diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -10,12 +10,12 @@ tags: - text-classification - generated_from_setfit_trainer widget: -- text: 크리니크 치크 팝 21번 ballerina pop 3.5g/박스정품, 한글텍 발레리나 21번정품+브러쉬 오씨네 -- text: Suave Professionals 안티 프리즈 크림, 날렵 - 3.5온스 옵션없음 가나오 -- text: 더페이스샵 미감수 브라이트 클렌징 티슈 50매 옵션없음 (주)글로벌세림 -- text: Nexus Climate Control Gel Cream Curl Defined for Cashmere Curls, StyleProtect - Technology 8oz Curl Define 가나구 -- text: 잔스 튤립괄사+하트괄사+오일30ml+마스크 옵션없음 엘로컴퍼니 +- text: '[단품] 뮤트 치크팔레트 (로즈가든/모브가든) 03. 로즈가든 디퍼런트밀리언즈(주)' +- text: 나투리아 케라틴 워터팩 250g 옵션없음 나투리아 공식몰 +- text: 순수자아 원스텝 워터 클렌징 패드 100매 옵션없음 바라글로벌 +- text: Hair Identifier Spray for Face Shaving 2024 Skin Dermaplaning Moisturizing + and Care Dermaplaner 2 PC 옵션없음 젠틀스토어 +- text: 블랑네이처 5배 매직 티트리 오일 대용량 20ML 옵션없음 에스지헬스케어 inference: true model-index: - name: SetFit with klue/roberta-base @@ -29,7 +29,7 @@ model-index: split: test metrics: - type: accuracy - value: 0.6650017028039505 + value: 0.6668180270178227 name: Accuracy --- @@ -61,135 +61,135 @@ The model has been trained using an efficient few-shot learning technique that i - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels -| Label | Examples | -|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| 251 | | -| 1046 | | -| 506 | | -| 215 | | -| 395 | | -| 993 | | -| 462 | | -| 393 | | -| 390 | | -| 575 | | -| 1051 | | -| 456 | | -| 463 | | -| 313 | | -| 318 | | -| 453 | | -| 211 | | -| 439 | | -| 381 | | -| 216 | | -| 459 | | -| 464 | | -| 468 | | -| 992 | | -| 504 | | -| 469 | | -| 213 | | -| 1002 | | -| 443 | | -| 392 | | -| 467 | | -| 209 | | -| 1062 | | -| 455 | | -| 1003 | | -| 1063 | | -| 221 | | -| 997 | | -| 576 | | -| 579 | | -| 384 | | -| 1058 | | -| 508 | | -| 578 | | -| 574 | | -| 461 | | -| 452 | | -| 444 | | -| 1047 | | -| 312 | | -| 387 | | -| 457 | | -| 391 | | -| 317 | | -| 454 | | -| 994 | | -| 1060 | | -| 389 | | -| 397 | | -| 465 | | -| 460 | | -| 212 | | -| 315 | | -| 1061 | | -| 1050 | | -| 1065 | | -| 217 | | -| 311 | | -| 245 | | -| 998 | | -| 999 | | -| 1001 | | -| 996 | | -| 582 | | -| 250 | | -| 440 | | -| 385 | | -| 1044 | | -| 505 | | -| 584 | | -| 316 | | -| 1049 | | -| 219 | | -| 451 | | -| 1045 | | -| 214 | | -| 249 | | -| 458 | | -| 507 | | -| 577 | | -| 246 | | -| 581 | | -| 208 | | -| 509 | | -| 210 | | -| 382 | | -| 441 | | -| 1059 | | -| 383 | | -| 450 | | -| 1052 | | -| 396 | | -| 388 | | -| 1048 | | -| 466 | | -| 1064 | | -| 1000 | | -| 314 | | -| 995 | | -| 445 | | -| 583 | | -| 247 | | -| 449 | | -| 580 | | -| 394 | | -| 218 | | -| 386 | | -| 442 | | -| 510 | | -| 248 | | +| Label | Examples | +|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| 19 | | +| 104 | | +| 77 | | +| 8 | | +| 30 | | +| 99 | | +| 61 | | +| 36 | | +| 37 | | +| 91 | | +| 108 | | +| 60 | | +| 66 | | +| 26 | | +| 27 | | +| 53 | | +| 3 | | +| 48 | | +| 39 | | +| 4 | | +| 62 | | +| 67 | | +| 71 | | +| 94 | | +| 79 | | +| 72 | | +| 5 | | +| 100 | | +| 51 | | +| 38 | | +| 70 | | +| 1 | | +| 117 | | +| 55 | | +| 98 | | +| 118 | | +| 12 | | +| 97 | | +| 90 | | +| 83 | | +| 35 | | +| 113 | | +| 80 | | +| 88 | | +| 84 | | +| 65 | | +| 59 | | +| 52 | | +| 107 | | +| 28 | | +| 43 | | +| 63 | | +| 34 | | +| 21 | | +| 54 | | +| 101 | | +| 115 | | +| 44 | | +| 42 | | +| 73 | | +| 68 | | +| 9 | | +| 25 | | +| 116 | | +| 112 | | +| 120 | | +| 6 | | +| 23 | | +| 15 | | +| 96 | | +| 95 | | +| 92 | | +| 103 | | +| 85 | | +| 20 | | +| 47 | | +| 33 | | +| 106 | | +| 78 | | +| 86 | | +| 24 | | +| 111 | | +| 10 | | +| 57 | | +| 105 | | +| 13 | | +| 18 | | +| 64 | | +| 76 | | +| 89 | | +| 14 | | +| 87 | | +| 0 | | +| 75 | | +| 2 | | +| 45 | | +| 50 | | +| 114 | | +| 41 | | +| 58 | | +| 109 | | +| 32 | | +| 40 | | +| 110 | | +| 69 | | +| 119 | | +| 93 | | +| 22 | | +| 102 | | +| 49 | | +| 81 | | +| 16 | | +| 56 | | +| 82 | | +| 31 | | +| 7 | | +| 29 | | +| 46 | | +| 74 | | +| 17 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| -| **all** | 0.6650 | +| **all** | 0.6668 | ## Uses @@ -209,7 +209,7 @@ from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_item_bt_test_flat") # Run inference -preds = model("잔스 튤립괄사+하트괄사+오일30ml+마스크 옵션없음 엘로컴퍼니") +preds = model("나투리아 케라틴 워터팩 250g 옵션없음 나투리아 공식몰") ```