--- base_model: klue/roberta-base library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 닥터시드 모이스처 바디워시 1000ml 약산성, 꿀&우유 추출물, 자연유래 바디워시 1000ml_망고선셋 Naverstore > 화장품/미용 > 바디케어 > 바디클렌저 - text: NEW 아이오페 UV쉴드 에센셜 톤업 선 50ml SPF50+ MinSellAmount Gmarket > 뷰티 > 화장품/향수 > 스킨케어 > 로션/에멀젼 - text: 아비노 액티브 네츄럴 스킨 릴리프 젠틀 향 로션 너리 싱 코코넛 354ml 4개 (개당 21,000원 - 코드884p) Coupang > 뷰티 > 바디 > 바디로션/크림 > 바디로션 - text: '[MAC] 러브 미 립스틱 트레 블라제 HMALL > 뷰티 > 메이크업 > 립메이크업' - text: (현대백화점)부르조아 벨벳 립펜슬 13 프랑부아즈 그리프 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 > 립스틱 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: accuracy value: 0.85 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:** 100 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 | |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 1 | | | 58 | | | 53 | | | 95 | | | 45 | | | 52 | | | 55 | | | 86 | | | 29 | | | 82 | | | 28 | | | 40 | | | 37 | | | 67 | | | 59 | | | 93 | | | 74 | | | 64 | | | 7 | | | 22 | | | 79 | | | 91 | | | 33 | | | 99 | | | 47 | | | 75 | | | 69 | | | 44 | | | 90 | | | 98 | | | 42 | | | 65 | | | 32 | | | 88 | | | 39 | | | 16 | | | 76 | | | 4 | | | 50 | | | 66 | | | 18 | | | 70 | | | 30 | | | 57 | | | 36 | | | 38 | | | 15 | | | 63 | | | 87 | | | 68 | | | 23 | | | 85 | | | 25 | | | 48 | | | 84 | | | 71 | | | 51 | | | 92 | | | 77 | | | 5 | | | 80 | | | 13 | | | 54 | | | 12 | | | 89 | | | 46 | | | 10 | | | 94 | | | 8 | | | 49 | | | 60 | | | 2 | | | 27 | | | 43 | | | 62 | | | 78 | | | 56 | | | 31 | | | 97 | | | 19 | | | 26 | | | 0 | | | 96 | | | 61 | | | 17 | | | 81 | | | 83 | | | 34 | | | 3 | | | 24 | | | 9 | | | 14 | | | 11 | | | 21 | | | 41 | | | 73 | | | 20 | | | 6 | | | 72 | | | 35 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.85 | ## 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_item_bt_test_org_tcate") # Run inference preds = model("[MAC] 러브 미 립스틱 트레 블라제 HMALL > 뷰티 > 메이크업 > 립메이크업") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 6 | 17.1348 | 40 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 50 | | 1 | 50 | | 2 | 50 | | 3 | 50 | | 4 | 50 | | 5 | 50 | | 6 | 50 | | 7 | 41 | | 8 | 50 | | 9 | 50 | | 10 | 50 | | 11 | 50 | | 12 | 50 | | 13 | 50 | | 14 | 50 | | 15 | 50 | | 16 | 50 | | 17 | 50 | | 18 | 50 | | 19 | 50 | | 20 | 50 | | 21 | 50 | | 22 | 50 | | 23 | 50 | | 24 | 50 | | 25 | 50 | | 26 | 50 | | 27 | 50 | | 28 | 50 | | 29 | 50 | | 30 | 50 | | 31 | 50 | | 32 | 50 | | 33 | 50 | | 34 | 50 | | 35 | 50 | | 36 | 50 | | 37 | 50 | | 38 | 50 | | 39 | 50 | | 40 | 50 | | 41 | 50 | | 42 | 50 | | 43 | 50 | | 44 | 50 | | 45 | 50 | | 46 | 50 | | 47 | 50 | | 48 | 50 | | 49 | 50 | | 50 | 50 | | 51 | 50 | | 52 | 50 | | 53 | 50 | | 54 | 50 | | 55 | 50 | | 56 | 50 | | 57 | 50 | | 58 | 50 | | 59 | 50 | | 60 | 50 | | 61 | 50 | | 62 | 50 | | 63 | 50 | | 64 | 50 | | 65 | 50 | | 66 | 50 | | 67 | 50 | | 68 | 50 | | 69 | 50 | | 70 | 50 | | 71 | 50 | | 72 | 50 | | 73 | 50 | | 74 | 50 | | 75 | 50 | | 76 | 50 | | 77 | 50 | | 78 | 50 | | 79 | 50 | | 80 | 50 | | 81 | 50 | | 82 | 50 | | 83 | 50 | | 84 | 50 | | 85 | 50 | | 86 | 50 | | 87 | 50 | | 88 | 50 | | 89 | 50 | | 90 | 50 | | 91 | 50 | | 92 | 50 | | 93 | 50 | | 94 | 50 | | 95 | 50 | | 96 | 50 | | 97 | 50 | | 98 | 50 | | 99 | 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 | |:-------:|:-----:|:-------------:|:---------------:| | 0.0004 | 1 | 0.4723 | - | | 0.0214 | 50 | 0.4278 | - | | 0.0427 | 100 | 0.4098 | - | | 0.0641 | 150 | 0.3728 | - | | 0.0855 | 200 | 0.3617 | - | | 0.1068 | 250 | 0.3294 | - | | 0.1282 | 300 | 0.2829 | - | | 0.1496 | 350 | 0.2541 | - | | 0.1709 | 400 | 0.2338 | - | | 0.1923 | 450 | 0.204 | - | | 0.2137 | 500 | 0.1824 | - | | 0.2350 | 550 | 0.1606 | - | | 0.2564 | 600 | 0.1309 | - | | 0.2778 | 650 | 0.1195 | - | | 0.2991 | 700 | 0.1065 | - | | 0.3205 | 750 | 0.0977 | - | | 0.3419 | 800 | 0.0946 | - | | 0.3632 | 850 | 0.0843 | - | | 0.3846 | 900 | 0.0851 | - | | 0.4060 | 950 | 0.0772 | - | | 0.4274 | 1000 | 0.0717 | - | | 0.4487 | 1050 | 0.0695 | - | | 0.4701 | 1100 | 0.0653 | - | | 0.4915 | 1150 | 0.0585 | - | | 0.5128 | 1200 | 0.0633 | - | | 0.5342 | 1250 | 0.0569 | - | | 0.5556 | 1300 | 0.0528 | - | | 0.5769 | 1350 | 0.0518 | - | | 0.5983 | 1400 | 0.0499 | - | | 0.6197 | 1450 | 0.0428 | - | | 0.6410 | 1500 | 0.045 | - | | 0.6624 | 1550 | 0.0458 | - | | 0.6838 | 1600 | 0.0415 | - | | 0.7051 | 1650 | 0.0377 | - | | 0.7265 | 1700 | 0.0392 | - | | 0.7479 | 1750 | 0.0359 | - | | 0.7692 | 1800 | 0.0371 | - | | 0.7906 | 1850 | 0.0341 | - | | 0.8120 | 1900 | 0.0331 | - | | 0.8333 | 1950 | 0.0331 | - | | 0.8547 | 2000 | 0.03 | - | | 0.8761 | 2050 | 0.03 | - | | 0.8974 | 2100 | 0.0321 | - | | 0.9188 | 2150 | 0.0266 | - | | 0.9402 | 2200 | 0.0281 | - | | 0.9615 | 2250 | 0.0294 | - | | 0.9829 | 2300 | 0.0277 | - | | 1.0043 | 2350 | 0.0277 | - | | 1.0256 | 2400 | 0.0267 | - | | 1.0470 | 2450 | 0.0266 | - | | 1.0684 | 2500 | 0.0256 | - | | 1.0897 | 2550 | 0.0251 | - | | 1.1111 | 2600 | 0.0233 | - | | 1.1325 | 2650 | 0.0215 | - | | 1.1538 | 2700 | 0.0219 | - | | 1.1752 | 2750 | 0.0254 | - | | 1.1966 | 2800 | 0.0221 | - | | 1.2179 | 2850 | 0.0218 | - | | 1.2393 | 2900 | 0.0221 | - | | 1.2607 | 2950 | 0.0193 | - | | 1.2821 | 3000 | 0.0207 | - | | 1.3034 | 3050 | 0.0197 | - | | 1.3248 | 3100 | 0.0183 | - | | 1.3462 | 3150 | 0.0178 | - | | 1.3675 | 3200 | 0.0192 | - | | 1.3889 | 3250 | 0.0182 | - | | 1.4103 | 3300 | 0.0174 | - | | 1.4316 | 3350 | 0.0186 | - | | 1.4530 | 3400 | 0.0187 | - | | 1.4744 | 3450 | 0.0189 | - | | 1.4957 | 3500 | 0.0176 | - | | 1.5171 | 3550 | 0.0161 | - | | 1.5385 | 3600 | 0.0164 | - | | 1.5598 | 3650 | 0.0161 | - | | 1.5812 | 3700 | 0.0161 | - | | 1.6026 | 3750 | 0.0169 | - | | 1.6239 | 3800 | 0.0143 | - | | 1.6453 | 3850 | 0.0162 | - | | 1.6667 | 3900 | 0.014 | - | | 1.6880 | 3950 | 0.0156 | - | | 1.7094 | 4000 | 0.0137 | - | | 1.7308 | 4050 | 0.0129 | - | | 1.7521 | 4100 | 0.0137 | - | | 1.7735 | 4150 | 0.0138 | - | | 1.7949 | 4200 | 0.0136 | - | | 1.8162 | 4250 | 0.0131 | - | | 1.8376 | 4300 | 0.0122 | - | | 1.8590 | 4350 | 0.0137 | - | | 1.8803 | 4400 | 0.0119 | - | | 1.9017 | 4450 | 0.0107 | - | | 1.9231 | 4500 | 0.0088 | - | | 1.9444 | 4550 | 0.0106 | - | | 1.9658 | 4600 | 0.0121 | - | | 1.9872 | 4650 | 0.0103 | - | | 2.0085 | 4700 | 0.0087 | - | | 2.0299 | 4750 | 0.009 | - | | 2.0513 | 4800 | 0.0097 | - | | 2.0726 | 4850 | 0.0101 | - | | 2.0940 | 4900 | 0.0102 | - | | 2.1154 | 4950 | 0.0121 | - | | 2.1368 | 5000 | 0.0085 | - | | 2.1581 | 5050 | 0.0087 | - | | 2.1795 | 5100 | 0.0095 | - | | 2.2009 | 5150 | 0.008 | - | | 2.2222 | 5200 | 0.0109 | - | | 2.2436 | 5250 | 0.0111 | - | | 2.2650 | 5300 | 0.009 | - | | 2.2863 | 5350 | 0.0102 | - | | 2.3077 | 5400 | 0.009 | - | | 2.3291 | 5450 | 0.0077 | - | | 2.3504 | 5500 | 0.01 | - | | 2.3718 | 5550 | 0.0103 | - | | 2.3932 | 5600 | 0.0072 | - | | 2.4145 | 5650 | 0.0104 | - | | 2.4359 | 5700 | 0.0076 | - | | 2.4573 | 5750 | 0.0099 | - | | 2.4786 | 5800 | 0.009 | - | | 2.5 | 5850 | 0.0085 | - | | 2.5214 | 5900 | 0.0097 | - | | 2.5427 | 5950 | 0.0073 | - | | 2.5641 | 6000 | 0.0084 | - | | 2.5855 | 6050 | 0.0072 | - | | 2.6068 | 6100 | 0.0085 | - | | 2.6282 | 6150 | 0.0069 | - | | 2.6496 | 6200 | 0.0091 | - | | 2.6709 | 6250 | 0.0065 | - | | 2.6923 | 6300 | 0.0064 | - | | 2.7137 | 6350 | 0.0073 | - | | 2.7350 | 6400 | 0.008 | - | | 2.7564 | 6450 | 0.0091 | - | | 2.7778 | 6500 | 0.008 | - | | 2.7991 | 6550 | 0.0066 | - | | 2.8205 | 6600 | 0.0071 | - | | 2.8419 | 6650 | 0.0066 | - | | 2.8632 | 6700 | 0.0082 | - | | 2.8846 | 6750 | 0.0061 | - | | 2.9060 | 6800 | 0.0055 | - | | 2.9274 | 6850 | 0.0068 | - | | 2.9487 | 6900 | 0.006 | - | | 2.9701 | 6950 | 0.0063 | - | | 2.9915 | 7000 | 0.0056 | - | | 3.0128 | 7050 | 0.0078 | - | | 3.0342 | 7100 | 0.0091 | - | | 3.0556 | 7150 | 0.0061 | - | | 3.0769 | 7200 | 0.0068 | - | | 3.0983 | 7250 | 0.0073 | - | | 3.1197 | 7300 | 0.0054 | - | | 3.1410 | 7350 | 0.0061 | - | | 3.1624 | 7400 | 0.0056 | - | | 3.1838 | 7450 | 0.0062 | - | | 3.2051 | 7500 | 0.0046 | - | | 3.2265 | 7550 | 0.0052 | - | | 3.2479 | 7600 | 0.0046 | - | | 3.2692 | 7650 | 0.0043 | - | | 3.2906 | 7700 | 0.0046 | - | | 3.3120 | 7750 | 0.0053 | - | | 3.3333 | 7800 | 0.0045 | - | | 3.3547 | 7850 | 0.0037 | - | | 3.3761 | 7900 | 0.0045 | - | | 3.3974 | 7950 | 0.0056 | - | | 3.4188 | 8000 | 0.005 | - | | 3.4402 | 8050 | 0.0059 | - | | 3.4615 | 8100 | 0.0042 | - | | 3.4829 | 8150 | 0.0049 | - | | 3.5043 | 8200 | 0.0041 | - | | 3.5256 | 8250 | 0.0042 | - | | 3.5470 | 8300 | 0.0041 | - | | 3.5684 | 8350 | 0.0033 | - | | 3.5897 | 8400 | 0.0041 | - | | 3.6111 | 8450 | 0.0035 | - | | 3.6325 | 8500 | 0.0041 | - | | 3.6538 | 8550 | 0.0027 | - | | 3.6752 | 8600 | 0.0033 | - | | 3.6966 | 8650 | 0.0043 | - | | 3.7179 | 8700 | 0.0038 | - | | 3.7393 | 8750 | 0.0041 | - | | 3.7607 | 8800 | 0.0034 | - | | 3.7821 | 8850 | 0.0045 | - | | 3.8034 | 8900 | 0.0041 | - | | 3.8248 | 8950 | 0.0042 | - | | 3.8462 | 9000 | 0.0034 | - | | 3.8675 | 9050 | 0.004 | - | | 3.8889 | 9100 | 0.0028 | - | | 3.9103 | 9150 | 0.0037 | - | | 3.9316 | 9200 | 0.0029 | - | | 3.9530 | 9250 | 0.0033 | - | | 3.9744 | 9300 | 0.0031 | - | | 3.9957 | 9350 | 0.0032 | - | | 4.0171 | 9400 | 0.0032 | - | | 4.0385 | 9450 | 0.004 | - | | 4.0598 | 9500 | 0.0026 | - | | 4.0812 | 9550 | 0.0034 | - | | 4.1026 | 9600 | 0.0033 | - | | 4.1239 | 9650 | 0.0037 | - | | 4.1453 | 9700 | 0.003 | - | | 4.1667 | 9750 | 0.0042 | - | | 4.1880 | 9800 | 0.0032 | - | | 4.2094 | 9850 | 0.0035 | - | | 4.2308 | 9900 | 0.0027 | - | | 4.2521 | 9950 | 0.0031 | - | | 4.2735 | 10000 | 0.0023 | - | | 4.2949 | 10050 | 0.0031 | - | | 4.3162 | 10100 | 0.0028 | - | | 4.3376 | 10150 | 0.0027 | - | | 4.3590 | 10200 | 0.0029 | - | | 4.3803 | 10250 | 0.0023 | - | | 4.4017 | 10300 | 0.0023 | - | | 4.4231 | 10350 | 0.0026 | - | | 4.4444 | 10400 | 0.0025 | - | | 4.4658 | 10450 | 0.002 | - | | 4.4872 | 10500 | 0.0019 | - | | 4.5085 | 10550 | 0.0023 | - | | 4.5299 | 10600 | 0.0026 | - | | 4.5513 | 10650 | 0.0031 | - | | 4.5726 | 10700 | 0.0023 | - | | 4.5940 | 10750 | 0.0027 | - | | 4.6154 | 10800 | 0.0021 | - | | 4.6368 | 10850 | 0.0021 | - | | 4.6581 | 10900 | 0.0029 | - | | 4.6795 | 10950 | 0.003 | - | | 4.7009 | 11000 | 0.0026 | - | | 4.7222 | 11050 | 0.0025 | - | | 4.7436 | 11100 | 0.002 | - | | 4.7650 | 11150 | 0.0017 | - | | 4.7863 | 11200 | 0.0023 | - | | 4.8077 | 11250 | 0.0021 | - | | 4.8291 | 11300 | 0.0033 | - | | 4.8504 | 11350 | 0.0024 | - | | 4.8718 | 11400 | 0.0016 | - | | 4.8932 | 11450 | 0.0013 | - | | 4.9145 | 11500 | 0.0017 | - | | 4.9359 | 11550 | 0.0023 | - | | 4.9573 | 11600 | 0.0014 | - | | 4.9786 | 11650 | 0.0022 | - | | 5.0 | 11700 | 0.0024 | - | | 5.0214 | 11750 | 0.0011 | - | | 5.0427 | 11800 | 0.0021 | - | | 5.0641 | 11850 | 0.0017 | - | | 5.0855 | 11900 | 0.0018 | - | | 5.1068 | 11950 | 0.0019 | - | | 5.1282 | 12000 | 0.0023 | - | | 5.1496 | 12050 | 0.0024 | - | | 5.1709 | 12100 | 0.0017 | - | | 5.1923 | 12150 | 0.0029 | - | | 5.2137 | 12200 | 0.0047 | - | | 5.2350 | 12250 | 0.0024 | - | | 5.2564 | 12300 | 0.0023 | - | | 5.2778 | 12350 | 0.0015 | - | | 5.2991 | 12400 | 0.0032 | - | | 5.3205 | 12450 | 0.0018 | - | | 5.3419 | 12500 | 0.0018 | - | | 5.3632 | 12550 | 0.002 | - | | 5.3846 | 12600 | 0.0021 | - | | 5.4060 | 12650 | 0.0015 | - | | 5.4274 | 12700 | 0.0014 | - | | 5.4487 | 12750 | 0.002 | - | | 5.4701 | 12800 | 0.0018 | - | | 5.4915 | 12850 | 0.002 | - | | 5.5128 | 12900 | 0.001 | - | | 5.5342 | 12950 | 0.0019 | - | | 5.5556 | 13000 | 0.0021 | - | | 5.5769 | 13050 | 0.0018 | - | | 5.5983 | 13100 | 0.0039 | - | | 5.6197 | 13150 | 0.0046 | - | | 5.6410 | 13200 | 0.0029 | - | | 5.6624 | 13250 | 0.0015 | - | | 5.6838 | 13300 | 0.0017 | - | | 5.7051 | 13350 | 0.0021 | - | | 5.7265 | 13400 | 0.0017 | - | | 5.7479 | 13450 | 0.0026 | - | | 5.7692 | 13500 | 0.0022 | - | | 5.7906 | 13550 | 0.0024 | - | | 5.8120 | 13600 | 0.0015 | - | | 5.8333 | 13650 | 0.0023 | - | | 5.8547 | 13700 | 0.0018 | - | | 5.8761 | 13750 | 0.0026 | - | | 5.8974 | 13800 | 0.0017 | - | | 5.9188 | 13850 | 0.001 | - | | 5.9402 | 13900 | 0.002 | - | | 5.9615 | 13950 | 0.0012 | - | | 5.9829 | 14000 | 0.0011 | - | | 6.0043 | 14050 | 0.0024 | - | | 6.0256 | 14100 | 0.0011 | - | | 6.0470 | 14150 | 0.0019 | - | | 6.0684 | 14200 | 0.0016 | - | | 6.0897 | 14250 | 0.0017 | - | | 6.1111 | 14300 | 0.0008 | - | | 6.1325 | 14350 | 0.0017 | - | | 6.1538 | 14400 | 0.0018 | - | | 6.1752 | 14450 | 0.0011 | - | | 6.1966 | 14500 | 0.0023 | - | | 6.2179 | 14550 | 0.0014 | - | | 6.2393 | 14600 | 0.0008 | - | | 6.2607 | 14650 | 0.0014 | - | | 6.2821 | 14700 | 0.0014 | - | | 6.3034 | 14750 | 0.0015 | - | | 6.3248 | 14800 | 0.0014 | - | | 6.3462 | 14850 | 0.0008 | - | | 6.3675 | 14900 | 0.0016 | - | | 6.3889 | 14950 | 0.0009 | - | | 6.4103 | 15000 | 0.0017 | - | | 6.4316 | 15050 | 0.0008 | - | | 6.4530 | 15100 | 0.002 | - | | 6.4744 | 15150 | 0.0014 | - | | 6.4957 | 15200 | 0.0012 | - | | 6.5171 | 15250 | 0.0007 | - | | 6.5385 | 15300 | 0.0013 | - | | 6.5598 | 15350 | 0.003 | - | | 6.5812 | 15400 | 0.0049 | - | | 6.6026 | 15450 | 0.0024 | - | | 6.6239 | 15500 | 0.0023 | - | | 6.6453 | 15550 | 0.0019 | - | | 6.6667 | 15600 | 0.0022 | - | | 6.6880 | 15650 | 0.0018 | - | | 6.7094 | 15700 | 0.0019 | - | | 6.7308 | 15750 | 0.0014 | - | | 6.7521 | 15800 | 0.001 | - | | 6.7735 | 15850 | 0.0016 | - | | 6.7949 | 15900 | 0.0016 | - | | 6.8162 | 15950 | 0.0015 | - | | 6.8376 | 16000 | 0.0012 | - | | 6.8590 | 16050 | 0.0014 | - | | 6.8803 | 16100 | 0.0014 | - | | 6.9017 | 16150 | 0.0014 | - | | 6.9231 | 16200 | 0.001 | - | | 6.9444 | 16250 | 0.0013 | - | | 6.9658 | 16300 | 0.0018 | - | | 6.9872 | 16350 | 0.0005 | - | | 7.0085 | 16400 | 0.0013 | - | | 7.0299 | 16450 | 0.0019 | - | | 7.0513 | 16500 | 0.0007 | - | | 7.0726 | 16550 | 0.0009 | - | | 7.0940 | 16600 | 0.0015 | - | | 7.1154 | 16650 | 0.0016 | - | | 7.1368 | 16700 | 0.001 | - | | 7.1581 | 16750 | 0.0011 | - | | 7.1795 | 16800 | 0.0015 | - | | 7.2009 | 16850 | 0.0012 | - | | 7.2222 | 16900 | 0.0015 | - | | 7.2436 | 16950 | 0.0011 | - | | 7.2650 | 17000 | 0.0013 | - | | 7.2863 | 17050 | 0.002 | - | | 7.3077 | 17100 | 0.0012 | - | | 7.3291 | 17150 | 0.0023 | - | | 7.3504 | 17200 | 0.0021 | - | | 7.3718 | 17250 | 0.0013 | - | | 7.3932 | 17300 | 0.0015 | - | | 7.4145 | 17350 | 0.0013 | - | | 7.4359 | 17400 | 0.0011 | - | | 7.4573 | 17450 | 0.0014 | - | | 7.4786 | 17500 | 0.0005 | - | | 7.5 | 17550 | 0.0014 | - | | 7.5214 | 17600 | 0.0006 | - | | 7.5427 | 17650 | 0.0011 | - | | 7.5641 | 17700 | 0.0014 | - | | 7.5855 | 17750 | 0.0009 | - | | 7.6068 | 17800 | 0.0012 | - | | 7.6282 | 17850 | 0.0014 | - | | 7.6496 | 17900 | 0.001 | - | | 7.6709 | 17950 | 0.0012 | - | | 7.6923 | 18000 | 0.0013 | - | | 7.7137 | 18050 | 0.0013 | - | | 7.7350 | 18100 | 0.0007 | - | | 7.7564 | 18150 | 0.0009 | - | | 7.7778 | 18200 | 0.0015 | - | | 7.7991 | 18250 | 0.0006 | - | | 7.8205 | 18300 | 0.0012 | - | | 7.8419 | 18350 | 0.0007 | - | | 7.8632 | 18400 | 0.0005 | - | | 7.8846 | 18450 | 0.0007 | - | | 7.9060 | 18500 | 0.0003 | - | | 7.9274 | 18550 | 0.0007 | - | | 7.9487 | 18600 | 0.0005 | - | | 7.9701 | 18650 | 0.0015 | - | | 7.9915 | 18700 | 0.001 | - | | 8.0128 | 18750 | 0.0014 | - | | 8.0342 | 18800 | 0.0009 | - | | 8.0556 | 18850 | 0.0016 | - | | 8.0769 | 18900 | 0.0024 | - | | 8.0983 | 18950 | 0.0016 | - | | 8.1197 | 19000 | 0.0009 | - | | 8.1410 | 19050 | 0.001 | - | | 8.1624 | 19100 | 0.0006 | - | | 8.1838 | 19150 | 0.0008 | - | | 8.2051 | 19200 | 0.0009 | - | | 8.2265 | 19250 | 0.0005 | - | | 8.2479 | 19300 | 0.0006 | - | | 8.2692 | 19350 | 0.0006 | - | | 8.2906 | 19400 | 0.0009 | - | | 8.3120 | 19450 | 0.0007 | - | | 8.3333 | 19500 | 0.001 | - | | 8.3547 | 19550 | 0.0011 | - | | 8.3761 | 19600 | 0.0004 | - | | 8.3974 | 19650 | 0.0009 | - | | 8.4188 | 19700 | 0.0009 | - | | 8.4402 | 19750 | 0.001 | - | | 8.4615 | 19800 | 0.0013 | - | | 8.4829 | 19850 | 0.0013 | - | | 8.5043 | 19900 | 0.0011 | - | | 8.5256 | 19950 | 0.0007 | - | | 8.5470 | 20000 | 0.0006 | - | | 8.5684 | 20050 | 0.0006 | - | | 8.5897 | 20100 | 0.0011 | - | | 8.6111 | 20150 | 0.0013 | - | | 8.6325 | 20200 | 0.0008 | - | | 8.6538 | 20250 | 0.0006 | - | | 8.6752 | 20300 | 0.0008 | - | | 8.6966 | 20350 | 0.0008 | - | | 8.7179 | 20400 | 0.0007 | - | | 8.7393 | 20450 | 0.0007 | - | | 8.7607 | 20500 | 0.001 | - | | 8.7821 | 20550 | 0.0006 | - | | 8.8034 | 20600 | 0.0005 | - | | 8.8248 | 20650 | 0.0007 | - | | 8.8462 | 20700 | 0.0003 | - | | 8.8675 | 20750 | 0.0007 | - | | 8.8889 | 20800 | 0.001 | - | | 8.9103 | 20850 | 0.0004 | - | | 8.9316 | 20900 | 0.0005 | - | | 8.9530 | 20950 | 0.0004 | - | | 8.9744 | 21000 | 0.001 | - | | 8.9957 | 21050 | 0.0012 | - | | 9.0171 | 21100 | 0.0008 | - | | 9.0385 | 21150 | 0.0007 | - | | 9.0598 | 21200 | 0.0008 | - | | 9.0812 | 21250 | 0.0008 | - | | 9.1026 | 21300 | 0.0011 | - | | 9.1239 | 21350 | 0.0017 | - | | 9.1453 | 21400 | 0.0014 | - | | 9.1667 | 21450 | 0.0008 | - | | 9.1880 | 21500 | 0.0011 | - | | 9.2094 | 21550 | 0.0003 | - | | 9.2308 | 21600 | 0.0003 | - | | 9.2521 | 21650 | 0.0006 | - | | 9.2735 | 21700 | 0.0005 | - | | 9.2949 | 21750 | 0.0004 | - | | 9.3162 | 21800 | 0.0003 | - | | 9.3376 | 21850 | 0.0005 | - | | 9.3590 | 21900 | 0.0007 | - | | 9.3803 | 21950 | 0.0003 | - | | 9.4017 | 22000 | 0.0005 | - | | 9.4231 | 22050 | 0.0009 | - | | 9.4444 | 22100 | 0.0003 | - | | 9.4658 | 22150 | 0.0008 | - | | 9.4872 | 22200 | 0.0006 | - | | 9.5085 | 22250 | 0.0005 | - | | 9.5299 | 22300 | 0.0003 | - | | 9.5513 | 22350 | 0.0002 | - | | 9.5726 | 22400 | 0.0012 | - | | 9.5940 | 22450 | 0.0005 | - | | 9.6154 | 22500 | 0.0003 | - | | 9.6368 | 22550 | 0.0007 | - | | 9.6581 | 22600 | 0.0004 | - | | 9.6795 | 22650 | 0.0004 | - | | 9.7009 | 22700 | 0.0006 | - | | 9.7222 | 22750 | 0.0004 | - | | 9.7436 | 22800 | 0.0001 | - | | 9.7650 | 22850 | 0.0004 | - | | 9.7863 | 22900 | 0.0007 | - | | 9.8077 | 22950 | 0.0007 | - | | 9.8291 | 23000 | 0.0004 | - | | 9.8504 | 23050 | 0.0008 | - | | 9.8718 | 23100 | 0.0002 | - | | 9.8932 | 23150 | 0.0002 | - | | 9.9145 | 23200 | 0.0014 | - | | 9.9359 | 23250 | 0.002 | - | | 9.9573 | 23300 | 0.0013 | - | | 9.9786 | 23350 | 0.0006 | - | | 10.0 | 23400 | 0.0011 | - | | 10.0214 | 23450 | 0.0018 | - | | 10.0427 | 23500 | 0.0013 | - | | 10.0641 | 23550 | 0.0006 | - | | 10.0855 | 23600 | 0.0011 | - | | 10.1068 | 23650 | 0.0004 | - | | 10.1282 | 23700 | 0.0006 | - | | 10.1496 | 23750 | 0.0007 | - | | 10.1709 | 23800 | 0.0008 | - | | 10.1923 | 23850 | 0.0009 | - | | 10.2137 | 23900 | 0.0006 | - | | 10.2350 | 23950 | 0.0008 | - | | 10.2564 | 24000 | 0.0005 | - | | 10.2778 | 24050 | 0.0005 | - | | 10.2991 | 24100 | 0.0005 | - | | 10.3205 | 24150 | 0.0004 | - | | 10.3419 | 24200 | 0.0009 | - | | 10.3632 | 24250 | 0.0006 | - | | 10.3846 | 24300 | 0.0009 | - | | 10.4060 | 24350 | 0.0002 | - | | 10.4274 | 24400 | 0.0001 | - | | 10.4487 | 24450 | 0.0003 | - | | 10.4701 | 24500 | 0.0004 | - | | 10.4915 | 24550 | 0.0002 | - | | 10.5128 | 24600 | 0.0005 | - | | 10.5342 | 24650 | 0.0005 | - | | 10.5556 | 24700 | 0.0003 | - | | 10.5769 | 24750 | 0.0002 | - | | 10.5983 | 24800 | 0.0004 | - | | 10.6197 | 24850 | 0.0002 | - | | 10.6410 | 24900 | 0.0003 | - | | 10.6624 | 24950 | 0.0002 | - | | 10.6838 | 25000 | 0.0003 | - | | 10.7051 | 25050 | 0.0007 | - | | 10.7265 | 25100 | 0.0004 | - | | 10.7479 | 25150 | 0.0007 | - | | 10.7692 | 25200 | 0.0004 | - | | 10.7906 | 25250 | 0.0005 | - | | 10.8120 | 25300 | 0.0003 | - | | 10.8333 | 25350 | 0.0005 | - | | 10.8547 | 25400 | 0.0005 | - | | 10.8761 | 25450 | 0.0002 | - | | 10.8974 | 25500 | 0.0004 | - | | 10.9188 | 25550 | 0.0009 | - | | 10.9402 | 25600 | 0.0003 | - | | 10.9615 | 25650 | 0.0003 | - | | 10.9829 | 25700 | 0.0004 | - | | 11.0043 | 25750 | 0.0001 | - | | 11.0256 | 25800 | 0.0004 | - | | 11.0470 | 25850 | 0.0007 | - | | 11.0684 | 25900 | 0.0005 | - | | 11.0897 | 25950 | 0.0006 | - | | 11.1111 | 26000 | 0.0003 | - | | 11.1325 | 26050 | 0.0007 | - | | 11.1538 | 26100 | 0.0013 | - | | 11.1752 | 26150 | 0.001 | - | | 11.1966 | 26200 | 0.0006 | - | | 11.2179 | 26250 | 0.0007 | - | | 11.2393 | 26300 | 0.0013 | - | | 11.2607 | 26350 | 0.0019 | - | | 11.2821 | 26400 | 0.0006 | - | | 11.3034 | 26450 | 0.0008 | - | | 11.3248 | 26500 | 0.0013 | - | | 11.3462 | 26550 | 0.0011 | - | | 11.3675 | 26600 | 0.0011 | - | | 11.3889 | 26650 | 0.0006 | - | | 11.4103 | 26700 | 0.0004 | - | | 11.4316 | 26750 | 0.0009 | - | | 11.4530 | 26800 | 0.0007 | - | | 11.4744 | 26850 | 0.0007 | - | | 11.4957 | 26900 | 0.0005 | - | | 11.5171 | 26950 | 0.0004 | - | | 11.5385 | 27000 | 0.0007 | - | | 11.5598 | 27050 | 0.0004 | - | | 11.5812 | 27100 | 0.0002 | - | | 11.6026 | 27150 | 0.0005 | - | | 11.6239 | 27200 | 0.001 | - | | 11.6453 | 27250 | 0.0009 | - | | 11.6667 | 27300 | 0.0004 | - | | 11.6880 | 27350 | 0.0004 | - | | 11.7094 | 27400 | 0.001 | - | | 11.7308 | 27450 | 0.0006 | - | | 11.7521 | 27500 | 0.0003 | - | | 11.7735 | 27550 | 0.0007 | - | | 11.7949 | 27600 | 0.0011 | - | | 11.8162 | 27650 | 0.0005 | - | | 11.8376 | 27700 | 0.0004 | - | | 11.8590 | 27750 | 0.0005 | - | | 11.8803 | 27800 | 0.0003 | - | | 11.9017 | 27850 | 0.0008 | - | | 11.9231 | 27900 | 0.0002 | - | | 11.9444 | 27950 | 0.0002 | - | | 11.9658 | 28000 | 0.0003 | - | | 11.9872 | 28050 | 0.0001 | - | | 12.0085 | 28100 | 0.0001 | - | | 12.0299 | 28150 | 0.0002 | - | | 12.0513 | 28200 | 0.0004 | - | | 12.0726 | 28250 | 0.0002 | - | | 12.0940 | 28300 | 0.0001 | - | | 12.1154 | 28350 | 0.0001 | - | | 12.1368 | 28400 | 0.0003 | - | | 12.1581 | 28450 | 0.0004 | - | | 12.1795 | 28500 | 0.0004 | - | | 12.2009 | 28550 | 0.0004 | - | | 12.2222 | 28600 | 0.0003 | - | | 12.2436 | 28650 | 0.0001 | - | | 12.2650 | 28700 | 0.0006 | - | | 12.2863 | 28750 | 0.0005 | - | | 12.3077 | 28800 | 0.0005 | - | | 12.3291 | 28850 | 0.0001 | - | | 12.3504 | 28900 | 0.0004 | - | | 12.3718 | 28950 | 0.0003 | - | | 12.3932 | 29000 | 0.0006 | - | | 12.4145 | 29050 | 0.0001 | - | | 12.4359 | 29100 | 0.0007 | - | | 12.4573 | 29150 | 0.0001 | - | | 12.4786 | 29200 | 0.0001 | - | | 12.5 | 29250 | 0.0001 | - | | 12.5214 | 29300 | 0.0001 | - | | 12.5427 | 29350 | 0.0001 | - | | 12.5641 | 29400 | 0.0001 | - | | 12.5855 | 29450 | 0.0002 | - | | 12.6068 | 29500 | 0.0004 | - | | 12.6282 | 29550 | 0.0003 | - | | 12.6496 | 29600 | 0.0002 | - | | 12.6709 | 29650 | 0.0002 | - | | 12.6923 | 29700 | 0.0002 | - | | 12.7137 | 29750 | 0.0003 | - | | 12.7350 | 29800 | 0.0002 | - | | 12.7564 | 29850 | 0.0001 | - | | 12.7778 | 29900 | 0.0002 | - | | 12.7991 | 29950 | 0.0001 | - | | 12.8205 | 30000 | 0.0001 | - | | 12.8419 | 30050 | 0.0003 | - | | 12.8632 | 30100 | 0.0001 | - | | 12.8846 | 30150 | 0.0004 | - | | 12.9060 | 30200 | 0.0004 | - | | 12.9274 | 30250 | 0.0002 | - | | 12.9487 | 30300 | 0.0002 | - | | 12.9701 | 30350 | 0.0001 | - | | 12.9915 | 30400 | 0.0004 | - | | 13.0128 | 30450 | 0.0001 | - | | 13.0342 | 30500 | 0.0002 | - | | 13.0556 | 30550 | 0.0002 | - | | 13.0769 | 30600 | 0.0005 | - | | 13.0983 | 30650 | 0.0005 | - | | 13.1197 | 30700 | 0.0001 | - | | 13.1410 | 30750 | 0.0001 | - | | 13.1624 | 30800 | 0.0001 | - | | 13.1838 | 30850 | 0.0001 | - | | 13.2051 | 30900 | 0.0002 | - | | 13.2265 | 30950 | 0.0002 | - | | 13.2479 | 31000 | 0.0006 | - | | 13.2692 | 31050 | 0.0002 | - | | 13.2906 | 31100 | 0.0004 | - | | 13.3120 | 31150 | 0.0001 | - | | 13.3333 | 31200 | 0.0001 | - | | 13.3547 | 31250 | 0.0002 | - | | 13.3761 | 31300 | 0.0002 | - | | 13.3974 | 31350 | 0.0001 | - | | 13.4188 | 31400 | 0.0001 | - | | 13.4402 | 31450 | 0.0003 | - | | 13.4615 | 31500 | 0.0004 | - | | 13.4829 | 31550 | 0.0003 | - | | 13.5043 | 31600 | 0.0003 | - | | 13.5256 | 31650 | 0.0001 | - | | 13.5470 | 31700 | 0.0001 | - | | 13.5684 | 31750 | 0.0003 | - | | 13.5897 | 31800 | 0.0001 | - | | 13.6111 | 31850 | 0.0001 | - | | 13.6325 | 31900 | 0.0001 | - | | 13.6538 | 31950 | 0.0001 | - | | 13.6752 | 32000 | 0.0001 | - | | 13.6966 | 32050 | 0.0 | - | | 13.7179 | 32100 | 0.0002 | - | | 13.7393 | 32150 | 0.0004 | - | | 13.7607 | 32200 | 0.0002 | - | | 13.7821 | 32250 | 0.0002 | - | | 13.8034 | 32300 | 0.0001 | - | | 13.8248 | 32350 | 0.0001 | - | | 13.8462 | 32400 | 0.0001 | - | | 13.8675 | 32450 | 0.0001 | - | | 13.8889 | 32500 | 0.0001 | - | | 13.9103 | 32550 | 0.0002 | - | | 13.9316 | 32600 | 0.0001 | - | | 13.9530 | 32650 | 0.0002 | - | | 13.9744 | 32700 | 0.0001 | - | | 13.9957 | 32750 | 0.0001 | - | | 14.0171 | 32800 | 0.0002 | - | | 14.0385 | 32850 | 0.0003 | - | | 14.0598 | 32900 | 0.0001 | - | | 14.0812 | 32950 | 0.0001 | - | | 14.1026 | 33000 | 0.0001 | - | | 14.1239 | 33050 | 0.0001 | - | | 14.1453 | 33100 | 0.0 | - | | 14.1667 | 33150 | 0.0005 | - | | 14.1880 | 33200 | 0.0001 | - | | 14.2094 | 33250 | 0.0001 | - | | 14.2308 | 33300 | 0.0001 | - | | 14.2521 | 33350 | 0.0001 | - | | 14.2735 | 33400 | 0.0001 | - | | 14.2949 | 33450 | 0.0 | - | | 14.3162 | 33500 | 0.0003 | - | | 14.3376 | 33550 | 0.0003 | - | | 14.3590 | 33600 | 0.0001 | - | | 14.3803 | 33650 | 0.0 | - | | 14.4017 | 33700 | 0.0 | - | | 14.4231 | 33750 | 0.0 | - | | 14.4444 | 33800 | 0.0002 | - | | 14.4658 | 33850 | 0.0001 | - | | 14.4872 | 33900 | 0.0 | - | | 14.5085 | 33950 | 0.0 | - | | 14.5299 | 34000 | 0.0002 | - | | 14.5513 | 34050 | 0.0 | - | | 14.5726 | 34100 | 0.0003 | - | | 14.5940 | 34150 | 0.0002 | - | | 14.6154 | 34200 | 0.0002 | - | | 14.6368 | 34250 | 0.0006 | - | | 14.6581 | 34300 | 0.0004 | - | | 14.6795 | 34350 | 0.0006 | - | | 14.7009 | 34400 | 0.0002 | - | | 14.7222 | 34450 | 0.0002 | - | | 14.7436 | 34500 | 0.0002 | - | | 14.7650 | 34550 | 0.0002 | - | | 14.7863 | 34600 | 0.0005 | - | | 14.8077 | 34650 | 0.0001 | - | | 14.8291 | 34700 | 0.0005 | - | | 14.8504 | 34750 | 0.0008 | - | | 14.8718 | 34800 | 0.0002 | - | | 14.8932 | 34850 | 0.0001 | - | | 14.9145 | 34900 | 0.0001 | - | | 14.9359 | 34950 | 0.0002 | - | | 14.9573 | 35000 | 0.0001 | - | | 14.9786 | 35050 | 0.0002 | - | | 15.0 | 35100 | 0.0002 | - | | 15.0214 | 35150 | 0.0002 | - | | 15.0427 | 35200 | 0.0001 | - | | 15.0641 | 35250 | 0.0001 | - | | 15.0855 | 35300 | 0.0003 | - | | 15.1068 | 35350 | 0.0003 | - | | 15.1282 | 35400 | 0.0001 | - | | 15.1496 | 35450 | 0.0002 | - | | 15.1709 | 35500 | 0.0001 | - | | 15.1923 | 35550 | 0.0001 | - | | 15.2137 | 35600 | 0.0004 | - | | 15.2350 | 35650 | 0.0002 | - | | 15.2564 | 35700 | 0.0001 | - | | 15.2778 | 35750 | 0.0001 | - | | 15.2991 | 35800 | 0.0001 | - | | 15.3205 | 35850 | 0.0001 | - | | 15.3419 | 35900 | 0.0002 | - | | 15.3632 | 35950 | 0.0001 | - | | 15.3846 | 36000 | 0.0 | - | | 15.4060 | 36050 | 0.0 | - | | 15.4274 | 36100 | 0.0 | - | | 15.4487 | 36150 | 0.0001 | - | | 15.4701 | 36200 | 0.0004 | - | | 15.4915 | 36250 | 0.0001 | - | | 15.5128 | 36300 | 0.0002 | - | | 15.5342 | 36350 | 0.0002 | - | | 15.5556 | 36400 | 0.0001 | - | | 15.5769 | 36450 | 0.0001 | - | | 15.5983 | 36500 | 0.0001 | - | | 15.6197 | 36550 | 0.0001 | - | | 15.6410 | 36600 | 0.0002 | - | | 15.6624 | 36650 | 0.0001 | - | | 15.6838 | 36700 | 0.0 | - | | 15.7051 | 36750 | 0.0001 | - | | 15.7265 | 36800 | 0.0001 | - | | 15.7479 | 36850 | 0.0 | - | | 15.7692 | 36900 | 0.0001 | - | | 15.7906 | 36950 | 0.0001 | - | | 15.8120 | 37000 | 0.0 | - | | 15.8333 | 37050 | 0.0002 | - | | 15.8547 | 37100 | 0.0002 | - | | 15.8761 | 37150 | 0.0001 | - | | 15.8974 | 37200 | 0.0001 | - | | 15.9188 | 37250 | 0.0001 | - | | 15.9402 | 37300 | 0.0001 | - | | 15.9615 | 37350 | 0.0001 | - | | 15.9829 | 37400 | 0.0001 | - | | 16.0043 | 37450 | 0.0001 | - | | 16.0256 | 37500 | 0.0001 | - | | 16.0470 | 37550 | 0.0 | - | | 16.0684 | 37600 | 0.0 | - | | 16.0897 | 37650 | 0.0001 | - | | 16.1111 | 37700 | 0.0001 | - | | 16.1325 | 37750 | 0.0001 | - | | 16.1538 | 37800 | 0.0001 | - | | 16.1752 | 37850 | 0.0001 | - | | 16.1966 | 37900 | 0.0002 | - | | 16.2179 | 37950 | 0.0001 | - | | 16.2393 | 38000 | 0.0001 | - | | 16.2607 | 38050 | 0.0001 | - | | 16.2821 | 38100 | 0.0001 | - | | 16.3034 | 38150 | 0.0001 | - | | 16.3248 | 38200 | 0.0001 | - | | 16.3462 | 38250 | 0.0001 | - | | 16.3675 | 38300 | 0.0 | - | | 16.3889 | 38350 | 0.0001 | - | | 16.4103 | 38400 | 0.0001 | - | | 16.4316 | 38450 | 0.0 | - | | 16.4530 | 38500 | 0.0 | - | | 16.4744 | 38550 | 0.0002 | - | | 16.4957 | 38600 | 0.0001 | - | | 16.5171 | 38650 | 0.0002 | - | | 16.5385 | 38700 | 0.0001 | - | | 16.5598 | 38750 | 0.0001 | - | | 16.5812 | 38800 | 0.0003 | - | | 16.6026 | 38850 | 0.0001 | - | | 16.6239 | 38900 | 0.0001 | - | | 16.6453 | 38950 | 0.0001 | - | | 16.6667 | 39000 | 0.0001 | - | | 16.6880 | 39050 | 0.0 | - | | 16.7094 | 39100 | 0.0001 | - | | 16.7308 | 39150 | 0.0001 | - | | 16.7521 | 39200 | 0.0001 | - | | 16.7735 | 39250 | 0.0 | - | | 16.7949 | 39300 | 0.0 | - | | 16.8162 | 39350 | 0.0002 | - | | 16.8376 | 39400 | 0.0003 | - | | 16.8590 | 39450 | 0.0 | - | | 16.8803 | 39500 | 0.0001 | - | | 16.9017 | 39550 | 0.0002 | - | | 16.9231 | 39600 | 0.0001 | - | | 16.9444 | 39650 | 0.0001 | - | | 16.9658 | 39700 | 0.0001 | - | | 16.9872 | 39750 | 0.0001 | - | | 17.0085 | 39800 | 0.0 | - | | 17.0299 | 39850 | 0.0001 | - | | 17.0513 | 39900 | 0.0 | - | | 17.0726 | 39950 | 0.0001 | - | | 17.0940 | 40000 | 0.0001 | - | | 17.1154 | 40050 | 0.0001 | - | | 17.1368 | 40100 | 0.0 | - | | 17.1581 | 40150 | 0.0 | - | | 17.1795 | 40200 | 0.0 | - | | 17.2009 | 40250 | 0.0 | - | | 17.2222 | 40300 | 0.0001 | - | | 17.2436 | 40350 | 0.0 | - | | 17.2650 | 40400 | 0.0002 | - | | 17.2863 | 40450 | 0.0001 | - | | 17.3077 | 40500 | 0.0 | - | | 17.3291 | 40550 | 0.0 | - | | 17.3504 | 40600 | 0.0 | - | | 17.3718 | 40650 | 0.0 | - | | 17.3932 | 40700 | 0.0 | - | | 17.4145 | 40750 | 0.0 | - | | 17.4359 | 40800 | 0.0001 | - | | 17.4573 | 40850 | 0.0001 | - | | 17.4786 | 40900 | 0.0001 | - | | 17.5 | 40950 | 0.0 | - | | 17.5214 | 41000 | 0.0 | - | | 17.5427 | 41050 | 0.0 | - | | 17.5641 | 41100 | 0.0001 | - | | 17.5855 | 41150 | 0.0001 | - | | 17.6068 | 41200 | 0.0001 | - | | 17.6282 | 41250 | 0.0001 | - | | 17.6496 | 41300 | 0.0 | - | | 17.6709 | 41350 | 0.0 | - | | 17.6923 | 41400 | 0.0 | - | | 17.7137 | 41450 | 0.0001 | - | | 17.7350 | 41500 | 0.0001 | - | | 17.7564 | 41550 | 0.0001 | - | | 17.7778 | 41600 | 0.0 | - | | 17.7991 | 41650 | 0.0 | - | | 17.8205 | 41700 | 0.0001 | - | | 17.8419 | 41750 | 0.0 | - | | 17.8632 | 41800 | 0.0 | - | | 17.8846 | 41850 | 0.0 | - | | 17.9060 | 41900 | 0.0 | - | | 17.9274 | 41950 | 0.0 | - | | 17.9487 | 42000 | 0.0 | - | | 17.9701 | 42050 | 0.0 | - | | 17.9915 | 42100 | 0.0 | - | | 18.0128 | 42150 | 0.0 | - | | 18.0342 | 42200 | 0.0 | - | | 18.0556 | 42250 | 0.0 | - | | 18.0769 | 42300 | 0.0 | - | | 18.0983 | 42350 | 0.0 | - | | 18.1197 | 42400 | 0.0001 | - | | 18.1410 | 42450 | 0.0 | - | | 18.1624 | 42500 | 0.0 | - | | 18.1838 | 42550 | 0.0001 | - | | 18.2051 | 42600 | 0.0 | - | | 18.2265 | 42650 | 0.0001 | - | | 18.2479 | 42700 | 0.0001 | - | | 18.2692 | 42750 | 0.0 | - | | 18.2906 | 42800 | 0.0001 | - | | 18.3120 | 42850 | 0.0 | - | | 18.3333 | 42900 | 0.0001 | - | | 18.3547 | 42950 | 0.0 | - | | 18.3761 | 43000 | 0.0 | - | | 18.3974 | 43050 | 0.0 | - | | 18.4188 | 43100 | 0.0 | - | | 18.4402 | 43150 | 0.0 | - | | 18.4615 | 43200 | 0.0 | - | | 18.4829 | 43250 | 0.0 | - | | 18.5043 | 43300 | 0.0001 | - | | 18.5256 | 43350 | 0.0 | - | | 18.5470 | 43400 | 0.0 | - | | 18.5684 | 43450 | 0.0 | - | | 18.5897 | 43500 | 0.0 | - | | 18.6111 | 43550 | 0.0001 | - | | 18.6325 | 43600 | 0.0 | - | | 18.6538 | 43650 | 0.0 | - | | 18.6752 | 43700 | 0.0002 | - | | 18.6966 | 43750 | 0.0 | - | | 18.7179 | 43800 | 0.0 | - | | 18.7393 | 43850 | 0.0 | - | | 18.7607 | 43900 | 0.0 | - | | 18.7821 | 43950 | 0.0001 | - | | 18.8034 | 44000 | 0.0 | - | | 18.8248 | 44050 | 0.0 | - | | 18.8462 | 44100 | 0.0001 | - | | 18.8675 | 44150 | 0.0 | - | | 18.8889 | 44200 | 0.0 | - | | 18.9103 | 44250 | 0.0 | - | | 18.9316 | 44300 | 0.0 | - | | 18.9530 | 44350 | 0.0 | - | | 18.9744 | 44400 | 0.0 | - | | 18.9957 | 44450 | 0.0 | - | | 19.0171 | 44500 | 0.0 | - | | 19.0385 | 44550 | 0.0 | - | | 19.0598 | 44600 | 0.0001 | - | | 19.0812 | 44650 | 0.0 | - | | 19.1026 | 44700 | 0.0 | - | | 19.1239 | 44750 | 0.0 | - | | 19.1453 | 44800 | 0.0001 | - | | 19.1667 | 44850 | 0.0001 | - | | 19.1880 | 44900 | 0.0 | - | | 19.2094 | 44950 | 0.0 | - | | 19.2308 | 45000 | 0.0 | - | | 19.2521 | 45050 | 0.0001 | - | | 19.2735 | 45100 | 0.0 | - | | 19.2949 | 45150 | 0.0 | - | | 19.3162 | 45200 | 0.0001 | - | | 19.3376 | 45250 | 0.0001 | - | | 19.3590 | 45300 | 0.0 | - | | 19.3803 | 45350 | 0.0 | - | | 19.4017 | 45400 | 0.0 | - | | 19.4231 | 45450 | 0.0 | - | | 19.4444 | 45500 | 0.0001 | - | | 19.4658 | 45550 | 0.0001 | - | | 19.4872 | 45600 | 0.0 | - | | 19.5085 | 45650 | 0.0 | - | | 19.5299 | 45700 | 0.0001 | - | | 19.5513 | 45750 | 0.0 | - | | 19.5726 | 45800 | 0.0 | - | | 19.5940 | 45850 | 0.0 | - | | 19.6154 | 45900 | 0.0001 | - | | 19.6368 | 45950 | 0.0 | - | | 19.6581 | 46000 | 0.0 | - | | 19.6795 | 46050 | 0.0001 | - | | 19.7009 | 46100 | 0.0001 | - | | 19.7222 | 46150 | 0.0001 | - | | 19.7436 | 46200 | 0.0 | - | | 19.7650 | 46250 | 0.0 | - | | 19.7863 | 46300 | 0.0 | - | | 19.8077 | 46350 | 0.0 | - | | 19.8291 | 46400 | 0.0 | - | | 19.8504 | 46450 | 0.0 | - | | 19.8718 | 46500 | 0.0 | - | | 19.8932 | 46550 | 0.0001 | - | | 19.9145 | 46600 | 0.0 | - | | 19.9359 | 46650 | 0.0001 | - | | 19.9573 | 46700 | 0.0 | - | | 19.9786 | 46750 | 0.0001 | - | | 20.0 | 46800 | 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} } ```