--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: '상품명 : 틸리전트 강아지 안전문 울타리 펜스 매핑_카테고리2 : T200 > Naverstore > 반려동물용품 > 강아지용품 > 리빙용품 > 안전문 매핑_카테고리1 : (#M)생활/건강>반려동물>리빙용품>안전문 옵션명 : 단일색상_059-066cm ' - text: '상품명 : 내추럴발란스 인도어 고양이 포뮬라 1.36kg 옵션명 : 매핑_카테고리1 : 홈>고양이사료;(#M)홈>고양이사료>내추럴발란스 매핑_카테고리2 : Naverstore > 반려동물용품 > 고양이용품 > 사료 > 건식 ' - text: '상품명 : 멸균우유 표준화우유 수입멸균 밀키스마 200ml X 24개 267498 옵션명 : ' - text: '상품명 : 간식+오가앤리프 유기농 가수분해 강아지사료 눈물 피부 1.8kg 5.6kg 옵션명 : 오리&초록입홍합 1.8kg_불리스틱(초소형) 2개 ' - text: '옵션명 : 스테인리스 캠핑앤펫보틀 1.1L_블루실버 상품명 : 캐나다 아소부 강아지물통 산책 물병 ' metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true model-index: - name: SetFit results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.8503521126760564 name: Accuracy --- # SetFit This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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 - **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:** 124 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 | |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 122 | | | 6 | | | 116 | | | 0 | | | 24 | | | 15 | | | 21 | | | 52 | | | 14 | | | 33 | | | 53 | | | 45 | | | 76 | | | 65 | | | 77 | | | 48 | | | 71 | | | 64 | | | 107 | | | 117 | | | 70 | | | 98 | | | 44 | | | 29 | | | 102 | | | 88 | | | 86 | | | 62 | | | 30 | | | 108 | | | 73 | | | 22 | | | 57 | | | 31 | | | 23 | | | 75 | | | 100 | | | 95 | | | 85 | | | 105 | | | 96 | | | 63 | | | 39 | | | 69 | | | 82 | | | 37 | | | 47 | | | 2 | | | 42 | | | 58 | | | 18 | | | 7 | | | 81 | | | 50 | | | 49 | | | 87 | | | 83 | | | 46 | | | 60 | | | 28 | | | 66 | | | 84 | | | 61 | | | 26 | | | 109 | | | 4 | | | 120 | | | 104 | | | 13 | | | 72 | | | 59 | | | 19 | | | 106 | | | 51 | | | 101 | | | 36 | | | 91 | | | 113 | | | 119 | | | 112 | | | 10 | | | 34 | | | 78 | | | 103 | | | 1 | | | 89 | | | 8 | | | 25 | | | 110 | | | 41 | | | 35 | | | 94 | | | 12 | | | 97 | | | 90 | | | 56 | | | 3 | | | 93 | | | 38 | | | 80 | | | 43 | | | 111 | | | 55 | | | 118 | | | 99 | | | 115 | | | 16 | | | 79 | | | 9 | | | 27 | | | 114 | | | 5 | | | 74 | | | 121 | | | 40 | | | 20 | | | 17 | | | 11 | | | 92 | | | 54 | | | 67 | | | 32 | | | 123 | | | 68 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.8504 | ## 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_ps_flat") # Run inference preds = model("상품명 : 멸균우유 표준화우유 수입멸균 밀키스마 200ml X 24개 267498 옵션명 : ") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 7 | 26.6223 | 54 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 250 | | 1 | 250 | | 2 | 162 | | 3 | 250 | | 4 | 250 | | 5 | 250 | | 6 | 250 | | 7 | 250 | | 8 | 77 | | 9 | 250 | | 10 | 250 | | 11 | 53 | | 12 | 19 | | 13 | 250 | | 14 | 250 | | 15 | 166 | | 16 | 81 | | 17 | 94 | | 18 | 207 | | 19 | 135 | | 20 | 29 | | 21 | 229 | | 22 | 250 | | 23 | 250 | | 24 | 250 | | 25 | 211 | | 26 | 250 | | 27 | 250 | | 28 | 250 | | 29 | 223 | | 30 | 230 | | 31 | 250 | | 32 | 50 | | 33 | 250 | | 34 | 250 | | 35 | 250 | | 36 | 250 | | 37 | 250 | | 38 | 96 | | 39 | 250 | | 40 | 250 | | 41 | 241 | | 42 | 93 | | 43 | 250 | | 44 | 116 | | 45 | 250 | | 46 | 250 | | 47 | 138 | | 48 | 250 | | 49 | 184 | | 50 | 123 | | 51 | 151 | | 52 | 250 | | 53 | 250 | | 54 | 45 | | 55 | 250 | | 56 | 250 | | 57 | 250 | | 58 | 250 | | 59 | 250 | | 60 | 249 | | 61 | 250 | | 62 | 250 | | 63 | 250 | | 64 | 250 | | 65 | 195 | | 66 | 132 | | 67 | 35 | | 68 | 10 | | 69 | 194 | | 70 | 250 | | 71 | 250 | | 72 | 250 | | 73 | 250 | | 74 | 250 | | 75 | 250 | | 76 | 250 | | 77 | 250 | | 78 | 250 | | 79 | 250 | | 80 | 205 | | 81 | 250 | | 82 | 250 | | 83 | 250 | | 84 | 79 | | 85 | 250 | | 86 | 227 | | 87 | 152 | | 88 | 250 | | 89 | 250 | | 90 | 250 | | 91 | 198 | | 92 | 76 | | 93 | 250 | | 94 | 250 | | 95 | 250 | | 96 | 250 | | 97 | 195 | | 98 | 250 | | 99 | 231 | | 100 | 250 | | 101 | 196 | | 102 | 250 | | 103 | 250 | | 104 | 250 | | 105 | 250 | | 106 | 250 | | 107 | 250 | | 108 | 250 | | 109 | 250 | | 110 | 250 | | 111 | 250 | | 112 | 250 | | 113 | 250 | | 114 | 250 | | 115 | 250 | | 116 | 250 | | 117 | 250 | | 118 | 250 | | 119 | 250 | | 120 | 250 | | 121 | 250 | | 122 | 250 | | 123 | 14 | ### Training Hyperparameters - batch_size: (64, 64) - num_epochs: (50, 50) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: BatchAllTripletLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: True - 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 | |:-------:|:-----:|:-------------:|:---------------:| | 0.0024 | 1 | 0.147 | - | | 0.1205 | 50 | 0.1757 | - | | 0.2410 | 100 | 0.1596 | - | | 0.3614 | 150 | 0.158 | - | | 0.4819 | 200 | 0.1595 | - | | 0.6024 | 250 | 0.1731 | - | | 0.7229 | 300 | 0.1626 | - | | 0.8434 | 350 | 0.163 | - | | 0.9639 | 400 | 0.1721 | - | | 1.0843 | 450 | 0.1519 | - | | 1.2048 | 500 | 0.1512 | - | | 1.3253 | 550 | 0.1595 | - | | 1.4458 | 600 | 0.1484 | - | | 1.5663 | 650 | 0.1726 | - | | 1.6867 | 700 | 0.159 | - | | 1.8072 | 750 | 0.162 | - | | 1.9277 | 800 | 0.1602 | - | | 2.0482 | 850 | 0.1544 | - | | 2.1687 | 900 | 0.163 | - | | 2.2892 | 950 | 0.1574 | - | | 2.4096 | 1000 | 0.1542 | - | | 2.5301 | 1050 | 0.1707 | - | | 2.6506 | 1100 | 0.1552 | - | | 2.7711 | 1150 | 0.1632 | - | | 2.8916 | 1200 | 0.1524 | - | | 3.0120 | 1250 | 0.1439 | - | | 3.1325 | 1300 | 0.1512 | - | | 3.2530 | 1350 | 0.1526 | - | | 3.3735 | 1400 | 0.1519 | - | | 3.4940 | 1450 | 0.1485 | - | | 3.6145 | 1500 | 0.1487 | - | | 3.7349 | 1550 | 0.1607 | - | | 3.8554 | 1600 | 0.1415 | - | | 3.9759 | 1650 | 0.1487 | - | | 4.0964 | 1700 | 0.1514 | - | | 4.2169 | 1750 | 0.1488 | - | | 4.3373 | 1800 | 0.1544 | - | | 4.4578 | 1850 | 0.1529 | - | | 4.5783 | 1900 | 0.1446 | - | | 4.6988 | 1950 | 0.1591 | - | | 4.8193 | 2000 | 0.1546 | - | | 4.9398 | 2050 | 0.1514 | - | | 5.0602 | 2100 | 0.1505 | - | | 5.1807 | 2150 | 0.1518 | - | | 5.3012 | 2200 | 0.1508 | - | | 5.4217 | 2250 | 0.1504 | - | | 5.5422 | 2300 | 0.1572 | - | | 5.6627 | 2350 | 0.1438 | - | | 5.7831 | 2400 | 0.1504 | - | | 5.9036 | 2450 | 0.1501 | - | | 6.0241 | 2500 | 0.1537 | - | | 6.1446 | 2550 | 0.1526 | - | | 6.2651 | 2600 | 0.1439 | - | | 6.3855 | 2650 | 0.1464 | - | | 6.5060 | 2700 | 0.1431 | - | | 6.6265 | 2750 | 0.1462 | - | | 6.7470 | 2800 | 0.1525 | - | | 6.8675 | 2850 | 0.1482 | - | | 6.9880 | 2900 | 0.1545 | - | | 7.1084 | 2950 | 0.1436 | - | | 7.2289 | 3000 | 0.1551 | - | | 7.3494 | 3050 | 0.1397 | - | | 7.4699 | 3100 | 0.1656 | - | | 7.5904 | 3150 | 0.1483 | - | | 7.7108 | 3200 | 0.1634 | - | | 7.8313 | 3250 | 0.1529 | - | | 7.9518 | 3300 | 0.1429 | - | | 8.0723 | 3350 | 0.1564 | - | | 8.1928 | 3400 | 0.1464 | - | | 8.3133 | 3450 | 0.1432 | - | | 8.4337 | 3500 | 0.1592 | - | | 8.5542 | 3550 | 0.148 | - | | 8.6747 | 3600 | 0.1366 | - | | 8.7952 | 3650 | 0.1493 | - | | 8.9157 | 3700 | 0.1421 | - | | 9.0361 | 3750 | 0.146 | - | | 9.1566 | 3800 | 0.1425 | - | | 9.2771 | 3850 | 0.142 | - | | 9.3976 | 3900 | 0.1384 | - | | 9.5181 | 3950 | 0.1376 | - | | 9.6386 | 4000 | 0.149 | - | | 9.7590 | 4050 | 0.1472 | - | | 9.8795 | 4100 | 0.1394 | - | | 10.0 | 4150 | 0.1379 | - | | 10.1205 | 4200 | 0.1497 | - | | 10.2410 | 4250 | 0.1406 | - | | 10.3614 | 4300 | 0.1284 | - | | 10.4819 | 4350 | 0.1516 | - | | 10.6024 | 4400 | 0.1499 | - | | 10.7229 | 4450 | 0.1367 | - | | 10.8434 | 4500 | 0.1418 | - | | 10.9639 | 4550 | 0.1477 | - | | 11.0843 | 4600 | 0.1425 | - | | 11.2048 | 4650 | 0.1401 | - | | 11.3253 | 4700 | 0.1344 | - | | 11.4458 | 4750 | 0.1351 | - | | 11.5663 | 4800 | 0.1475 | - | | 11.6867 | 4850 | 0.1337 | - | | 11.8072 | 4900 | 0.1452 | - | | 11.9277 | 4950 | 0.1463 | - | | 12.0482 | 5000 | 0.1361 | - | | 12.1687 | 5050 | 0.1331 | - | | 12.2892 | 5100 | 0.1387 | - | | 12.4096 | 5150 | 0.1494 | - | | 12.5301 | 5200 | 0.1341 | - | | 12.6506 | 5250 | 0.1411 | - | | 12.7711 | 5300 | 0.1373 | - | | 12.8916 | 5350 | 0.1376 | - | | 13.0120 | 5400 | 0.1359 | - | | 13.1325 | 5450 | 0.1404 | - | | 13.2530 | 5500 | 0.1331 | - | | 13.3735 | 5550 | 0.1424 | - | | 13.4940 | 5600 | 0.1444 | - | | 13.6145 | 5650 | 0.1469 | - | | 13.7349 | 5700 | 0.1387 | - | | 13.8554 | 5750 | 0.1378 | - | | 13.9759 | 5800 | 0.1331 | - | | 14.0964 | 5850 | 0.1229 | - | | 14.2169 | 5900 | 0.1352 | - | | 14.3373 | 5950 | 0.1182 | - | | 14.4578 | 6000 | 0.1281 | - | | 14.5783 | 6050 | 0.1359 | - | | 14.6988 | 6100 | 0.1399 | - | | 14.8193 | 6150 | 0.1394 | - | | 14.9398 | 6200 | 0.1352 | - | | 15.0602 | 6250 | 0.126 | - | | 15.1807 | 6300 | 0.1302 | - | | 15.3012 | 6350 | 0.1324 | - | | 15.4217 | 6400 | 0.1302 | - | | 15.5422 | 6450 | 0.1321 | - | | 15.6627 | 6500 | 0.1389 | - | | 15.7831 | 6550 | 0.1203 | - | | 15.9036 | 6600 | 0.1388 | - | | 16.0241 | 6650 | 0.119 | - | | 16.1446 | 6700 | 0.1289 | - | | 16.2651 | 6750 | 0.1282 | - | | 16.3855 | 6800 | 0.1313 | - | | 16.5060 | 6850 | 0.1305 | - | | 16.6265 | 6900 | 0.1194 | - | | 16.7470 | 6950 | 0.1256 | - | | 16.8675 | 7000 | 0.1396 | - | | 16.9880 | 7050 | 0.1203 | - | | 17.1084 | 7100 | 0.1408 | - | | 17.2289 | 7150 | 0.1279 | - | | 17.3494 | 7200 | 0.1132 | - | | 17.4699 | 7250 | 0.1188 | - | | 17.5904 | 7300 | 0.1359 | - | | 17.7108 | 7350 | 0.1216 | - | | 17.8313 | 7400 | 0.1149 | - | | 17.9518 | 7450 | 0.1461 | - | | 18.0723 | 7500 | 0.1258 | - | | 18.1928 | 7550 | 0.1239 | - | | 18.3133 | 7600 | 0.1232 | - | | 18.4337 | 7650 | 0.1384 | - | | 18.5542 | 7700 | 0.1232 | - | | 18.6747 | 7750 | 0.1043 | - | | 18.7952 | 7800 | 0.1274 | - | | 18.9157 | 7850 | 0.1247 | - | | 19.0361 | 7900 | 0.1333 | - | | 19.1566 | 7950 | 0.1243 | - | | 19.2771 | 8000 | 0.115 | - | | 19.3976 | 8050 | 0.1264 | - | | 19.5181 | 8100 | 0.1224 | - | | 19.6386 | 8150 | 0.1214 | - | | 19.7590 | 8200 | 0.1381 | - | | 19.8795 | 8250 | 0.122 | - | | 20.0 | 8300 | 0.1097 | - | | 20.1205 | 8350 | 0.1144 | - | | 20.2410 | 8400 | 0.1248 | - | | 20.3614 | 8450 | 0.1233 | - | | 20.4819 | 8500 | 0.1184 | - | | 20.6024 | 8550 | 0.1038 | - | | 20.7229 | 8600 | 0.1274 | - | | 20.8434 | 8650 | 0.13 | - | | 20.9639 | 8700 | 0.1239 | - | | 21.0843 | 8750 | 0.1378 | - | | 21.2048 | 8800 | 0.1144 | - | | 21.3253 | 8850 | 0.1177 | - | | 21.4458 | 8900 | 0.127 | - | | 21.5663 | 8950 | 0.1143 | - | | 21.6867 | 9000 | 0.1242 | - | | 21.8072 | 9050 | 0.1309 | - | | 21.9277 | 9100 | 0.121 | - | | 22.0482 | 9150 | 0.1229 | - | | 22.1687 | 9200 | 0.1212 | - | | 22.2892 | 9250 | 0.1212 | - | | 22.4096 | 9300 | 0.1201 | - | | 22.5301 | 9350 | 0.1381 | - | | 22.6506 | 9400 | 0.1188 | - | | 22.7711 | 9450 | 0.128 | - | | 22.8916 | 9500 | 0.1019 | - | | 23.0120 | 9550 | 0.1194 | - | | 23.1325 | 9600 | 0.1057 | - | | 23.2530 | 9650 | 0.1239 | - | | 23.3735 | 9700 | 0.128 | - | | 23.4940 | 9750 | 0.1249 | - | | 23.6145 | 9800 | 0.1105 | - | | 23.7349 | 9850 | 0.1264 | - | | 23.8554 | 9900 | 0.1192 | - | | 23.9759 | 9950 | 0.1169 | - | | 24.0964 | 10000 | 0.1075 | - | | 24.2169 | 10050 | 0.1172 | - | | 24.3373 | 10100 | 0.1196 | - | | 24.4578 | 10150 | 0.1153 | - | | 24.5783 | 10200 | 0.1148 | - | | 24.6988 | 10250 | 0.0947 | - | | 24.8193 | 10300 | 0.11 | - | | 24.9398 | 10350 | 0.1147 | - | | 25.0602 | 10400 | 0.1193 | - | | 25.1807 | 10450 | 0.1152 | - | | 25.3012 | 10500 | 0.1285 | - | | 25.4217 | 10550 | 0.0971 | - | | 25.5422 | 10600 | 0.104 | - | | 25.6627 | 10650 | 0.1019 | - | | 25.7831 | 10700 | 0.127 | - | | 25.9036 | 10750 | 0.1089 | - | | 26.0241 | 10800 | 0.1129 | - | | 26.1446 | 10850 | 0.1047 | - | | 26.2651 | 10900 | 0.1144 | - | | 26.3855 | 10950 | 0.1158 | - | | 26.5060 | 11000 | 0.1267 | - | | 26.6265 | 11050 | 0.0987 | - | | 26.7470 | 11100 | 0.1081 | - | | 26.8675 | 11150 | 0.1097 | - | | 26.9880 | 11200 | 0.102 | - | | 27.1084 | 11250 | 0.1307 | - | | 27.2289 | 11300 | 0.1054 | - | | 27.3494 | 11350 | 0.1018 | - | | 27.4699 | 11400 | 0.0981 | - | | 27.5904 | 11450 | 0.1102 | - | | 27.7108 | 11500 | 0.1125 | - | | 27.8313 | 11550 | 0.1241 | - | | 27.9518 | 11600 | 0.1036 | - | | 28.0723 | 11650 | 0.0996 | - | | 28.1928 | 11700 | 0.0966 | - | | 28.3133 | 11750 | 0.1129 | - | | 28.4337 | 11800 | 0.1177 | - | | 28.5542 | 11850 | 0.1128 | - | | 28.6747 | 11900 | 0.1077 | - | | 28.7952 | 11950 | 0.0999 | - | | 28.9157 | 12000 | 0.0825 | - | | 29.0361 | 12050 | 0.1173 | - | | 29.1566 | 12100 | 0.088 | - | | 29.2771 | 12150 | 0.103 | - | | 29.3976 | 12200 | 0.0955 | - | | 29.5181 | 12250 | 0.0924 | - | | 29.6386 | 12300 | 0.1024 | - | | 29.7590 | 12350 | 0.0923 | - | | 29.8795 | 12400 | 0.1128 | - | | 30.0 | 12450 | 0.1218 | - | | 30.1205 | 12500 | 0.1047 | - | | 30.2410 | 12550 | 0.1109 | - | | 30.3614 | 12600 | 0.1002 | - | | 30.4819 | 12650 | 0.1168 | - | | 30.6024 | 12700 | 0.1041 | - | | 30.7229 | 12750 | 0.1107 | - | | 30.8434 | 12800 | 0.1082 | - | | 30.9639 | 12850 | 0.1131 | - | | 31.0843 | 12900 | 0.1014 | - | | 31.2048 | 12950 | 0.0991 | - | | 31.3253 | 13000 | 0.1067 | - | | 31.4458 | 13050 | 0.1099 | - | | 31.5663 | 13100 | 0.0944 | - | | 31.6867 | 13150 | 0.1063 | - | | 31.8072 | 13200 | 0.1105 | - | | 31.9277 | 13250 | 0.0926 | - | | 32.0482 | 13300 | 0.0965 | - | | 32.1687 | 13350 | 0.0877 | - | | 32.2892 | 13400 | 0.1137 | - | | 32.4096 | 13450 | 0.108 | - | | 32.5301 | 13500 | 0.0988 | - | | 32.6506 | 13550 | 0.1013 | - | | 32.7711 | 13600 | 0.1024 | - | | 32.8916 | 13650 | 0.1008 | - | | 33.0120 | 13700 | 0.0985 | - | | 33.1325 | 13750 | 0.0774 | - | | 33.2530 | 13800 | 0.0975 | - | | 33.3735 | 13850 | 0.096 | - | | 33.4940 | 13900 | 0.0999 | - | | 33.6145 | 13950 | 0.1258 | - | | 33.7349 | 14000 | 0.0868 | - | | 33.8554 | 14050 | 0.0966 | - | | 33.9759 | 14100 | 0.0955 | - | | 34.0964 | 14150 | 0.1044 | - | | 34.2169 | 14200 | 0.0745 | - | | 34.3373 | 14250 | 0.0896 | - | | 34.4578 | 14300 | 0.0938 | - | | 34.5783 | 14350 | 0.1008 | - | | 34.6988 | 14400 | 0.0952 | - | | 34.8193 | 14450 | 0.0929 | - | | 34.9398 | 14500 | 0.0856 | - | | 35.0602 | 14550 | 0.0952 | - | | 35.1807 | 14600 | 0.1017 | - | | 35.3012 | 14650 | 0.0892 | - | | 35.4217 | 14700 | 0.0892 | - | | 35.5422 | 14750 | 0.0988 | - | | 35.6627 | 14800 | 0.0943 | - | | 35.7831 | 14850 | 0.0713 | - | | 35.9036 | 14900 | 0.0839 | - | | 36.0241 | 14950 | 0.0894 | - | | 36.1446 | 15000 | 0.0757 | - | | 36.2651 | 15050 | 0.0824 | - | | 36.3855 | 15100 | 0.0822 | - | | 36.5060 | 15150 | 0.0863 | - | | 36.6265 | 15200 | 0.0965 | - | | 36.7470 | 15250 | 0.0864 | - | | 36.8675 | 15300 | 0.0879 | - | | 36.9880 | 15350 | 0.0834 | - | | 37.1084 | 15400 | 0.0885 | - | | 37.2289 | 15450 | 0.0735 | - | | 37.3494 | 15500 | 0.0891 | - | | 37.4699 | 15550 | 0.0923 | - | | 37.5904 | 15600 | 0.0687 | - | | 37.7108 | 15650 | 0.0882 | - | | 37.8313 | 15700 | 0.0695 | - | | 37.9518 | 15750 | 0.0888 | - | | 38.0723 | 15800 | 0.0875 | - | | 38.1928 | 15850 | 0.0773 | - | | 38.3133 | 15900 | 0.0973 | - | | 38.4337 | 15950 | 0.0961 | - | | 38.5542 | 16000 | 0.0922 | - | | 38.6747 | 16050 | 0.0961 | - | | 38.7952 | 16100 | 0.0722 | - | | 38.9157 | 16150 | 0.0991 | - | | 39.0361 | 16200 | 0.081 | - | | 39.1566 | 16250 | 0.0847 | - | | 39.2771 | 16300 | 0.0796 | - | | 39.3976 | 16350 | 0.0922 | - | | 39.5181 | 16400 | 0.0942 | - | | 39.6386 | 16450 | 0.0791 | - | | 39.7590 | 16500 | 0.0838 | - | | 39.8795 | 16550 | 0.1005 | - | | 40.0 | 16600 | 0.0755 | - | | 40.1205 | 16650 | 0.1 | - | | 40.2410 | 16700 | 0.0737 | - | | 40.3614 | 16750 | 0.0659 | - | | 40.4819 | 16800 | 0.083 | - | | 40.6024 | 16850 | 0.0729 | - | | 40.7229 | 16900 | 0.0637 | - | | 40.8434 | 16950 | 0.0776 | - | | 40.9639 | 17000 | 0.0697 | - | | 41.0843 | 17050 | 0.0548 | - | | 41.2048 | 17100 | 0.0612 | - | | 41.3253 | 17150 | 0.078 | - | | 41.4458 | 17200 | 0.0713 | - | | 41.5663 | 17250 | 0.0808 | - | | 41.6867 | 17300 | 0.0761 | - | | 41.8072 | 17350 | 0.0878 | - | | 41.9277 | 17400 | 0.0798 | - | | 42.0482 | 17450 | 0.082 | - | | 42.1687 | 17500 | 0.0757 | - | | 42.2892 | 17550 | 0.0706 | - | | 42.4096 | 17600 | 0.0882 | - | | 42.5301 | 17650 | 0.0833 | - | | 42.6506 | 17700 | 0.0851 | - | | 42.7711 | 17750 | 0.078 | - | | 42.8916 | 17800 | 0.0875 | - | | 43.0120 | 17850 | 0.053 | - | | 43.1325 | 17900 | 0.0751 | - | | 43.2530 | 17950 | 0.0953 | - | | 43.3735 | 18000 | 0.0702 | - | | 43.4940 | 18050 | 0.0764 | - | | 43.6145 | 18100 | 0.0626 | - | | 43.7349 | 18150 | 0.0824 | - | | 43.8554 | 18200 | 0.0714 | - | | 43.9759 | 18250 | 0.0749 | - | | 44.0964 | 18300 | 0.0648 | - | | 44.2169 | 18350 | 0.088 | - | | 44.3373 | 18400 | 0.0631 | - | | 44.4578 | 18450 | 0.0773 | - | | 44.5783 | 18500 | 0.0497 | - | | 44.6988 | 18550 | 0.0797 | - | | 44.8193 | 18600 | 0.0758 | - | | 44.9398 | 18650 | 0.0808 | - | | 45.0602 | 18700 | 0.067 | - | | 45.1807 | 18750 | 0.0655 | - | | 45.3012 | 18800 | 0.0714 | - | | 45.4217 | 18850 | 0.0597 | - | | 45.5422 | 18900 | 0.0738 | - | | 45.6627 | 18950 | 0.0582 | - | | 45.7831 | 19000 | 0.057 | - | | 45.9036 | 19050 | 0.0725 | - | | 46.0241 | 19100 | 0.0533 | - | | 46.1446 | 19150 | 0.0584 | - | | 46.2651 | 19200 | 0.0616 | - | | 46.3855 | 19250 | 0.0749 | - | | 46.5060 | 19300 | 0.0682 | - | | 46.6265 | 19350 | 0.0724 | - | | 46.7470 | 19400 | 0.0673 | - | | 46.8675 | 19450 | 0.0665 | - | | 46.9880 | 19500 | 0.0691 | - | | 47.1084 | 19550 | 0.0547 | - | | 47.2289 | 19600 | 0.0551 | - | | 47.3494 | 19650 | 0.0519 | - | | 47.4699 | 19700 | 0.079 | - | | 47.5904 | 19750 | 0.0728 | - | | 47.7108 | 19800 | 0.0575 | - | | 47.8313 | 19850 | 0.0611 | - | | 47.9518 | 19900 | 0.0703 | - | | 48.0723 | 19950 | 0.0642 | - | | 48.1928 | 20000 | 0.07 | - | | 48.3133 | 20050 | 0.0681 | - | | 48.4337 | 20100 | 0.0531 | - | | 48.5542 | 20150 | 0.0623 | - | | 48.6747 | 20200 | 0.0603 | - | | 48.7952 | 20250 | 0.0551 | - | | 48.9157 | 20300 | 0.0721 | - | | 49.0361 | 20350 | 0.0563 | - | | 49.1566 | 20400 | 0.0719 | - | | 49.2771 | 20450 | 0.069 | - | | 49.3976 | 20500 | 0.0847 | - | | 49.5181 | 20550 | 0.0869 | - | | 49.6386 | 20600 | 0.0553 | - | | 49.7590 | 20650 | 0.0423 | - | | 49.8795 | 20700 | 0.0712 | - | | 50.0 | 20750 | 0.0497 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.2 - Sentence Transformers: 4.1.0 - Transformers: 4.52.1 - PyTorch: 2.7.0+cu126 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## 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} } ```