795 | 12100 | 0.0013 | - | | 5.9038 | 12150 | 0.001 | - | | 5.9281 | 12200 | 0.0009 | - | | 5.9524 | 12250 | 0.0018 | - | | 5.9767 | 12300 | 0.0021 | - | | 6.0010 | 12350 | 0.0027 | - | | 6.0253 | 12400 | 0.0031 | - | | 6.0496 | 12450 | 0.0032 | - | | 6.0739 | 12500 | 0.0026 | - | | 6.0982 | 12550 | 0.0019 | - | | 6.1224 | 12600 | 0.0012 | - | | 6.1467 | 12650 | 0.0006 | - | | 6.1710 | 12700 | 0.0015 | - | | 6.1953 | 12750 | 0.0011 | - | | 6.2196 | 12800 | 0.0014 | - | | 6.2439 | 12850 | 0.0017 | - | | 6.2682 | 12900 | 0.0016 | - | | 6.2925 | 12950 | 0.0008 | - | | 6.3168 | 13000 | 0.0006 | - | | 6.3411 | 13050 | 0.0019 | - | | 6.3654 | 13100 | 0.0018 | - | | 6.3897 | 13150 | 0.0007 | - | | 6.4140 | 13200 | 0.0011 | - | | 6.4383 | 13250 | 0.0009 | - | | 6.4626 | 13300 | 0.0012 | - | | 6.4869 | 13350 | 0.0012 | - | | 6.5112 | 13400 | 0.0013 | - | | 6.5355 | 13450 | 0.0018 | - | | 6.5598 | 13500 | 0.0011 | - | | 6.5841 | 13550 | 0.0016 | - | | 6.6084 | 13600 | 0.0007 | - | | 6.6327 | 13650 | 0.0007 | - | | 6.6569 | 13700 | 0.0014 | - | | 6.6812 | 13750 | 0.0017 | - | | 6.7055 | 13800 | 0.0018 | - | | 6.7298 | 13850 | 0.0012 | - | | 6.7541 | 13900 | 0.0016 | - | | 6.7784 | 13950 | 0.0012 | - | | 6.8027 | 14000 | 0.0007 | - | | 6.8270 | 14050 | 0.0015 | - | | 6.8513 | 14100 | 0.0019 | - | | 6.8756 | 14150 | 0.0016 | - | | 6.8999 | 14200 | 0.0017 | - | | 6.9242 | 14250 | 0.0014 | - | | 6.9485 | 14300 | 0.0016 | - | | 6.9728 | 14350 | 0.0007 | - | | 6.9971 | 14400 | 0.0007 | - | | 7.0214 | 14450 | 0.0011 | - | | 7.0457 | 14500 | 0.0007 | - | | 7.0700 | 14550 | 0.0008 | - | | 7.0943 | 14600 | 0.0004 | - | | 7.1186 | 14650 | 0.0012 | - | | 7.1429 | 14700 | 0.0009 | - | | 7.1672 | 14750 | 0.0007 | - | | 7.1914 | 14800 | 0.0023 | - | | 7.2157 | 14850 | 0.0023 | - | | 7.2400 | 14900 | 0.0016 | - | | 7.2643 | 14950 | 0.0021 | - | | 7.2886 | 15000 | 0.0014 | - | | 7.3129 | 15050 | 0.0009 | - | | 7.3372 | 15100 | 0.0009 | - | | 7.3615 | 15150 | 0.0013 | - | | 7.3858 | 15200 | 0.0009 | - | | 7.4101 | 15250 | 0.0012 | - | | 7.4344 | 15300 | 0.0007 | - | | 7.4587 | 15350 | 0.001 | - | | 7.4830 | 15400 | 0.0006 | - | | 7.5073 | 15450 | 0.0019 | - | | 7.5316 | 15500 | 0.0017 | - | | 7.5559 | 15550 | 0.0015 | - | | 7.5802 | 15600 | 0.0022 | - | | 7.6045 | 15650 | 0.0009 | - | | 7.6288 | 15700 | 0.0008 | - | | 7.6531 | 15750 | 0.0011 | - | | 7.6774 | 15800 | 0.001 | - | | 7.7017 | 15850 | 0.001 | - | | 7.7259 | 15900 | 0.0009 | - | | 7.7502 | 15950 | 0.0007 | - | | 7.7745 | 16000 | 0.0004 | - | | 7.7988 | 16050 | 0.0016 | - | | 7.8231 | 16100 | 0.0003 | - | | 7.8474 | 16150 | 0.001 | - | | 7.8717 | 16200 | 0.0008 | - | | 7.8960 | 16250 | 0.0014 | - | | 7.9203 | 16300 | 0.0006 | - | | 7.9446 | 16350 | 0.0013 | - | | 7.9689 | 16400 | 0.0012 | - | | 7.9932 | 16450 | 0.0012 | - | | 8.0175 | 16500 | 0.001 | - | | 8.0418 | 16550 | 0.0009 | - | | 8.0661 | 16600 | 0.0006 | - | | 8.0904 | 16650 | 0.0007 | - | | 8.1147 | 16700 | 0.0005 | - | | 8.1390 | 16750 | 0.001 | - | | 8.1633 | 16800 | 0.0008 | - | | 8.1876 | 16850 | 0.0014 | - | | 8.2119 | 16900 | 0.0011 | - | | 8.2362 | 16950 | 0.0009 | - | | 8.2604 | 17000 | 0.0003 | - | | 8.2847 | 17050 | 0.001 | - | | 8.3090 | 17100 | 0.0008 | - | | 8.3333 | 17150 | 0.0009 | - | | 8.3576 | 17200 | 0.0008 | - | | 8.3819 | 17250 | 0.0005 | - | | 8.4062 | 17300 | 0.0005 | - | | 8.4305 | 17350 | 0.0013 | - | | 8.4548 | 17400 | 0.0006 | - | | 8.4791 | 17450 | 0.0005 | - | | 8.5034 | 17500 | 0.001 | - | | 8.5277 | 17550 | 0.0013 | - | | 8.5520 | 17600 | 0.0007 | - | | 8.5763 | 17650 | 0.0008 | - | | 8.6006 | 17700 | 0.0011 | - | | 8.6249 | 17750 | 0.0005 | - | | 8.6492 | 17800 | 0.0007 | - | | 8.6735 | 17850 | 0.0008 | - | | 8.6978 | 17900 | 0.0021 | - | | 8.7221 | 17950 | 0.0027 | - | | 8.7464 | 18000 | 0.002 | - | | 8.7707 | 18050 | 0.0011 | - | | 8.7949 | 18100 | 0.0005 | - | | 8.8192 | 18150 | 0.0006 | - | | 8.8435 | 18200 | 0.0011 | - | | 8.8678 | 18250 | 0.0008 | - | | 8.8921 | 18300 | 0.0005 | - | | 8.9164 | 18350 | 0.0009 | - | | 8.9407 | 18400 | 0.0011 | - | | 8.9650 | 18450 | 0.001 | - | | 8.9893 | 18500 | 0.0009 | - | | 9.0136 | 18550 | 0.0005 | - | | 9.0379 | 18600 | 0.0008 | - | | 9.0622 | 18650 | 0.0006 | - | | 9.0865 | 18700 | 0.001 | - | | 9.1108 | 18750 | 0.0006 | - | | 9.1351 | 18800 | 0.0003 | - | | 9.1594 | 18850 | 0.0005 | - | | 9.1837 | 18900 | 0.0006 | - | | 9.2080 | 18950 | 0.0012 | - | | 9.2323 | 19000 | 0.0004 | - | | 9.2566 | 19050 | 0.0004 | - | | 9.2809 | 19100 | 0.001 | - | | 9.3052 | 19150 | 0.0007 | - | | 9.3294 | 19200 | 0.0006 | - | | 9.3537 | 19250 | 0.0002 | - | | 9.3780 | 19300 | 0.0008 | - | | 9.4023 | 19350 | 0.0005 | - | | 9.4266 | 19400 | 0.0012 | - | | 9.4509 | 19450 | 0.0009 | - | | 9.4752 | 19500 | 0.0008 | - | | 9.4995 | 19550 | 0.0004 | - | | 9.5238 | 19600 | 0.001 | - | | 9.5481 | 19650 | 0.001 | - | | 9.5724 | 19700 | 0.0009 | - | | 9.5967 | 19750 | 0.0006 | - | | 9.6210 | 19800 | 0.0008 | - | | 9.6453 | 19850 | 0.0008 | - | | 9.6696 | 19900 | 0.0006 | - | | 9.6939 | 19950 | 0.0011 | - | | 9.7182 | 20000 | 0.0008 | - | | 9.7425 | 20050 | 0.0006 | - | | 9.7668 | 20100 | 0.0008 | - | | 9.7911 | 20150 | 0.001 | - | | 9.8154 | 20200 | 0.0006 | - | | 9.8397 | 20250 | 0.0004 | - | | 9.8639 | 20300 | 0.0005 | - | | 9.8882 | 20350 | 0.0007 | - | | 9.9125 | 20400 | 0.0005 | - | | 9.9368 | 20450 | 0.0011 | - | | 9.9611 | 20500 | 0.0004 | - | | 9.9854 | 20550 | 0.0011 | - | | 10.0097 | 20600 | 0.0004 | - | | 10.0340 | 20650 | 0.0006 | - | | 10.0583 | 20700 | 0.0002 | - | | 10.0826 | 20750 | 0.0012 | - | | 10.1069 | 20800 | 0.0011 | - | | 10.1312 | 20850 | 0.0008 | - | | 10.1555 | 20900 | 0.0005 | - | | 10.1798 | 20950 | 0.0007 | - | | 10.2041 | 21000 | 0.0005 | - | | 10.2284 | 21050 | 0.0002 | - | | 10.2527 | 21100 | 0.0006 | - | | 10.2770 | 21150 | 0.0004 | - | | 10.3013 | 21200 | 0.0014 | - | | 10.3256 | 21250 | 0.0008 | - | | 10.3499 | 21300 | 0.0005 | - | | 10.3741 | 21350 | 0.0004 | - | | 10.3984 | 21400 | 0.0011 | - | | 10.4227 | 21450 | 0.0004 | - | | 10.4470 | 21500 | 0.0007 | - | | 10.4713 | 21550 | 0.0004 | - | | 10.4956 | 21600 | 0.0005 | - | | 10.5199 | 21650 | 0.0006 | - | | 10.5442 | 21700 | 0.0005 | - | | 10.5685 | 21750 | 0.0004 | - | | 10.5928 | 21800 | 0.0005 | - | | 10.6171 | 21850 | 0.0008 | - | | 10.6414 | 21900 | 0.0004 | - | | 10.6657 | 21950 | 0.0007 | - | | 10.6900 | 22000 | 0.0003 | - | | 10.7143 | 22050 | 0.0005 | - | | 10.7386 | 22100 | 0.0005 | - | | 10.7629 | 22150 | 0.0006 | - | | 10.7872 | 22200 | 0.0005 | - | | 10.8115 | 22250 | 0.0008 | - | | 10.8358 | 22300 | 0.0011 | - | | 10.8601 | 22350 | 0.0009 | - | | 10.8844 | 22400 | 0.002 | - | | 10.9086 | 22450 | 0.003 | - | | 10.9329 | 22500 | 0.0024 | - | | 10.9572 | 22550 | 0.001 | - | | 10.9815 | 22600 | 0.0015 | - | | 11.0058 | 22650 | 0.0011 | - | | 11.0301 | 22700 | 0.0012 | - | | 11.0544 | 22750 | 0.0009 | - | | 11.0787 | 22800 | 0.0006 | - | | 11.1030 | 22850 | 0.0006 | - | | 11.1273 | 22900 | 0.0005 | - | | 11.1516 | 22950 | 0.0003 | - | | 11.1759 | 23000 | 0.0003 | - | | 11.2002 | 23050 | 0.0007 | - | | 11.2245 | 23100 | 0.0004 | - | | 11.2488 | 23150 | 0.0005 | - | | 11.2731 | 23200 | 0.0004 | - | | 11.2974 | 23250 | 0.0001 | - | | 11.3217 | 23300 | 0.0002 | - | | 11.3460 | 23350 | 0.0005 | - | | 11.3703 | 23400 | 0.0009 | - | | 11.3946 | 23450 | 0.0004 | - | | 11.4189 | 23500 | 0.0006 | - | | 11.4431 | 23550 | 0.0007 | - | | 11.4674 | 23600 | 0.0004 | - | | 11.4917 | 23650 | 0.0005 | - | | 11.5160 | 23700 | 0.0002 | - | | 11.5403 | 23750 | 0.0008 | - | | 11.5646 | 23800 | 0.0007 | - | | 11.5889 | 23850 | 0.0002 | - | | 11.6132 | 23900 | 0.0005 | - | | 11.6375 | 23950 | 0.0005 | - | | 11.6618 | 24000 | 0.0002 | - | | 11.6861 | 24050 | 0.0007 | - | | 11.7104 | 24100 | 0.0004 | - | | 11.7347 | 24150 | 0.0003 | - | | 11.7590 | 24200 | 0.0006 | - | | 11.7833 | 24250 | 0.0004 | - | | 11.8076 | 24300 | 0.001 | - | | 11.8319 | 24350 | 0.0004 | - | | 11.8562 | 24400 | 0.0005 | - | | 11.8805 | 24450 | 0.0003 | - | | 11.9048 | 24500 | 0.0004 | - | | 11.9291 | 24550 | 0.0005 | - | | 11.9534 | 24600 | 0.0008 | - | | 11.9776 | 24650 | 0.0004 | - | | 12.0019 | 24700 | 0.0008 | - | | 12.0262 | 24750 | 0.0006 | - | | 12.0505 | 24800 | 0.0008 | - | | 12.0748 | 24850 | 0.0004 | - | | 12.0991 | 24900 | 0.0002 | - | | 12.1234 | 24950 | 0.0005 | - | | 12.1477 | 25000 | 0.0005 | - | | 12.1720 | 25050 | 0.0002 | - | | 12.1963 | 25100 | 0.0003 | - | | 12.2206 | 25150 | 0.0013 | - | | 12.2449 | 25200 | 0.0009 | - | | 12.2692 | 25250 | 0.0007 | - | | 12.2935 | 25300 | 0.0 | - | | 12.3178 | 25350 | 0.0004 | - | | 12.3421 | 25400 | 0.0002 | - | | 12.3664 | 25450 | 0.0003 | - | | 12.3907 | 25500 | 0.0005 | - | | 12.4150 | 25550 | 0.0005 | - | | 12.4393 | 25600 | 0.0002 | - | | 12.4636 | 25650 | 0.0002 | - | | 12.4879 | 25700 | 0.0005 | - | | 12.5121 | 25750 | 0.0002 | - | | 12.5364 | 25800 | 0.0005 | - | | 12.5607 | 25850 | 0.0006 | - | | 12.5850 | 25900 | 0.0006 | - | | 12.6093 | 25950 | 0.0007 | - | | 12.6336 | 26000 | 0.0002 | - | | 12.6579 | 26050 | 0.0004 | - | | 12.6822 | 26100 | 0.0004 | - | | 12.7065 | 26150 | 0.0004 | - | | 12.7308 | 26200 | 0.0004 | - | | 12.7551 | 26250 | 0.0006 | - | | 12.7794 | 26300 | 0.0004 | - | | 12.8037 | 26350 | 0.0003 | - | | 12.8280 | 26400 | 0.0009 | - | | 12.8523 | 26450 | 0.0004 | - | | 12.8766 | 26500 | 0.0004 | - | | 12.9009 | 26550 | 0.0004 | - | | 12.9252 | 26600 | 0.0003 | - | | 12.9495 | 26650 | 0.0002 | - | | 12.9738 | 26700 | 0.0005 | - | | 12.9981 | 26750 | 0.0005 | - | | 13.0224 | 26800 | 0.0005 | - | | 13.0466 | 26850 | 0.0007 | - | | 13.0709 | 26900 | 0.0003 | - | | 13.0952 | 26950 | 0.0004 | - | | 13.1195 | 27000 | 0.0003 | - | | 13.1438 | 27050 | 0.0003 | - | | 13.1681 | 27100 | 0.0001 | - | | 13.1924 | 27150 | 0.0001 | - | | 13.2167 | 27200 | 0.0003 | - | | 13.2410 | 27250 | 0.0 | - | | 13.2653 | 27300 | 0.0002 | - | | 13.2896 | 27350 | 0.0002 | - | | 13.3139 | 27400 | 0.0002 | - | | 13.3382 | 27450 | 0.0007 | - | | 13.3625 | 27500 | 0.0003 | - | | 13.3868 | 27550 | 0.0003 | - | | 13.4111 | 27600 | 0.0003 | - | | 13.4354 | 27650 | 0.0 | - | | 13.4597 | 27700 | 0.0004 | - | | 13.4840 | 27750 | 0.0011 | - | | 13.5083 | 27800 | 0.0002 | - | | 13.5326 | 27850 | 0.0002 | - | | 13.5569 | 27900 | 0.0003 | - | | 13.5811 | 27950 | 0.0006 | - | | 13.6054 | 28000 | 0.0003 | - | | 13.6297 | 28050 | 0.0002 | - | | 13.6540 | 28100 | 0.0006 | - | | 13.6783 | 28150 | 0.0003 | - | | 13.7026 | 28200 | 0.0006 | - | | 13.7269 | 28250 | 0.0003 | - | | 13.7512 | 28300 | 0.0 | - | | 13.7755 | 28350 | 0.0007 | - | | 13.7998 | 28400 | 0.0002 | - | | 13.8241 | 28450 | 0.0005 | - | | 13.8484 | 28500 | 0.0002 | - | | 13.8727 | 28550 | 0.0006 | - | | 13.8970 | 28600 | 0.0002 | - | | 13.9213 | 28650 | 0.0002 | - | | 13.9456 | 28700 | 0.0 | - | | 13.9699 | 28750 | 0.0002 | - | | 13.9942 | 28800 | 0.0 | - | | 14.0185 | 28850 | 0.0002 | - | | 14.0428 | 28900 | 0.0 | - | | 14.0671 | 28950 | 0.0006 | - | | 14.0914 | 29000 | 0.0009 | - | | 14.1156 | 29050 | 0.0002 | - | | 14.1399 | 29100 | 0.0002 | - | | 14.1642 | 29150 | 0.0001 | - | | 14.1885 | 29200 | 0.0005 | - | | 14.2128 | 29250 | 0.0005 | - | | 14.2371 | 29300 | 0.0005 | - | | 14.2614 | 29350 | 0.0004 | - | | 14.2857 | 29400 | 0.0001 | - | | 14.3100 | 29450 | 0.0003 | - | | 14.3343 | 29500 | 0.0003 | - | | 14.3586 | 29550 | 0.0002 | - | | 14.3829 | 29600 | 0.0002 | - | | 14.4072 | 29650 | 0.0002 | - | | 14.4315 | 29700 | 0.0 | - | | 14.4558 | 29750 | 0.0005 | - | | 14.4801 | 29800 | 0.0004 | - | | 14.5044 | 29850 | 0.0006 | - | | 14.5287 | 29900 | 0.0002 | - | | 14.5530 | 29950 | 0.0008 | - | | 14.5773 | 30000 | 0.0 | - | | 14.6016 | 30050 | 0.0 | - | | 14.6259 | 30100 | 0.0002 | - | | 14.6501 | 30150 | 0.0 | - | | 14.6744 | 30200 | 0.0 | - | | 14.6987 | 30250 | 0.0002 | - | | 14.7230 | 30300 | 0.0003 | - | | 14.7473 | 30350 | 0.0002 | - | | 14.7716 | 30400 | 0.0002 | - | | 14.7959 | 30450 | 0.0002 | - | | 14.8202 | 30500 | 0.0006 | - | | 14.8445 | 30550 | 0.0009 | - | | 14.8688 | 30600 | 0.0006 | - | | 14.8931 | 30650 | 0.0002 | - | | 14.9174 | 30700 | 0.0008 | - | | 14.9417 | 30750 | 0.0005 | - | | 14.9660 | 30800 | 0.0004 | - | | 14.9903 | 30850 | 0.0006 | - | | 15.0146 | 30900 | 0.0 | - | | 15.0389 | 30950 | 0.0004 | - | | 15.0632 | 31000 | 0.0003 | - | | 15.0875 | 31050 | 0.0006 | - | | 15.1118 | 31100 | 0.0001 | - | | 15.1361 | 31150 | 0.0 | - | | 15.1603 | 31200 | 0.0002 | - | | 15.1846 | 31250 | 0.0005 | - | | 15.2089 | 31300 | 0.0002 | - | | 15.2332 | 31350 | 0.0004 | - | | 15.2575 | 31400 | 0.0003 | - | | 15.2818 | 31450 | 0.0004 | - | | 15.3061 | 31500 | 0.0 | - | | 15.3304 | 31550 | 0.0002 | - | | 15.3547 | 31600 | 0.0005 | - | | 15.3790 | 31650 | 0.0004 | - | | 15.4033 | 31700 | 0.0 | - | | 15.4276 | 31750 | 0.0002 | - | | 15.4519 | 31800 | 0.0 | - | | 15.4762 | 31850 | 0.0005 | - | | 15.5005 | 31900 | 0.0005 | - | | 15.5248 | 31950 | 0.0002 | - | | 15.5491 | 32000 | 0.0001 | - | | 15.5734 | 32050 | 0.0002 | - | | 15.5977 | 32100 | 0.0004 | - | | 15.6220 | 32150 | 0.0007 | - | | 15.6463 | 32200 | 0.0001 | - | | 15.6706 | 32250 | 0.0003 | - | | 15.6948 | 32300 | 0.0002 | - | | 15.7191 | 32350 | 0.0005 | - | | 15.7434 | 32400 | 0.0004 | - | | 15.7677 | 32450 | 0.0003 | - | | 15.7920 | 32500 | 0.0004 | - | | 15.8163 | 32550 | 0.0004 | - | | 15.8406 | 32600 | 0.0005 | - | | 15.8649 | 32650 | 0.0004 | - | | 15.8892 | 32700 | 0.0002 | - | | 15.9135 | 32750 | 0.0005 | - | | 15.9378 | 32800 | 0.0002 | - | | 15.9621 | 32850 | 0.0006 | - | | 15.9864 | 32900 | 0.0004 | - | | 16.0107 | 32950 | 0.0004 | - | | 16.0350 | 33000 | 0.0003 | - | | 16.0593 | 33050 | 0.0001 | - | | 16.0836 | 33100 | 0.0005 | - | | 16.1079 | 33150 | 0.0003 | - | | 16.1322 | 33200 | 0.0001 | - | | 16.1565 | 33250 | 0.0002 | - | | 16.1808 | 33300 | 0.0002 | - | | 16.2051 | 33350 | 0.0001 | - | | 16.2293 | 33400 | 0.0003 | - | | 16.2536 | 33450 | 0.0001 | - | | 16.2779 | 33500 | 0.0 | - | | 16.3022 | 33550 | 0.0003 | - | | 16.3265 | 33600 | 0.0002 | - | | 16.3508 | 33650 | 0.0002 | - | | 16.3751 | 33700 | 0.0 | - | | 16.3994 | 33750 | 0.0001 | - | | 16.4237 | 33800 | 0.0002 | - | | 16.4480 | 33850 | 0.0002 | - | | 16.4723 | 33900 | 0.0001 | - | | 16.4966 | 33950 | 0.0004 | - | | 16.5209 | 34000 | 0.0002 | - | | 16.5452 | 34050 | 0.0002 | - | | 16.5695 | 34100 | 0.0002 | - | | 16.5938 | 34150 | 0.0 | - | | 16.6181 | 34200 | 0.0001 | - | | 16.6424 | 34250 | 0.0001 | - | | 16.6667 | 34300 | 0.0 | - | | 16.6910 | 34350 | 0.0001 | - | | 16.7153 | 34400 | 0.0002 | - | | 16.7396 | 34450 | 0.0002 | - | | 16.7638 | 34500 | 0.0002 | - | | 16.7881 | 34550 | 0.0 | - | | 16.8124 | 34600 | 0.0003 | - | | 16.8367 | 34650 | 0.0 | - | | 16.8610 | 34700 | 0.0002 | - | | 16.8853 | 34750 | 0.0001 | - | | 16.9096 | 34800 | 0.0001 | - | | 16.9339 | 34850 | 0.0 | - | | 16.9582 | 34900 | 0.0 | - | | 16.9825 | 34950 | 0.0001 | - | | 17.0068 | 35000 | 0.0001 | - | | 17.0311 | 35050 | 0.0001 | - | | 17.0554 | 35100 | 0.0 | - | | 17.0797 | 35150 | 0.0 | - | | 17.1040 | 35200 | 0.0002 | - | | 17.1283 | 35250 | 0.0002 | - | | 17.1526 | 35300 | 0.0 | - | | 17.1769 | 35350 | 0.0 | - | | 17.2012 | 35400 | 0.0001 | - | | 17.2255 | 35450 | 0.0 | - | | 17.2498 | 35500 | 0.0 | - | | 17.2741 | 35550 | 0.0 | - | | 17.2983 | 35600 | 0.0 | - | | 17.3226 | 35650 | 0.0 | - | | 17.3469 | 35700 | 0.0 | - | | 17.3712 | 35750 | 0.0 | - | | 17.3955 | 35800 | 0.0 | - | | 17.4198 | 35850 | 0.0001 | - | | 17.4441 | 35900 | 0.0 | - | | 17.4684 | 35950 | 0.0 | - | | 17.4927 | 36000 | 0.0 | - | | 17.5170 | 36050 | 0.0 | - | | 17.5413 | 36100 | 0.0 | - | | 17.5656 | 36150 | 0.0 | - | | 17.5899 | 36200 | 0.0 | - | | 17.6142 | 36250 | 0.0 | - | | 17.6385 | 36300 | 0.0 | - | | 17.6628 | 36350 | 0.0 | - | | 17.6871 | 36400 | 0.0 | - | | 17.7114 | 36450 | 0.0 | - | | 17.7357 | 36500 | 0.0 | - | | 17.7600 | 36550 | 0.0 | - | | 17.7843 | 36600 | 0.0 | - | | 17.8086 | 36650 | 0.0002 | - | | 17.8328 | 36700 | 0.0 | - | | 17.8571 | 36750 | 0.0 | - | | 17.8814 | 36800 | 0.0001 | - | | 17.9057 | 36850 | 0.0 | - | | 17.9300 | 36900 | 0.0 | - | | 17.9543 | 36950 | 0.0 | - | | 17.9786 | 37000 | 0.0 | - | | 18.0029 | 37050 | 0.0 | - | | 18.0272 | 37100 | 0.0 | - | | 18.0515 | 37150 | 0.0 | - | | 18.0758 | 37200 | 0.0002 | - | | 18.1001 | 37250 | 0.0 | - | | 18.1244 | 37300 | 0.0 | - | | 18.1487 | 37350 | 0.0 | - | | 18.1730 | 37400 | 0.0 | - | | 18.1973 | 37450 | 0.0 | - | | 18.2216 | 37500 | 0.0 | - | | 18.2459 | 37550 | 0.0 | - | | 18.2702 | 37600 | 0.0 | - | | 18.2945 | 37650 | 0.0 | - | | 18.3188 | 37700 | 0.0 | - | | 18.3431 | 37750 | 0.0 | - | | 18.3673 | 37800 | 0.0 | - | | 18.3916 | 37850 | 0.0 | - | | 18.4159 | 37900 | 0.0 | - | | 18.4402 | 37950 | 0.0 | - | | 18.4645 | 38000 | 0.0 | - | | 18.4888 | 38050 | 0.0 | - | | 18.5131 | 38100 | 0.0 | - | | 18.5374 | 38150 | 0.0 | - | | 18.5617 | 38200 | 0.0 | - | | 18.5860 | 38250 | 0.0 | - | | 18.6103 | 38300 | 0.0 | - | | 18.6346 | 38350 | 0.0 | - | | 18.6589 | 38400 | 0.0 | - | | 18.6832 | 38450 | 0.0 | - | | 18.7075 | 38500 | 0.0 | - | | 18.7318 | 38550 | 0.0 | - | | 18.7561 | 38600 | 0.0 | - | | 18.7804 | 38650 | 0.0 | - | | 18.8047 | 38700 | 0.0 | - | | 18.8290 | 38750 | 0.0 | - | | 18.8533 | 38800 | 0.0 | - | | 18.8776 | 38850 | 0.0 | - | | 18.9018 | 38900 | 0.0 | - | | 18.9261 | 38950 | 0.0 | - | | 18.9504 | 39000 | 0.0001 | - | | 18.9747 | 39050 | 0.0 | - | | 18.9990 | 39100 | 0.0 | - | | 19.0233 | 39150 | 0.0 | - | | 19.0476 | 39200 | 0.0 | - | | 19.0719 | 39250 | 0.0 | - | | 19.0962 | 39300 | 0.0 | - | | 19.1205 | 39350 | 0.0 | - | | 19.1448 | 39400 | 0.0 | - | | 19.1691 | 39450 | 0.0 | - | | 19.1934 | 39500 | 0.0 | - | | 19.2177 | 39550 | 0.0 | - | | 19.2420 | 39600 | 0.0 | - | | 19.2663 | 39650 | 0.0 | - | | 19.2906 | 39700 | 0.0 | - | | 19.3149 | 39750 | 0.0 | - | | 19.3392 | 39800 | 0.0 | - | | 19.3635 | 39850 | 0.0 | - | | 19.3878 | 39900 | 0.0 | - | | 19.4121 | 39950 | 0.0 | - | | 19.4363 | 40000 | 0.0 | - | | 19.4606 | 40050 | 0.0 | - | | 19.4849 | 40100 | 0.0 | - | | 19.5092 | 40150 | 0.0 | - | | 19.5335 | 40200 | 0.0 | - | | 19.5578 | 40250 | 0.0 | - | | 19.5821 | 40300 | 0.0002 | - | | 19.6064 | 40350 | 0.0 | - | | 19.6307 | 40400 | 0.0 | - | | 19.6550 | 40450 | 0.0 | - | | 19.6793 | 40500 | 0.0 | - | | 19.7036 | 40550 | 0.0 | - | | 19.7279 | 40600 | 0.0 | - | | 19.7522 | 40650 | 0.0 | - | | 19.7765 | 40700 | 0.0 | - | | 19.8008 | 40750 | 0.0 | - | | 19.8251 | 40800 | 0.0 | - | | 19.8494 | 40850 | 0.0 | - | | 19.8737 | 40900 | 0.0 | - | | 19.8980 | 40950 | 0.0 | - | | 19.9223 | 41000 | 0.0 | - | | 19.9466 | 41050 | 0.0 | - | | 19.9708 | 41100 | 0.0 | - | | 19.9951 | 41150 | 0.0 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0 - Sentence Transformers: 3.3.1 - Transformers: 4.44.2 - PyTorch: 2.2.0a0+81ea7a4 - Datasets: 3.2.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```