diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -5,24 +5,22 @@ tags: - text-classification - generated_from_setfit_trainer widget: -- text: 찰흑미 국산 1kg 20 ea 박스 순성 양곡 식품 농산물 본품 (#M)식품>농산물>쌀>흑미 T200 > Naverstore > 식품 - > 쌀/잡곡 > 쌀 > 흑미 -- text: 티아시아키친 치킨 마크니 커리 170g 골든 버터 난 8p 200g (#M)식품>냉동/간편조리식품>카레/짜장 T200 > Naverstore - > 식품 > 간편조리식품 > 카레/짜장 -- text: 공기방울세척기 손세척 1위정품 괴산절임배추 20kg 정품 중상품 괴산절임배추20kg_12월16일수도착 (#M)신선식품>김치>절임배추 +- text: 해남 농협 열무김치 3kg 이맑은 김치 (#M)신선식품>김치>열무김치 Auction > 식품/마트/유아 > 식품 > 신선식품 > 김치 + > 열무김치 +- text: 1위정품 괴산영농조합 버블세척기 괴산절임배추20kg 정품 중상품 괴산절임배추20kg_11월09일월도착 (#M)신선식품>김치>절임배추 Auction > 식품/마트/유아 > 식품 > 신선식품 > 김치 > 절임배추 -- text: 오메기명원 제주 오메기떡 수제 5종 30개입 1세트 팥 콩고물 씨앗 감귤 흑임자 5종40(팥8씨8콩8흑8귤8) (#M)식품>과자/베이커리>떡 - T200 > Naverstore > 식품 > 과자/떡/베이커리 > 떡 -- text: 오뚜기 맛있는 오뚜기밥 210g × 32개 (#M)쿠팡 홈 Coupang > 식품 > 면/통조림/가공식품 > 즉석밥/누룽지 > 즉석밥/누룽지 - > 즉석백미밥 > 오뚜기 +- text: 1+1 티백 100개입 작두콩차 호박팥차 돼지감자 생강 결명자 대추 야관문 오미자 쑥차 ★★하루허브 메리골드 50티백 1+1 총2팩 + 홈>식품>음료>차류>기타차;홈>★1+1팩★;(#M)홈>💖1+1팩💖 Naverstore > 식품 > 음료 > 차류 > 기타차 +- text: 정식품 달콤한 베지밀B 190ml (#M)식품>음료>두유 T200 > Naverstore > 식품 > 생수/음료 > 두유 +- text: 던킨도너츠 센트럴파크 블렌드 캡슐커피 - 5g x 20개 5g × 20개 LotteOn > 식품 > 커피/원두/차 > 캡슐/드립백/더치 + LotteOn > 식품 > 커피/원두/차 > 캡슐/드립백/더치 metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true -base_model: intfloat/multilingual-e5-base model-index: -- name: SetFit with intfloat/multilingual-e5-base +- name: SetFit results: - task: type: text-classification @@ -33,13 +31,13 @@ model-index: split: test metrics: - type: accuracy - value: 0.9024586925578953 + value: 0.9263923628936245 name: Accuracy --- -# SetFit with intfloat/multilingual-e5-base +# SetFit -This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-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. +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: @@ -50,7 +48,7 @@ The model has been trained using an efficient few-shot learning technique that i ### Model Description - **Model Type:** SetFit -- **Sentence Transformer body:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-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:** 598 classes @@ -65,613 +63,613 @@ 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 | 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| | +| 114 | | +| 339 | | +| 458 | | +| 422 | | +| 96 | | +| 516 | | +| 103 | | +| 307 | | +| 484 | | +| 590 | | +| 162 | | +| 280 | | +| 156 | | +| 84 | | +| 365 | | +| 87 | | +| 371 | | +| 313 | | +| 507 | | +| 234 | | +| 108 | | +| 301 | | +| 62 | | +| 187 | | +| 460 | | +| 491 | | +| 200 | | +| 140 | | +| 267 | | +| 436 | | +| 415 | | +| 151 | | +| 182 | | +| 179 | | +| 227 | | +| 11 | | +| 260 | | +| 150 | | +| 213 | | +| 289 | | +| 69 | | +| 317 | | +| 579 | | +| 271 | | +| 142 | | +| 223 | | +| 417 | | +| 20 | | +| 337 | | +| 72 | | +| 404 | | +| 40 | | +| 438 | | +| 321 | | +| 596 | | +| 361 | | +| 90 | | +| 121 | | +| 15 | | +| 517 | | +| 395 | | +| 429 | | +| 489 | | +| 122 | | +| 105 | | +| 431 | | +| 419 | | +| 469 | | +| 115 | | +| 357 | | +| 167 | | +| 327 | | +| 426 | | +| 154 | | +| 537 | | +| 583 | | +| 170 | | +| 279 | | +| 258 | | +| 459 | | +| 202 | | +| 418 | | +| 492 | | +| 306 | | +| 31 | | +| 549 | | +| 363 | | +| 252 | | +| 212 | | +| 428 | | +| 392 | | +| 485 | | +| 440 | | +| 130 | | +| 328 | | +| 235 | | +| 94 | | +| 297 | | +| 401 | | +| 312 | | +| 597 | | +| 135 | | +| 338 | | +| 582 | | +| 211 | | +| 292 | | +| 201 | | +| 144 | | +| 88 | | +| 360 | | +| 54 | | +| 199 | | +| 434 | | +| 377 | | +| 341 | | +| 370 | | +| 48 | | +| 30 | | +| 386 | | +| 264 | | +| 158 | | +| 553 | | +| 356 | | +| 560 | | +| 464 | | +| 451 | | +| 185 | | +| 441 | | +| 228 | | +| 236 | | +| 209 | | +| 592 | | +| 405 | | +| 41 | | +| 505 | | +| 188 | | +| 315 | | +| 93 | | +| 443 | | +| 513 | | +| 393 | | +| 294 | | +| 430 | | +| 515 | | +| 137 | | +| 57 | | +| 6 | | +| 2 | | +| 466 | | +| 501 | | +| 427 | | +| 402 | | +| 45 | | +| 22 | | +| 288 | | +| 468 | | +| 226 | | +| 132 | | +| 161 | | +| 70 | | +| 400 | | +| 33 | | +| 346 | | +| 343 | | +| 334 | | +| 229 | | +| 75 | | +| 186 | | +| 67 | | +| 387 | | +| 488 | | +| 117 | | +| 207 | | +| 342 | | +| 448 | | +| 325 | | +| 588 | | +| 569 | | +| 543 | | +| 38 | | +| 49 | | +| 225 | | +| 379 | | +| 324 | | +| 433 | | +| 265 | | +| 248 | | +| 124 | | +| 238 | | +| 586 | | +| 403 | | +| 362 | | +| 138 | | +| 66 | | +| 498 | | +| 191 | | +| 50 | | +| 508 | | +| 531 | | +| 36 | | +| 300 | | +| 159 | | +| 32 | | +| 218 | | +| 587 | | +| 39 | | +| 35 | | +| 524 | | +| 239 | | +| 23 | | +| 89 | | +| 193 | | +| 246 | | +| 71 | | +| 111 | | +| 470 | | +| 68 | | +| 215 | | +| 203 | | +| 595 | | +| 551 | | +| 472 | | +| 584 | | +| 376 | | +| 542 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| -| **all** | 0.9025 | +| **all** | 0.9264 | ## Uses @@ -691,7 +689,7 @@ from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_item_top_fd_flat") # Run inference -preds = model("찰흑미 국산 1kg 20 ea 박스 순성 양곡 식품 농산물 본품 (#M)식품>농산물>쌀>흑미 T200 > Naverstore > 식품 > 쌀/잡곡 > 쌀 > 흑미") +preds = model("정식품 달콤한 베지밀B 190ml (#M)식품>음료>두유 T200 > Naverstore > 식품 > 생수/음료 > 두유") ```