diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,1520 @@ +--- +tags: +- setfit +- sentence-transformers +- 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 + 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 +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 + results: + - task: + type: text-classification + name: Text Classification + dataset: + name: Unknown + type: unknown + split: test + metrics: + - type: accuracy + value: 0.9024586925578953 + name: Accuracy +--- + +# SetFit with intfloat/multilingual-e5-base + +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. + +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:** [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 + + + + +### 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 | +|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| 505 | | +| 502 | | +| 457 | | +| 319 | | +| 324 | | +| 290 | | +| 484 | | +| 164 | | +| 99 | | +| 591 | | +| 272 | | +| 588 | | +| 378 | | +| 504 | | +| 257 | | +| 267 | | +| 517 | | +| 223 | | +| 575 | | +| 171 | | +| 559 | | +| 236 | | +| 436 | | +| 78 | | +| 478 | | +| 65 | | +| 430 | | +| 52 | | +| 593 | | +| 243 | | +| 355 | | +| 144 | | +| 231 | | +| 562 | | +| 403 | | +| 416 | | +| 528 | | +| 396 | | +| 369 | | +| 27 | | +| 331 | | +| 380 | | +| 398 | | +| 287 | | +| 340 | | +| 567 | | +| 126 | | +| 363 | | +| 59 | | +| 550 | | +| 53 | | +| 192 | | +| 489 | | +| 64 | | +| 520 | | +| 57 | | +| 194 | | +| 170 | | +| 336 | | +| 73 | | +| 154 | | +| 29 | | +| 54 | | +| 9 | | +| 474 | | +| 429 | | +| 592 | | +| 188 | | +| 368 | | +| 365 | | +| 90 | | +| 235 | | +| 330 | | +| 501 | | +| 463 | | +| 359 | | +| 543 | | +| 440 | | +| 161 | | +| 410 | | +| 158 | | +| 462 | | +| 458 | | +| 221 | | +| 202 | | +| 437 | | +| 116 | | +| 347 | | +| 315 | | +| 295 | | +| 381 | | +| 548 | | +| 343 | | +| 476 | | +| 16 | | +| 174 | | +| 187 | | +| 586 | | +| 449 | | +| 4 | | +| 557 | | +| 482 | | +| 487 | | +| 273 | | +| 373 | | +| 366 | | +| 583 | | +| 298 | | +| 284 | | +| 469 | | +| 242 | | +| 277 | | +| 531 | | +| 282 | | +| 216 | | +| 255 | | +| 530 | | +| 173 | | +| 12 | | +| 485 | | +| 128 | | +| 492 | | +| 471 | | +| 506 | | +| 303 | | +| 169 | | +| 10 | | +| 596 | | +| 300 | | +| 228 | | +| 412 | | +| 237 | | +| 288 | | +| 317 | | +| 406 | | +| 350 | | +| 160 | | +| 572 | | +| 577 | | +| 338 | | +| 532 | | +| 246 | | +| 233 | | +| 153 | | +| 264 | | +| 240 | | +| 348 | | +| 198 | | +| 419 | | +| 408 | | +| 510 | | +| 515 | | +| 354 | | +| 219 | | +| 590 | | +| 442 | | +| 274 | | +| 130 | | +| 180 | | +| 305 | | +| 220 | | +| 265 | | +| 297 | | +| 564 | | +| 217 | | +| 8 | | +| 342 | | +| 539 | | +| 33 | | +| 241 | | +| 280 | | +| 85 | | +| 183 | | +| 486 | | +| 538 | | +| 385 | | +| 125 | | +| 122 | | +| 167 | | +| 79 | | +| 248 | | +| 44 | | +| 227 | | +| 86 | | +| 571 | | +| 409 | | +| 428 | | +| 328 | | +| 20 | | +| 309 | | +| 326 | | +| 477 | | +| 271 | | +| 208 | | +| 499 | | +| 17 | | +| 88 | | +| 417 | | +| 534 | | +| 554 | | +| 500 | | +| 289 | | +| 134 | | +| 535 | | +| 113 | | +| 312 | | +| 24 | | +| 569 | | +| 15 | | +| 232 | | +| 218 | | +| 527 | | +| 589 | | +| 1 | | +| 43 | | +| 101 | | +| 372 | | +| 25 | | +| 28 | | +| 250 | | +| 155 | | +| 432 | | +| 566 | | +| 507 | | +| 49 | | +| 314 | | +| 140 | | +| 104 | | +| 304 | | +| 491 | | +| 214 | | +| 565 | | +| 518 | | +| 299 | | +| 245 | | +| 345 | | +| 97 | | +| 553 | | +| 185 | | +| 195 | | +| 286 | | +| 120 | | +| 393 | | +| 80 | | +| 133 | | +| 361 | | +| 197 | | +| 35 | | +| 522 | | +| 42 | | +| 446 | | +| 66 | | +| 182 | | +| 495 | | +| 181 | | +| 81 | | +| 332 | | +| 175 | | +| 281 | | +| 92 | | +| 401 | | +| 483 | | +| 519 | | +| 587 | | +| 540 | | +| 344 | | +| 480 | | +| 184 | | +| 310 | | +| 6 | | +| 263 | | +| 585 | | +| 151 | | +| 561 | | +| 472 | | +| 360 | | +| 244 | | +| 461 | | +| 525 | | +| 581 | | +| 370 | | +| 353 | | +| 176 | | +| 456 | | +| 459 | | +| 283 | | +| 275 | | +| 438 | | +| 105 | | +| 498 | | +| 524 | | +| 325 | | +| 269 | | +| 420 | | +| 423 | | +| 346 | | +| 172 | | +| 552 | | +| 468 | | +| 479 | | +| 433 | | +| 146 | | +| 392 | | +| 493 | | +| 424 | | +| 497 | | +| 60 | | +| 203 | | +| 5 | | +| 249 | | +| 193 | | +| 253 | | +| 276 | | +| 546 | | +| 210 | | +| 168 | | +| 323 | | +| 222 | | +| 296 | | +| 427 | | +| 45 | | +| 136 | | +| 311 | | +| 364 | | +| 148 | | +| 50 | | +| 503 | | +| 18 | | +| 156 | | +| 239 | | +| 207 | | +| 123 | | +| 382 | | +| 377 | | +| 268 | | +| 560 | | +| 190 | | +| 371 | | +| 450 | | +| 481 | | +| 259 | | +| 11 | | +| 191 | | +| 204 | | +| 121 | | +| 26 | | +| 358 | | +| 387 | | +| 405 | | +| 56 | | +| 93 | | +| 132 | | +| 211 | | +| 341 | | +| 544 | | +| 102 | | +| 301 | | +| 568 | | +| 334 | | +| 318 | | +| 55 | | +| 127 | | +| 395 | | +| 157 | | +| 594 | | +| 431 | | +| 19 | | +| 149 | | +| 14 | | +| 578 | | +| 397 | | +| 466 | | +| 46 | | +| 215 | | +| 516 | | +| 256 | | +| 509 | | +| 72 | | +| 83 | | +| 96 | | +| 163 | | +| 76 | | +| 69 | | +| 200 | | +| 21 | | +| 526 | | +| 434 | | +| 467 | | +| 384 | | +| 597 | | +| 74 | | +| 573 | | +| 521 | | +| 536 | | +| 390 | | +| 143 | | +| 435 | | +| 41 | | +| 558 | | +| 407 | | +| 470 | | +| 138 | | +| 0 | | +| 119 | | +| 445 | | +| 230 | | +| 337 | | +| 582 | | +| 152 | | +| 508 | | +| 475 | | +| 294 | | +| 247 | | +| 464 | | +| 537 | | +| 367 | | +| 413 | | +| 145 | | +| 523 | | +| 404 | | +| 321 | | +| 209 | | +| 91 | | +| 131 | | +| 201 | | +| 441 | | +| 421 | | +| 426 | | +| 386 | | +| 189 | | +| 279 | | +| 394 | | +| 159 | | +| 87 | | +| 178 | | +| 490 | | +| 447 | | +| 162 | | +| 388 | | +| 452 | | +| 261 | | +| 306 | | +| 453 | | +| 316 | | +| 551 | | +| 302 | | +| 13 | | +| 141 | | +| 252 | | +| 262 | | +| 322 | | +| 494 | | +| 177 | | +| 100 | | +| 226 | | +| 61 | | +| 270 | | +| 529 | | +| 75 | | +| 451 | | +| 23 | | +| 51 | | +| 251 | | +| 95 | | +| 47 | | +| 439 | | +| 473 | | +| 415 | | +| 31 | | +| 425 | | +| 391 | | +| 335 | | +| 260 | | +| 389 | | +| 2 | | +| 574 | | +| 541 | | +| 327 | | +| 333 | | +| 40 | | +| 117 | | +| 3 | | +| 77 | | +| 547 | | +| 139 | | +| 488 | | +| 511 | | +| 422 | | +| 213 | | +| 196 | | +| 375 | | +| 576 | | +| 374 | | +| 129 | | +| 135 | | +| 107 | | +| 455 | | +| 307 | | +| 234 | | +| 512 | | +| 555 | | +| 402 | | +| 579 | | +| 513 | | +| 48 | | +| 320 | | +| 254 | | +| 443 | | +| 147 | | +| 67 | | +| 383 | | +| 212 | | +| 106 | | +| 34 | | +| 109 | | +| 352 | | +| 496 | | +| 313 | | +| 124 | | +| 411 | | +| 563 | | +| 570 | | +| 399 | | +| 225 | | +| 179 | | +| 556 | | +| 329 | | +| 533 | | +| 414 | | +| 349 | | +| 32 | | +| 94 | | +| 70 | | +| 229 | | +| 22 | | +| 285 | | +| 465 | | +| 36 | | +| 549 | | +| 542 | | +| 7 | | +| 238 | | +| 84 | | +| 137 | | +| 278 | | +| 114 | | +| 186 | | +| 357 | | +| 199 | | +| 266 | | +| 224 | | +| 308 | | +| 30 | | +| 595 | | +| 111 | | +| 514 | | +| 71 | | +| 142 | | +| 58 | | +| 293 | | +| 454 | | +| 118 | | +| 545 | | +| 351 | | +| 62 | | +| 379 | | +| 98 | | +| 580 | | +| 418 | | +| 356 | | +| 37 | | +| 206 | | +| 38 | | +| 110 | | +| 258 | | +| 400 | | +| 108 | | +| 112 | | +| 165 | | +| 82 | | +| 205 | | +| 339 | | +| 460 | | +| 89 | | +| 291 | | +| 584 | | +| 166 | | +| 63 | | +| 103 | | +| 150 | | +| 115 | | +| 292 | | +| 448 | | +| 444 | | +| 376 | | +| 39 | | +| 68 | | +| 362 | | + +## Evaluation + +### Metrics +| Label | Accuracy | +|:--------|:---------| +| **all** | 0.9025 | + +## 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_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") +``` + + + + + + + + + +## Training Details + +### Training Set Metrics +| Training set | Min | Median | Max | +|:-------------|:----|:--------|:----| +| Word count | 9 | 20.6877 | 89 | + +| Label | Training Sample Count | +|:------|:----------------------| +| 0 | 70 | +| 1 | 70 | +| 2 | 70 | +| 3 | 70 | +| 4 | 70 | +| 5 | 70 | +| 6 | 70 | +| 7 | 70 | +| 8 | 70 | +| 9 | 70 | +| 10 | 41 | +| 11 | 70 | +| 12 | 70 | +| 13 | 70 | +| 14 | 70 | +| 15 | 70 | +| 16 | 70 | +| 17 | 70 | +| 18 | 70 | +| 19 | 70 | +| 20 | 70 | +| 21 | 70 | +| 22 | 70 | +| 23 | 70 | +| 24 | 70 | +| 25 | 70 | +| 26 | 70 | +| 27 | 70 | +| 28 | 70 | +| 29 | 70 | +| 30 | 70 | +| 31 | 70 | +| 32 | 33 | +| 33 | 70 | +| 34 | 28 | +| 35 | 70 | +| 36 | 18 | +| 37 | 12 | +| 38 | 70 | +| 39 | 24 | +| 40 | 70 | +| 41 | 70 | +| 42 | 64 | +| 43 | 70 | +| 44 | 70 | +| 45 | 70 | +| 46 | 70 | +| 47 | 70 | +| 48 | 70 | +| 49 | 70 | +| 50 | 70 | +| 51 | 55 | +| 52 | 70 | +| 53 | 70 | +| 54 | 70 | +| 55 | 70 | +| 56 | 70 | +| 57 | 70 | +| 58 | 70 | +| 59 | 70 | +| 60 | 70 | +| 61 | 70 | +| 62 | 70 | +| 63 | 70 | +| 64 | 70 | +| 65 | 70 | +| 66 | 70 | +| 67 | 70 | +| 68 | 13 | +| 69 | 70 | +| 70 | 36 | +| 71 | 49 | +| 72 | 70 | +| 73 | 70 | +| 74 | 70 | +| 75 | 70 | +| 76 | 70 | +| 77 | 70 | +| 78 | 70 | +| 79 | 70 | +| 80 | 70 | +| 81 | 70 | +| 82 | 70 | +| 83 | 70 | +| 84 | 70 | +| 85 | 70 | +| 86 | 70 | +| 87 | 70 | +| 88 | 70 | +| 89 | 70 | +| 90 | 70 | +| 91 | 70 | +| 92 | 70 | +| 93 | 70 | +| 94 | 70 | +| 95 | 70 | +| 96 | 70 | +| 97 | 70 | +| 98 | 70 | +| 99 | 70 | +| 100 | 70 | +| 101 | 70 | +| 102 | 70 | +| 103 | 70 | +| 104 | 70 | +| 105 | 70 | +| 106 | 70 | +| 107 | 70 | +| 108 | 70 | +| 109 | 70 | +| 110 | 69 | +| 111 | 24 | +| 112 | 70 | +| 113 | 70 | +| 114 | 70 | +| 115 | 70 | +| 116 | 70 | +| 117 | 70 | +| 118 | 22 | +| 119 | 70 | +| 120 | 70 | +| 121 | 70 | +| 122 | 70 | +| 123 | 70 | +| 124 | 70 | +| 125 | 70 | +| 126 | 70 | +| 127 | 70 | +| 128 | 70 | +| 129 | 70 | +| 130 | 70 | +| 131 | 70 | +| 132 | 70 | +| 133 | 70 | +| 134 | 70 | +| 135 | 70 | +| 136 | 70 | +| 137 | 70 | +| 138 | 70 | +| 139 | 70 | +| 140 | 70 | +| 141 | 70 | +| 142 | 70 | +| 143 | 70 | +| 144 | 70 | +| 145 | 70 | +| 146 | 70 | +| 147 | 70 | +| 148 | 70 | +| 149 | 70 | +| 150 | 70 | +| 151 | 70 | +| 152 | 70 | +| 153 | 70 | +| 154 | 70 | +| 155 | 70 | +| 156 | 70 | +| 157 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+| 229 | 51 | +| 230 | 70 | +| 231 | 70 | +| 232 | 69 | +| 233 | 70 | +| 234 | 70 | +| 235 | 70 | +| 236 | 70 | +| 237 | 70 | +| 238 | 70 | +| 239 | 70 | +| 240 | 70 | +| 241 | 70 | +| 242 | 70 | +| 243 | 70 | +| 244 | 70 | +| 245 | 70 | +| 246 | 70 | +| 247 | 70 | +| 248 | 70 | +| 249 | 70 | +| 250 | 70 | +| 251 | 70 | +| 252 | 70 | +| 253 | 70 | +| 254 | 69 | +| 255 | 70 | +| 256 | 70 | +| 257 | 70 | +| 258 | 70 | +| 259 | 70 | +| 260 | 70 | +| 261 | 70 | +| 262 | 70 | +| 263 | 70 | +| 264 | 70 | +| 265 | 70 | +| 266 | 70 | +| 267 | 70 | +| 268 | 70 | +| 269 | 70 | +| 270 | 70 | +| 271 | 70 | +| 272 | 70 | +| 273 | 70 | +| 274 | 70 | +| 275 | 70 | +| 276 | 70 | +| 277 | 70 | +| 278 | 70 | +| 279 | 70 | +| 280 | 70 | +| 281 | 70 | +| 282 | 70 | +| 283 | 70 | +| 284 | 70 | +| 285 | 70 | +| 286 | 70 | +| 287 | 70 | +| 288 | 70 | +| 289 | 70 | +| 290 | 70 | +| 291 | 70 | +| 292 | 70 | +| 293 | 70 | +| 294 | 70 | +| 295 | 70 | +| 296 | 70 | +| 297 | 34 | +| 298 | 70 | +| 299 | 70 | +| 300 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+| 372 | 70 | +| 373 | 70 | +| 374 | 70 | +| 375 | 70 | +| 376 | 12 | +| 377 | 70 | +| 378 | 70 | +| 379 | 35 | +| 380 | 70 | +| 381 | 70 | +| 382 | 70 | +| 383 | 70 | +| 384 | 70 | +| 385 | 70 | +| 386 | 70 | +| 387 | 70 | +| 388 | 70 | +| 389 | 70 | +| 390 | 70 | +| 391 | 70 | +| 392 | 70 | +| 393 | 70 | +| 394 | 70 | +| 395 | 70 | +| 396 | 70 | +| 397 | 70 | +| 398 | 70 | +| 399 | 70 | +| 400 | 70 | +| 401 | 70 | +| 402 | 70 | +| 403 | 70 | +| 404 | 70 | +| 405 | 70 | +| 406 | 70 | +| 407 | 70 | +| 408 | 70 | +| 409 | 70 | +| 410 | 70 | +| 411 | 70 | +| 412 | 70 | +| 413 | 70 | +| 414 | 70 | +| 415 | 70 | +| 416 | 70 | +| 417 | 70 | +| 418 | 70 | +| 419 | 70 | +| 420 | 70 | +| 421 | 70 | +| 422 | 70 | +| 423 | 70 | +| 424 | 70 | +| 425 | 70 | +| 426 | 70 | +| 427 | 70 | +| 428 | 70 | +| 429 | 70 | +| 430 | 70 | +| 431 | 70 | +| 432 | 70 | +| 433 | 70 | +| 434 | 70 | +| 435 | 70 | +| 436 | 70 | +| 437 | 70 | +| 438 | 70 | +| 439 | 70 | +| 440 | 70 | +| 441 | 70 | +| 442 | 70 | +| 443 | 70 | +| 444 | 70 | +| 445 | 70 | +| 446 | 70 | +| 447 | 70 | +| 448 | 15 | +| 449 | 70 | +| 450 | 70 | +| 451 | 70 | +| 452 | 70 | +| 453 | 70 | +| 454 | 70 | +| 455 | 70 | +| 456 | 70 | +| 457 | 70 | +| 458 | 70 | +| 459 | 70 | +| 460 | 70 | +| 461 | 70 | +| 462 | 70 | +| 463 | 70 | +| 464 | 70 | +| 465 | 70 | +| 466 | 70 | +| 467 | 70 | +| 468 | 70 | +| 469 | 70 | +| 470 | 13 | +| 471 | 70 | +| 472 | 13 | +| 473 | 70 | +| 474 | 70 | +| 475 | 70 | +| 476 | 70 | +| 477 | 70 | +| 478 | 70 | +| 479 | 70 | +| 480 | 70 | +| 481 | 70 | +| 482 | 70 | +| 483 | 53 | +| 484 | 70 | +| 485 | 70 | +| 486 | 70 | +| 487 | 70 | +| 488 | 70 | +| 489 | 70 | +| 490 | 70 | +| 491 | 70 | +| 492 | 70 | +| 493 | 70 | +| 494 | 70 | +| 495 | 70 | +| 496 | 70 | +| 497 | 70 | +| 498 | 70 | +| 499 | 69 | +| 500 | 70 | +| 501 | 70 | +| 502 | 70 | +| 503 | 70 | +| 504 | 70 | +| 505 | 70 | +| 506 | 70 | +| 507 | 69 | +| 508 | 70 | +| 509 | 70 | +| 510 | 70 | +| 511 | 70 | +| 512 | 70 | +| 513 | 70 | +| 514 | 70 | +| 515 | 70 | +| 516 | 70 | +| 517 | 70 | +| 518 | 70 | +| 519 | 70 | +| 520 | 70 | +| 521 | 70 | +| 522 | 70 | +| 523 | 70 | +| 524 | 70 | +| 525 | 70 | +| 526 | 70 | +| 527 | 70 | +| 528 | 70 | +| 529 | 70 | +| 530 | 70 | +| 531 | 69 | +| 532 | 70 | +| 533 | 70 | +| 534 | 70 | +| 535 | 70 | +| 536 | 70 | +| 537 | 70 | +| 538 | 70 | +| 539 | 70 | +| 540 | 70 | +| 541 | 70 | +| 542 | 12 | +| 543 | 70 | +| 544 | 70 | +| 545 | 70 | +| 546 | 70 | +| 547 | 70 | +| 548 | 59 | +| 549 | 70 | +| 550 | 70 | +| 551 | 24 | +| 552 | 70 | +| 553 | 70 | +| 554 | 70 | +| 555 | 70 | +| 556 | 70 | +| 557 | 70 | +| 558 | 70 | +| 559 | 70 | +| 560 | 70 | +| 561 | 70 | +| 562 | 70 | +| 563 | 70 | +| 564 | 70 | +| 565 | 70 | +| 566 | 70 | +| 567 | 70 | +| 568 | 70 | +| 569 | 70 | +| 570 | 70 | +| 571 | 70 | +| 572 | 70 | +| 573 | 70 | +| 574 | 70 | +| 575 | 70 | +| 576 | 70 | +| 577 | 70 | +| 578 | 70 | +| 579 | 70 | +| 580 | 70 | +| 581 | 70 | +| 582 | 70 | +| 583 | 70 | +| 584 | 14 | +| 585 | 70 | +| 586 | 18 | +| 587 | 70 | +| 588 | 70 | +| 589 | 70 | +| 590 | 70 | +| 591 | 70 | +| 592 | 70 | +| 593 | 70 | +| 594 | 70 | +| 595 | 16 | +| 596 | 70 | +| 597 | 70 | + +### Training Hyperparameters +- batch_size: (64, 64) +- num_epochs: (10, 10) +- max_steps: -1 +- sampling_strategy: oversampling +- num_iterations: 30 +- 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: 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 | +|:------:|:----:|:-------------:|:---------------:| +| 0.0016 | 1 | 0.1866 | - | +| 0.0791 | 50 | 0.1829 | - | +| 0.1582 | 100 | 0.0985 | - | +| 0.2373 | 150 | 0.0853 | - | +| 0.3165 | 200 | 0.0859 | - | +| 0.3956 | 250 | 0.099 | - | +| 0.4747 | 300 | 0.0738 | - | +| 0.5538 | 350 | 0.0886 | - | +| 0.6329 | 400 | 0.0583 | - | +| 0.7120 | 450 | 0.0589 | - | +| 0.7911 | 500 | 0.0731 | - | +| 0.8703 | 550 | 0.068 | - | +| 0.9494 | 600 | 0.0632 | - | +| 1.0285 | 650 | 0.0891 | - | +| 1.1076 | 700 | 0.0581 | - | +| 1.1867 | 750 | 0.076 | - | +| 1.2658 | 800 | 0.043 | - | +| 1.3449 | 850 | 0.081 | - | +| 1.4241 | 900 | 0.0769 | - | +| 1.5032 | 950 | 0.0847 | - | +| 1.5823 | 1000 | 0.0741 | - | +| 1.6614 | 1050 | 0.0685 | - | +| 1.7405 | 1100 | 0.0586 | - | +| 1.8196 | 1150 | 0.0661 | - | +| 1.8987 | 1200 | 0.0766 | - | +| 1.9778 | 1250 | 0.0703 | - | +| 2.0570 | 1300 | 0.0584 | - | +| 2.1361 | 1350 | 0.0555 | - | +| 2.2152 | 1400 | 0.0672 | - | +| 2.2943 | 1450 | 0.0576 | - | +| 2.3734 | 1500 | 0.0677 | - | +| 2.4525 | 1550 | 0.0609 | - | +| 2.5316 | 1600 | 0.0445 | - | +| 2.6108 | 1650 | 0.058 | - | +| 2.6899 | 1700 | 0.0455 | - | +| 2.7690 | 1750 | 0.0798 | - | +| 2.8481 | 1800 | 0.0694 | - | +| 2.9272 | 1850 | 0.0638 | - | +| 3.0063 | 1900 | 0.0586 | - | +| 3.0854 | 1950 | 0.0489 | - | +| 3.1646 | 2000 | 0.0612 | - | +| 3.2437 | 2050 | 0.0626 | - | +| 3.3228 | 2100 | 0.0563 | - | +| 3.4019 | 2150 | 0.0437 | - | +| 3.4810 | 2200 | 0.0604 | - | +| 3.5601 | 2250 | 0.0576 | - | +| 3.6392 | 2300 | 0.0552 | - | +| 3.7184 | 2350 | 0.066 | - | +| 3.7975 | 2400 | 0.0475 | - | +| 3.8766 | 2450 | 0.0594 | - | +| 3.9557 | 2500 | 0.0391 | - | +| 4.0348 | 2550 | 0.0605 | - | +| 4.1139 | 2600 | 0.0453 | - | +| 4.1930 | 2650 | 0.043 | - | +| 4.2722 | 2700 | 0.045 | - | +| 4.3513 | 2750 | 0.0595 | - | +| 4.4304 | 2800 | 0.0482 | - | +| 4.5095 | 2850 | 0.0382 | - | +| 4.5886 | 2900 | 0.0612 | - | +| 4.6677 | 2950 | 0.0623 | - | +| 4.7468 | 3000 | 0.0609 | - | +| 4.8259 | 3050 | 0.0625 | - | +| 4.9051 | 3100 | 0.0602 | - | +| 4.9842 | 3150 | 0.0454 | - | +| 5.0633 | 3200 | 0.051 | - | +| 5.1424 | 3250 | 0.0567 | - | +| 5.2215 | 3300 | 0.0535 | - | +| 5.3006 | 3350 | 0.0447 | - | +| 5.3797 | 3400 | 0.0432 | - | +| 5.4589 | 3450 | 0.0374 | - | +| 5.5380 | 3500 | 0.0481 | - | +| 5.6171 | 3550 | 0.0466 | - | +| 5.6962 | 3600 | 0.0597 | - | +| 5.7753 | 3650 | 0.0338 | - | +| 5.8544 | 3700 | 0.0433 | - | +| 5.9335 | 3750 | 0.0482 | - | +| 6.0127 | 3800 | 0.0538 | - | +| 6.0918 | 3850 | 0.0365 | - | +| 6.1709 | 3900 | 0.0452 | - | +| 6.25 | 3950 | 0.0604 | - | +| 6.3291 | 4000 | 0.0694 | - | +| 6.4082 | 4050 | 0.0538 | - | +| 6.4873 | 4100 | 0.0467 | - | +| 6.5665 | 4150 | 0.055 | - | +| 6.6456 | 4200 | 0.0587 | - | +| 6.7247 | 4250 | 0.0408 | - | +| 6.8038 | 4300 | 0.0412 | - | +| 6.8829 | 4350 | 0.0667 | - | +| 6.9620 | 4400 | 0.0436 | - | +| 7.0411 | 4450 | 0.0467 | - | +| 7.1203 | 4500 | 0.0476 | - | +| 7.1994 | 4550 | 0.038 | - | +| 7.2785 | 4600 | 0.0538 | - | +| 7.3576 | 4650 | 0.0503 | - | +| 7.4367 | 4700 | 0.0299 | - | +| 7.5158 | 4750 | 0.0355 | - | +| 7.5949 | 4800 | 0.0674 | - | +| 7.6741 | 4850 | 0.0389 | - | +| 7.7532 | 4900 | 0.0521 | - | +| 7.8323 | 4950 | 0.0463 | - | +| 7.9114 | 5000 | 0.0609 | - | +| 7.9905 | 5050 | 0.0581 | - | +| 8.0696 | 5100 | 0.0405 | - | +| 8.1487 | 5150 | 0.0553 | - | +| 8.2278 | 5200 | 0.0423 | - | +| 8.3070 | 5250 | 0.0422 | - | +| 8.3861 | 5300 | 0.0383 | - | +| 8.4652 | 5350 | 0.052 | - | +| 8.5443 | 5400 | 0.0512 | - | +| 8.6234 | 5450 | 0.0564 | - | +| 8.7025 | 5500 | 0.0393 | - | +| 8.7816 | 5550 | 0.0369 | - | +| 8.8608 | 5600 | 0.0427 | - | +| 8.9399 | 5650 | 0.0209 | - | +| 9.0190 | 5700 | 0.0525 | - | +| 9.0981 | 5750 | 0.0445 | - | +| 9.1772 | 5800 | 0.0509 | - | +| 9.2563 | 5850 | 0.0428 | - | +| 9.3354 | 5900 | 0.0417 | - | +| 9.4146 | 5950 | 0.0345 | - | +| 9.4937 | 6000 | 0.0476 | - | +| 9.5728 | 6050 | 0.0341 | - | +| 9.6519 | 6100 | 0.0489 | - | +| 9.7310 | 6150 | 0.0396 | - | +| 9.8101 | 6200 | 0.0341 | - | +| 9.8892 | 6250 | 0.0405 | - | +| 9.9684 | 6300 | 0.027 | - | + +### Framework Versions +- Python: 3.10.12 +- SetFit: 1.1.2 +- Sentence Transformers: 4.1.0 +- Transformers: 4.51.3 +- PyTorch: 2.1.0+cu118 +- 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} +} +``` + + + + + + \ No newline at end of file