--- 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: 백화점정품 샤넬 루쥬 알뤼르 잉크 6ml 140-AMOUREUX_. (#M)쿠팡 홈>뷰티>메이크업>립 메이크업>립스틱 Coupang > 뷰티 > 메이크업 > 립 메이크업 > 립스틱 - text: 더페이스샵 모노큐브 아이섀도우 2g 매트_앙 버터 (#M)화장품/향수>색조메이크업>아이섀도 Gmarket > 뷰티 > 화장품/향수 > 색조메이크업 > 아이섀도 - text: 3CE BLUR WATER TINT 블러 워터 틴트 FRE_SEPIA LOREAL > LotteOn > 입생로랑 > Generic > 틴트 LotteOn > 뷰티 > 메이크업 > 립메이크업 > 립틴트 - text: 3CE 페이스 블러쉬 ROSE BEIGE 홈>전체상품;(#M)홈>FACE>치크 Naverstore > 화장품/미용 > 색조메이크업 > 블러셔 - text: 섀도 듀오 4g 6호 라이커블 LotteOn > 뷰티 > 색조메이크업 > 아이메이크업 LotteOn > 뷰티 > 메이크업 > 아이메이크업 > 아이섀도우 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: 0.48148148148148145 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:** 11 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 | | | 4 | | | 8 | | | 9 | | | 6 | | | 1 | | | 3 | | | 7 | | | 10 | | | 2 | | | 5 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.4815 | ## 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_bt6_test_flat_top_cate") # Run inference preds = model("3CE 페이스 블러쉬 ROSE BEIGE 홈>전체상품;(#M)홈>FACE>치크 Naverstore > 화장품/미용 > 색조메이크업 > 블러셔") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 10 | 24.2404 | 79 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 50 | | 1 | 50 | | 2 | 50 | | 3 | 50 | | 4 | 50 | | 5 | 50 | | 6 | 50 | | 7 | 50 | | 8 | 50 | | 9 | 50 | | 10 | 49 | ### 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 | |:-------:|:-----:|:-------------:|:---------------:| | 0.0012 | 1 | 0.4756 | - | | 0.0583 | 50 | 0.447 | - | | 0.1166 | 100 | 0.4629 | - | | 0.1748 | 150 | 0.4327 | - | | 0.2331 | 200 | 0.4182 | - | | 0.2914 | 250 | 0.3863 | - | | 0.3497 | 300 | 0.3492 | - | | 0.4079 | 350 | 0.3277 | - | | 0.4662 | 400 | 0.2987 | - | | 0.5245 | 450 | 0.2729 | - | | 0.5828 | 500 | 0.2637 | - | | 0.6410 | 550 | 0.2554 | - | | 0.6993 | 600 | 0.252 | - | | 0.7576 | 650 | 0.2419 | - | | 0.8159 | 700 | 0.2382 | - | | 0.8741 | 750 | 0.239 | - | | 0.9324 | 800 | 0.2294 | - | | 0.9907 | 850 | 0.2274 | - | | 1.0490 | 900 | 0.2237 | - | | 1.1072 | 950 | 0.2241 | - | | 1.1655 | 1000 | 0.2196 | - | | 1.2238 | 1050 | 0.2164 | - | | 1.2821 | 1100 | 0.2119 | - | | 1.3403 | 1150 | 0.2048 | - | | 1.3986 | 1200 | 0.2007 | - | | 1.4569 | 1250 | 0.1969 | - | | 1.5152 | 1300 | 0.1898 | - | | 1.5734 | 1350 | 0.1857 | - | | 1.6317 | 1400 | 0.1753 | - | | 1.6900 | 1450 | 0.1703 | - | | 1.7483 | 1500 | 0.1552 | - | | 1.8065 | 1550 | 0.1481 | - | | 1.8648 | 1600 | 0.1341 | - | | 1.9231 | 1650 | 0.1254 | - | | 1.9814 | 1700 | 0.1077 | - | | 2.0396 | 1750 | 0.0895 | - | | 2.0979 | 1800 | 0.0806 | - | | 2.1562 | 1850 | 0.0674 | - | | 2.2145 | 1900 | 0.0618 | - | | 2.2727 | 1950 | 0.056 | - | | 2.3310 | 2000 | 0.0549 | - | | 2.3893 | 2050 | 0.0492 | - | | 2.4476 | 2100 | 0.0438 | - | | 2.5058 | 2150 | 0.0394 | - | | 2.5641 | 2200 | 0.0395 | - | | 2.6224 | 2250 | 0.0358 | - | | 2.6807 | 2300 | 0.0373 | - | | 2.7389 | 2350 | 0.0303 | - | | 2.7972 | 2400 | 0.0321 | - | | 2.8555 | 2450 | 0.0267 | - | | 2.9138 | 2500 | 0.029 | - | | 2.9720 | 2550 | 0.0314 | - | | 3.0303 | 2600 | 0.031 | - | | 3.0886 | 2650 | 0.019 | - | | 3.1469 | 2700 | 0.02 | - | | 3.2051 | 2750 | 0.0223 | - | | 3.2634 | 2800 | 0.0206 | - | | 3.3217 | 2850 | 0.0173 | - | | 3.3800 | 2900 | 0.016 | - | | 3.4382 | 2950 | 0.0181 | - | | 3.4965 | 3000 | 0.0102 | - | | 3.5548 | 3050 | 0.0078 | - | | 3.6131 | 3100 | 0.0107 | - | | 3.6713 | 3150 | 0.0094 | - | | 3.7296 | 3200 | 0.0089 | - | | 3.7879 | 3250 | 0.0097 | - | | 3.8462 | 3300 | 0.0094 | - | | 3.9044 | 3350 | 0.0111 | - | | 3.9627 | 3400 | 0.0102 | - | | 4.0210 | 3450 | 0.0091 | - | | 4.0793 | 3500 | 0.0082 | - | | 4.1375 | 3550 | 0.0048 | - | | 4.1958 | 3600 | 0.0022 | - | | 4.2541 | 3650 | 0.0007 | - | | 4.3124 | 3700 | 0.0007 | - | | 4.3706 | 3750 | 0.0012 | - | | 4.4289 | 3800 | 0.0009 | - | | 4.4872 | 3850 | 0.0006 | - | | 4.5455 | 3900 | 0.0002 | - | | 4.6037 | 3950 | 0.0002 | - | | 4.6620 | 4000 | 0.0002 | - | | 4.7203 | 4050 | 0.0002 | - | | 4.7786 | 4100 | 0.0002 | - | | 4.8368 | 4150 | 0.0001 | - | | 4.8951 | 4200 | 0.0001 | - | | 4.9534 | 4250 | 0.0001 | - | | 5.0117 | 4300 | 0.0001 | - | | 5.0699 | 4350 | 0.0001 | - | | 5.1282 | 4400 | 0.0001 | - | | 5.1865 | 4450 | 0.0001 | - | | 5.2448 | 4500 | 0.0001 | - | | 5.3030 | 4550 | 0.001 | - | | 5.3613 | 4600 | 0.0003 | - | | 5.4196 | 4650 | 0.0005 | - | | 5.4779 | 4700 | 0.0014 | - | | 5.5361 | 4750 | 0.0005 | - | | 5.5944 | 4800 | 0.0016 | - | | 5.6527 | 4850 | 0.0007 | - | | 5.7110 | 4900 | 0.0003 | - | | 5.7692 | 4950 | 0.0001 | - | | 5.8275 | 5000 | 0.0005 | - | | 5.8858 | 5050 | 0.0004 | - | | 5.9441 | 5100 | 0.0003 | - | | 6.0023 | 5150 | 0.0011 | - | | 6.0606 | 5200 | 0.0001 | - | | 6.1189 | 5250 | 0.0001 | - | | 6.1772 | 5300 | 0.0001 | - | | 6.2354 | 5350 | 0.0001 | - | | 6.2937 | 5400 | 0.0002 | - | | 6.3520 | 5450 | 0.0004 | - | | 6.4103 | 5500 | 0.0009 | - | | 6.4685 | 5550 | 0.0002 | - | | 6.5268 | 5600 | 0.0001 | - | | 6.5851 | 5650 | 0.0 | - | | 6.6434 | 5700 | 0.0 | - | | 6.7016 | 5750 | 0.0 | - | | 6.7599 | 5800 | 0.0 | - | | 6.8182 | 5850 | 0.0001 | - | | 6.8765 | 5900 | 0.0006 | - | | 6.9347 | 5950 | 0.0008 | - | | 6.9930 | 6000 | 0.0013 | - | | 7.0513 | 6050 | 0.0015 | - | | 7.1096 | 6100 | 0.0007 | - | | 7.1678 | 6150 | 0.003 | - | | 7.2261 | 6200 | 0.0031 | - | | 7.2844 | 6250 | 0.0013 | - | | 7.3427 | 6300 | 0.0019 | - | | 7.4009 | 6350 | 0.0025 | - | | 7.4592 | 6400 | 0.0009 | - | | 7.5175 | 6450 | 0.0008 | - | | 7.5758 | 6500 | 0.0001 | - | | 7.6340 | 6550 | 0.0001 | - | | 7.6923 | 6600 | 0.0001 | - | | 7.7506 | 6650 | 0.0 | - | | 7.8089 | 6700 | 0.0 | - | | 7.8671 | 6750 | 0.0 | - | | 7.9254 | 6800 | 0.0 | - | | 7.9837 | 6850 | 0.0 | - | | 8.0420 | 6900 | 0.0 | - | | 8.1002 | 6950 | 0.0 | - | | 8.1585 | 7000 | 0.0 | - | | 8.2168 | 7050 | 0.0 | - | | 8.2751 | 7100 | 0.0 | - | | 8.3333 | 7150 | 0.0 | - | | 8.3916 | 7200 | 0.0 | - | | 8.4499 | 7250 | 0.0 | - | | 8.5082 | 7300 | 0.0 | - | | 8.5664 | 7350 | 0.0 | - | | 8.6247 | 7400 | 0.0 | - | | 8.6830 | 7450 | 0.0 | - | | 8.7413 | 7500 | 0.0 | - | | 8.7995 | 7550 | 0.0 | - | | 8.8578 | 7600 | 0.0 | - | | 8.9161 | 7650 | 0.0 | - | | 8.9744 | 7700 | 0.0 | - | | 9.0326 | 7750 | 0.0 | - | | 9.0909 | 7800 | 0.0 | - | | 9.1492 | 7850 | 0.0 | - | | 9.2075 | 7900 | 0.0 | - | | 9.2657 | 7950 | 0.0 | - | | 9.3240 | 8000 | 0.0 | - | | 9.3823 | 8050 | 0.0 | - | | 9.4406 | 8100 | 0.0 | - | | 9.4988 | 8150 | 0.0 | - | | 9.5571 | 8200 | 0.0 | - | | 9.6154 | 8250 | 0.0 | - | | 9.6737 | 8300 | 0.0 | - | | 9.7319 | 8350 | 0.0 | - | | 9.7902 | 8400 | 0.0 | - | | 9.8485 | 8450 | 0.0 | - | | 9.9068 | 8500 | 0.0 | - | | 9.9650 | 8550 | 0.0 | - | | 10.0233 | 8600 | 0.0 | - | | 10.0816 | 8650 | 0.0001 | - | | 10.1399 | 8700 | 0.0036 | - | | 10.1981 | 8750 | 0.0148 | - | | 10.2564 | 8800 | 0.0142 | - | | 10.3147 | 8850 | 0.0132 | - | | 10.3730 | 8900 | 0.0116 | - | | 10.4312 | 8950 | 0.0041 | - | | 10.4895 | 9000 | 0.0005 | - | | 10.5478 | 9050 | 0.0001 | - | | 10.6061 | 9100 | 0.0003 | - | | 10.6643 | 9150 | 0.0003 | - | | 10.7226 | 9200 | 0.0002 | - | | 10.7809 | 9250 | 0.0001 | - | | 10.8392 | 9300 | 0.0003 | - | | 10.8974 | 9350 | 0.0 | - | | 10.9557 | 9400 | 0.0 | - | | 11.0140 | 9450 | 0.0001 | - | | 11.0723 | 9500 | 0.0 | - | | 11.1305 | 9550 | 0.0 | - | | 11.1888 | 9600 | 0.0 | - | | 11.2471 | 9650 | 0.0 | - | | 11.3054 | 9700 | 0.0 | - | | 11.3636 | 9750 | 0.0 | - | | 11.4219 | 9800 | 0.0 | - | | 11.4802 | 9850 | 0.0007 | - | | 11.5385 | 9900 | 0.0001 | - | | 11.5967 | 9950 | 0.0002 | - | | 11.6550 | 10000 | 0.0014 | - | | 11.7133 | 10050 | 0.0006 | - | | 11.7716 | 10100 | 0.0003 | - | | 11.8298 | 10150 | 0.0003 | - | | 11.8881 | 10200 | 0.0 | - | | 11.9464 | 10250 | 0.0 | - | | 12.0047 | 10300 | 0.0 | - | | 12.0629 | 10350 | 0.0 | - | | 12.1212 | 10400 | 0.0 | - | | 12.1795 | 10450 | 0.0 | - | | 12.2378 | 10500 | 0.002 | - | | 12.2960 | 10550 | 0.0005 | - | | 12.3543 | 10600 | 0.0002 | - | | 12.4126 | 10650 | 0.0 | - | | 12.4709 | 10700 | 0.0 | - | | 12.5291 | 10750 | 0.0 | - | | 12.5874 | 10800 | 0.0 | - | | 12.6457 | 10850 | 0.0002 | - | | 12.7040 | 10900 | 0.0 | - | | 12.7622 | 10950 | 0.0 | - | | 12.8205 | 11000 | 0.0 | - | | 12.8788 | 11050 | 0.0 | - | | 12.9371 | 11100 | 0.0 | - | | 12.9953 | 11150 | 0.0 | - | | 13.0536 | 11200 | 0.0001 | - | | 13.1119 | 11250 | 0.0 | - | | 13.1702 | 11300 | 0.0005 | - | | 13.2284 | 11350 | 0.0008 | - | | 13.2867 | 11400 | 0.0002 | - | | 13.3450 | 11450 | 0.0005 | - | | 13.4033 | 11500 | 0.0001 | - | | 13.4615 | 11550 | 0.0 | - | | 13.5198 | 11600 | 0.0 | - | | 13.5781 | 11650 | 0.0 | - | | 13.6364 | 11700 | 0.0 | - | | 13.6946 | 11750 | 0.0 | - | | 13.7529 | 11800 | 0.0 | - | | 13.8112 | 11850 | 0.0 | - | | 13.8695 | 11900 | 0.0 | - | | 13.9277 | 11950 | 0.0 | - | | 13.9860 | 12000 | 0.0002 | - | | 14.0443 | 12050 | 0.0009 | - | | 14.1026 | 12100 | 0.0 | - | | 14.1608 | 12150 | 0.0 | - | | 14.2191 | 12200 | 0.0 | - | | 14.2774 | 12250 | 0.0 | - | | 14.3357 | 12300 | 0.0 | - | | 14.3939 | 12350 | 0.0 | - | | 14.4522 | 12400 | 0.0 | - | | 14.5105 | 12450 | 0.0 | - | | 14.5688 | 12500 | 0.0 | - | | 14.6270 | 12550 | 0.0 | - | | 14.6853 | 12600 | 0.0 | - | | 14.7436 | 12650 | 0.0 | - | | 14.8019 | 12700 | 0.0 | - | | 14.8601 | 12750 | 0.0 | - | | 14.9184 | 12800 | 0.0 | - | | 14.9767 | 12850 | 0.0 | - | | 15.0350 | 12900 | 0.0 | - | | 15.0932 | 12950 | 0.0 | - | | 15.1515 | 13000 | 0.0 | - | | 15.2098 | 13050 | 0.0 | - | | 15.2681 | 13100 | 0.0 | - | | 15.3263 | 13150 | 0.0 | - | | 15.3846 | 13200 | 0.0 | - | | 15.4429 | 13250 | 0.0 | - | | 15.5012 | 13300 | 0.0 | - | | 15.5594 | 13350 | 0.0 | - | | 15.6177 | 13400 | 0.0 | - | | 15.6760 | 13450 | 0.0 | - | | 15.7343 | 13500 | 0.0 | - | | 15.7925 | 13550 | 0.0 | - | | 15.8508 | 13600 | 0.0 | - | | 15.9091 | 13650 | 0.0 | - | | 15.9674 | 13700 | 0.0 | - | | 16.0256 | 13750 | 0.0 | - | | 16.0839 | 13800 | 0.0 | - | | 16.1422 | 13850 | 0.0 | - | | 16.2005 | 13900 | 0.0 | - | | 16.2587 | 13950 | 0.0 | - | | 16.3170 | 14000 | 0.0 | - | | 16.3753 | 14050 | 0.0 | - | | 16.4336 | 14100 | 0.0 | - | | 16.4918 | 14150 | 0.0 | - | | 16.5501 | 14200 | 0.0 | - | | 16.6084 | 14250 | 0.0 | - | | 16.6667 | 14300 | 0.0 | - | | 16.7249 | 14350 | 0.0 | - | | 16.7832 | 14400 | 0.0 | - | | 16.8415 | 14450 | 0.0 | - | | 16.8998 | 14500 | 0.0 | - | | 16.9580 | 14550 | 0.0 | - | | 17.0163 | 14600 | 0.0 | - | | 17.0746 | 14650 | 0.0 | - | | 17.1329 | 14700 | 0.0 | - | | 17.1911 | 14750 | 0.0 | - | | 17.2494 | 14800 | 0.0 | - | | 17.3077 | 14850 | 0.0 | - | | 17.3660 | 14900 | 0.0 | - | | 17.4242 | 14950 | 0.0 | - | | 17.4825 | 15000 | 0.0 | - | | 17.5408 | 15050 | 0.0 | - | | 17.5991 | 15100 | 0.0 | - | | 17.6573 | 15150 | 0.0 | - | | 17.7156 | 15200 | 0.0 | - | | 17.7739 | 15250 | 0.0 | - | | 17.8322 | 15300 | 0.0 | - | | 17.8904 | 15350 | 0.0 | - | | 17.9487 | 15400 | 0.0 | - | | 18.0070 | 15450 | 0.0 | - | | 18.0653 | 15500 | 0.0 | - | | 18.1235 | 15550 | 0.0 | - | | 18.1818 | 15600 | 0.0 | - | | 18.2401 | 15650 | 0.0 | - | | 18.2984 | 15700 | 0.0 | - | | 18.3566 | 15750 | 0.0 | - | | 18.4149 | 15800 | 0.0 | - | | 18.4732 | 15850 | 0.0 | - | | 18.5315 | 15900 | 0.0 | - | | 18.5897 | 15950 | 0.0 | - | | 18.6480 | 16000 | 0.0 | - | | 18.7063 | 16050 | 0.0 | - | | 18.7646 | 16100 | 0.0 | - | | 18.8228 | 16150 | 0.0 | - | | 18.8811 | 16200 | 0.0 | - | | 18.9394 | 16250 | 0.0 | - | | 18.9977 | 16300 | 0.0 | - | | 19.0559 | 16350 | 0.0 | - | | 19.1142 | 16400 | 0.0 | - | | 19.1725 | 16450 | 0.0 | - | | 19.2308 | 16500 | 0.0 | - | | 19.2890 | 16550 | 0.0 | - | | 19.3473 | 16600 | 0.0 | - | | 19.4056 | 16650 | 0.0 | - | | 19.4639 | 16700 | 0.0 | - | | 19.5221 | 16750 | 0.0 | - | | 19.5804 | 16800 | 0.0 | - | | 19.6387 | 16850 | 0.0 | - | | 19.6970 | 16900 | 0.0 | - | | 19.7552 | 16950 | 0.0 | - | | 19.8135 | 17000 | 0.0 | - | | 19.8718 | 17050 | 0.0 | - | | 19.9301 | 17100 | 0.0 | - | | 19.9883 | 17150 | 0.0 | - | | 20.0466 | 17200 | 0.0 | - | | 20.1049 | 17250 | 0.0 | - | | 20.1632 | 17300 | 0.0 | - | | 20.2214 | 17350 | 0.0 | - | | 20.2797 | 17400 | 0.0 | - | | 20.3380 | 17450 | 0.0 | - | | 20.3963 | 17500 | 0.0 | - | | 20.4545 | 17550 | 0.0 | - | | 20.5128 | 17600 | 0.0 | - | | 20.5711 | 17650 | 0.0 | - | | 20.6294 | 17700 | 0.0 | - | | 20.6876 | 17750 | 0.0 | - | | 20.7459 | 17800 | 0.0 | - | | 20.8042 | 17850 | 0.0 | - | | 20.8625 | 17900 | 0.0 | - | | 20.9207 | 17950 | 0.0 | - | | 20.9790 | 18000 | 0.0 | - | | 21.0373 | 18050 | 0.0 | - | | 21.0956 | 18100 | 0.0 | - | | 21.1538 | 18150 | 0.0 | - | | 21.2121 | 18200 | 0.0 | - | | 21.2704 | 18250 | 0.0 | - | | 21.3287 | 18300 | 0.0 | - | | 21.3869 | 18350 | 0.0 | - | | 21.4452 | 18400 | 0.0 | - | | 21.5035 | 18450 | 0.0 | - | | 21.5618 | 18500 | 0.0 | - | | 21.6200 | 18550 | 0.0 | - | | 21.6783 | 18600 | 0.0 | - | | 21.7366 | 18650 | 0.0 | - | | 21.7949 | 18700 | 0.0 | - | | 21.8531 | 18750 | 0.0 | - | | 21.9114 | 18800 | 0.0 | - | | 21.9697 | 18850 | 0.0 | - | | 22.0280 | 18900 | 0.0 | - | | 22.0862 | 18950 | 0.0 | - | | 22.1445 | 19000 | 0.0 | - | | 22.2028 | 19050 | 0.0 | - | | 22.2611 | 19100 | 0.0 | - | | 22.3193 | 19150 | 0.0 | - | | 22.3776 | 19200 | 0.0 | - | | 22.4359 | 19250 | 0.0 | - | | 22.4942 | 19300 | 0.0 | - | | 22.5524 | 19350 | 0.0 | - | | 22.6107 | 19400 | 0.0 | - | | 22.6690 | 19450 | 0.0 | - | | 22.7273 | 19500 | 0.0 | - | | 22.7855 | 19550 | 0.0 | - | | 22.8438 | 19600 | 0.0 | - | | 22.9021 | 19650 | 0.0 | - | | 22.9604 | 19700 | 0.0 | - | | 23.0186 | 19750 | 0.0 | - | | 23.0769 | 19800 | 0.0 | - | | 23.1352 | 19850 | 0.0 | - | | 23.1935 | 19900 | 0.0 | - | | 23.2517 | 19950 | 0.0 | - | | 23.3100 | 20000 | 0.0 | - | | 23.3683 | 20050 | 0.0 | - | | 23.4266 | 20100 | 0.0 | - | | 23.4848 | 20150 | 0.0 | - | | 23.5431 | 20200 | 0.0 | - | | 23.6014 | 20250 | 0.0 | - | | 23.6597 | 20300 | 0.0 | - | | 23.7179 | 20350 | 0.0 | - | | 23.7762 | 20400 | 0.0 | - | | 23.8345 | 20450 | 0.0 | - | | 23.8928 | 20500 | 0.0 | - | | 23.9510 | 20550 | 0.0 | - | | 24.0093 | 20600 | 0.0 | - | | 24.0676 | 20650 | 0.0 | - | | 24.1259 | 20700 | 0.0 | - | | 24.1841 | 20750 | 0.0 | - | | 24.2424 | 20800 | 0.0 | - | | 24.3007 | 20850 | 0.0 | - | | 24.3590 | 20900 | 0.0 | - | | 24.4172 | 20950 | 0.0 | - | | 24.4755 | 21000 | 0.0 | - | | 24.5338 | 21050 | 0.0 | - | | 24.5921 | 21100 | 0.0 | - | | 24.6503 | 21150 | 0.0 | - | | 24.7086 | 21200 | 0.0 | - | | 24.7669 | 21250 | 0.0 | - | | 24.8252 | 21300 | 0.0 | - | | 24.8834 | 21350 | 0.0 | - | | 24.9417 | 21400 | 0.0 | - | | 25.0 | 21450 | 0.0 | - | | 25.0583 | 21500 | 0.0 | - | | 25.1166 | 21550 | 0.0 | - | | 25.1748 | 21600 | 0.0 | - | | 25.2331 | 21650 | 0.0 | - | | 25.2914 | 21700 | 0.0 | - | | 25.3497 | 21750 | 0.0 | - | | 25.4079 | 21800 | 0.0 | - | | 25.4662 | 21850 | 0.0 | - | | 25.5245 | 21900 | 0.0 | - | | 25.5828 | 21950 | 0.0 | - | | 25.6410 | 22000 | 0.0 | - | | 25.6993 | 22050 | 0.0 | - | | 25.7576 | 22100 | 0.0 | - | | 25.8159 | 22150 | 0.0 | - | | 25.8741 | 22200 | 0.0 | - | | 25.9324 | 22250 | 0.0 | - | | 25.9907 | 22300 | 0.0 | - | | 26.0490 | 22350 | 0.0 | - | | 26.1072 | 22400 | 0.0 | - | | 26.1655 | 22450 | 0.0 | - | | 26.2238 | 22500 | 0.0 | - | | 26.2821 | 22550 | 0.0 | - | | 26.3403 | 22600 | 0.0 | - | | 26.3986 | 22650 | 0.0 | - | | 26.4569 | 22700 | 0.0 | - | | 26.5152 | 22750 | 0.0 | - | | 26.5734 | 22800 | 0.0 | - | | 26.6317 | 22850 | 0.0 | - | | 26.6900 | 22900 | 0.0 | - | | 26.7483 | 22950 | 0.0 | - | | 26.8065 | 23000 | 0.0 | - | | 26.8648 | 23050 | 0.0 | - | | 26.9231 | 23100 | 0.0 | - | | 26.9814 | 23150 | 0.0 | - | | 27.0396 | 23200 | 0.0 | - | | 27.0979 | 23250 | 0.0 | - | | 27.1562 | 23300 | 0.0 | - | | 27.2145 | 23350 | 0.0 | - | | 27.2727 | 23400 | 0.0 | - | | 27.3310 | 23450 | 0.0 | - | | 27.3893 | 23500 | 0.0 | - | | 27.4476 | 23550 | 0.0 | - | | 27.5058 | 23600 | 0.0 | - | | 27.5641 | 23650 | 0.0 | - | | 27.6224 | 23700 | 0.0 | - | | 27.6807 | 23750 | 0.0 | - | | 27.7389 | 23800 | 0.0 | - | | 27.7972 | 23850 | 0.0 | - | | 27.8555 | 23900 | 0.0 | - | | 27.9138 | 23950 | 0.0 | - | | 27.9720 | 24000 | 0.0 | - | | 28.0303 | 24050 | 0.0 | - | | 28.0886 | 24100 | 0.0 | - | | 28.1469 | 24150 | 0.0 | - | | 28.2051 | 24200 | 0.0 | - | | 28.2634 | 24250 | 0.0 | - | | 28.3217 | 24300 | 0.0 | - | | 28.3800 | 24350 | 0.0 | - | | 28.4382 | 24400 | 0.0 | - | | 28.4965 | 24450 | 0.0 | - | | 28.5548 | 24500 | 0.0 | - | | 28.6131 | 24550 | 0.0 | - | | 28.6713 | 24600 | 0.0 | - | | 28.7296 | 24650 | 0.0 | - | | 28.7879 | 24700 | 0.0 | - | | 28.8462 | 24750 | 0.0 | - | | 28.9044 | 24800 | 0.0 | - | | 28.9627 | 24850 | 0.0 | - | | 29.0210 | 24900 | 0.0 | - | | 29.0793 | 24950 | 0.0 | - | | 29.1375 | 25000 | 0.0 | - | | 29.1958 | 25050 | 0.0 | - | | 29.2541 | 25100 | 0.0 | - | | 29.3124 | 25150 | 0.0 | - | | 29.3706 | 25200 | 0.0 | - | | 29.4289 | 25250 | 0.0 | - | | 29.4872 | 25300 | 0.0 | - | | 29.5455 | 25350 | 0.0 | - | | 29.6037 | 25400 | 0.0 | - | | 29.6620 | 25450 | 0.0 | - | | 29.7203 | 25500 | 0.0 | - | | 29.7786 | 25550 | 0.0 | - | | 29.8368 | 25600 | 0.0 | - | | 29.8951 | 25650 | 0.0 | - | | 29.9534 | 25700 | 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} } ```