--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - 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: 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 model-index: - name: SetFit results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.9263923628936245 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:** 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 | |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 131 | | | 29 | | | 244 | | | 559 | | | 477 | | | 63 | | | 581 | | | 500 | | | 112 | | | 533 | | | 148 | | | 43 | | | 152 | | | 27 | | | 333 | | | 141 | | | 556 | | | 409 | | | 139 | | | 133 | | | 34 | | | 504 | | | 109 | | | 286 | | | 12 | | | 13 | | | 570 | | | 580 | | | 502 | | | 523 | | | 99 | | | 394 | | | 210 | | | 277 | | | 354 | | | 174 | | | 318 | | | 256 | | | 283 | | | 589 | | | 567 | | | 562 | | | 506 | | | 398 | | | 83 | | | 79 | | | 166 | | | 118 | | | 349 | | | 171 | | | 557 | | | 447 | | | 482 | | | 275 | | | 444 | | | 311 | | | 367 | | | 169 | | | 368 | | | 512 | | | 473 | | | 295 | | | 183 | | | 198 | | | 571 | | | 522 | | | 180 | | | 220 | | | 565 | | | 396 | | | 304 | | | 453 | | | 123 | | | 146 | | | 462 | | | 511 | | | 7 | | | 550 | | | 319 | | | 380 | | | 555 | | | 269 | | | 520 | | | 375 | | | 561 | | | 320 | | | 136 | | | 175 | | | 412 | | | 435 | | | 391 | | | 532 | | | 1 | | | 554 | | | 56 | | | 486 | | | 268 | | | 384 | | | 408 | | | 113 | | | 173 | | | 52 | | | 119 | | | 389 | | | 85 | | | 181 | | | 355 | | | 527 | | | 127 | | | 454 | | | 358 | | | 528 | | | 483 | | | 205 | | | 224 | | | 254 | | | 572 | | | 421 | | | 237 | | | 18 | | | 25 | | | 16 | | | 21 | | | 514 | | | 95 | | | 120 | | | 216 | | | 206 | | | 243 | | | 493 | | | 456 | | | 552 | | | 204 | | | 310 | | | 64 | | | 5 | | | 302 | | | 420 | | | 568 | | | 397 | | | 374 | | | 457 | | | 575 | | | 450 | | | 348 | | | 165 | | | 116 | | | 241 | | | 160 | | | 28 | | | 177 | | | 285 | | | 168 | | | 80 | | | 529 | | | 51 | | | 303 | | | 432 | | | 17 | | | 381 | | | 98 | | | 414 | | | 495 | | | 388 | | | 287 | | | 424 | | | 290 | | | 134 | | | 262 | | | 461 | | | 510 | | | 439 | | | 184 | | | 521 | | | 195 | | | 411 | | | 476 | | | 452 | | | 593 | | | 230 | | | 274 | | | 541 | | | 129 | | | 172 | | | 530 | | | 65 | | | 540 | | | 442 | | | 77 | | | 293 | | | 273 | | | 276 | | | 406 | | | 192 | | | 344 | | | 467 | | | 539 | | | 503 | | | 544 | | | 373 | | | 253 | | | 257 | | | 126 | | | 463 | | | 323 | | | 494 | | | 490 | | | 298 | | | 208 | | | 272 | | | 155 | | | 499 | | | 107 | | | 3 | | | 345 | | | 413 | | | 576 | | | 221 | | | 385 | | | 232 | | | 591 | | | 585 | | | 340 | | | 291 | | | 125 | | | 308 | | | 390 | | | 359 | | | 309 | | | 245 | | | 353 | | | 372 | | | 217 | | | 240 | | | 4 | | | 100 | | | 425 | | | 255 | | | 329 | | | 407 | | | 189 | | | 478 | | | 534 | | | 410 | | | 281 | | | 369 | | | 474 | | | 176 | | | 270 | | | 24 | | | 382 | | | 296 | | | 42 | | | 163 | | | 445 | | | 242 | | | 145 | | | 60 | | | 92 | | | 314 | | | 465 | | | 335 | | | 259 | | | 222 | | | 53 | | | 487 | | | 347 | | | 197 | | | 496 | | | 61 | | | 563 | | | 14 | | | 475 | | | 86 | | | 263 | | | 558 | | | 74 | | | 564 | | | 46 | | | 251 | | | 545 | | | 566 | | | 8 | | | 82 | | | 55 | | | 261 | | | 331 | | | 322 | | | 366 | | | 546 | | | 164 | | | 316 | | | 231 | | | 278 | | | 509 | | | 91 | | | 9 | | | 47 | | | 233 | | | 536 | | | 101 | | | 81 | | | 535 | | | 455 | | | 284 | | | 73 | | | 416 | | | 480 | | | 128 | | | 0 | | | 578 | | | 594 | | | 282 | | | 247 | | | 519 | | | 59 | | | 102 | | | 383 | | | 351 | | | 399 | | | 446 | | | 471 | | | 58 | | | 19 | | | 157 | | | 577 | | | 266 | | | 350 | | | 37 | | | 423 | | | 525 | | | 299 | | | 481 | | | 573 | | | 78 | | | 249 | | | 194 | | | 190 | | | 518 | | | 26 | | | 332 | | | 153 | | | 149 | | | 196 | | | 110 | | | 106 | | | 336 | | | 378 | | | 330 | | | 104 | | | 574 | | | 548 | | | 10 | | | 526 | | | 547 | | | 178 | | | 326 | | | 219 | | | 497 | | | 364 | | | 76 | | | 97 | | | 449 | | | 538 | | | 250 | | | 437 | | | 352 | | | 214 | | | 479 | | | 147 | | | 44 | | | 305 | | | 143 | | | 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** | 0.9264 | ## 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("정식품 달콤한 베지밀B 190ml (#M)식품>음료>두유 T200 > Naverstore > 식품 > 생수/음료 > 두유") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 7 | 21.2132 | 90 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 100 | | 1 | 100 | | 2 | 100 | | 3 | 100 | | 4 | 100 | | 5 | 100 | | 6 | 100 | | 7 | 100 | | 8 | 100 | | 9 | 100 | | 10 | 41 | | 11 | 100 | | 12 | 100 | | 13 | 100 | | 14 | 100 | | 15 | 100 | | 16 | 100 | | 17 | 100 | | 18 | 100 | | 19 | 95 | | 20 | 100 | | 21 | 100 | | 22 | 100 | | 23 | 100 | | 24 | 100 | | 25 | 100 | | 26 | 100 | | 27 | 98 | | 28 | 100 | | 29 | 100 | | 30 | 100 | | 31 | 100 | | 32 | 33 | | 33 | 100 | | 34 | 28 | | 35 | 100 | | 36 | 18 | | 37 | 12 | | 38 | 100 | | 39 | 24 | | 40 | 83 | | 41 | 100 | | 42 | 82 | | 43 | 100 | | 44 | 100 | | 45 | 95 | | 46 | 100 | | 47 | 100 | | 48 | 100 | | 49 | 100 | | 50 | 100 | | 51 | 55 | | 52 | 100 | | 53 | 100 | | 54 | 100 | | 55 | 100 | | 56 | 100 | | 57 | 100 | | 58 | 100 | | 59 | 100 | | 60 | 100 | | 61 | 100 | | 62 | 85 | | 63 | 100 | | 64 | 100 | | 65 | 100 | | 66 | 100 | | 67 | 100 | | 68 | 13 | | 69 | 100 | | 70 | 36 | | 71 | 49 | | 72 | 100 | | 73 | 100 | | 74 | 100 | | 75 | 100 | | 76 | 100 | | 77 | 100 | | 78 | 100 | | 79 | 100 | | 80 | 100 | | 81 | 100 | | 82 | 80 | | 83 | 100 | | 84 | 100 | | 85 | 100 | | 86 | 100 | | 87 | 100 | | 88 | 93 | | 89 | 74 | | 90 | 100 | | 91 | 100 | | 92 | 100 | | 93 | 100 | | 94 | 100 | | 95 | 100 | | 96 | 100 | | 97 | 76 | | 98 | 70 | | 99 | 100 | | 100 | 100 | | 101 | 100 | | 102 | 100 | | 103 | 100 | | 104 | 100 | | 105 | 100 | | 106 | 100 | | 107 | 100 | | 108 | 100 | | 109 | 100 | | 110 | 69 | | 111 | 24 | | 112 | 100 | | 113 | 100 | | 114 | 100 | | 115 | 100 | | 116 | 100 | | 117 | 100 | | 118 | 22 | | 119 | 100 | | 120 | 100 | | 121 | 100 | | 122 | 100 | | 123 | 100 | | 124 | 100 | | 125 | 100 | | 126 | 100 | | 127 | 100 | | 128 | 100 | | 129 | 100 | | 130 | 100 | | 131 | 100 | | 132 | 100 | | 133 | 100 | | 134 | 100 | | 135 | 100 | | 136 | 100 | | 137 | 100 | | 138 | 100 | | 139 | 100 | | 140 | 100 | | 141 | 100 | | 142 | 100 | | 143 | 100 | | 144 | 100 | | 145 | 100 | | 146 | 100 | | 147 | 100 | | 148 | 100 | | 149 | 100 | | 150 | 100 | | 151 | 100 | | 152 | 100 | | 153 | 100 | | 154 | 100 | | 155 | 100 | | 156 | 100 | | 157 | 100 | | 158 | 100 | | 159 | 100 | | 160 | 100 | | 161 | 100 | | 162 | 100 | | 163 | 100 | | 164 | 100 | | 165 | 100 | | 166 | 100 | | 167 | 100 | | 168 | 100 | | 169 | 100 | | 170 | 100 | | 171 | 100 | | 172 | 100 | | 173 | 100 | | 174 | 100 | | 175 | 100 | | 176 | 100 | | 177 | 100 | | 178 | 100 | | 179 | 100 | | 180 | 100 | | 181 | 100 | | 182 | 100 | | 183 | 100 | | 184 | 100 | | 185 | 100 | | 186 | 100 | | 187 | 100 | | 188 | 100 | | 189 | 100 | | 190 | 100 | | 191 | 100 | | 192 | 100 | | 193 | 100 | | 194 | 100 | | 195 | 100 | | 196 | 100 | | 197 | 100 | | 198 | 100 | | 199 | 100 | | 200 | 100 | | 201 | 100 | | 202 | 40 | | 203 | 31 | | 204 | 100 | | 205 | 100 | | 206 | 100 | | 207 | 100 | | 208 | 100 | | 209 | 100 | | 210 | 100 | | 211 | 100 | | 212 | 100 | | 213 | 100 | | 214 | 100 | | 215 | 25 | | 216 | 100 | | 217 | 68 | | 218 | 100 | | 219 | 100 | | 220 | 100 | | 221 | 100 | | 222 | 100 | | 223 | 79 | | 224 | 100 | | 225 | 100 | | 226 | 97 | | 227 | 100 | | 228 | 100 | | 229 | 51 | | 230 | 100 | | 231 | 100 | | 232 | 100 | | 233 | 100 | | 234 | 100 | | 235 | 100 | | 236 | 100 | | 237 | 100 | | 238 | 100 | | 239 | 100 | | 240 | 100 | | 241 | 99 | | 242 | 100 | | 243 | 100 | | 244 | 100 | | 245 | 100 | | 246 | 82 | | 247 | 100 | | 248 | 100 | | 249 | 100 | | 250 | 100 | | 251 | 100 | | 252 | 97 | | 253 | 100 | | 254 | 100 | | 255 | 100 | | 256 | 100 | | 257 | 100 | | 258 | 100 | | 259 | 100 | | 260 | 100 | | 261 | 100 | | 262 | 100 | | 263 | 100 | | 264 | 100 | | 265 | 100 | | 266 | 100 | | 267 | 100 | | 268 | 100 | | 269 | 100 | | 270 | 100 | | 271 | 100 | | 272 | 100 | | 273 | 100 | | 274 | 100 | | 275 | 100 | | 276 | 100 | | 277 | 100 | | 278 | 100 | | 279 | 100 | | 280 | 100 | | 281 | 100 | | 282 | 100 | | 283 | 100 | | 284 | 100 | | 285 | 100 | | 286 | 100 | | 287 | 100 | | 288 | 100 | | 289 | 100 | | 290 | 100 | | 291 | 100 | | 292 | 100 | | 293 | 100 | | 294 | 100 | | 295 | 100 | | 296 | 100 | | 297 | 34 | | 298 | 100 | | 299 | 100 | | 300 | 100 | | 301 | 99 | | 302 | 100 | | 303 | 100 | | 304 | 100 | | 305 | 100 | | 306 | 100 | | 307 | 100 | | 308 | 100 | | 309 | 100 | | 310 | 100 | | 311 | 100 | | 312 | 100 | | 313 | 100 | | 314 | 100 | | 315 | 100 | | 316 | 100 | | 317 | 100 | | 318 | 100 | | 319 | 100 | | 320 | 100 | | 321 | 100 | | 322 | 100 | | 323 | 100 | | 324 | 100 | | 325 | 100 | | 326 | 100 | | 327 | 100 | | 328 | 100 | | 329 | 100 | | 330 | 100 | | 331 | 100 | | 332 | 100 | | 333 | 100 | | 334 | 100 | | 335 | 100 | | 336 | 100 | | 337 | 100 | | 338 | 46 | | 339 | 100 | | 340 | 100 | | 341 | 100 | | 342 | 17 | | 343 | 100 | | 344 | 100 | | 345 | 100 | | 346 | 100 | | 347 | 100 | | 348 | 100 | | 349 | 100 | | 350 | 100 | | 351 | 20 | | 352 | 100 | | 353 | 100 | | 354 | 100 | | 355 | 100 | | 356 | 100 | | 357 | 86 | | 358 | 100 | | 359 | 100 | | 360 | 100 | | 361 | 100 | | 362 | 10 | | 363 | 100 | | 364 | 100 | | 365 | 100 | | 366 | 100 | | 367 | 100 | | 368 | 100 | | 369 | 100 | | 370 | 83 | | 371 | 100 | | 372 | 100 | | 373 | 100 | | 374 | 100 | | 375 | 100 | | 376 | 12 | | 377 | 100 | | 378 | 100 | | 379 | 35 | | 380 | 100 | | 381 | 100 | | 382 | 100 | | 383 | 100 | | 384 | 100 | | 385 | 100 | | 386 | 100 | | 387 | 100 | | 388 | 100 | | 389 | 100 | | 390 | 100 | | 391 | 100 | | 392 | 100 | | 393 | 100 | | 394 | 100 | | 395 | 100 | | 396 | 100 | | 397 | 100 | | 398 | 100 | | 399 | 100 | | 400 | 100 | | 401 | 100 | | 402 | 100 | | 403 | 100 | | 404 | 100 | | 405 | 100 | | 406 | 100 | | 407 | 100 | | 408 | 100 | | 409 | 100 | | 410 | 100 | | 411 | 100 | | 412 | 100 | | 413 | 100 | | 414 | 100 | | 415 | 100 | | 416 | 100 | | 417 | 100 | | 418 | 100 | | 419 | 100 | | 420 | 100 | | 421 | 100 | | 422 | 100 | | 423 | 100 | | 424 | 100 | | 425 | 100 | | 426 | 100 | | 427 | 100 | | 428 | 100 | | 429 | 100 | | 430 | 100 | | 431 | 100 | | 432 | 100 | | 433 | 100 | | 434 | 100 | | 435 | 100 | | 436 | 100 | | 437 | 100 | | 438 | 100 | | 439 | 100 | | 440 | 100 | | 441 | 100 | | 442 | 100 | | 443 | 100 | | 444 | 100 | | 445 | 100 | | 446 | 100 | | 447 | 100 | | 448 | 15 | | 449 | 100 | | 450 | 100 | | 451 | 100 | | 452 | 100 | | 453 | 100 | | 454 | 100 | | 455 | 100 | | 456 | 100 | | 457 | 100 | | 458 | 100 | | 459 | 100 | | 460 | 100 | | 461 | 100 | | 462 | 100 | | 463 | 100 | | 464 | 100 | | 465 | 100 | | 466 | 100 | | 467 | 100 | | 468 | 100 | | 469 | 100 | | 470 | 13 | | 471 | 100 | | 472 | 13 | | 473 | 100 | | 474 | 100 | | 475 | 100 | | 476 | 100 | | 477 | 100 | | 478 | 100 | | 479 | 100 | | 480 | 100 | | 481 | 100 | | 482 | 100 | | 483 | 53 | | 484 | 100 | | 485 | 100 | | 486 | 95 | | 487 | 100 | | 488 | 100 | | 489 | 100 | | 490 | 100 | | 491 | 100 | | 492 | 100 | | 493 | 100 | | 494 | 100 | | 495 | 100 | | 496 | 100 | | 497 | 100 | | 498 | 100 | | 499 | 100 | | 500 | 100 | | 501 | 100 | | 502 | 100 | | 503 | 100 | | 504 | 100 | | 505 | 100 | | 506 | 100 | | 507 | 100 | | 508 | 100 | | 509 | 100 | | 510 | 100 | | 511 | 100 | | 512 | 100 | | 513 | 100 | | 514 | 100 | | 515 | 100 | | 516 | 100 | | 517 | 100 | | 518 | 100 | | 519 | 100 | | 520 | 100 | | 521 | 100 | | 522 | 100 | | 523 | 100 | | 524 | 100 | | 525 | 100 | | 526 | 100 | | 527 | 100 | | 528 | 100 | | 529 | 100 | | 530 | 100 | | 531 | 100 | | 532 | 100 | | 533 | 100 | | 534 | 100 | | 535 | 100 | | 536 | 100 | | 537 | 100 | | 538 | 100 | | 539 | 100 | | 540 | 100 | | 541 | 100 | | 542 | 12 | | 543 | 100 | | 544 | 100 | | 545 | 99 | | 546 | 100 | | 547 | 100 | | 548 | 59 | | 549 | 100 | | 550 | 100 | | 551 | 24 | | 552 | 100 | | 553 | 100 | | 554 | 100 | | 555 | 100 | | 556 | 99 | | 557 | 100 | | 558 | 100 | | 559 | 100 | | 560 | 100 | | 561 | 100 | | 562 | 100 | | 563 | 100 | | 564 | 100 | | 565 | 100 | | 566 | 100 | | 567 | 100 | | 568 | 100 | | 569 | 100 | | 570 | 100 | | 571 | 100 | | 572 | 100 | | 573 | 100 | | 574 | 100 | | 575 | 100 | | 576 | 100 | | 577 | 100 | | 578 | 100 | | 579 | 100 | | 580 | 100 | | 581 | 100 | | 582 | 100 | | 583 | 100 | | 584 | 14 | | 585 | 100 | | 586 | 18 | | 587 | 100 | | 588 | 100 | | 589 | 100 | | 590 | 100 | | 591 | 100 | | 592 | 100 | | 593 | 100 | | 594 | 100 | | 595 | 16 | | 596 | 100 | | 597 | 100 | ### Training Hyperparameters - batch_size: (64, 64) - num_epochs: (30, 30) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 50 - 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 | |:-------:|:-----:|:-------------:|:---------------:| | 0.0011 | 1 | 0.0 | - | | 0.0560 | 50 | 0.0697 | - | | 0.1120 | 100 | 0.0809 | - | | 0.1680 | 150 | 0.0834 | - | | 0.2240 | 200 | 0.0474 | - | | 0.2800 | 250 | 0.0819 | - | | 0.3359 | 300 | 0.0574 | - | | 0.3919 | 350 | 0.0563 | - | | 0.4479 | 400 | 0.0586 | - | | 0.5039 | 450 | 0.0739 | - | | 0.5599 | 500 | 0.0595 | - | | 0.6159 | 550 | 0.0523 | - | | 0.6719 | 600 | 0.0845 | - | | 0.7279 | 650 | 0.0641 | - | | 0.7839 | 700 | 0.062 | - | | 0.8399 | 750 | 0.0634 | - | | 0.8959 | 800 | 0.0685 | - | | 0.9518 | 850 | 0.0512 | - | | 1.0078 | 900 | 0.0557 | - | | 1.0638 | 950 | 0.0683 | - | | 1.1198 | 1000 | 0.0589 | - | | 1.1758 | 1050 | 0.0761 | - | | 1.2318 | 1100 | 0.0506 | - | | 1.2878 | 1150 | 0.052 | - | | 1.3438 | 1200 | 0.0892 | - | | 1.3998 | 1250 | 0.0583 | - | | 1.4558 | 1300 | 0.056 | - | | 1.5118 | 1350 | 0.0675 | - | | 1.5677 | 1400 | 0.06 | - | | 1.6237 | 1450 | 0.0606 | - | | 1.6797 | 1500 | 0.068 | - | | 1.7357 | 1550 | 0.0614 | - | | 1.7917 | 1600 | 0.0728 | - | | 1.8477 | 1650 | 0.0805 | - | | 1.9037 | 1700 | 0.0625 | - | | 1.9597 | 1750 | 0.049 | - | | 2.0157 | 1800 | 0.0724 | - | | 2.0717 | 1850 | 0.0642 | - | | 2.1277 | 1900 | 0.0613 | - | | 2.1837 | 1950 | 0.0772 | - | | 2.2396 | 2000 | 0.0481 | - | | 2.2956 | 2050 | 0.0696 | - | | 2.3516 | 2100 | 0.0745 | - | | 2.4076 | 2150 | 0.0785 | - | | 2.4636 | 2200 | 0.0693 | - | | 2.5196 | 2250 | 0.0614 | - | | 2.5756 | 2300 | 0.0667 | - | | 2.6316 | 2350 | 0.0778 | - | | 2.6876 | 2400 | 0.0619 | - | | 2.7436 | 2450 | 0.0689 | - | | 2.7996 | 2500 | 0.0784 | - | | 2.8555 | 2550 | 0.0631 | - | | 2.9115 | 2600 | 0.0567 | - | | 2.9675 | 2650 | 0.0723 | - | | 3.0235 | 2700 | 0.0696 | - | | 3.0795 | 2750 | 0.0679 | - | | 3.1355 | 2800 | 0.0568 | - | | 3.1915 | 2850 | 0.0708 | - | | 3.2475 | 2900 | 0.0596 | - | | 3.3035 | 2950 | 0.0776 | - | | 3.3595 | 3000 | 0.0604 | - | | 3.4155 | 3050 | 0.0603 | - | | 3.4714 | 3100 | 0.081 | - | | 3.5274 | 3150 | 0.0797 | - | | 3.5834 | 3200 | 0.0687 | - | | 3.6394 | 3250 | 0.0672 | - | | 3.6954 | 3300 | 0.0556 | - | | 3.7514 | 3350 | 0.0558 | - | | 3.8074 | 3400 | 0.056 | - | | 3.8634 | 3450 | 0.0729 | - | | 3.9194 | 3500 | 0.075 | - | | 3.9754 | 3550 | 0.0565 | - | | 4.0314 | 3600 | 0.0726 | - | | 4.0873 | 3650 | 0.0534 | - | | 4.1433 | 3700 | 0.0531 | - | | 4.1993 | 3750 | 0.0747 | - | | 4.2553 | 3800 | 0.0593 | - | | 4.3113 | 3850 | 0.0657 | - | | 4.3673 | 3900 | 0.0708 | - | | 4.4233 | 3950 | 0.0667 | - | | 4.4793 | 4000 | 0.0757 | - | | 4.5353 | 4050 | 0.0553 | - | | 4.5913 | 4100 | 0.0537 | - | | 4.6473 | 4150 | 0.0514 | - | | 4.7032 | 4200 | 0.0629 | - | | 4.7592 | 4250 | 0.0792 | - | | 4.8152 | 4300 | 0.0714 | - | | 4.8712 | 4350 | 0.0878 | - | | 4.9272 | 4400 | 0.0476 | - | | 4.9832 | 4450 | 0.0709 | - | | 5.0392 | 4500 | 0.0682 | - | | 5.0952 | 4550 | 0.0817 | - | | 5.1512 | 4600 | 0.0652 | - | | 5.2072 | 4650 | 0.0627 | - | | 5.2632 | 4700 | 0.0471 | - | | 5.3191 | 4750 | 0.0755 | - | | 5.3751 | 4800 | 0.0651 | - | | 5.4311 | 4850 | 0.0566 | - | | 5.4871 | 4900 | 0.065 | - | | 5.5431 | 4950 | 0.0759 | - | | 5.5991 | 5000 | 0.0876 | - | | 5.6551 | 5050 | 0.0796 | - | | 5.7111 | 5100 | 0.0571 | - | | 5.7671 | 5150 | 0.0681 | - | | 5.8231 | 5200 | 0.0542 | - | | 5.8791 | 5250 | 0.0815 | - | | 5.9351 | 5300 | 0.071 | - | | 5.9910 | 5350 | 0.0555 | - | | 6.0470 | 5400 | 0.0637 | - | | 6.1030 | 5450 | 0.0491 | - | | 6.1590 | 5500 | 0.061 | - | | 6.2150 | 5550 | 0.0509 | - | | 6.2710 | 5600 | 0.0626 | - | | 6.3270 | 5650 | 0.0799 | - | | 6.3830 | 5700 | 0.0568 | - | | 6.4390 | 5750 | 0.0613 | - | | 6.4950 | 5800 | 0.0606 | - | | 6.5510 | 5850 | 0.0582 | - | | 6.6069 | 5900 | 0.0469 | - | | 6.6629 | 5950 | 0.0413 | - | | 6.7189 | 6000 | 0.0578 | - | | 6.7749 | 6050 | 0.0686 | - | | 6.8309 | 6100 | 0.0482 | - | | 6.8869 | 6150 | 0.0602 | - | | 6.9429 | 6200 | 0.0578 | - | | 6.9989 | 6250 | 0.1035 | - | | 7.0549 | 6300 | 0.0464 | - | | 7.1109 | 6350 | 0.0673 | - | | 7.1669 | 6400 | 0.0816 | - | | 7.2228 | 6450 | 0.0617 | - | | 7.2788 | 6500 | 0.0728 | - | | 7.3348 | 6550 | 0.0605 | - | | 7.3908 | 6600 | 0.0646 | - | | 7.4468 | 6650 | 0.0582 | - | | 7.5028 | 6700 | 0.0394 | - | | 7.5588 | 6750 | 0.079 | - | | 7.6148 | 6800 | 0.0536 | - | | 7.6708 | 6850 | 0.0432 | - | | 7.7268 | 6900 | 0.0591 | - | | 7.7828 | 6950 | 0.0598 | - | | 7.8387 | 7000 | 0.0513 | - | | 7.8947 | 7050 | 0.0623 | - | | 7.9507 | 7100 | 0.0676 | - | | 8.0067 | 7150 | 0.0514 | - | | 8.0627 | 7200 | 0.0513 | - | | 8.1187 | 7250 | 0.0645 | - | | 8.1747 | 7300 | 0.0434 | - | | 8.2307 | 7350 | 0.0674 | - | | 8.2867 | 7400 | 0.0478 | - | | 8.3427 | 7450 | 0.0662 | - | | 8.3987 | 7500 | 0.0574 | - | | 8.4546 | 7550 | 0.055 | - | | 8.5106 | 7600 | 0.0537 | - | | 8.5666 | 7650 | 0.0659 | - | | 8.6226 | 7700 | 0.0501 | - | | 8.6786 | 7750 | 0.0555 | - | | 8.7346 | 7800 | 0.052 | - | | 8.7906 | 7850 | 0.0593 | - | | 8.8466 | 7900 | 0.0603 | - | | 8.9026 | 7950 | 0.0466 | - | | 8.9586 | 8000 | 0.0694 | - | | 9.0146 | 8050 | 0.0699 | - | | 9.0705 | 8100 | 0.0655 | - | | 9.1265 | 8150 | 0.0582 | - | | 9.1825 | 8200 | 0.063 | - | | 9.2385 | 8250 | 0.0533 | - | | 9.2945 | 8300 | 0.0535 | - | | 9.3505 | 8350 | 0.0476 | - | | 9.4065 | 8400 | 0.0568 | - | | 9.4625 | 8450 | 0.0667 | - | | 9.5185 | 8500 | 0.0527 | - | | 9.5745 | 8550 | 0.0533 | - | | 9.6305 | 8600 | 0.0781 | - | | 9.6865 | 8650 | 0.0801 | - | | 9.7424 | 8700 | 0.0429 | - | | 9.7984 | 8750 | 0.0437 | - | | 9.8544 | 8800 | 0.0384 | - | | 9.9104 | 8850 | 0.0527 | - | | 9.9664 | 8900 | 0.0556 | - | | 10.0224 | 8950 | 0.0462 | - | | 10.0784 | 9000 | 0.0608 | - | | 10.1344 | 9050 | 0.0407 | - | | 10.1904 | 9100 | 0.0484 | - | | 10.2464 | 9150 | 0.0642 | - | | 10.3024 | 9200 | 0.0665 | - | | 10.3583 | 9250 | 0.0558 | - | | 10.4143 | 9300 | 0.0504 | - | | 10.4703 | 9350 | 0.0841 | - | | 10.5263 | 9400 | 0.0651 | - | | 10.5823 | 9450 | 0.049 | - | | 10.6383 | 9500 | 0.0707 | - | | 10.6943 | 9550 | 0.0456 | - | | 10.7503 | 9600 | 0.0553 | - | | 10.8063 | 9650 | 0.0461 | - | | 10.8623 | 9700 | 0.0577 | - | | 10.9183 | 9750 | 0.0756 | - | | 10.9742 | 9800 | 0.0699 | - | | 11.0302 | 9850 | 0.0497 | - | | 11.0862 | 9900 | 0.0624 | - | | 11.1422 | 9950 | 0.078 | - | | 11.1982 | 10000 | 0.0646 | - | | 11.2542 | 10050 | 0.0636 | - | | 11.3102 | 10100 | 0.0655 | - | | 11.3662 | 10150 | 0.0582 | - | | 11.4222 | 10200 | 0.0594 | - | | 11.4782 | 10250 | 0.0597 | - | | 11.5342 | 10300 | 0.038 | - | | 11.5901 | 10350 | 0.0684 | - | | 11.6461 | 10400 | 0.0381 | - | | 11.7021 | 10450 | 0.0504 | - | | 11.7581 | 10500 | 0.0584 | - | | 11.8141 | 10550 | 0.0474 | - | | 11.8701 | 10600 | 0.0659 | - | | 11.9261 | 10650 | 0.0437 | - | | 11.9821 | 10700 | 0.0558 | - | | 12.0381 | 10750 | 0.0557 | - | | 12.0941 | 10800 | 0.0639 | - | | 12.1501 | 10850 | 0.0377 | - | | 12.2060 | 10900 | 0.0506 | - | | 12.2620 | 10950 | 0.0711 | - | | 12.3180 | 11000 | 0.0345 | - | | 12.3740 | 11050 | 0.0513 | - | | 12.4300 | 11100 | 0.0512 | - | | 12.4860 | 11150 | 0.0456 | - | | 12.5420 | 11200 | 0.0701 | - | | 12.5980 | 11250 | 0.0389 | - | | 12.6540 | 11300 | 0.0496 | - | | 12.7100 | 11350 | 0.0545 | - | | 12.7660 | 11400 | 0.0404 | - | | 12.8219 | 11450 | 0.0405 | - | | 12.8779 | 11500 | 0.0621 | - | | 12.9339 | 11550 | 0.0738 | - | | 12.9899 | 11600 | 0.0394 | - | | 13.0459 | 11650 | 0.054 | - | | 13.1019 | 11700 | 0.0609 | - | | 13.1579 | 11750 | 0.0526 | - | | 13.2139 | 11800 | 0.0461 | - | | 13.2699 | 11850 | 0.0378 | - | | 13.3259 | 11900 | 0.0384 | - | | 13.3819 | 11950 | 0.0738 | - | | 13.4378 | 12000 | 0.0471 | - | | 13.4938 | 12050 | 0.0578 | - | | 13.5498 | 12100 | 0.0551 | - | | 13.6058 | 12150 | 0.036 | - | | 13.6618 | 12200 | 0.0453 | - | | 13.7178 | 12250 | 0.0514 | - | | 13.7738 | 12300 | 0.0771 | - | | 13.8298 | 12350 | 0.0557 | - | | 13.8858 | 12400 | 0.0479 | - | | 13.9418 | 12450 | 0.0548 | - | | 13.9978 | 12500 | 0.071 | - | | 14.0538 | 12550 | 0.0465 | - | | 14.1097 | 12600 | 0.055 | - | | 14.1657 | 12650 | 0.049 | - | | 14.2217 | 12700 | 0.0521 | - | | 14.2777 | 12750 | 0.0695 | - | | 14.3337 | 12800 | 0.0532 | - | | 14.3897 | 12850 | 0.0379 | - | | 14.4457 | 12900 | 0.0502 | - | | 14.5017 | 12950 | 0.0508 | - | | 14.5577 | 13000 | 0.0508 | - | | 14.6137 | 13050 | 0.0464 | - | | 14.6697 | 13100 | 0.0539 | - | | 14.7256 | 13150 | 0.0673 | - | | 14.7816 | 13200 | 0.0653 | - | | 14.8376 | 13250 | 0.0683 | - | | 14.8936 | 13300 | 0.05 | - | | 14.9496 | 13350 | 0.0475 | - | | 15.0056 | 13400 | 0.0429 | - | | 15.0616 | 13450 | 0.0656 | - | | 15.1176 | 13500 | 0.0559 | - | | 15.1736 | 13550 | 0.0358 | - | | 15.2296 | 13600 | 0.0705 | - | | 15.2856 | 13650 | 0.0391 | - | | 15.3415 | 13700 | 0.0494 | - | | 15.3975 | 13750 | 0.0502 | - | | 15.4535 | 13800 | 0.0488 | - | | 15.5095 | 13850 | 0.046 | - | | 15.5655 | 13900 | 0.0481 | - | | 15.6215 | 13950 | 0.0441 | - | | 15.6775 | 14000 | 0.0383 | - | | 15.7335 | 14050 | 0.0498 | - | | 15.7895 | 14100 | 0.0477 | - | | 15.8455 | 14150 | 0.0586 | - | | 15.9015 | 14200 | 0.0434 | - | | 15.9574 | 14250 | 0.0496 | - | | 16.0134 | 14300 | 0.0486 | - | | 16.0694 | 14350 | 0.0348 | - | | 16.1254 | 14400 | 0.0359 | - | | 16.1814 | 14450 | 0.0496 | - | | 16.2374 | 14500 | 0.0314 | - | | 16.2934 | 14550 | 0.0507 | - | | 16.3494 | 14600 | 0.0698 | - | | 16.4054 | 14650 | 0.0479 | - | | 16.4614 | 14700 | 0.0614 | - | | 16.5174 | 14750 | 0.0413 | - | | 16.5733 | 14800 | 0.0743 | - | | 16.6293 | 14850 | 0.0492 | - | | 16.6853 | 14900 | 0.0355 | - | | 16.7413 | 14950 | 0.0507 | - | | 16.7973 | 15000 | 0.0512 | - | | 16.8533 | 15050 | 0.0317 | - | | 16.9093 | 15100 | 0.0649 | - | | 16.9653 | 15150 | 0.0658 | - | | 17.0213 | 15200 | 0.0627 | - | | 17.0773 | 15250 | 0.0314 | - | | 17.1333 | 15300 | 0.0389 | - | | 17.1892 | 15350 | 0.05 | - | | 17.2452 | 15400 | 0.0488 | - | | 17.3012 | 15450 | 0.0481 | - | | 17.3572 | 15500 | 0.0457 | - | | 17.4132 | 15550 | 0.0692 | - | | 17.4692 | 15600 | 0.0648 | - | | 17.5252 | 15650 | 0.0523 | - | | 17.5812 | 15700 | 0.0419 | - | | 17.6372 | 15750 | 0.061 | - | | 17.6932 | 15800 | 0.0554 | - | | 17.7492 | 15850 | 0.0466 | - | | 17.8052 | 15900 | 0.0597 | - | | 17.8611 | 15950 | 0.0655 | - | | 17.9171 | 16000 | 0.0442 | - | | 17.9731 | 16050 | 0.0398 | - | | 18.0291 | 16100 | 0.0663 | - | | 18.0851 | 16150 | 0.0449 | - | | 18.1411 | 16200 | 0.0518 | - | | 18.1971 | 16250 | 0.0497 | - | | 18.2531 | 16300 | 0.0541 | - | | 18.3091 | 16350 | 0.0638 | - | | 18.3651 | 16400 | 0.0543 | - | | 18.4211 | 16450 | 0.0518 | - | | 18.4770 | 16500 | 0.0534 | - | | 18.5330 | 16550 | 0.0446 | - | | 18.5890 | 16600 | 0.0389 | - | | 18.6450 | 16650 | 0.0692 | - | | 18.7010 | 16700 | 0.0346 | - | | 18.7570 | 16750 | 0.0359 | - | | 18.8130 | 16800 | 0.0518 | - | | 18.8690 | 16850 | 0.0399 | - | | 18.9250 | 16900 | 0.063 | - | | 18.9810 | 16950 | 0.0578 | - | | 19.0370 | 17000 | 0.0409 | - | | 19.0929 | 17050 | 0.0534 | - | | 19.1489 | 17100 | 0.039 | - | | 19.2049 | 17150 | 0.0478 | - | | 19.2609 | 17200 | 0.0561 | - | | 19.3169 | 17250 | 0.0433 | - | | 19.3729 | 17300 | 0.0322 | - | | 19.4289 | 17350 | 0.0452 | - | | 19.4849 | 17400 | 0.0811 | - | | 19.5409 | 17450 | 0.0314 | - | | 19.5969 | 17500 | 0.039 | - | | 19.6529 | 17550 | 0.0395 | - | | 19.7088 | 17600 | 0.0413 | - | | 19.7648 | 17650 | 0.0343 | - | | 19.8208 | 17700 | 0.0392 | - | | 19.8768 | 17750 | 0.0543 | - | | 19.9328 | 17800 | 0.0507 | - | | 19.9888 | 17850 | 0.0415 | - | | 20.0448 | 17900 | 0.0414 | - | | 20.1008 | 17950 | 0.0324 | - | | 20.1568 | 18000 | 0.0384 | - | | 20.2128 | 18050 | 0.0636 | - | | 20.2688 | 18100 | 0.0401 | - | | 20.3247 | 18150 | 0.0401 | - | | 20.3807 | 18200 | 0.0746 | - | | 20.4367 | 18250 | 0.0608 | - | | 20.4927 | 18300 | 0.0491 | - | | 20.5487 | 18350 | 0.0576 | - | | 20.6047 | 18400 | 0.0497 | - | | 20.6607 | 18450 | 0.0334 | - | | 20.7167 | 18500 | 0.0518 | - | | 20.7727 | 18550 | 0.057 | - | | 20.8287 | 18600 | 0.0436 | - | | 20.8847 | 18650 | 0.0442 | - | | 20.9406 | 18700 | 0.0486 | - | | 20.9966 | 18750 | 0.0533 | - | | 21.0526 | 18800 | 0.043 | - | | 21.1086 | 18850 | 0.0606 | - | | 21.1646 | 18900 | 0.052 | - | | 21.2206 | 18950 | 0.0504 | - | | 21.2766 | 19000 | 0.0453 | - | | 21.3326 | 19050 | 0.0406 | - | | 21.3886 | 19100 | 0.0481 | - | | 21.4446 | 19150 | 0.0442 | - | | 21.5006 | 19200 | 0.0462 | - | | 21.5566 | 19250 | 0.0411 | - | | 21.6125 | 19300 | 0.0392 | - | | 21.6685 | 19350 | 0.0375 | - | | 21.7245 | 19400 | 0.0419 | - | | 21.7805 | 19450 | 0.0332 | - | | 21.8365 | 19500 | 0.0393 | - | | 21.8925 | 19550 | 0.0571 | - | | 21.9485 | 19600 | 0.0412 | - | | 22.0045 | 19650 | 0.0383 | - | | 22.0605 | 19700 | 0.0465 | - | | 22.1165 | 19750 | 0.0771 | - | | 22.1725 | 19800 | 0.0389 | - | | 22.2284 | 19850 | 0.0394 | - | | 22.2844 | 19900 | 0.0426 | - | | 22.3404 | 19950 | 0.0422 | - | | 22.3964 | 20000 | 0.0342 | - | | 22.4524 | 20050 | 0.0441 | - | | 22.5084 | 20100 | 0.0579 | - | | 22.5644 | 20150 | 0.053 | - | | 22.6204 | 20200 | 0.0547 | - | | 22.6764 | 20250 | 0.0361 | - | | 22.7324 | 20300 | 0.0413 | - | | 22.7884 | 20350 | 0.045 | - | | 22.8443 | 20400 | 0.0369 | - | | 22.9003 | 20450 | 0.0606 | - | | 22.9563 | 20500 | 0.0571 | - | | 23.0123 | 20550 | 0.0264 | - | | 23.0683 | 20600 | 0.0402 | - | | 23.1243 | 20650 | 0.0465 | - | | 23.1803 | 20700 | 0.0495 | - | | 23.2363 | 20750 | 0.0289 | - | | 23.2923 | 20800 | 0.0381 | - | | 23.3483 | 20850 | 0.0397 | - | | 23.4043 | 20900 | 0.0553 | - | | 23.4602 | 20950 | 0.0271 | - | | 23.5162 | 21000 | 0.0419 | - | | 23.5722 | 21050 | 0.0364 | - | | 23.6282 | 21100 | 0.0265 | - | | 23.6842 | 21150 | 0.0333 | - | | 23.7402 | 21200 | 0.029 | - | | 23.7962 | 21250 | 0.0485 | - | | 23.8522 | 21300 | 0.0423 | - | | 23.9082 | 21350 | 0.0418 | - | | 23.9642 | 21400 | 0.0327 | - | | 24.0202 | 21450 | 0.0482 | - | | 24.0761 | 21500 | 0.0339 | - | | 24.1321 | 21550 | 0.0453 | - | | 24.1881 | 21600 | 0.0435 | - | | 24.2441 | 21650 | 0.0488 | - | | 24.3001 | 21700 | 0.0394 | - | | 24.3561 | 21750 | 0.047 | - | | 24.4121 | 21800 | 0.0373 | - | | 24.4681 | 21850 | 0.0241 | - | | 24.5241 | 21900 | 0.0294 | - | | 24.5801 | 21950 | 0.0487 | - | | 24.6361 | 22000 | 0.032 | - | | 24.6920 | 22050 | 0.0446 | - | | 24.7480 | 22100 | 0.0273 | - | | 24.8040 | 22150 | 0.0386 | - | | 24.8600 | 22200 | 0.0404 | - | | 24.9160 | 22250 | 0.0378 | - | | 24.9720 | 22300 | 0.0517 | - | | 25.0280 | 22350 | 0.0517 | - | | 25.0840 | 22400 | 0.0475 | - | | 25.1400 | 22450 | 0.0523 | - | | 25.1960 | 22500 | 0.0306 | - | | 25.2520 | 22550 | 0.0477 | - | | 25.3080 | 22600 | 0.033 | - | | 25.3639 | 22650 | 0.0557 | - | | 25.4199 | 22700 | 0.0339 | - | | 25.4759 | 22750 | 0.0259 | - | | 25.5319 | 22800 | 0.0471 | - | | 25.5879 | 22850 | 0.0362 | - | | 25.6439 | 22900 | 0.0332 | - | | 25.6999 | 22950 | 0.0417 | - | | 25.7559 | 23000 | 0.0406 | - | | 25.8119 | 23050 | 0.034 | - | | 25.8679 | 23100 | 0.0293 | - | | 25.9239 | 23150 | 0.0609 | - | | 25.9798 | 23200 | 0.0372 | - | | 26.0358 | 23250 | 0.0525 | - | | 26.0918 | 23300 | 0.0372 | - | | 26.1478 | 23350 | 0.0582 | - | | 26.2038 | 23400 | 0.058 | - | | 26.2598 | 23450 | 0.0446 | - | | 26.3158 | 23500 | 0.0463 | - | | 26.3718 | 23550 | 0.0295 | - | | 26.4278 | 23600 | 0.0423 | - | | 26.4838 | 23650 | 0.0423 | - | | 26.5398 | 23700 | 0.0336 | - | | 26.5957 | 23750 | 0.0704 | - | | 26.6517 | 23800 | 0.0305 | - | | 26.7077 | 23850 | 0.0428 | - | | 26.7637 | 23900 | 0.0422 | - | | 26.8197 | 23950 | 0.04 | - | | 26.8757 | 24000 | 0.0404 | - | | 26.9317 | 24050 | 0.0518 | - | | 26.9877 | 24100 | 0.0269 | - | | 27.0437 | 24150 | 0.0493 | - | | 27.0997 | 24200 | 0.0334 | - | | 27.1557 | 24250 | 0.0315 | - | | 27.2116 | 24300 | 0.0314 | - | | 27.2676 | 24350 | 0.0487 | - | | 27.3236 | 24400 | 0.0358 | - | | 27.3796 | 24450 | 0.0423 | - | | 27.4356 | 24500 | 0.0323 | - | | 27.4916 | 24550 | 0.0528 | - | | 27.5476 | 24600 | 0.0182 | - | | 27.6036 | 24650 | 0.0391 | - | | 27.6596 | 24700 | 0.0486 | - | | 27.7156 | 24750 | 0.0387 | - | | 27.7716 | 24800 | 0.0349 | - | | 27.8275 | 24850 | 0.0337 | - | | 27.8835 | 24900 | 0.0411 | - | | 27.9395 | 24950 | 0.0438 | - | | 27.9955 | 25000 | 0.0366 | - | | 28.0515 | 25050 | 0.0374 | - | | 28.1075 | 25100 | 0.0313 | - | | 28.1635 | 25150 | 0.054 | - | | 28.2195 | 25200 | 0.0422 | - | | 28.2755 | 25250 | 0.0443 | - | | 28.3315 | 25300 | 0.0324 | - | | 28.3875 | 25350 | 0.0432 | - | | 28.4434 | 25400 | 0.0193 | - | | 28.4994 | 25450 | 0.0236 | - | | 28.5554 | 25500 | 0.0627 | - | | 28.6114 | 25550 | 0.0461 | - | | 28.6674 | 25600 | 0.0418 | - | | 28.7234 | 25650 | 0.038 | - | | 28.7794 | 25700 | 0.0412 | - | | 28.8354 | 25750 | 0.035 | - | | 28.8914 | 25800 | 0.0616 | - | | 28.9474 | 25850 | 0.0322 | - | | 29.0034 | 25900 | 0.0461 | - | | 29.0594 | 25950 | 0.0423 | - | | 29.1153 | 26000 | 0.0242 | - | | 29.1713 | 26050 | 0.0559 | - | | 29.2273 | 26100 | 0.0317 | - | | 29.2833 | 26150 | 0.0333 | - | | 29.3393 | 26200 | 0.0235 | - | | 29.3953 | 26250 | 0.033 | - | | 29.4513 | 26300 | 0.0404 | - | | 29.5073 | 26350 | 0.0379 | - | | 29.5633 | 26400 | 0.0413 | - | | 29.6193 | 26450 | 0.0343 | - | | 29.6753 | 26500 | 0.0247 | - | | 29.7312 | 26550 | 0.0237 | - | | 29.7872 | 26600 | 0.0285 | - | | 29.8432 | 26650 | 0.0416 | - | | 29.8992 | 26700 | 0.0426 | - | | 29.9552 | 26750 | 0.0508 | - | ### 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} } ```