--- base_model: mini1013/master_domain library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 아이깨끗해 핸드워시 490ml용기+450ml리필 2개 29.리필 200ml 5개(순) 홈>전체상품;홈>인기상품;(#M)홈>▶판매 BEST Naverstore > 화장품/미용 > 바디케어 > 핸드케어 - text: 몰튼브라운 바디워시 300ml 21종 4. 네온 앰버 (#M)홈>화장품/미용>바디케어>바디클렌저 Naverstore > 화장품/미용 > 바디케어 > 바디클렌저 - text: Biotherm Homme Day Control Antiperspirant Roll-On Multicolor, 2.53oz, 1 pack 비오템 옴/8837866 LotteOn > 뷰티 > 바디케어 > 데오드란트 LotteOn > 뷰티 > 바디케어 > 데오드란트 - text: 에스테소피 스크럽 솔트 솝 진저 1kg (#M)11st>바디케어>바디스크럽>바디스크럽 11st > 뷰티 > 바디케어 > 바디스크럽 - text: LUSH BUBBLE BAR Creamy Candy 러쉬 입욕제 버블 바 크리미 캔디 100g 2팩 (#M)홈>화장품/미용>바디케어>입욕제 Naverstore > 화장품/미용 > 바디케어 > 입욕제 inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.8482412060301507 name: Accuracy --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) 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:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - **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:** 15 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 | |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | | | 10 | | | 4 | | | 13 | | | 7 | | | 14 | | | 1 | | | 3 | | | 6 | | | 11 | | | 12 | | | 9 | | | 5 | | | 8 | | | 2 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.8482 | ## 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_bt3_test_flat_top_cate") # Run inference preds = model("에스테소피 스크럽 솔트 솝 진저 1kg (#M)11st>바디케어>바디스크럽>바디스크럽 11st > 뷰티 > 바디케어 > 바디스크럽") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 11 | 21.6635 | 51 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 50 | | 1 | 50 | | 2 | 50 | | 3 | 50 | | 4 | 50 | | 5 | 50 | | 6 | 50 | | 7 | 46 | | 8 | 50 | | 9 | 50 | | 10 | 50 | | 11 | 50 | | 12 | 50 | | 13 | 50 | | 14 | 50 | ### Training Hyperparameters - batch_size: (64, 64) - num_epochs: (30, 30) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 100 - 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 | |:-------:|:-----:|:-------------:|:---------------:| | 0.0009 | 1 | 0.421 | - | | 0.0429 | 50 | 0.4469 | - | | 0.0858 | 100 | 0.4667 | - | | 0.1286 | 150 | 0.4451 | - | | 0.1715 | 200 | 0.4292 | - | | 0.2144 | 250 | 0.4105 | - | | 0.2573 | 300 | 0.4006 | - | | 0.3002 | 350 | 0.3816 | - | | 0.3431 | 400 | 0.3448 | - | | 0.3859 | 450 | 0.3177 | - | | 0.4288 | 500 | 0.2957 | - | | 0.4717 | 550 | 0.2719 | - | | 0.5146 | 600 | 0.2574 | - | | 0.5575 | 650 | 0.2516 | - | | 0.6003 | 700 | 0.2601 | - | | 0.6432 | 750 | 0.2533 | - | | 0.6861 | 800 | 0.2498 | - | | 0.7290 | 850 | 0.2401 | - | | 0.7719 | 900 | 0.2253 | - | | 0.8148 | 950 | 0.2273 | - | | 0.8576 | 1000 | 0.223 | - | | 0.9005 | 1050 | 0.22 | - | | 0.9434 | 1100 | 0.2089 | - | | 0.9863 | 1150 | 0.2111 | - | | 1.0292 | 1200 | 0.2048 | - | | 1.0720 | 1250 | 0.2072 | - | | 1.1149 | 1300 | 0.1999 | - | | 1.1578 | 1350 | 0.1977 | - | | 1.2007 | 1400 | 0.1938 | - | | 1.2436 | 1450 | 0.1805 | - | | 1.2864 | 1500 | 0.1769 | - | | 1.3293 | 1550 | 0.1764 | - | | 1.3722 | 1600 | 0.1716 | - | | 1.4151 | 1650 | 0.1635 | - | | 1.4580 | 1700 | 0.1529 | - | | 1.5009 | 1750 | 0.1563 | - | | 1.5437 | 1800 | 0.148 | - | | 1.5866 | 1850 | 0.1465 | - | | 1.6295 | 1900 | 0.1393 | - | | 1.6724 | 1950 | 0.1278 | - | | 1.7153 | 2000 | 0.1262 | - | | 1.7581 | 2050 | 0.12 | - | | 1.8010 | 2100 | 0.1123 | - | | 1.8439 | 2150 | 0.1051 | - | | 1.8868 | 2200 | 0.0968 | - | | 1.9297 | 2250 | 0.0902 | - | | 1.9726 | 2300 | 0.0843 | - | | 2.0154 | 2350 | 0.0784 | - | | 2.0583 | 2400 | 0.0698 | - | | 2.1012 | 2450 | 0.0671 | - | | 2.1441 | 2500 | 0.0605 | - | | 2.1870 | 2550 | 0.0601 | - | | 2.2298 | 2600 | 0.0494 | - | | 2.2727 | 2650 | 0.0484 | - | | 2.3156 | 2700 | 0.0442 | - | | 2.3585 | 2750 | 0.0376 | - | | 2.4014 | 2800 | 0.0356 | - | | 2.4443 | 2850 | 0.0308 | - | | 2.4871 | 2900 | 0.0313 | - | | 2.5300 | 2950 | 0.0321 | - | | 2.5729 | 3000 | 0.0279 | - | | 2.6158 | 3050 | 0.0293 | - | | 2.6587 | 3100 | 0.0304 | - | | 2.7015 | 3150 | 0.0211 | - | | 2.7444 | 3200 | 0.0233 | - | | 2.7873 | 3250 | 0.0204 | - | | 2.8302 | 3300 | 0.0177 | - | | 2.8731 | 3350 | 0.0181 | - | | 2.9160 | 3400 | 0.0183 | - | | 2.9588 | 3450 | 0.0145 | - | | 3.0017 | 3500 | 0.0163 | - | | 3.0446 | 3550 | 0.0145 | - | | 3.0875 | 3600 | 0.0131 | - | | 3.1304 | 3650 | 0.0113 | - | | 3.1732 | 3700 | 0.0136 | - | | 3.2161 | 3750 | 0.012 | - | | 3.2590 | 3800 | 0.0109 | - | | 3.3019 | 3850 | 0.011 | - | | 3.3448 | 3900 | 0.0113 | - | | 3.3877 | 3950 | 0.0105 | - | | 3.4305 | 4000 | 0.0095 | - | | 3.4734 | 4050 | 0.008 | - | | 3.5163 | 4100 | 0.0072 | - | | 3.5592 | 4150 | 0.0077 | - | | 3.6021 | 4200 | 0.0057 | - | | 3.6449 | 4250 | 0.0056 | - | | 3.6878 | 4300 | 0.0061 | - | | 3.7307 | 4350 | 0.004 | - | | 3.7736 | 4400 | 0.0049 | - | | 3.8165 | 4450 | 0.0041 | - | | 3.8593 | 4500 | 0.0028 | - | | 3.9022 | 4550 | 0.002 | - | | 3.9451 | 4600 | 0.0015 | - | | 3.9880 | 4650 | 0.0012 | - | | 4.0309 | 4700 | 0.0011 | - | | 4.0738 | 4750 | 0.0021 | - | | 4.1166 | 4800 | 0.0014 | - | | 4.1595 | 4850 | 0.0006 | - | | 4.2024 | 4900 | 0.0008 | - | | 4.2453 | 4950 | 0.0006 | - | | 4.2882 | 5000 | 0.0005 | - | | 4.3310 | 5050 | 0.0003 | - | | 4.3739 | 5100 | 0.0003 | - | | 4.4168 | 5150 | 0.0002 | - | | 4.4597 | 5200 | 0.0002 | - | | 4.5026 | 5250 | 0.0002 | - | | 4.5455 | 5300 | 0.0002 | - | | 4.5883 | 5350 | 0.0002 | - | | 4.6312 | 5400 | 0.0002 | - | | 4.6741 | 5450 | 0.0003 | - | | 4.7170 | 5500 | 0.0001 | - | | 4.7599 | 5550 | 0.0001 | - | | 4.8027 | 5600 | 0.0001 | - | | 4.8456 | 5650 | 0.0001 | - | | 4.8885 | 5700 | 0.0001 | - | | 4.9314 | 5750 | 0.0001 | - | | 4.9743 | 5800 | 0.0001 | - | | 5.0172 | 5850 | 0.0001 | - | | 5.0600 | 5900 | 0.0001 | - | | 5.1029 | 5950 | 0.0001 | - | | 5.1458 | 6000 | 0.0001 | - | | 5.1887 | 6050 | 0.0001 | - | | 5.2316 | 6100 | 0.0001 | - | | 5.2744 | 6150 | 0.0001 | - | | 5.3173 | 6200 | 0.0002 | - | | 5.3602 | 6250 | 0.0001 | - | | 5.4031 | 6300 | 0.0001 | - | | 5.4460 | 6350 | 0.0001 | - | | 5.4889 | 6400 | 0.0 | - | | 5.5317 | 6450 | 0.0 | - | | 5.5746 | 6500 | 0.0001 | - | | 5.6175 | 6550 | 0.0 | - | | 5.6604 | 6600 | 0.0001 | - | | 5.7033 | 6650 | 0.0 | - | | 5.7461 | 6700 | 0.0001 | - | | 5.7890 | 6750 | 0.0 | - | | 5.8319 | 6800 | 0.0 | - | | 5.8748 | 6850 | 0.0001 | - | | 5.9177 | 6900 | 0.0 | - | | 5.9605 | 6950 | 0.0001 | - | | 6.0034 | 7000 | 0.0023 | - | | 6.0463 | 7050 | 0.0094 | - | | 6.0892 | 7100 | 0.0089 | - | | 6.1321 | 7150 | 0.0075 | - | | 6.1750 | 7200 | 0.0033 | - | | 6.2178 | 7250 | 0.0026 | - | | 6.2607 | 7300 | 0.0023 | - | | 6.3036 | 7350 | 0.0034 | - | | 6.3465 | 7400 | 0.0013 | - | | 6.3894 | 7450 | 0.0008 | - | | 6.4322 | 7500 | 0.0004 | - | | 6.4751 | 7550 | 0.0002 | - | | 6.5180 | 7600 | 0.0001 | - | | 6.5609 | 7650 | 0.0001 | - | | 6.6038 | 7700 | 0.0001 | - | | 6.6467 | 7750 | 0.0002 | - | | 6.6895 | 7800 | 0.0001 | - | | 6.7324 | 7850 | 0.0002 | - | | 6.7753 | 7900 | 0.0001 | - | | 6.8182 | 7950 | 0.0 | - | | 6.8611 | 8000 | 0.0021 | - | | 6.9039 | 8050 | 0.0003 | - | | 6.9468 | 8100 | 0.0011 | - | | 6.9897 | 8150 | 0.0013 | - | | 7.0326 | 8200 | 0.0001 | - | | 7.0755 | 8250 | 0.0001 | - | | 7.1184 | 8300 | 0.0 | - | | 7.1612 | 8350 | 0.0001 | - | | 7.2041 | 8400 | 0.0002 | - | | 7.2470 | 8450 | 0.0015 | - | | 7.2899 | 8500 | 0.0008 | - | | 7.3328 | 8550 | 0.0001 | - | | 7.3756 | 8600 | 0.0 | - | | 7.4185 | 8650 | 0.0 | - | | 7.4614 | 8700 | 0.0 | - | | 7.5043 | 8750 | 0.0 | - | | 7.5472 | 8800 | 0.0001 | - | | 7.5901 | 8850 | 0.0 | - | | 7.6329 | 8900 | 0.0001 | - | | 7.6758 | 8950 | 0.0001 | - | | 7.7187 | 9000 | 0.0 | - | | 7.7616 | 9050 | 0.0001 | - | | 7.8045 | 9100 | 0.0003 | - | | 7.8473 | 9150 | 0.0002 | - | | 7.8902 | 9200 | 0.0 | - | | 7.9331 | 9250 | 0.0 | - | | 7.9760 | 9300 | 0.0 | - | | 8.0189 | 9350 | 0.0 | - | | 8.0617 | 9400 | 0.0 | - | | 8.1046 | 9450 | 0.0005 | - | | 8.1475 | 9500 | 0.0092 | - | | 8.1904 | 9550 | 0.009 | - | | 8.2333 | 9600 | 0.0042 | - | | 8.2762 | 9650 | 0.0011 | - | | 8.3190 | 9700 | 0.0001 | - | | 8.3619 | 9750 | 0.0003 | - | | 8.4048 | 9800 | 0.0001 | - | | 8.4477 | 9850 | 0.0003 | - | | 8.4906 | 9900 | 0.0 | - | | 8.5334 | 9950 | 0.0 | - | | 8.5763 | 10000 | 0.0002 | - | | 8.6192 | 10050 | 0.0003 | - | | 8.6621 | 10100 | 0.0 | - | | 8.7050 | 10150 | 0.0 | - | | 8.7479 | 10200 | 0.0 | - | | 8.7907 | 10250 | 0.0 | - | | 8.8336 | 10300 | 0.0 | - | | 8.8765 | 10350 | 0.0 | - | | 8.9194 | 10400 | 0.0 | - | | 8.9623 | 10450 | 0.0 | - | | 9.0051 | 10500 | 0.0 | - | | 9.0480 | 10550 | 0.0 | - | | 9.0909 | 10600 | 0.0 | - | | 9.1338 | 10650 | 0.0 | - | | 9.1767 | 10700 | 0.0 | - | | 9.2196 | 10750 | 0.0 | - | | 9.2624 | 10800 | 0.0 | - | | 9.3053 | 10850 | 0.0018 | - | | 9.3482 | 10900 | 0.0016 | - | | 9.3911 | 10950 | 0.0012 | - | | 9.4340 | 11000 | 0.0007 | - | | 9.4768 | 11050 | 0.0075 | - | | 9.5197 | 11100 | 0.0044 | - | | 9.5626 | 11150 | 0.004 | - | | 9.6055 | 11200 | 0.004 | - | | 9.6484 | 11250 | 0.0019 | - | | 9.6913 | 11300 | 0.0015 | - | | 9.7341 | 11350 | 0.0017 | - | | 9.7770 | 11400 | 0.0011 | - | | 9.8199 | 11450 | 0.0003 | - | | 9.8628 | 11500 | 0.0001 | - | | 9.9057 | 11550 | 0.0001 | - | | 9.9485 | 11600 | 0.0001 | - | | 9.9914 | 11650 | 0.0 | - | | 10.0343 | 11700 | 0.0 | - | | 10.0772 | 11750 | 0.0 | - | | 10.1201 | 11800 | 0.0 | - | | 10.1630 | 11850 | 0.0 | - | | 10.2058 | 11900 | 0.0 | - | | 10.2487 | 11950 | 0.0 | - | | 10.2916 | 12000 | 0.0 | - | | 10.3345 | 12050 | 0.0 | - | | 10.3774 | 12100 | 0.0 | - | | 10.4202 | 12150 | 0.0 | - | | 10.4631 | 12200 | 0.0 | - | | 10.5060 | 12250 | 0.0 | - | | 10.5489 | 12300 | 0.0 | - | | 10.5918 | 12350 | 0.0 | - | | 10.6346 | 12400 | 0.0 | - | | 10.6775 | 12450 | 0.0 | - | | 10.7204 | 12500 | 0.0 | - | | 10.7633 | 12550 | 0.0 | - | | 10.8062 | 12600 | 0.0 | - | | 10.8491 | 12650 | 0.0 | - | | 10.8919 | 12700 | 0.0 | - | | 10.9348 | 12750 | 0.0003 | - | | 10.9777 | 12800 | 0.0014 | - | | 11.0206 | 12850 | 0.0004 | - | | 11.0635 | 12900 | 0.0001 | - | | 11.1063 | 12950 | 0.0 | - | | 11.1492 | 13000 | 0.0 | - | | 11.1921 | 13050 | 0.0 | - | | 11.2350 | 13100 | 0.0 | - | | 11.2779 | 13150 | 0.0 | - | | 11.3208 | 13200 | 0.0 | - | | 11.3636 | 13250 | 0.0 | - | | 11.4065 | 13300 | 0.0 | - | | 11.4494 | 13350 | 0.0 | - | | 11.4923 | 13400 | 0.0 | - | | 11.5352 | 13450 | 0.0 | - | | 11.5780 | 13500 | 0.0 | - | | 11.6209 | 13550 | 0.0 | - | | 11.6638 | 13600 | 0.0 | - | | 11.7067 | 13650 | 0.0 | - | | 11.7496 | 13700 | 0.0 | - | | 11.7925 | 13750 | 0.0 | - | | 11.8353 | 13800 | 0.0 | - | | 11.8782 | 13850 | 0.0 | - | | 11.9211 | 13900 | 0.0 | - | | 11.9640 | 13950 | 0.0 | - | | 12.0069 | 14000 | 0.0 | - | | 12.0497 | 14050 | 0.0 | - | | 12.0926 | 14100 | 0.0 | - | | 12.1355 | 14150 | 0.0 | - | | 12.1784 | 14200 | 0.0 | - | | 12.2213 | 14250 | 0.0 | - | | 12.2642 | 14300 | 0.0 | - | | 12.3070 | 14350 | 0.0 | - | | 12.3499 | 14400 | 0.0 | - | | 12.3928 | 14450 | 0.0 | - | | 12.4357 | 14500 | 0.0 | - | | 12.4786 | 14550 | 0.0 | - | | 12.5214 | 14600 | 0.0 | - | | 12.5643 | 14650 | 0.0 | - | | 12.6072 | 14700 | 0.0 | - | | 12.6501 | 14750 | 0.0 | - | | 12.6930 | 14800 | 0.0 | - | | 12.7358 | 14850 | 0.0 | - | | 12.7787 | 14900 | 0.0 | - | | 12.8216 | 14950 | 0.0 | - | | 12.8645 | 15000 | 0.0 | - | | 12.9074 | 15050 | 0.0 | - | | 12.9503 | 15100 | 0.0001 | - | | 12.9931 | 15150 | 0.0068 | - | | 13.0360 | 15200 | 0.0085 | - | | 13.0789 | 15250 | 0.007 | - | | 13.1218 | 15300 | 0.0059 | - | | 13.1647 | 15350 | 0.0036 | - | | 13.2075 | 15400 | 0.0041 | - | | 13.2504 | 15450 | 0.0054 | - | | 13.2933 | 15500 | 0.007 | - | | 13.3362 | 15550 | 0.0047 | - | | 13.3791 | 15600 | 0.0041 | - | | 13.4220 | 15650 | 0.0019 | - | | 13.4648 | 15700 | 0.002 | - | | 13.5077 | 15750 | 0.0004 | - | | 13.5506 | 15800 | 0.0001 | - | | 13.5935 | 15850 | 0.0 | - | | 13.6364 | 15900 | 0.0001 | - | | 13.6792 | 15950 | 0.0 | - | | 13.7221 | 16000 | 0.0002 | - | | 13.7650 | 16050 | 0.0 | - | | 13.8079 | 16100 | 0.0 | - | | 13.8508 | 16150 | 0.0 | - | | 13.8937 | 16200 | 0.0 | - | | 13.9365 | 16250 | 0.0 | - | | 13.9794 | 16300 | 0.0 | - | | 14.0223 | 16350 | 0.0 | - | | 14.0652 | 16400 | 0.0 | - | | 14.1081 | 16450 | 0.0 | - | | 14.1509 | 16500 | 0.0 | - | | 14.1938 | 16550 | 0.0 | - | | 14.2367 | 16600 | 0.0 | - | | 14.2796 | 16650 | 0.0 | - | | 14.3225 | 16700 | 0.0 | - | | 14.3654 | 16750 | 0.0 | - | | 14.4082 | 16800 | 0.0 | - | | 14.4511 | 16850 | 0.0 | - | | 14.4940 | 16900 | 0.0 | - | | 14.5369 | 16950 | 0.0 | - | | 14.5798 | 17000 | 0.0 | - | | 14.6226 | 17050 | 0.0 | - | | 14.6655 | 17100 | 0.0 | - | | 14.7084 | 17150 | 0.0 | - | | 14.7513 | 17200 | 0.0 | - | | 14.7942 | 17250 | 0.0 | - | | 14.8370 | 17300 | 0.0 | - | | 14.8799 | 17350 | 0.0 | - | | 14.9228 | 17400 | 0.0 | - | | 14.9657 | 17450 | 0.0 | - | | 15.0086 | 17500 | 0.0 | - | | 15.0515 | 17550 | 0.0 | - | | 15.0943 | 17600 | 0.0 | - | | 15.1372 | 17650 | 0.0 | - | | 15.1801 | 17700 | 0.0 | - | | 15.2230 | 17750 | 0.0 | - | | 15.2659 | 17800 | 0.0 | - | | 15.3087 | 17850 | 0.0 | - | | 15.3516 | 17900 | 0.0 | - | | 15.3945 | 17950 | 0.0 | - | | 15.4374 | 18000 | 0.0 | - | | 15.4803 | 18050 | 0.0 | - | | 15.5232 | 18100 | 0.0 | - | | 15.5660 | 18150 | 0.0 | - | | 15.6089 | 18200 | 0.0004 | - | | 15.6518 | 18250 | 0.002 | - | | 15.6947 | 18300 | 0.0015 | - | | 15.7376 | 18350 | 0.0016 | - | | 15.7804 | 18400 | 0.002 | - | | 15.8233 | 18450 | 0.0009 | - | | 15.8662 | 18500 | 0.0007 | - | | 15.9091 | 18550 | 0.0011 | - | | 15.9520 | 18600 | 0.0004 | - | | 15.9949 | 18650 | 0.0004 | - | | 16.0377 | 18700 | 0.0001 | - | | 16.0806 | 18750 | 0.0 | - | | 16.1235 | 18800 | 0.0 | - | | 16.1664 | 18850 | 0.0002 | - | | 16.2093 | 18900 | 0.0 | - | | 16.2521 | 18950 | 0.0 | - | | 16.2950 | 19000 | 0.0 | - | | 16.3379 | 19050 | 0.0 | - | | 16.3808 | 19100 | 0.0 | - | | 16.4237 | 19150 | 0.0 | - | | 16.4666 | 19200 | 0.0 | - | | 16.5094 | 19250 | 0.0 | - | | 16.5523 | 19300 | 0.0 | - | | 16.5952 | 19350 | 0.0 | - | | 16.6381 | 19400 | 0.0 | - | | 16.6810 | 19450 | 0.0 | - | | 16.7238 | 19500 | 0.0 | - | | 16.7667 | 19550 | 0.0001 | - | | 16.8096 | 19600 | 0.0001 | - | | 16.8525 | 19650 | 0.0007 | - | | 16.8954 | 19700 | 0.0002 | - | | 16.9383 | 19750 | 0.0003 | - | | 16.9811 | 19800 | 0.0 | - | | 17.0240 | 19850 | 0.0 | - | | 17.0669 | 19900 | 0.0 | - | | 17.1098 | 19950 | 0.0 | - | | 17.1527 | 20000 | 0.0 | - | | 17.1955 | 20050 | 0.0 | - | | 17.2384 | 20100 | 0.0 | - | | 17.2813 | 20150 | 0.0 | - | | 17.3242 | 20200 | 0.0 | - | | 17.3671 | 20250 | 0.0 | - | | 17.4099 | 20300 | 0.0 | - | | 17.4528 | 20350 | 0.0 | - | | 17.4957 | 20400 | 0.0 | - | | 17.5386 | 20450 | 0.0 | - | | 17.5815 | 20500 | 0.0 | - | | 17.6244 | 20550 | 0.0 | - | | 17.6672 | 20600 | 0.0 | - | | 17.7101 | 20650 | 0.0 | - | | 17.7530 | 20700 | 0.0 | - | | 17.7959 | 20750 | 0.0 | - | | 17.8388 | 20800 | 0.0 | - | | 17.8816 | 20850 | 0.0 | - | | 17.9245 | 20900 | 0.0 | - | | 17.9674 | 20950 | 0.0 | - | | 18.0103 | 21000 | 0.0 | - | | 18.0532 | 21050 | 0.0 | - | | 18.0961 | 21100 | 0.0 | - | | 18.1389 | 21150 | 0.0 | - | | 18.1818 | 21200 | 0.0 | - | | 18.2247 | 21250 | 0.0 | - | | 18.2676 | 21300 | 0.0 | - | | 18.3105 | 21350 | 0.0 | - | | 18.3533 | 21400 | 0.0 | - | | 18.3962 | 21450 | 0.0 | - | | 18.4391 | 21500 | 0.0 | - | | 18.4820 | 21550 | 0.0 | - | | 18.5249 | 21600 | 0.0 | - | | 18.5678 | 21650 | 0.0 | - | | 18.6106 | 21700 | 0.0 | - | | 18.6535 | 21750 | 0.0 | - | | 18.6964 | 21800 | 0.0 | - | | 18.7393 | 21850 | 0.0 | - | | 18.7822 | 21900 | 0.0 | - | | 18.8250 | 21950 | 0.0 | - | | 18.8679 | 22000 | 0.0 | - | | 18.9108 | 22050 | 0.0 | - | | 18.9537 | 22100 | 0.0 | - | | 18.9966 | 22150 | 0.0 | - | | 19.0395 | 22200 | 0.0 | - | | 19.0823 | 22250 | 0.0 | - | | 19.1252 | 22300 | 0.0 | - | | 19.1681 | 22350 | 0.0 | - | | 19.2110 | 22400 | 0.0 | - | | 19.2539 | 22450 | 0.0 | - | | 19.2967 | 22500 | 0.0 | - | | 19.3396 | 22550 | 0.0 | - | | 19.3825 | 22600 | 0.0 | - | | 19.4254 | 22650 | 0.0 | - | | 19.4683 | 22700 | 0.0 | - | | 19.5111 | 22750 | 0.0 | - | | 19.5540 | 22800 | 0.0 | - | | 19.5969 | 22850 | 0.0 | - | | 19.6398 | 22900 | 0.0 | - | | 19.6827 | 22950 | 0.0 | - | | 19.7256 | 23000 | 0.0 | - | | 19.7684 | 23050 | 0.0 | - | | 19.8113 | 23100 | 0.0 | - | | 19.8542 | 23150 | 0.0 | - | | 19.8971 | 23200 | 0.0 | - | | 19.9400 | 23250 | 0.0 | - | | 19.9828 | 23300 | 0.0 | - | | 20.0257 | 23350 | 0.0 | - | | 20.0686 | 23400 | 0.0 | - | | 20.1115 | 23450 | 0.0 | - | | 20.1544 | 23500 | 0.0 | - | | 20.1973 | 23550 | 0.0 | - | | 20.2401 | 23600 | 0.0 | - | | 20.2830 | 23650 | 0.0 | - | | 20.3259 | 23700 | 0.0 | - | | 20.3688 | 23750 | 0.0 | - | | 20.4117 | 23800 | 0.0 | - | | 20.4545 | 23850 | 0.0 | - | | 20.4974 | 23900 | 0.0 | - | | 20.5403 | 23950 | 0.0 | - | | 20.5832 | 24000 | 0.0 | - | | 20.6261 | 24050 | 0.0 | - | | 20.6690 | 24100 | 0.0 | - | | 20.7118 | 24150 | 0.0 | - | | 20.7547 | 24200 | 0.0 | - | | 20.7976 | 24250 | 0.0 | - | | 20.8405 | 24300 | 0.0 | - | | 20.8834 | 24350 | 0.0 | - | | 20.9262 | 24400 | 0.0 | - | | 20.9691 | 24450 | 0.0 | - | | 21.0120 | 24500 | 0.0 | - | | 21.0549 | 24550 | 0.0 | - | | 21.0978 | 24600 | 0.0 | - | | 21.1407 | 24650 | 0.0 | - | | 21.1835 | 24700 | 0.0 | - | | 21.2264 | 24750 | 0.0 | - | | 21.2693 | 24800 | 0.0 | - | | 21.3122 | 24850 | 0.0 | - | | 21.3551 | 24900 | 0.0 | - | | 21.3979 | 24950 | 0.0 | - | | 21.4408 | 25000 | 0.0 | - | | 21.4837 | 25050 | 0.0 | - | | 21.5266 | 25100 | 0.0 | - | | 21.5695 | 25150 | 0.0 | - | | 21.6123 | 25200 | 0.0 | - | | 21.6552 | 25250 | 0.0 | - | | 21.6981 | 25300 | 0.0 | - | | 21.7410 | 25350 | 0.0005 | - | | 21.7839 | 25400 | 0.0022 | - | | 21.8268 | 25450 | 0.0021 | - | | 21.8696 | 25500 | 0.0001 | - | | 21.9125 | 25550 | 0.0 | - | | 21.9554 | 25600 | 0.0 | - | | 21.9983 | 25650 | 0.0 | - | | 22.0412 | 25700 | 0.0 | - | | 22.0840 | 25750 | 0.0 | - | | 22.1269 | 25800 | 0.0 | - | | 22.1698 | 25850 | 0.0 | - | | 22.2127 | 25900 | 0.0 | - | | 22.2556 | 25950 | 0.0 | - | | 22.2985 | 26000 | 0.0 | - | | 22.3413 | 26050 | 0.0 | - | | 22.3842 | 26100 | 0.0 | - | | 22.4271 | 26150 | 0.0 | - | | 22.4700 | 26200 | 0.0 | - | | 22.5129 | 26250 | 0.0 | - | | 22.5557 | 26300 | 0.0 | - | | 22.5986 | 26350 | 0.0 | - | | 22.6415 | 26400 | 0.0 | - | | 22.6844 | 26450 | 0.0 | - | | 22.7273 | 26500 | 0.0 | - | | 22.7702 | 26550 | 0.0 | - | | 22.8130 | 26600 | 0.0 | - | | 22.8559 | 26650 | 0.0 | - | | 22.8988 | 26700 | 0.0 | - | | 22.9417 | 26750 | 0.0 | - | | 22.9846 | 26800 | 0.0 | - | | 23.0274 | 26850 | 0.0 | - | | 23.0703 | 26900 | 0.0 | - | | 23.1132 | 26950 | 0.0 | - | | 23.1561 | 27000 | 0.0 | - | | 23.1990 | 27050 | 0.0 | - | | 23.2419 | 27100 | 0.0 | - | | 23.2847 | 27150 | 0.0 | - | | 23.3276 | 27200 | 0.0 | - | | 23.3705 | 27250 | 0.0 | - | | 23.4134 | 27300 | 0.0 | - | | 23.4563 | 27350 | 0.0 | - | | 23.4991 | 27400 | 0.0 | - | | 23.5420 | 27450 | 0.0 | - | | 23.5849 | 27500 | 0.0 | - | | 23.6278 | 27550 | 0.0 | - | | 23.6707 | 27600 | 0.0 | - | | 23.7136 | 27650 | 0.0 | - | | 23.7564 | 27700 | 0.0 | - | | 23.7993 | 27750 | 0.0 | - | | 23.8422 | 27800 | 0.0 | - | | 23.8851 | 27850 | 0.0 | - | | 23.9280 | 27900 | 0.0 | - | | 23.9708 | 27950 | 0.0 | - | | 24.0137 | 28000 | 0.0 | - | | 24.0566 | 28050 | 0.0 | - | | 24.0995 | 28100 | 0.0 | - | | 24.1424 | 28150 | 0.0 | - | | 24.1852 | 28200 | 0.0 | - | | 24.2281 | 28250 | 0.0 | - | | 24.2710 | 28300 | 0.0 | - | | 24.3139 | 28350 | 0.0 | - | | 24.3568 | 28400 | 0.0 | - | | 24.3997 | 28450 | 0.0 | - | | 24.4425 | 28500 | 0.0 | - | | 24.4854 | 28550 | 0.0 | - | | 24.5283 | 28600 | 0.0 | - | | 24.5712 | 28650 | 0.0 | - | | 24.6141 | 28700 | 0.0 | - | | 24.6569 | 28750 | 0.0 | - | | 24.6998 | 28800 | 0.0 | - | | 24.7427 | 28850 | 0.0 | - | | 24.7856 | 28900 | 0.0 | - | | 24.8285 | 28950 | 0.0 | - | | 24.8714 | 29000 | 0.0 | - | | 24.9142 | 29050 | 0.0 | - | | 24.9571 | 29100 | 0.0 | - | | 25.0 | 29150 | 0.0 | - | | 25.0429 | 29200 | 0.0 | - | | 25.0858 | 29250 | 0.0 | - | | 25.1286 | 29300 | 0.0 | - | | 25.1715 | 29350 | 0.0 | - | | 25.2144 | 29400 | 0.0 | - | | 25.2573 | 29450 | 0.0 | - | | 25.3002 | 29500 | 0.0 | - | | 25.3431 | 29550 | 0.0 | - | | 25.3859 | 29600 | 0.0 | - | | 25.4288 | 29650 | 0.0 | - | | 25.4717 | 29700 | 0.0 | - | | 25.5146 | 29750 | 0.0 | - | | 25.5575 | 29800 | 0.0 | - | | 25.6003 | 29850 | 0.0 | - | | 25.6432 | 29900 | 0.0 | - | | 25.6861 | 29950 | 0.0 | - | | 25.7290 | 30000 | 0.0 | - | | 25.7719 | 30050 | 0.0 | - | | 25.8148 | 30100 | 0.0 | - | | 25.8576 | 30150 | 0.0 | - | | 25.9005 | 30200 | 0.0 | - | | 25.9434 | 30250 | 0.0 | - | | 25.9863 | 30300 | 0.0 | - | | 26.0292 | 30350 | 0.0 | - | | 26.0720 | 30400 | 0.0 | - | | 26.1149 | 30450 | 0.0 | - | | 26.1578 | 30500 | 0.0 | - | | 26.2007 | 30550 | 0.0 | - | | 26.2436 | 30600 | 0.0 | - | | 26.2864 | 30650 | 0.0 | - | | 26.3293 | 30700 | 0.0 | - | | 26.3722 | 30750 | 0.0 | - | | 26.4151 | 30800 | 0.0 | - | | 26.4580 | 30850 | 0.0 | - | | 26.5009 | 30900 | 0.0 | - | | 26.5437 | 30950 | 0.0 | - | | 26.5866 | 31000 | 0.0 | - | | 26.6295 | 31050 | 0.0 | - | | 26.6724 | 31100 | 0.0 | - | | 26.7153 | 31150 | 0.0 | - | | 26.7581 | 31200 | 0.0 | - | | 26.8010 | 31250 | 0.0 | - | | 26.8439 | 31300 | 0.0 | - | | 26.8868 | 31350 | 0.0 | - | | 26.9297 | 31400 | 0.0 | - | | 26.9726 | 31450 | 0.0 | - | | 27.0154 | 31500 | 0.0 | - | | 27.0583 | 31550 | 0.0 | - | | 27.1012 | 31600 | 0.0 | - | | 27.1441 | 31650 | 0.0 | - | | 27.1870 | 31700 | 0.0 | - | | 27.2298 | 31750 | 0.0 | - | | 27.2727 | 31800 | 0.0 | - | | 27.3156 | 31850 | 0.0 | - | | 27.3585 | 31900 | 0.0 | - | | 27.4014 | 31950 | 0.0 | - | | 27.4443 | 32000 | 0.0 | - | | 27.4871 | 32050 | 0.0 | - | | 27.5300 | 32100 | 0.0 | - | | 27.5729 | 32150 | 0.0 | - | | 27.6158 | 32200 | 0.0 | - | | 27.6587 | 32250 | 0.0 | - | | 27.7015 | 32300 | 0.0 | - | | 27.7444 | 32350 | 0.0 | - | | 27.7873 | 32400 | 0.0 | - | | 27.8302 | 32450 | 0.0 | - | | 27.8731 | 32500 | 0.0 | - | | 27.9160 | 32550 | 0.0 | - | | 27.9588 | 32600 | 0.0 | - | | 28.0017 | 32650 | 0.0 | - | | 28.0446 | 32700 | 0.0 | - | | 28.0875 | 32750 | 0.0 | - | | 28.1304 | 32800 | 0.0 | - | | 28.1732 | 32850 | 0.0 | - | | 28.2161 | 32900 | 0.0 | - | | 28.2590 | 32950 | 0.0 | - | | 28.3019 | 33000 | 0.0 | - | | 28.3448 | 33050 | 0.0 | - | | 28.3877 | 33100 | 0.0 | - | | 28.4305 | 33150 | 0.0 | - | | 28.4734 | 33200 | 0.0 | - | | 28.5163 | 33250 | 0.0 | - | | 28.5592 | 33300 | 0.0 | - | | 28.6021 | 33350 | 0.0 | - | | 28.6449 | 33400 | 0.0 | - | | 28.6878 | 33450 | 0.0 | - | | 28.7307 | 33500 | 0.0 | - | | 28.7736 | 33550 | 0.0 | - | | 28.8165 | 33600 | 0.0 | - | | 28.8593 | 33650 | 0.0 | - | | 28.9022 | 33700 | 0.0 | - | | 28.9451 | 33750 | 0.0 | - | | 28.9880 | 33800 | 0.0 | - | | 29.0309 | 33850 | 0.0 | - | | 29.0738 | 33900 | 0.0 | - | | 29.1166 | 33950 | 0.0 | - | | 29.1595 | 34000 | 0.0 | - | | 29.2024 | 34050 | 0.0 | - | | 29.2453 | 34100 | 0.0 | - | | 29.2882 | 34150 | 0.0 | - | | 29.3310 | 34200 | 0.0 | - | | 29.3739 | 34250 | 0.0 | - | | 29.4168 | 34300 | 0.0 | - | | 29.4597 | 34350 | 0.0 | - | | 29.5026 | 34400 | 0.0 | - | | 29.5455 | 34450 | 0.0 | - | | 29.5883 | 34500 | 0.0 | - | | 29.6312 | 34550 | 0.0 | - | | 29.6741 | 34600 | 0.0 | - | | 29.7170 | 34650 | 0.0 | - | | 29.7599 | 34700 | 0.0 | - | | 29.8027 | 34750 | 0.0 | - | | 29.8456 | 34800 | 0.0 | - | | 29.8885 | 34850 | 0.0 | - | | 29.9314 | 34900 | 0.0 | - | | 29.9743 | 34950 | 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} } ```