diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,1333 @@ +--- +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: 러쉬 배쓰밤 20종 러쉬 입욕제 티스티 토스티 (#M)홈>러쉬 Naverstore > 화장품/미용 > 바디케어 > 입욕제 +- text: 설화수 자음 2종 단품 세트 품 3종 설화수 자음 2종 세트 증정품 3종 (#M)홈>화장품/미용>스킨케어>화장품세트 Naverstore + > 화장품/미용 > 스킨케어 > 화장품세트 +- text: 논픽션 젠틀나잇 바디로션 300ml Happy Birthday (#M)화장품/미용>바디케어>바디로션 Naverstore > 화장품/미용 + > 바디케어 > 바디로션 +- text: '[8월] 캔디 글레이즈 컬러밤 듀오 세트(+핸드 미러) 7호_12호 LotteOn > 뷰티 > 메이크업 > 립메이크업 > 립스틱 LotteOn + > 뷰티 > 메이크업 > 립메이크업 > 립스틱' +- text: 3CE SOFT MATTE LIPSTICK 소프트 매트 립 (딥언더모어딥) SENSUAL BREEZE_FRE (#M)화장품/향수>색조메이크업>립스틱 + 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: 0.854 + 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 | +|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| 42 | | +| 60 | | +| 1 | | +| 68 | | +| 52 | | +| 33 | | +| 71 | | +| 48 | | +| 18 | | +| 38 | | +| 98 | | +| 87 | | +| 47 | | +| 67 | | +| 96 | | +| 22 | | +| 30 | | +| 24 | | +| 79 | | +| 80 | | +| 23 | | +| 50 | | +| 5 | | +| 19 | | +| 55 | | +| 7 | | +| 64 | | +| 9 | | +| 74 | | +| 90 | | +| 40 | | +| 92 | | +| 59 | | +| 29 | | +| 70 | | +| 56 | | +| 13 | | +| 82 | | +| 58 | | +| 25 | | +| 37 | | +| 78 | | +| 27 | | +| 66 | | +| 41 | | +| 16 | | +| 76 | | +| 73 | | +| 86 | | +| 83 | | +| 17 | | +| 85 | | +| 63 | | +| 51 | | +| 89 | | +| 28 | | +| 8 | | +| 46 | | +| 75 | | +| 15 | | +| 81 | | +| 61 | | +| 21 | | +| 72 | | +| 31 | | +| 84 | | +| 11 | | +| 99 | | +| 43 | | +| 91 | | +| 49 | | +| 53 | | +| 26 | | +| 77 | | +| 94 | | +| 2 | | +| 12 | | +| 93 | | +| 35 | | +| 54 | | +| 45 | | +| 6 | | +| 39 | | +| 0 | | +| 65 | | +| 4 | | +| 95 | | +| 88 | | +| 20 | | +| 62 | | +| 3 | | +| 32 | | +| 10 | | +| 69 | | +| 14 | | +| 34 | | +| 36 | | +| 44 | | +| 97 | | +| 57 | | + +## Evaluation + +### Metrics +| Label | Accuracy | +|:--------|:---------| +| **all** | 0.854 | + +## 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_allcate") +# Run inference +preds = model("러쉬 배쓰밤 20종 러쉬 입욕제 티스티 토스티 (#M)홈>러쉬 Naverstore > 화장품/미용 > 바디케어 > 입욕제") +``` + + + + + + + + + +## Training Details + +### Training Set Metrics +| Training set | Min | Median | Max | +|:-------------|:----|:--------|:----| +| Word count | 10 | 23.0575 | 125 | + +| 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 | 49 | +| 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 | 49 | +| 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 | 0.4519 | - | +| 0.0214 | 50 | 0.4285 | - | +| 0.0427 | 100 | 0.4177 | - | +| 0.0641 | 150 | 0.3848 | - | +| 0.0854 | 200 | 0.3587 | - | +| 0.1068 | 250 | 0.3257 | - | +| 0.1282 | 300 | 0.2752 | - | +| 0.1495 | 350 | 0.2489 | - | +| 0.1709 | 400 | 0.2172 | - | +| 0.1922 | 450 | 0.1901 | - | +| 0.2136 | 500 | 0.1643 | - | +| 0.2349 | 550 | 0.1478 | - | +| 0.2563 | 600 | 0.1345 | - | +| 0.2777 | 650 | 0.1151 | - | +| 0.2990 | 700 | 0.1073 | - | +| 0.3204 | 750 | 0.1002 | - | +| 0.3417 | 800 | 0.0929 | - | +| 0.3631 | 850 | 0.0902 | - | +| 0.3845 | 900 | 0.0857 | - | +| 0.4058 | 950 | 0.0749 | - | +| 0.4272 | 1000 | 0.0756 | - | +| 0.4485 | 1050 | 0.0665 | - | +| 0.4699 | 1100 | 0.0659 | - | +| 0.4912 | 1150 | 0.0604 | - | +| 0.5126 | 1200 | 0.0561 | - | +| 0.5340 | 1250 | 0.0555 | - | +| 0.5553 | 1300 | 0.0499 | - | +| 0.5767 | 1350 | 0.0505 | - | +| 0.5980 | 1400 | 0.0492 | - | +| 0.6194 | 1450 | 0.0478 | - | +| 0.6408 | 1500 | 0.0429 | - | +| 0.6621 | 1550 | 0.0419 | - | +| 0.6835 | 1600 | 0.04 | - | +| 0.7048 | 1650 | 0.0376 | - | +| 0.7262 | 1700 | 0.0385 | - | +| 0.7475 | 1750 | 0.0368 | - | +| 0.7689 | 1800 | 0.0345 | - | +| 0.7903 | 1850 | 0.0322 | - | +| 0.8116 | 1900 | 0.0333 | - | +| 0.8330 | 1950 | 0.03 | - | +| 0.8543 | 2000 | 0.0301 | - | +| 0.8757 | 2050 | 0.0321 | - | +| 0.8971 | 2100 | 0.0293 | - | +| 0.9184 | 2150 | 0.0299 | - | +| 0.9398 | 2200 | 0.0289 | - | +| 0.9611 | 2250 | 0.0273 | - | +| 0.9825 | 2300 | 0.0269 | - | +| 1.0038 | 2350 | 0.0272 | - | +| 1.0252 | 2400 | 0.0275 | - | +| 1.0466 | 2450 | 0.0263 | - | +| 1.0679 | 2500 | 0.024 | - | +| 1.0893 | 2550 | 0.0243 | - | +| 1.1106 | 2600 | 0.0213 | - | +| 1.1320 | 2650 | 0.0227 | - | +| 1.1534 | 2700 | 0.0204 | - | +| 1.1747 | 2750 | 0.0243 | - | +| 1.1961 | 2800 | 0.0256 | - | +| 1.2174 | 2850 | 0.0209 | - | +| 1.2388 | 2900 | 0.0231 | - | +| 1.2601 | 2950 | 0.0252 | - | +| 1.2815 | 3000 | 0.0208 | - | +| 1.3029 | 3050 | 0.0219 | - | +| 1.3242 | 3100 | 0.0218 | - | +| 1.3456 | 3150 | 0.022 | - | +| 1.3669 | 3200 | 0.0208 | - | +| 1.3883 | 3250 | 0.0205 | - | +| 1.4097 | 3300 | 0.0198 | - | +| 1.4310 | 3350 | 0.0184 | - | +| 1.4524 | 3400 | 0.0176 | - | +| 1.4737 | 3450 | 0.0178 | - | +| 1.4951 | 3500 | 0.0179 | - | +| 1.5164 | 3550 | 0.0147 | - | +| 1.5378 | 3600 | 0.0168 | - | +| 1.5592 | 3650 | 0.0183 | - | +| 1.5805 | 3700 | 0.0183 | - | +| 1.6019 | 3750 | 0.0173 | - | +| 1.6232 | 3800 | 0.0182 | - | +| 1.6446 | 3850 | 0.0165 | - | +| 1.6660 | 3900 | 0.0165 | - | +| 1.6873 | 3950 | 0.0158 | - | +| 1.7087 | 4000 | 0.0151 | - | +| 1.7300 | 4050 | 0.017 | - | +| 1.7514 | 4100 | 0.0152 | - | +| 1.7727 | 4150 | 0.0144 | - | +| 1.7941 | 4200 | 0.015 | - | +| 1.8155 | 4250 | 0.0133 | - | +| 1.8368 | 4300 | 0.0143 | - | +| 1.8582 | 4350 | 0.0139 | - | +| 1.8795 | 4400 | 0.0119 | - | +| 1.9009 | 4450 | 0.016 | - | +| 1.9223 | 4500 | 0.0119 | - | +| 1.9436 | 4550 | 0.0116 | - | +| 1.9650 | 4600 | 0.0111 | - | +| 1.9863 | 4650 | 0.0129 | - | +| 2.0077 | 4700 | 0.0126 | - | +| 2.0290 | 4750 | 0.0116 | - | +| 2.0504 | 4800 | 0.0095 | - | +| 2.0718 | 4850 | 0.0088 | - | +| 2.0931 | 4900 | 0.0086 | - | +| 2.1145 | 4950 | 0.0085 | - | +| 2.1358 | 5000 | 0.0101 | - | +| 2.1572 | 5050 | 0.0093 | - | +| 2.1786 | 5100 | 0.0105 | - | +| 2.1999 | 5150 | 0.0103 | - | +| 2.2213 | 5200 | 0.0086 | - | +| 2.2426 | 5250 | 0.0091 | - | +| 2.2640 | 5300 | 0.011 | - | +| 2.2853 | 5350 | 0.0092 | - | +| 2.3067 | 5400 | 0.0083 | - | +| 2.3281 | 5450 | 0.0092 | - | +| 2.3494 | 5500 | 0.0083 | - | +| 2.3708 | 5550 | 0.0085 | - | +| 2.3921 | 5600 | 0.0068 | - | +| 2.4135 | 5650 | 0.0081 | - | +| 2.4349 | 5700 | 0.0083 | - | +| 2.4562 | 5750 | 0.0073 | - | +| 2.4776 | 5800 | 0.0083 | - | +| 2.4989 | 5850 | 0.0069 | - | +| 2.5203 | 5900 | 0.0062 | - | +| 2.5416 | 5950 | 0.0067 | - | +| 2.5630 | 6000 | 0.008 | - | +| 2.5844 | 6050 | 0.008 | - | +| 2.6057 | 6100 | 0.0072 | - | +| 2.6271 | 6150 | 0.0063 | - | +| 2.6484 | 6200 | 0.0079 | - | +| 2.6698 | 6250 | 0.0086 | - | +| 2.6912 | 6300 | 0.0083 | - | +| 2.7125 | 6350 | 0.0076 | - | +| 2.7339 | 6400 | 0.0069 | - | +| 2.7552 | 6450 | 0.0084 | - | +| 2.7766 | 6500 | 0.0092 | - | +| 2.7979 | 6550 | 0.0068 | - | +| 2.8193 | 6600 | 0.0062 | - | +| 2.8407 | 6650 | 0.0064 | - | +| 2.8620 | 6700 | 0.0077 | - | +| 2.8834 | 6750 | 0.0053 | - | +| 2.9047 | 6800 | 0.0062 | - | +| 2.9261 | 6850 | 0.0066 | - | +| 2.9475 | 6900 | 0.0076 | - | +| 2.9688 | 6950 | 0.0052 | - | +| 2.9902 | 7000 | 0.0072 | - | +| 3.0115 | 7050 | 0.0073 | - | +| 3.0329 | 7100 | 0.0047 | - | +| 3.0543 | 7150 | 0.0055 | - | +| 3.0756 | 7200 | 0.0052 | - | +| 3.0970 | 7250 | 0.0048 | - | +| 3.1183 | 7300 | 0.0065 | - | +| 3.1397 | 7350 | 0.0059 | - | +| 3.1610 | 7400 | 0.0046 | - | +| 3.1824 | 7450 | 0.0047 | - | +| 3.2038 | 7500 | 0.0049 | - | +| 3.2251 | 7550 | 0.005 | - | +| 3.2465 | 7600 | 0.006 | - | +| 3.2678 | 7650 | 0.0055 | - | +| 3.2892 | 7700 | 0.0058 | - | +| 3.3106 | 7750 | 0.0048 | - | +| 3.3319 | 7800 | 0.0045 | - | +| 3.3533 | 7850 | 0.0047 | - | +| 3.3746 | 7900 | 0.0042 | - | +| 3.3960 | 7950 | 0.0052 | - | +| 3.4173 | 8000 | 0.0034 | - | +| 3.4387 | 8050 | 0.004 | - | +| 3.4601 | 8100 | 0.0029 | - | +| 3.4814 | 8150 | 0.0036 | - | +| 3.5028 | 8200 | 0.0049 | - | +| 3.5241 | 8250 | 0.0045 | - | +| 3.5455 | 8300 | 0.0038 | - | +| 3.5669 | 8350 | 0.0038 | - | +| 3.5882 | 8400 | 0.0043 | - | +| 3.6096 | 8450 | 0.0025 | - | +| 3.6309 | 8500 | 0.0048 | - | +| 3.6523 | 8550 | 0.004 | - | +| 3.6736 | 8600 | 0.004 | - | +| 3.6950 | 8650 | 0.0037 | - | +| 3.7164 | 8700 | 0.0037 | - | +| 3.7377 | 8750 | 0.0041 | - | +| 3.7591 | 8800 | 0.003 | - | +| 3.7804 | 8850 | 0.0036 | - | +| 3.8018 | 8900 | 0.0031 | - | +| 3.8232 | 8950 | 0.0032 | - | +| 3.8445 | 9000 | 0.0035 | - | +| 3.8659 | 9050 | 0.0035 | - | +| 3.8872 | 9100 | 0.0027 | - | +| 3.9086 | 9150 | 0.0031 | - | +| 3.9299 | 9200 | 0.0038 | - | +| 3.9513 | 9250 | 0.0032 | - | +| 3.9727 | 9300 | 0.0032 | - | +| 3.9940 | 9350 | 0.0032 | - | +| 4.0154 | 9400 | 0.0031 | - | +| 4.0367 | 9450 | 0.0023 | - | +| 4.0581 | 9500 | 0.0028 | - | +| 4.0795 | 9550 | 0.0029 | - | +| 4.1008 | 9600 | 0.0031 | - | +| 4.1222 | 9650 | 0.0024 | - | +| 4.1435 | 9700 | 0.0034 | - | +| 4.1649 | 9750 | 0.0031 | - | +| 4.1862 | 9800 | 0.0036 | - | +| 4.2076 | 9850 | 0.0042 | - | +| 4.2290 | 9900 | 0.0044 | - | +| 4.2503 | 9950 | 0.0034 | - | +| 4.2717 | 10000 | 0.0044 | - | +| 4.2930 | 10050 | 0.0038 | - | +| 4.3144 | 10100 | 0.0044 | - | +| 4.3358 | 10150 | 0.0039 | - | +| 4.3571 | 10200 | 0.0049 | - | +| 4.3785 | 10250 | 0.004 | - | +| 4.3998 | 10300 | 0.0031 | - | +| 4.4212 | 10350 | 0.0021 | - | +| 4.4425 | 10400 | 0.0025 | - | +| 4.4639 | 10450 | 0.0032 | - | +| 4.4853 | 10500 | 0.003 | - | +| 4.5066 | 10550 | 0.0027 | - | +| 4.5280 | 10600 | 0.0019 | - | +| 4.5493 | 10650 | 0.002 | - | +| 4.5707 | 10700 | 0.0026 | - | +| 4.5921 | 10750 | 0.0025 | - | +| 4.6134 | 10800 | 0.0028 | - | +| 4.6348 | 10850 | 0.0021 | - | +| 4.6561 | 10900 | 0.0031 | - | +| 4.6775 | 10950 | 0.0017 | - | +| 4.6988 | 11000 | 0.003 | - | +| 4.7202 | 11050 | 0.0036 | - | +| 4.7416 | 11100 | 0.0024 | - | +| 4.7629 | 11150 | 0.0017 | - | +| 4.7843 | 11200 | 0.0024 | - | +| 4.8056 | 11250 | 0.0016 | - | +| 4.8270 | 11300 | 0.0021 | - | +| 4.8484 | 11350 | 0.0022 | - | +| 4.8697 | 11400 | 0.0024 | - | +| 4.8911 | 11450 | 0.004 | - | +| 4.9124 | 11500 | 0.003 | - | +| 4.9338 | 11550 | 0.0032 | - | +| 4.9551 | 11600 | 0.0024 | - | +| 4.9765 | 11650 | 0.0016 | - | +| 4.9979 | 11700 | 0.002 | - | +| 5.0192 | 11750 | 0.0024 | - | +| 5.0406 | 11800 | 0.0022 | - | +| 5.0619 | 11850 | 0.0018 | - | +| 5.0833 | 11900 | 0.0015 | - | +| 5.1047 | 11950 | 0.0023 | - | +| 5.1260 | 12000 | 0.0021 | - | +| 5.1474 | 12050 | 0.0015 | - | +| 5.1687 | 12100 | 0.002 | - | +| 5.1901 | 12150 | 0.0014 | - | +| 5.2114 | 12200 | 0.0011 | - | +| 5.2328 | 12250 | 0.0016 | - | +| 5.2542 | 12300 | 0.0019 | - | +| 5.2755 | 12350 | 0.0019 | - | +| 5.2969 | 12400 | 0.0027 | - | +| 5.3182 | 12450 | 0.0013 | - | +| 5.3396 | 12500 | 0.0023 | - | +| 5.3610 | 12550 | 0.0015 | - | +| 5.3823 | 12600 | 0.0026 | - | +| 5.4037 | 12650 | 0.0014 | - | +| 5.4250 | 12700 | 0.0016 | - | +| 5.4464 | 12750 | 0.0017 | - | +| 5.4677 | 12800 | 0.0013 | - | +| 5.4891 | 12850 | 0.002 | - | +| 5.5105 | 12900 | 0.0028 | - | +| 5.5318 | 12950 | 0.0021 | - | +| 5.5532 | 13000 | 0.0028 | - | +| 5.5745 | 13050 | 0.0016 | - | +| 5.5959 | 13100 | 0.0012 | - | +| 5.6173 | 13150 | 0.0031 | - | +| 5.6386 | 13200 | 0.0023 | - | +| 5.6600 | 13250 | 0.0017 | - | +| 5.6813 | 13300 | 0.0016 | - | +| 5.7027 | 13350 | 0.0018 | - | +| 5.7240 | 13400 | 0.0028 | - | +| 5.7454 | 13450 | 0.0029 | - | +| 5.7668 | 13500 | 0.0013 | - | +| 5.7881 | 13550 | 0.0012 | - | +| 5.8095 | 13600 | 0.0017 | - | +| 5.8308 | 13650 | 0.001 | - | +| 5.8522 | 13700 | 0.0017 | - | +| 5.8736 | 13750 | 0.0019 | - | +| 5.8949 | 13800 | 0.0013 | - | +| 5.9163 | 13850 | 0.001 | - | +| 5.9376 | 13900 | 0.0015 | - | +| 5.9590 | 13950 | 0.0013 | - | +| 5.9804 | 14000 | 0.0012 | - | +| 6.0017 | 14050 | 0.0013 | - | +| 6.0231 | 14100 | 0.0006 | - | +| 6.0444 | 14150 | 0.0013 | - | +| 6.0658 | 14200 | 0.0013 | - | +| 6.0871 | 14250 | 0.0012 | - | +| 6.1085 | 14300 | 0.0009 | - | +| 6.1299 | 14350 | 0.0013 | - | +| 6.1512 | 14400 | 0.0015 | - | +| 6.1726 | 14450 | 0.004 | - | +| 6.1939 | 14500 | 0.0038 | - | +| 6.2153 | 14550 | 0.0026 | - | 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29550 | 0.0002 | - | +| 12.6442 | 29600 | 0.0001 | - | +| 12.6655 | 29650 | 0.0004 | - | +| 12.6869 | 29700 | 0.0008 | - | +| 12.7082 | 29750 | 0.0004 | - | +| 12.7296 | 29800 | 0.0004 | - | +| 12.7510 | 29850 | 0.0004 | - | +| 12.7723 | 29900 | 0.0003 | - | +| 12.7937 | 29950 | 0.0004 | - | +| 12.8150 | 30000 | 0.0004 | - | +| 12.8364 | 30050 | 0.0007 | - | +| 12.8578 | 30100 | 0.0007 | - | +| 12.8791 | 30150 | 0.0009 | - | +| 12.9005 | 30200 | 0.0003 | - | +| 12.9218 | 30250 | 0.0003 | - | +| 12.9432 | 30300 | 0.0002 | - | +| 12.9645 | 30350 | 0.0003 | - | +| 12.9859 | 30400 | 0.0001 | - | +| 13.0073 | 30450 | 0.0004 | - | +| 13.0286 | 30500 | 0.0002 | - | +| 13.0500 | 30550 | 0.0001 | - | +| 13.0713 | 30600 | 0.0002 | - | +| 13.0927 | 30650 | 0.0001 | - | +| 13.1141 | 30700 | 0.0001 | - | +| 13.1354 | 30750 | 0.0002 | - | +| 13.1568 | 30800 | 0.0004 | - | +| 13.1781 | 30850 | 0.0003 | - | +| 13.1995 | 30900 | 0.0001 | - | +| 13.2208 | 30950 | 0.0001 | - | +| 13.2422 | 31000 | 0.0002 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14.5237 | 34000 | 0.0 | - | +| 14.5451 | 34050 | 0.0002 | - | +| 14.5664 | 34100 | 0.0004 | - | +| 14.5878 | 34150 | 0.0001 | - | +| 14.6091 | 34200 | 0.0001 | - | +| 14.6305 | 34250 | 0.0001 | - | +| 14.6519 | 34300 | 0.0002 | - | +| 14.6732 | 34350 | 0.0004 | - | +| 14.6946 | 34400 | 0.0005 | - | +| 14.7159 | 34450 | 0.0001 | - | +| 14.7373 | 34500 | 0.0001 | - | +| 14.7587 | 34550 | 0.0001 | - | +| 14.7800 | 34600 | 0.0002 | - | +| 14.8014 | 34650 | 0.0001 | - | +| 14.8227 | 34700 | 0.0003 | - | +| 14.8441 | 34750 | 0.0001 | - | +| 14.8654 | 34800 | 0.0003 | - | +| 14.8868 | 34850 | 0.0001 | - | +| 14.9082 | 34900 | 0.0003 | - | +| 14.9295 | 34950 | 0.0002 | - | +| 14.9509 | 35000 | 0.0002 | - | +| 14.9722 | 35050 | 0.0003 | - | +| 14.9936 | 35100 | 0.0002 | - | +| 15.0150 | 35150 | 0.0002 | - | +| 15.0363 | 35200 | 0.0002 | - | +| 15.0577 | 35250 | 0.0001 | - | +| 15.0790 | 35300 | 0.0001 | - | +| 15.1004 | 35350 | 0.0001 | - | +| 15.1217 | 35400 | 0.0001 | - | +| 15.1431 | 35450 | 0.0001 | - | +| 15.1645 | 35500 | 0.0 | - | +| 15.1858 | 35550 | 0.0003 | - | +| 15.2072 | 35600 | 0.0001 | - | +| 15.2285 | 35650 | 0.0002 | - | +| 15.2499 | 35700 | 0.0003 | - | +| 15.2713 | 35750 | 0.0 | - | +| 15.2926 | 35800 | 0.0001 | - | +| 15.3140 | 35850 | 0.0005 | - | +| 15.3353 | 35900 | 0.0002 | - | +| 15.3567 | 35950 | 0.0002 | - | +| 15.3780 | 36000 | 0.0003 | - | +| 15.3994 | 36050 | 0.0001 | - | +| 15.4208 | 36100 | 0.0001 | - | +| 15.4421 | 36150 | 0.0002 | - | +| 15.4635 | 36200 | 0.0004 | - | +| 15.4848 | 36250 | 0.0001 | - | +| 15.5062 | 36300 | 0.0001 | - | +| 15.5276 | 36350 | 0.0 | - | +| 15.5489 | 36400 | 0.0001 | - | +| 15.5703 | 36450 | 0.0002 | - | +| 15.5916 | 36500 | 0.0001 | - | +| 15.6130 | 36550 | 0.0004 | - | +| 15.6343 | 36600 | 0.0004 | - | +| 15.6557 | 36650 | 0.0 | - | +| 15.6771 | 36700 | 0.0001 | - | +| 15.6984 | 36750 | 0.0 | - | +| 15.7198 | 36800 | 0.0003 | - | +| 15.7411 | 36850 | 0.0002 | - | +| 15.7625 | 36900 | 0.0001 | - | +| 15.7839 | 36950 | 0.0001 | - | +| 15.8052 | 37000 | 0.0 | - | +| 15.8266 | 37050 | 0.0002 | - | +| 15.8479 | 37100 | 0.0 | - | +| 15.8693 | 37150 | 0.0003 | - | +| 15.8906 | 37200 | 0.0002 | - | +| 15.9120 | 37250 | 0.0001 | - | +| 15.9334 | 37300 | 0.0001 | - | +| 15.9547 | 37350 | 0.0001 | - | +| 15.9761 | 37400 | 0.0001 | - | +| 15.9974 | 37450 | 0.0006 | - | +| 16.0188 | 37500 | 0.0002 | - | +| 16.0402 | 37550 | 0.0002 | - | +| 16.0615 | 37600 | 0.0003 | - | +| 16.0829 | 37650 | 0.0001 | - | +| 16.1042 | 37700 | 0.0 | - | +| 16.1256 | 37750 | 0.0004 | - | +| 16.1469 | 37800 | 0.0003 | - | +| 16.1683 | 37850 | 0.0001 | - | +| 16.1897 | 37900 | 0.0001 | - | +| 16.2110 | 37950 | 0.0003 | - | +| 16.2324 | 38000 | 0.0002 | - | +| 16.2537 | 38050 | 0.0004 | - | +| 16.2751 | 38100 | 0.0 | - | +| 16.2965 | 38150 | 0.0 | - | +| 16.3178 | 38200 | 0.0001 | - | +| 16.3392 | 38250 | 0.0001 | - | +| 16.3605 | 38300 | 0.0 | - | +| 16.3819 | 38350 | 0.0 | - | +| 16.4032 | 38400 | 0.0002 | - | +| 16.4246 | 38450 | 0.0004 | - | +| 16.4460 | 38500 | 0.0001 | - | +| 16.4673 | 38550 | 0.0003 | - | +| 16.4887 | 38600 | 0.0001 | - | +| 16.5100 | 38650 | 0.0001 | - | +| 16.5314 | 38700 | 0.0004 | - | +| 16.5528 | 38750 | 0.0001 | - | +| 16.5741 | 38800 | 0.0 | - | +| 16.5955 | 38850 | 0.0 | - | +| 16.6168 | 38900 | 0.0002 | - | +| 16.6382 | 38950 | 0.0 | - | +| 16.6595 | 39000 | 0.0004 | - | +| 16.6809 | 39050 | 0.0002 | - | +| 16.7023 | 39100 | 0.0001 | - | +| 16.7236 | 39150 | 0.0002 | - | +| 16.7450 | 39200 | 0.0001 | - | +| 16.7663 | 39250 | 0.0002 | - | +| 16.7877 | 39300 | 0.0001 | - | +| 16.8091 | 39350 | 0.0001 | - | +| 16.8304 | 39400 | 0.0001 | - | +| 16.8518 | 39450 | 0.0001 | - | +| 16.8731 | 39500 | 0.0004 | - | +| 16.8945 | 39550 | 0.0001 | - | +| 16.9158 | 39600 | 0.0003 | - | +| 16.9372 | 39650 | 0.0001 | - | +| 16.9586 | 39700 | 0.0002 | - | +| 16.9799 | 39750 | 0.0 | - | +| 17.0013 | 39800 | 0.0001 | - | +| 17.0226 | 39850 | 0.0001 | - | +| 17.0440 | 39900 | 0.0 | - | +| 17.0654 | 39950 | 0.0 | - | +| 17.0867 | 40000 | 0.0 | - | +| 17.1081 | 40050 | 0.0001 | - | +| 17.1294 | 40100 | 0.0 | - | +| 17.1508 | 40150 | 0.0002 | - | +| 17.1721 | 40200 | 0.0 | - | +| 17.1935 | 40250 | 0.0002 | - | +| 17.2149 | 40300 | 0.0004 | - | +| 17.2362 | 40350 | 0.0001 | - | +| 17.2576 | 40400 | 0.0002 | - | +| 17.2789 | 40450 | 0.0001 | - | +| 17.3003 | 40500 | 0.0001 | - | +| 17.3217 | 40550 | 0.0002 | - | +| 17.3430 | 40600 | 0.0001 | - | +| 17.3644 | 40650 | 0.0002 | - | +| 17.3857 | 40700 | 0.0 | - | +| 17.4071 | 40750 | 0.0002 | - | +| 17.4284 | 40800 | 0.0002 | - | +| 17.4498 | 40850 | 0.0002 | - | +| 17.4712 | 40900 | 0.0004 | - | +| 17.4925 | 40950 | 0.0002 | - | +| 17.5139 | 41000 | 0.0003 | - | +| 17.5352 | 41050 | 0.0002 | - | +| 17.5566 | 41100 | 0.0002 | - | +| 17.5780 | 41150 | 0.0002 | - | +| 17.5993 | 41200 | 0.0002 | - | +| 17.6207 | 41250 | 0.0002 | - | +| 17.6420 | 41300 | 0.0002 | - | +| 17.6634 | 41350 | 0.0001 | - | +| 17.6848 | 41400 | 0.0001 | - | +| 17.7061 | 41450 | 0.0002 | - | +| 17.7275 | 41500 | 0.0002 | - | +| 17.7488 | 41550 | 0.0002 | - | +| 17.7702 | 41600 | 0.0 | - | +| 17.7915 | 41650 | 0.0003 | - | +| 17.8129 | 41700 | 0.0001 | - | +| 17.8343 | 41750 | 0.0 | - | +| 17.8556 | 41800 | 0.0001 | - | +| 17.8770 | 41850 | 0.0001 | - | +| 17.8983 | 41900 | 0.0002 | - | +| 17.9197 | 41950 | 0.0001 | - | +| 17.9411 | 42000 | 0.0003 | - | +| 17.9624 | 42050 | 0.0001 | - | +| 17.9838 | 42100 | 0.0001 | - | +| 18.0051 | 42150 | 0.0001 | - | +| 18.0265 | 42200 | 0.0002 | - | +| 18.0478 | 42250 | 0.0005 | - | +| 18.0692 | 42300 | 0.0002 | - | +| 18.0906 | 42350 | 0.0003 | - | +| 18.1119 | 42400 | 0.0001 | - | +| 18.1333 | 42450 | 0.0004 | - | +| 18.1546 | 42500 | 0.0002 | - | +| 18.1760 | 42550 | 0.0002 | - | +| 18.1974 | 42600 | 0.0002 | - | +| 18.2187 | 42650 | 0.0003 | - | +| 18.2401 | 42700 | 0.0001 | - | +| 18.2614 | 42750 | 0.0001 | - | +| 18.2828 | 42800 | 0.0001 | - | +| 18.3041 | 42850 | 0.0 | - | +| 18.3255 | 42900 | 0.0 | - | +| 18.3469 | 42950 | 0.0001 | - | +| 18.3682 | 43000 | 0.0 | - | +| 18.3896 | 43050 | 0.0001 | - | +| 18.4109 | 43100 | 0.0001 | - | +| 18.4323 | 43150 | 0.0002 | - | +| 18.4537 | 43200 | 0.0001 | - | +| 18.4750 | 43250 | 0.0001 | - | +| 18.4964 | 43300 | 0.0001 | - | +| 18.5177 | 43350 | 0.0001 | - | +| 18.5391 | 43400 | 0.0 | - | +| 18.5604 | 43450 | 0.0 | - | +| 18.5818 | 43500 | 0.0001 | - | +| 18.6032 | 43550 | 0.0001 | - | +| 18.6245 | 43600 | 0.0001 | - | +| 18.6459 | 43650 | 0.0004 | - | +| 18.6672 | 43700 | 0.0003 | - | +| 18.6886 | 43750 | 0.0002 | - | +| 18.7100 | 43800 | 0.0001 | - | +| 18.7313 | 43850 | 0.0001 | - | +| 18.7527 | 43900 | 0.0001 | - | +| 18.7740 | 43950 | 0.0001 | - | +| 18.7954 | 44000 | 0.0002 | - | +| 18.8167 | 44050 | 0.0003 | - | +| 18.8381 | 44100 | 0.0001 | - | +| 18.8595 | 44150 | 0.0001 | - | +| 18.8808 | 44200 | 0.0001 | - | +| 18.9022 | 44250 | 0.0003 | - | +| 18.9235 | 44300 | 0.0001 | - | +| 18.9449 | 44350 | 0.0001 | - | +| 18.9663 | 44400 | 0.0 | - | +| 18.9876 | 44450 | 0.0 | - | +| 19.0090 | 44500 | 0.0003 | - | +| 19.0303 | 44550 | 0.0002 | - | +| 19.0517 | 44600 | 0.0002 | - | +| 19.0730 | 44650 | 0.0003 | - | +| 19.0944 | 44700 | 0.0002 | - | +| 19.1158 | 44750 | 0.0001 | - | +| 19.1371 | 44800 | 0.0001 | - | +| 19.1585 | 44850 | 0.0001 | - | +| 19.1798 | 44900 | 0.0 | - | +| 19.2012 | 44950 | 0.0001 | - | +| 19.2226 | 45000 | 0.0003 | - | +| 19.2439 | 45050 | 0.0001 | - | +| 19.2653 | 45100 | 0.0 | - | +| 19.2866 | 45150 | 0.0 | - | +| 19.3080 | 45200 | 0.0001 | - | +| 19.3293 | 45250 | 0.0001 | - | +| 19.3507 | 45300 | 0.0002 | - | +| 19.3721 | 45350 | 0.0002 | - | +| 19.3934 | 45400 | 0.0003 | - | +| 19.4148 | 45450 | 0.0001 | - | +| 19.4361 | 45500 | 0.0 | - | +| 19.4575 | 45550 | 0.0001 | - | +| 19.4789 | 45600 | 0.0 | - | +| 19.5002 | 45650 | 0.0002 | - | +| 19.5216 | 45700 | 0.0 | - | +| 19.5429 | 45750 | 0.0001 | - | +| 19.5643 | 45800 | 0.0001 | - | +| 19.5856 | 45850 | 0.0001 | - | +| 19.6070 | 45900 | 0.0001 | - | +| 19.6284 | 45950 | 0.0001 | - | +| 19.6497 | 46000 | 0.0 | - | +| 19.6711 | 46050 | 0.0001 | - | +| 19.6924 | 46100 | 0.0 | - | +| 19.7138 | 46150 | 0.0002 | - | +| 19.7352 | 46200 | 0.0001 | - | +| 19.7565 | 46250 | 0.0002 | - | +| 19.7779 | 46300 | 0.0002 | - | +| 19.7992 | 46350 | 0.0001 | - | +| 19.8206 | 46400 | 0.0001 | - | +| 19.8419 | 46450 | 0.0002 | - | +| 19.8633 | 46500 | 0.0003 | - | +| 19.8847 | 46550 | 0.0002 | - | +| 19.9060 | 46600 | 0.0002 | - | +| 19.9274 | 46650 | 0.0001 | - | +| 19.9487 | 46700 | 0.0001 | - | +| 19.9701 | 46750 | 0.0 | - | +| 19.9915 | 46800 | 0.0002 | - | + +### 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} +} +``` + + + + + + \ No newline at end of file