File size: 247,073 Bytes
4ca3a44 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 |
---
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:21502474
- loss:CachedContrastive
- code
- embeddings
- retrieval
- code search
base_model: mixedbread-ai/mxbai-edge-colbert-v0-17m
datasets:
- lightonai/cornstack
pipeline_tag: sentence-similarity
library_name: PyLate
license: apache-2.0
language:
- en
- code
metrics:
- MaxSim_accuracy@1
- MaxSim_accuracy@3
- MaxSim_accuracy@5
- MaxSim_accuracy@10
- MaxSim_precision@1
- MaxSim_precision@3
- MaxSim_precision@5
- MaxSim_precision@10
- MaxSim_recall@1
- MaxSim_recall@3
- MaxSim_recall@5
- MaxSim_recall@10
- MaxSim_ndcg@10
- MaxSim_mrr@10
- MaxSim_map@100
model-index:
- name: PyLate model based on mixedbread-ai/mxbai-edge-colbert-v0-17m
results:
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: CodeSearchNetPython
type: CodeSearchNetPython
metrics:
- type: MaxSim_accuracy@1
value: 0.887
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.965
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.979
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.985
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.887
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.32166666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.1958
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09850000000000002
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.887
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.965
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.979
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.985
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9420042614231547
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9275333333333337
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9279906305925549
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: CodeSearchNetJavascript
type: CodeSearchNetJavascript
metrics:
- type: MaxSim_accuracy@1
value: 0.721
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.824
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.845
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.874
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.721
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2746666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.169
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.0874
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.721
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.824
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.845
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.874
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8001831323944514
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7762785714285717
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7792248926162259
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: CodeSearchNetGo
type: CodeSearchNetGo
metrics:
- type: MaxSim_accuracy@1
value: 0.932
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.981
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.987
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.992
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.932
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.32699999999999996
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.19740000000000005
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09920000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.932
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.981
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.987
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.992
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9659167944666842
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9571384920634921
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9573657971657972
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: CodeSearchNetRuby
type: CodeSearchNetRuby
metrics:
- type: MaxSim_accuracy@1
value: 0.785
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.879
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.904
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.921
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.785
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2929999999999999
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18080000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09210000000000002
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.785
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.879
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.904
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.921
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8574057548820149
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8365297619047621
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.837978408503048
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: CodeSearchNetJava
type: CodeSearchNetJava
metrics:
- type: MaxSim_accuracy@1
value: 0.794
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.921
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.943
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.956
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.794
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.307
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18860000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09560000000000002
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.794
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.921
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.943
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.956
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8840941887402239
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8600476190476191
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.860999920629093
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: CodeSearchNetPhp
type: CodeSearchNetPhp
metrics:
- type: MaxSim_accuracy@1
value: 0.796
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.911
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.937
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.954
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.796
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.30366666666666664
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18740000000000004
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09540000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.796
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.911
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.937
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.954
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8806201216949195
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8564166666666669
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8570695835485463
name: Maxsim Map@100
- task:
type: code-search-network
name: Code Search Network
dataset:
name: CodeSearchNet mean
type: CodeSearchNet_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.8191666666666667
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.9135
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.9325
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.947
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.8191666666666667
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.30449999999999994
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18650000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.0947
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.8191666666666667
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9135
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9325
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.947
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8883707089335746
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8689907407407409
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8701048721758776
name: Maxsim Map@100
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/609bbe2f4932693ca2009d6a/BWfClY8hoQIS_Qf9rVa__.png" width="700" height="auto">
# LateOn-Code
The [LateOn-Code collection](https://huggingface.co/collections/lightonai/lateon-code) is composed of [PyLate](https://github.com/lightonai/pylate) models optimized for code retrieval. These late interaction models are first pre-trained following the methodology of [CoRNStack](https://arxiv.org/pdf/2412.01007). These pre-trained models are then further fine-tuned on train sets of CoIR using the [nv-retriever](https://arxiv.org/abs/2407.15831) methodology to mine hard negatives while preventing false negatives.
We started from the two best ColBERT models on the BEIR benchmark for their respective sizes. The first one, [LateOn-Code](https://huggingface.co/lightonai/LateOn-Code) is based on in-house LateOn model, a new version of [GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) built on ModernBERT-base (also developed at LightOn). This version underwent significantly deeper training, crossing the 57 mark on BEIR, almost a 2.5-point improvement and is thus SOTA by a large margin. We'll release this base model along with training data and boilerplates in the near future, so stay tuned\! The second, [LateOn-Code-edge](https://huggingface.co/lightonai/LateOn-Code-edge) is a smaller model based on the [edge-colbert model family from mixedbread](https://www.mixedbread.com/blog/edge-v0), using the [smallest variant (Ettin-17M)](https://huggingface.co/mixedbread-ai/mxbai-edge-colbert-v0-17m) for maximum efficiency. For more details on the training setup, please refer to our [blogpost](https://huggingface.co/blog/lightonai/colgrep-lateon-code).
The original [CoRNStack data](https://huggingface.co/collections/nomic-ai/cornstack) in a format compatible with PyLate can be found [here](https://huggingface.co/datasets/lightonai/cornstack) while the fine-tuning data can be found [here](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code). Training boilerplates can be found [here in the PyLate repository](https://github.com/lightonai/pylate/tree/main/examples/train/lateon_code)
## MTEB (Code, v1) benchmark results
<img src="https://cdn-uploads.huggingface.co/production/uploads/609bbe2f4932693ca2009d6a/Dw9JADjB5tdiSsv4wiDbe.png" width="1000" height="auto">
Pre-trained models achieve very competitive results as the 17M model outperforms the very strong granite-embedding-small-english-r2 by an average of 1.7. This is truly impressive, as the granite model is almost three times bigger (17M vs 48M), but is also a beast on its own in the <100M parameters range. It also outperforms the larger granite variant (149M). The larger version nicely scales by improving over the performance of its little sibling by 6.5 on average.
Although the pre-training results are already very impressive given that they are mostly out-of-domain, running a proper fine-tuning using the training data of CoIR significantly boost the performance of the models. Notably, the 17M model increases from 57.50 to 66.64 (+9.14), getting pretty close to EmbeddingGemma-300M while being 17 times smaller. The larger one increases from 63.77 to 74.12 (+10.35), strongly outperforming EmbeddingGemma-300M and getting closer to strong LLM models such as Qwen3-Embedding-0.6B and C2LLM-0.5B while being much smaller.
| Model | Params | Type | **Avg** | Apps | COIR CSNet | CodeEdit | CodeFB MT | CodeFB ST | CSNet CC | CSNet | CodeTrans Contest | CodeTrans DL | CosQA | StackOF QA | Synth T2SQL |
|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| **Baseline** | | | | | | | | | | | | | | | |
| BM25 | - | Lexical | 44.41 | 4.76 | 40.86 | 49.85 | 59.19 | 68.15 | 53.97 | 60.01 | 47.78 | 34.42 | 18.75 | 70.26 | 24.94 |
| **Small (≤50M)** | | | | | | | | | | | | | | | |
| granite-embedding-small-english-r2 | 47M | Single vector | 55.84 | 13.54 | 60.46 | 57.16 | 52.19 | 76.85 | 48.42 | 78.28 | **77.63** | 33.63 | 35.58 | **90.04** | 46.33 |
| [LateOn-Code-edge-pretrain](https://huggingface.co/lightonai/LateOn-Code-edge-pretrain) | 17M | Multi vector | 57.50 | 10.81 | 73.78 | 62.07 | 51.92 | 76.65 | 63.22 | **88.03** | 71.31 | 33.16 | 30.53 | 74.63 | 53.83 |
| [LateOn-Code-edge](https://huggingface.co/lightonai/LateOn-Code-edge) | 17M | Multi vector | **66.64** | **26.22** | **81.60** | **62.21** | **74.25** | **87.12** | **79.26** | 87.85 | 75.36 | **37.08** | **40.54** | 85.63 | **62.57** |
| *Δ (fine-tune - pretrain)* | | | *+9.14* | *+15.41* | *+7.82* | *+0.14* | *+22.33* | *+10.47* | *+16.04* | *-0.18* | *+4.05* | *+3.92* | *+10.01* | *+11.00* | *+8.74* |
| **Medium (100M–300M)** | | | | | | | | | | | | | | | |
| granite-embedding-english-r2 | 149M | Single vector | 57.22 | 13.96 | 64.65 | 59.35 | 52.54 | 77.18 | 47.67 | 80.79 | 77.07 | 35.03 | 37.01 | 91.80 | 49.55 |
| CodeRankEmbed | 137M | Single vector | 60.47 | 23.45 | 83.20 | 59.98 | 42.61 | 78.10 | 68.89 | 89.50 | 66.43 | 34.49 | 35.17 | 80.53 | 63.27 |
| GTE-ModernBERT | 149M | Single vector | 71.66 | 57.72 | 83.10 | 55.83 | **86.15** | 86.00 | **93.61** | 88.76 | 72.35 | 37.27 | 43.36 | 91.14 | **64.61** |
| embeddinggemma-300m | 300M | Single vector | 68.76 | **<u>84.39</u>** | 75.54 | 62.10 | 51.42 | 80.26 | 73.71 | 90.15 | 85.51 | 33.52 | 43.60 | 86.47 | 58.42 |
| [LateOn-Code-pretrain](https://huggingface.co/lightonai/LateOn-Code-pretrain) | 149M | Multi vector | 63.77 | 23.09 | 80.27 | **68.74** | 50.21 | 82.66 | 71.47 | **<u>91.05</u>** | 82.20 | 34.46 | 34.15 | 85.61 | 61.34 |
| [LateOn-Code](https://huggingface.co/lightonai/LateOn-Code) | 149M | Multi vector | **74.12** | 54.76 | **86.57** | 64.99 | 82.22 | **<u>90.40</u>** | 89.32 | 90.40 | **<u>87.44</u>** | **<u>41.00</u>** | **<u>45.23</u>** | **<u>93.43</u>** | 63.67 |
| *Δ (fine-tune - pretrain)* | | | *+10.35* | *+31.67* | *+6.30* | *-3.75* | *+32.01* | *+7.74* | *+17.85* | *-0.65* | *+5.24* | *+6.54* | *+11.08* | *+7.82* | *+2.33* |
| **Large (≥500M)** | | | | | | | | | | | | | | | |
| C2LLM-0.5B | 500M | Single vector | **<u>75.46</u>** | 61.02 | **<u>86.71</u>** | **<u>71.39</u>** | **<u>92.29</u>** | 88.63 | **<u>96.29</u>** | 89.20 | 84.27 | **33.99** | **38.30** | 89.40 | 74.08 |
| Qwen3-Embedding-0.6B | 600M | Single vector | 75.42 | **75.34** | 84.69 | 64.42 | 90.82 | **86.39** | 91.72 | **91.01** | **86.05** | 31.36 | 36.48 | **89.99** | **<u>76.74</u>** |
Best result across all sizes is <u>underlined</u>. Best within each size category is **bolded**.
# Colgrep
The LateOn-Code family model can easily be used within ColGrep, an easy-to-use search tool that give their powerful search capabilities to coding agent. It has been designed to extend grep capabilities to get the best of both world and is very effective to enhance the quality of the answer while diminishing answer time and tokens consumption. Given the performance of the very light-weight 17M model, it can easily run quickly on any computer.
## Install ColGrep
```bash
# macOS / Linux
curl --proto '=https' --tlsv1.2 -LsSf https://github.com/lightonai/next-plaid/releases/latest/download/colgrep-installer.sh | sh
# Windows (PowerShell)
powershell -c "irm https://github.com/lightonai/next-plaid/releases/latest/download/colgrep-installer.ps1 | iex"
```
## Search
```bash
# Semantic search — find code by meaning
colgrep "function that retries HTTP requests"
# Regex search
colgrep -e "async fn\s+\w+"
# Hybrid — regex narrows candidates, semantics ranks them
colgrep -e "Result<" "error handling" --include="*.rs"
```
## Install for Claude Code
```bash
colgrep --install-claude-code
```
## Choose a Model
```bash
# Set the model
colgrep set-model lightonai/LateOn-Code # default: lightonai/LateOn-Code-edge
```
For more information about ColGrep, please refer to the [official documentation](https://github.com/lightonai/next-plaid/tree/main/colgrep)
# PyLate model based on mixedbread-ai/mxbai-edge-colbert-v0-17m
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [mixedbread-ai/mxbai-edge-colbert-v0-17m](https://huggingface.co/mixedbread-ai/mxbai-edge-colbert-v0-17m) on the [python](https://huggingface.co/datasets/lightonai/cornstack), [php](https://huggingface.co/datasets/lightonai/cornstack), [go](https://huggingface.co/datasets/lightonai/cornstack), [ruby](https://huggingface.co/datasets/lightonai/cornstack), [javascript](https://huggingface.co/datasets/lightonai/cornstack) and [java](https://huggingface.co/datasets/lightonai/cornstack) datasets. It maps sentences & paragraphs to sequences of 48-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [mixedbread-ai/mxbai-edge-colbert-v0-17m](https://huggingface.co/mixedbread-ai/mxbai-edge-colbert-v0-17m) <!-- at revision 23ae07f5bf028bc0d1f80c82e6e2dd2311f13a46 -->
- **Document Length:** 2048 tokens
- **Query Length:** 256 tokens
- **Output Dimensionality:** 48 tokens
- **Similarity Function:** MaxSim
- **Training Datasets:**
- [python](https://huggingface.co/datasets/lightonai/cornstack)
- [php](https://huggingface.co/datasets/lightonai/cornstack)
- [go](https://huggingface.co/datasets/lightonai/cornstack)
- [ruby](https://huggingface.co/datasets/lightonai/cornstack)
- [javascript](https://huggingface.co/datasets/lightonai/cornstack)
- [java](https://huggingface.co/datasets/lightonai/cornstack)
- **Language:** English, code
- **License:** Apache 2.0
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 2047, 'do_lower_case': True, 'architecture': 'ModernBertModel'})
(1): Dense({'in_features': 256, 'out_features': 512, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
(2): Dense({'in_features': 512, 'out_features': 48, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.
#### Indexing documents
Load the ColBERT model and initialize the PLAID index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path="pylate_model_id",
)
# Step 2: Initialize the PLAID index
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path="pylate_model_id",
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Py Late Information Retrieval
* Dataset: `['CodeSearchNetPython', 'CodeSearchNetJavascript', 'CodeSearchNetGo', 'CodeSearchNetRuby', 'CodeSearchNetJava', 'CodeSearchNetPhp']`
* Evaluated with <code>pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator</code>
| Metric | CodeSearchNetPython | CodeSearchNetJavascript | CodeSearchNetGo | CodeSearchNetRuby | CodeSearchNetJava | CodeSearchNetPhp |
|:--------------------|:--------------------|:------------------------|:----------------|:------------------|:------------------|:-----------------|
| MaxSim_accuracy@1 | 0.887 | 0.721 | 0.932 | 0.785 | 0.794 | 0.796 |
| MaxSim_accuracy@3 | 0.965 | 0.824 | 0.981 | 0.879 | 0.921 | 0.911 |
| MaxSim_accuracy@5 | 0.979 | 0.845 | 0.987 | 0.904 | 0.943 | 0.937 |
| MaxSim_accuracy@10 | 0.985 | 0.874 | 0.992 | 0.921 | 0.956 | 0.954 |
| MaxSim_precision@1 | 0.887 | 0.721 | 0.932 | 0.785 | 0.794 | 0.796 |
| MaxSim_precision@3 | 0.3217 | 0.2747 | 0.327 | 0.293 | 0.307 | 0.3037 |
| MaxSim_precision@5 | 0.1958 | 0.169 | 0.1974 | 0.1808 | 0.1886 | 0.1874 |
| MaxSim_precision@10 | 0.0985 | 0.0874 | 0.0992 | 0.0921 | 0.0956 | 0.0954 |
| MaxSim_recall@1 | 0.887 | 0.721 | 0.932 | 0.785 | 0.794 | 0.796 |
| MaxSim_recall@3 | 0.965 | 0.824 | 0.981 | 0.879 | 0.921 | 0.911 |
| MaxSim_recall@5 | 0.979 | 0.845 | 0.987 | 0.904 | 0.943 | 0.937 |
| MaxSim_recall@10 | 0.985 | 0.874 | 0.992 | 0.921 | 0.956 | 0.954 |
| **MaxSim_ndcg@10** | **0.942** | **0.8002** | **0.9659** | **0.8574** | **0.8841** | **0.8806** |
| MaxSim_mrr@10 | 0.9275 | 0.7763 | 0.9571 | 0.8365 | 0.86 | 0.8564 |
| MaxSim_map@100 | 0.928 | 0.7792 | 0.9574 | 0.838 | 0.861 | 0.8571 |
#### Code Search Network
* Dataset: `CodeSearchNet_mean`
* Evaluated with <code>pylate.evaluation.code_stack_network_evaluator.CodeSearchNetworkEvaluator</code>
| Metric | Value |
|:--------------------|:-----------|
| MaxSim_accuracy@1 | 0.8192 |
| MaxSim_accuracy@3 | 0.9135 |
| MaxSim_accuracy@5 | 0.9325 |
| MaxSim_accuracy@10 | 0.947 |
| MaxSim_precision@1 | 0.8192 |
| MaxSim_precision@3 | 0.3045 |
| MaxSim_precision@5 | 0.1865 |
| MaxSim_precision@10 | 0.0947 |
| MaxSim_recall@1 | 0.8192 |
| MaxSim_recall@3 | 0.9135 |
| MaxSim_recall@5 | 0.9325 |
| MaxSim_recall@10 | 0.947 |
| **MaxSim_ndcg@10** | **0.8884** |
| MaxSim_mrr@10 | 0.869 |
| MaxSim_map@100 | 0.8701 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Datasets
#### python
* Dataset: [python](https://huggingface.co/datasets/lightonai/cornstack) at [d821c55](https://huggingface.co/datasets/lightonai/cornstack/tree/d821c5591c1397bce8e0c59efa7af64ad0fbbeab)
* Size: 6,889,731 training samples
* Approximate statistics based on the first 1000 samples:
| | query | document | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | negative_50 | negative_51 | negative_52 | negative_53 | negative_54 | negative_55 | negative_56 | negative_57 | negative_58 | negative_59 | negative_60 | negative_61 | negative_62 | negative_63 | negative_64 | negative_65 | negative_66 | negative_67 | negative_68 | negative_69 | negative_70 | negative_71 | negative_72 | negative_73 | negative_74 | negative_75 | negative_76 | negative_77 | negative_78 | negative_79 | negative_80 | negative_81 | negative_82 | negative_83 | negative_84 | negative_85 | negative_86 | negative_87 | negative_88 | negative_89 | negative_90 | negative_91 | negative_92 | negative_93 | negative_94 | negative_95 | negative_96 | negative_97 | negative_98 | negative_99 | negative_scores |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list |
| details | min: 7 tokens, mean: 23.89 tokens, max: 256 tokens | min: 13 tokens, mean: 124.0 tokens, max: 256 tokens | min: 6 tokens, mean: 102.79 tokens, max: 256 tokens | min: 6 tokens, mean: 100.77 tokens, max: 256 tokens | min: 6 tokens, mean: 98.71 tokens, max: 256 tokens | min: 6 tokens, mean: 97.52 tokens, max: 256 tokens | min: 7 tokens, mean: 100.06 tokens, max: 256 tokens | min: 8 tokens, mean: 99.1 tokens, max: 256 tokens | min: 6 tokens, mean: 99.12 tokens, max: 256 tokens | min: 6 tokens, mean: 97.54 tokens, max: 256 tokens | min: 6 tokens, mean: 100.49 tokens, max: 256 tokens | min: 7 tokens, mean: 102.03 tokens, max: 256 tokens | min: 8 tokens, mean: 99.51 tokens, max: 256 tokens | min: 6 tokens, mean: 105.16 tokens, max: 256 tokens | min: 6 tokens, mean: 105.02 tokens, max: 256 tokens | min: 8 tokens, mean: 97.72 tokens, max: 256 tokens | min: 7 tokens, mean: 102.63 tokens, max: 256 tokens | min: 6 tokens, mean: 100.13 tokens, max: 256 tokens | min: 8 tokens, mean: 97.77 tokens, max: 256 tokens | min: 6 tokens, mean: 100.67 tokens, max: 256 tokens | min: 6 tokens, mean: 100.03 tokens, max: 256 tokens | min: 8 tokens, mean: 94.66 tokens, max: 256 tokens | min: 6 tokens, mean: 101.89 tokens, max: 256 tokens | min: 7 tokens, mean: 97.09 tokens, max: 256 tokens | min: 8 tokens, mean: 99.05 tokens, max: 256 tokens | min: 6 tokens, mean: 98.86 tokens, max: 256 tokens | min: 6 tokens, mean: 104.3 tokens, max: 256 tokens | min: 6 tokens, mean: 99.98 tokens, max: 256 tokens | min: 6 tokens, mean: 104.6 tokens, max: 256 tokens | min: 6 tokens, mean: 104.02 tokens, max: 256 tokens | min: 6 tokens, mean: 101.09 tokens, max: 256 tokens | min: 6 tokens, mean: 102.33 tokens, max: 256 tokens | min: 6 tokens, mean: 103.75 tokens, max: 256 tokens | min: 8 tokens, mean: 100.34 tokens, max: 256 tokens | min: 6 tokens, mean: 100.95 tokens, max: 256 tokens | min: 6 tokens, mean: 101.89 tokens, max: 256 tokens | min: 6 tokens, mean: 103.91 tokens, max: 256 tokens | min: 7 tokens, mean: 102.53 tokens, max: 256 tokens | min: 6 tokens, mean: 104.06 tokens, max: 256 tokens | min: 6 tokens, mean: 104.39 tokens, max: 256 tokens | min: 7 tokens, mean: 105.59 tokens, max: 256 tokens | min: 6 tokens, mean: 102.49 tokens, max: 256 tokens | min: 6 tokens, mean: 100.08 tokens, max: 256 tokens | min: 6 tokens, mean: 104.22 tokens, max: 256 tokens | min: 6 tokens, mean: 104.7 tokens, max: 256 tokens | min: 6 tokens, mean: 104.41 tokens, max: 256 tokens | min: 6 tokens, mean: 99.97 tokens, max: 256 tokens | min: 6 tokens, mean: 105.69 tokens, max: 256 tokens | min: 8 tokens, mean: 103.23 tokens, max: 256 tokens | min: 6 tokens, mean: 107.67 tokens, max: 256 tokens | min: 6 tokens, mean: 103.96 tokens, max: 256 tokens | min: 6 tokens, mean: 102.4 tokens, max: 256 tokens | min: 6 tokens, mean: 106.0 tokens, max: 256 tokens | min: 7 tokens, mean: 107.58 tokens, max: 256 tokens | min: 6 tokens, mean: 104.34 tokens, max: 256 tokens | min: 6 tokens, mean: 106.04 tokens, max: 256 tokens | min: 6 tokens, mean: 104.49 tokens, max: 256 tokens | min: 6 tokens, mean: 101.76 tokens, max: 256 tokens | min: 6 tokens, mean: 99.04 tokens, max: 256 tokens | min: 8 tokens, mean: 102.33 tokens, max: 256 tokens | min: 8 tokens, mean: 103.6 tokens, max: 256 tokens | min: 8 tokens, mean: 103.8 tokens, max: 256 tokens | min: 8 tokens, mean: 101.44 tokens, max: 256 tokens | min: 8 tokens, mean: 103.98 tokens, max: 256 tokens | min: 6 tokens, mean: 104.34 tokens, max: 256 tokens | min: 6 tokens, mean: 103.43 tokens, max: 256 tokens | min: 6 tokens, mean: 104.42 tokens, max: 256 tokens | min: 6 tokens, mean: 102.78 tokens, max: 256 tokens | min: 6 tokens, mean: 103.66 tokens, max: 256 tokens | min: 6 tokens, mean: 106.97 tokens, max: 256 tokens | min: 6 tokens, mean: 106.03 tokens, max: 256 tokens | min: 8 tokens, mean: 103.5 tokens, max: 256 tokens | min: 6 tokens, mean: 102.39 tokens, max: 256 tokens | min: 9 tokens, mean: 101.24 tokens, max: 256 tokens | min: 6 tokens, mean: 102.41 tokens, max: 256 tokens | min: 6 tokens, mean: 105.79 tokens, max: 256 tokens | min: 6 tokens, mean: 104.16 tokens, max: 256 tokens | min: 6 tokens, mean: 101.31 tokens, max: 256 tokens | min: 6 tokens, mean: 103.75 tokens, max: 256 tokens | min: 6 tokens, mean: 104.15 tokens, max: 256 tokens | min: 6 tokens, mean: 104.32 tokens, max: 256 tokens | min: 6 tokens, mean: 105.33 tokens, max: 256 tokens | min: 6 tokens, mean: 103.74 tokens, max: 256 tokens | min: 6 tokens, mean: 103.79 tokens, max: 256 tokens | min: 8 tokens, mean: 103.77 tokens, max: 256 tokens | min: 7 tokens, mean: 105.83 tokens, max: 256 tokens | min: 6 tokens, mean: 102.59 tokens, max: 256 tokens | min: 6 tokens, mean: 101.91 tokens, max: 256 tokens | min: 6 tokens, mean: 103.66 tokens, max: 256 tokens | min: 6 tokens, mean: 101.27 tokens, max: 256 tokens | min: 6 tokens, mean: 105.1 tokens, max: 256 tokens | min: 6 tokens, mean: 104.97 tokens, max: 256 tokens | min: 6 tokens, mean: 107.13 tokens, max: 256 tokens | min: 6 tokens, mean: 108.49 tokens, max: 256 tokens | min: 8 tokens, mean: 108.61 tokens, max: 256 tokens | min: 6 tokens, mean: 101.23 tokens, max: 256 tokens | min: 6 tokens, mean: 105.16 tokens, max: 256 tokens | min: 7 tokens, mean: 105.71 tokens, max: 256 tokens | min: 8 tokens, mean: 105.51 tokens, max: 256 tokens | min: 8 tokens, mean: 103.51 tokens, max: 256 tokens | min: 6 tokens, mean: 102.81 tokens, max: 256 tokens | min: 6 tokens, mean: 102.48 tokens, max: 256 tokens | size: 100 elements |
* Samples:
| query | document | negative_0 |
|:------|:---------|:-----------|
| <code>Write the concordance entries to the output file(filename) See sample output files for format.</code> | <code>def write_concordance(self, filename):<br> all_keys = self.concordance_table.get_all_keys()<br> lines = []<br> for i in all_keys:<br> a = ""<br> a += i + ":"<br> f = self.concordance_table.get_value(i)<br> if f != None:<br> for s in f:<br> a += " " + str(s)<br> a += "\n"<br> lines.append(a)<br> a = open(filename, "w+")<br> for i in lines:<br> a.write(i)<br> a.close()</code> | <code>def write_concordance(self, filename):<br> out = ''<br> values = [x for x in self.concordance_table.hash_table if x is not None]<br> values.sort(key=lambda x: x[0])<br> for v in values:<br> out += f'{v[0]}: {" ".join(str(x) for x in sorted(set(v[1])))}\n' <br> with open(filename, 'w') as f:<br> f.write(out.rstrip())</code> |
* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
#### php
* Dataset: [php](https://huggingface.co/datasets/lightonai/cornstack) at [d821c55](https://huggingface.co/datasets/lightonai/cornstack/tree/d821c5591c1397bce8e0c59efa7af64ad0fbbeab)
* Size: 2,676,409 training samples
* Approximate statistics based on the first 1000 samples:
| | query | document | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | negative_50 | negative_51 | negative_52 | negative_53 | negative_54 | negative_55 | negative_56 | negative_57 | negative_58 | negative_59 | negative_60 | negative_61 | negative_62 | negative_63 | negative_64 | negative_65 | negative_66 | negative_67 | negative_68 | negative_69 | negative_70 | negative_71 | negative_72 | negative_73 | negative_74 | negative_75 | negative_76 | negative_77 | negative_78 | negative_79 | negative_80 | negative_81 | negative_82 | negative_83 | negative_84 | negative_85 | negative_86 | negative_87 | negative_88 | negative_89 | negative_90 | negative_91 | negative_92 | negative_93 | negative_94 | negative_95 | negative_96 | negative_97 | negative_98 | negative_99 | negative_scores |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list |
| details | min: 7 tokens, mean: 17.76 tokens, max: 220 tokens | min: 8 tokens, mean: 97.11 tokens, max: 256 tokens | min: 7 tokens, mean: 82.96 tokens, max: 256 tokens | min: 7 tokens, mean: 84.18 tokens, max: 256 tokens | min: 7 tokens, mean: 82.41 tokens, max: 256 tokens | min: 7 tokens, mean: 82.66 tokens, max: 256 tokens | min: 7 tokens, mean: 85.48 tokens, max: 256 tokens | min: 6 tokens, mean: 80.61 tokens, max: 256 tokens | min: 7 tokens, mean: 85.35 tokens, max: 256 tokens | min: 7 tokens, mean: 85.97 tokens, max: 256 tokens | min: 7 tokens, mean: 83.78 tokens, max: 256 tokens | min: 7 tokens, mean: 83.24 tokens, max: 256 tokens | min: 7 tokens, mean: 86.32 tokens, max: 256 tokens | min: 7 tokens, mean: 79.75 tokens, max: 256 tokens | min: 7 tokens, mean: 85.88 tokens, max: 256 tokens | min: 7 tokens, mean: 83.22 tokens, max: 256 tokens | min: 7 tokens, mean: 82.52 tokens, max: 256 tokens | min: 7 tokens, mean: 84.21 tokens, max: 256 tokens | min: 7 tokens, mean: 84.87 tokens, max: 256 tokens | min: 7 tokens, mean: 85.5 tokens, max: 256 tokens | min: 7 tokens, mean: 84.93 tokens, max: 256 tokens | min: 7 tokens, mean: 82.02 tokens, max: 256 tokens | min: 7 tokens, mean: 84.67 tokens, max: 256 tokens | min: 7 tokens, mean: 85.52 tokens, max: 256 tokens | min: 7 tokens, mean: 87.66 tokens, max: 256 tokens | min: 7 tokens, mean: 79.67 tokens, max: 256 tokens | min: 7 tokens, mean: 88.52 tokens, max: 256 tokens | min: 7 tokens, mean: 88.85 tokens, max: 256 tokens | min: 7 tokens, mean: 84.71 tokens, max: 256 tokens | min: 7 tokens, mean: 87.23 tokens, max: 256 tokens | min: 7 tokens, mean: 86.93 tokens, max: 256 tokens | min: 7 tokens, mean: 89.06 tokens, max: 256 tokens | min: 7 tokens, mean: 87.13 tokens, max: 256 tokens | min: 7 tokens, mean: 88.62 tokens, max: 256 tokens | min: 7 tokens, mean: 89.14 tokens, max: 256 tokens | min: 7 tokens, mean: 88.35 tokens, max: 256 tokens | min: 7 tokens, mean: 85.79 tokens, max: 256 tokens | min: 7 tokens, mean: 85.09 tokens, max: 256 tokens | min: 7 tokens, mean: 87.29 tokens, max: 256 tokens | min: 7 tokens, mean: 89.95 tokens, max: 256 tokens | min: 7 tokens, mean: 86.21 tokens, max: 256 tokens | min: 7 tokens, mean: 86.27 tokens, max: 256 tokens | min: 7 tokens, mean: 84.56 tokens, max: 256 tokens | min: 7 tokens, mean: 87.99 tokens, max: 256 tokens | min: 7 tokens, mean: 87.38 tokens, max: 256 tokens | min: 7 tokens, mean: 86.75 tokens, max: 256 tokens | min: 7 tokens, mean: 89.86 tokens, max: 256 tokens | min: 7 tokens, mean: 90.52 tokens, max: 256 tokens | min: 7 tokens, mean: 87.58 tokens, max: 256 tokens | min: 7 tokens, mean: 89.15 tokens, max: 256 tokens | min: 7 tokens, mean: 93.95 tokens, max: 256 tokens | min: 7 tokens, mean: 88.48 tokens, max: 256 tokens | min: 7 tokens, mean: 86.43 tokens, max: 256 tokens | min: 7 tokens, mean: 83.86 tokens, max: 256 tokens | min: 7 tokens, mean: 86.69 tokens, max: 256 tokens | min: 7 tokens, mean: 88.16 tokens, max: 256 tokens | min: 7 tokens, mean: 85.45 tokens, max: 256 tokens | min: 7 tokens, mean: 87.57 tokens, max: 256 tokens | min: 7 tokens, mean: 88.57 tokens, max: 256 tokens | min: 7 tokens, mean: 89.86 tokens, max: 256 tokens | min: 7 tokens, mean: 85.34 tokens, max: 256 tokens | min: 7 tokens, mean: 88.91 tokens, max: 256 tokens | min: 7 tokens, mean: 90.64 tokens, max: 256 tokens | min: 7 tokens, mean: 88.6 tokens, max: 256 tokens | min: 7 tokens, mean: 93.25 tokens, max: 256 tokens | min: 7 tokens, mean: 87.29 tokens, max: 256 tokens | min: 7 tokens, mean: 91.02 tokens, max: 256 tokens | min: 7 tokens, mean: 90.59 tokens, max: 256 tokens | min: 6 tokens, mean: 85.73 tokens, max: 256 tokens | min: 7 tokens, mean: 87.45 tokens, max: 256 tokens | min: 7 tokens, mean: 87.7 tokens, max: 256 tokens | min: 7 tokens, mean: 90.26 tokens, max: 256 tokens | min: 7 tokens, mean: 90.95 tokens, max: 256 tokens | min: 7 tokens, mean: 87.91 tokens, max: 256 tokens | min: 7 tokens, mean: 90.79 tokens, max: 256 tokens | min: 6 tokens, mean: 89.76 tokens, max: 256 tokens | min: 7 tokens, mean: 84.62 tokens, max: 256 tokens | min: 6 tokens, mean: 88.2 tokens, max: 256 tokens | min: 7 tokens, mean: 87.19 tokens, max: 256 tokens | min: 7 tokens, mean: 91.52 tokens, max: 256 tokens | min: 7 tokens, mean: 90.32 tokens, max: 256 tokens | min: 6 tokens, mean: 86.23 tokens, max: 256 tokens | min: 6 tokens, mean: 92.11 tokens, max: 256 tokens | min: 7 tokens, mean: 90.7 tokens, max: 256 tokens | min: 6 tokens, mean: 90.02 tokens, max: 256 tokens | min: 7 tokens, mean: 94.61 tokens, max: 256 tokens | min: 7 tokens, mean: 89.46 tokens, max: 256 tokens | min: 7 tokens, mean: 82.07 tokens, max: 256 tokens | min: 7 tokens, mean: 87.91 tokens, max: 256 tokens | min: 7 tokens, mean: 88.82 tokens, max: 256 tokens | min: 7 tokens, mean: 89.59 tokens, max: 256 tokens | min: 7 tokens, mean: 92.2 tokens, max: 256 tokens | min: 6 tokens, mean: 87.55 tokens, max: 256 tokens | min: 6 tokens, mean: 88.46 tokens, max: 256 tokens | min: 7 tokens, mean: 90.75 tokens, max: 256 tokens | min: 7 tokens, mean: 84.04 tokens, max: 256 tokens | min: 7 tokens, mean: 91.11 tokens, max: 256 tokens | min: 7 tokens, mean: 89.1 tokens, max: 256 tokens | min: 6 tokens, mean: 93.24 tokens, max: 256 tokens | min: 7 tokens, mean: 86.16 tokens, max: 256 tokens | min: 7 tokens, mean: 87.66 tokens, max: 256 tokens | min: 7 tokens, mean: 86.86 tokens, max: 256 tokens | size: 100 elements |
* Samples:
| query | document | negative_0 |
|:------|:---------|:-----------|
| <code>return boolean as string 'true' / 'false'</code> | <code>function bool2str($bool) {<br> if($bool ===false)<br> return 'false';<br> else<br> return 'true';<br>}</code> | <code>function bool_s($boolean) {<br> return ($boolean ? 'true' : 'false');<br>}</code> |
* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
#### go
* Dataset: [go](https://huggingface.co/datasets/lightonai/cornstack) at [d821c55](https://huggingface.co/datasets/lightonai/cornstack/tree/d821c5591c1397bce8e0c59efa7af64ad0fbbeab)
* Size: 5,815,734 training samples
* Approximate statistics based on the first 1000 samples:
| | query | document | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | negative_50 | negative_51 | negative_52 | negative_53 | negative_54 | negative_55 | negative_56 | negative_57 | negative_58 | negative_59 | negative_60 | negative_61 | negative_62 | negative_63 | negative_64 | negative_65 | negative_66 | negative_67 | negative_68 | negative_69 | negative_70 | negative_71 | negative_72 | negative_73 | negative_74 | negative_75 | negative_76 | negative_77 | negative_78 | negative_79 | negative_80 | negative_81 | negative_82 | negative_83 | negative_84 | negative_85 | negative_86 | negative_87 | negative_88 | negative_89 | negative_90 | negative_91 | negative_92 | negative_93 | negative_94 | negative_95 | negative_96 | negative_97 | negative_98 | negative_99 | negative_scores |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list |
| details | min: 7 tokens, mean: 23.66 tokens, max: 209 tokens | min: 13 tokens, mean: 109.45 tokens, max: 256 tokens | min: 15 tokens, mean: 107.28 tokens, max: 256 tokens | min: 12 tokens, mean: 102.78 tokens, max: 256 tokens | min: 14 tokens, mean: 103.84 tokens, max: 256 tokens | min: 10 tokens, mean: 106.7 tokens, max: 256 tokens | min: 9 tokens, mean: 105.97 tokens, max: 256 tokens | min: 9 tokens, mean: 106.33 tokens, max: 256 tokens | min: 9 tokens, mean: 107.86 tokens, max: 256 tokens | min: 9 tokens, mean: 104.82 tokens, max: 256 tokens | min: 12 tokens, mean: 106.33 tokens, max: 256 tokens | min: 11 tokens, mean: 105.48 tokens, max: 256 tokens | min: 9 tokens, mean: 108.54 tokens, max: 256 tokens | min: 11 tokens, mean: 108.51 tokens, max: 256 tokens | min: 11 tokens, mean: 110.58 tokens, max: 256 tokens | min: 12 tokens, mean: 106.19 tokens, max: 256 tokens | min: 12 tokens, mean: 106.53 tokens, max: 256 tokens | min: 14 tokens, mean: 106.89 tokens, max: 256 tokens | min: 11 tokens, mean: 102.98 tokens, max: 256 tokens | min: 9 tokens, mean: 107.35 tokens, max: 256 tokens | min: 13 tokens, mean: 105.37 tokens, max: 256 tokens | min: 14 tokens, mean: 105.34 tokens, max: 256 tokens | min: 12 tokens, mean: 104.52 tokens, max: 256 tokens | min: 13 tokens, mean: 111.48 tokens, max: 256 tokens | min: 14 tokens, mean: 107.38 tokens, max: 256 tokens | min: 10 tokens, mean: 107.31 tokens, max: 256 tokens | min: 10 tokens, mean: 102.55 tokens, max: 256 tokens | min: 8 tokens, mean: 108.53 tokens, max: 256 tokens | min: 10 tokens, mean: 111.4 tokens, max: 256 tokens | min: 12 tokens, mean: 105.16 tokens, max: 256 tokens | min: 11 tokens, mean: 108.63 tokens, max: 256 tokens | min: 15 tokens, mean: 107.94 tokens, max: 256 tokens | min: 12 tokens, mean: 105.72 tokens, max: 256 tokens | min: 10 tokens, mean: 106.51 tokens, max: 256 tokens | min: 13 tokens, mean: 105.31 tokens, max: 256 tokens | min: 13 tokens, mean: 104.63 tokens, max: 256 tokens | min: 11 tokens, mean: 106.5 tokens, max: 256 tokens | min: 14 tokens, mean: 105.02 tokens, max: 256 tokens | min: 13 tokens, mean: 107.19 tokens, max: 256 tokens | min: 10 tokens, mean: 110.87 tokens, max: 256 tokens | min: 12 tokens, mean: 106.04 tokens, max: 256 tokens | min: 16 tokens, mean: 109.84 tokens, max: 256 tokens | min: 16 tokens, mean: 109.89 tokens, max: 256 tokens | min: 10 tokens, mean: 108.69 tokens, max: 256 tokens | min: 11 tokens, mean: 110.42 tokens, max: 256 tokens | min: 15 tokens, mean: 107.62 tokens, max: 256 tokens | min: 12 tokens, mean: 108.6 tokens, max: 256 tokens | min: 12 tokens, mean: 106.39 tokens, max: 256 tokens | min: 9 tokens, mean: 105.92 tokens, max: 256 tokens | min: 12 tokens, mean: 111.52 tokens, max: 256 tokens | min: 11 tokens, mean: 108.31 tokens, max: 256 tokens | min: 11 tokens, mean: 104.39 tokens, max: 256 tokens | min: 8 tokens, mean: 112.34 tokens, max: 256 tokens | min: 14 tokens, mean: 110.01 tokens, max: 256 tokens | min: 8 tokens, mean: 108.58 tokens, max: 256 tokens | min: 14 tokens, mean: 103.8 tokens, max: 256 tokens | min: 14 tokens, mean: 108.41 tokens, max: 256 tokens | min: 13 tokens, mean: 104.5 tokens, max: 256 tokens | min: 12 tokens, mean: 109.63 tokens, max: 256 tokens | min: 10 tokens, mean: 107.77 tokens, max: 256 tokens | min: 12 tokens, mean: 107.46 tokens, max: 256 tokens | min: 14 tokens, mean: 106.32 tokens, max: 256 tokens | min: 9 tokens, mean: 111.96 tokens, max: 256 tokens | min: 11 tokens, mean: 108.63 tokens, max: 256 tokens | min: 13 tokens, mean: 108.7 tokens, max: 256 tokens | min: 12 tokens, mean: 110.76 tokens, max: 256 tokens | min: 14 tokens, mean: 105.31 tokens, max: 256 tokens | min: 14 tokens, mean: 108.63 tokens, max: 256 tokens | min: 14 tokens, mean: 111.21 tokens, max: 256 tokens | min: 14 tokens, mean: 106.53 tokens, max: 256 tokens | min: 13 tokens, mean: 110.22 tokens, max: 256 tokens | min: 12 tokens, mean: 110.26 tokens, max: 256 tokens | min: 12 tokens, mean: 111.13 tokens, max: 256 tokens | min: 12 tokens, mean: 110.15 tokens, max: 256 tokens | min: 14 tokens, mean: 108.58 tokens, max: 256 tokens | min: 13 tokens, mean: 110.5 tokens, max: 256 tokens | min: 15 tokens, mean: 111.2 tokens, max: 256 tokens | min: 14 tokens, mean: 104.21 tokens, max: 256 tokens | min: 10 tokens, mean: 108.67 tokens, max: 256 tokens | min: 11 tokens, mean: 110.96 tokens, max: 256 tokens | min: 14 tokens, mean: 110.88 tokens, max: 256 tokens | min: 13 tokens, mean: 109.85 tokens, max: 256 tokens | min: 12 tokens, mean: 105.19 tokens, max: 256 tokens | min: 14 tokens, mean: 111.65 tokens, max: 256 tokens | min: 7 tokens, mean: 108.81 tokens, max: 256 tokens | min: 13 tokens, mean: 110.15 tokens, max: 256 tokens | min: 9 tokens, mean: 105.6 tokens, max: 256 tokens | min: 12 tokens, mean: 108.67 tokens, max: 256 tokens | min: 14 tokens, mean: 105.41 tokens, max: 256 tokens | min: 10 tokens, mean: 107.35 tokens, max: 256 tokens | min: 12 tokens, mean: 108.56 tokens, max: 256 tokens | min: 13 tokens, mean: 108.36 tokens, max: 256 tokens | min: 16 tokens, mean: 110.39 tokens, max: 256 tokens | min: 14 tokens, mean: 112.55 tokens, max: 256 tokens | min: 11 tokens, mean: 108.5 tokens, max: 256 tokens | min: 14 tokens, mean: 109.81 tokens, max: 256 tokens | min: 12 tokens, mean: 108.61 tokens, max: 256 tokens | min: 14 tokens, mean: 111.78 tokens, max: 256 tokens | min: 16 tokens, mean: 111.52 tokens, max: 256 tokens | min: 9 tokens, mean: 110.59 tokens, max: 256 tokens | min: 10 tokens, mean: 107.75 tokens, max: 256 tokens | min: 14 tokens, mean: 110.05 tokens, max: 256 tokens | size: 100 elements |
* Samples:
| query | document | negative_0 |
|:------|:---------|:-----------|
| <code>Returns the value of the 'go_package' option of the first .proto file found in the same directory as projectFile</code> | <code>func detectGoPackageForProject(projectFile string) (string, error) {<br> var goPkg string<br> projectDir := filepath.Dir(projectFile)<br> if err := filepath.Walk(projectDir, func(protoFile string, info os.FileInfo, err error) error {<br> // already set<br> if goPkg != "" {<br> return nil<br> }<br> if !strings.HasSuffix(protoFile, ".proto") {<br> return nil<br> }<br> // search for go_package on protos in the same dir as the project.json<br> if projectDir != filepath.Dir(protoFile) {<br> return nil<br> }<br> content, err := ioutil.ReadFile(protoFile)<br> if err != nil {<br> return err<br> }<br> lines := strings.Split(string(content), "\n")<br> for _, line := range lines {<br> goPackage := goPackageStatementRegex.FindStringSubmatch(line)<br> if len(goPackage) == 0 {<br> continue<br> }<br> if len(goPackage) != 2 {<br> return errors.Errorf("parsing go_package error: from %v found %v", line, goPackage)<br> }<br> goPkg = goPackage[1]<br> break<br> }<br> return nil<br> }); err != nil {<br> return "", err<br> }<br> if goPkg == "" {<br> return "", errors.Er...</code> | <code>func (g *Generator) GoFilePackage(depfile *fdep.DepFile) string {<br> return fproto_wrap.BaseName(g.GoWrapPackage(depfile))<br>}</code> |
* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
#### ruby
* Dataset: [ruby](https://huggingface.co/datasets/lightonai/cornstack) at [d821c55](https://huggingface.co/datasets/lightonai/cornstack/tree/d821c5591c1397bce8e0c59efa7af64ad0fbbeab)
* Size: 631,161 training samples
* Approximate statistics based on the first 1000 samples:
| | query | document | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | negative_50 | negative_51 | negative_52 | negative_53 | negative_54 | negative_55 | negative_56 | negative_57 | negative_58 | negative_59 | negative_60 | negative_61 | negative_62 | negative_63 | negative_64 | negative_65 | negative_66 | negative_67 | negative_68 | negative_69 | negative_70 | negative_71 | negative_72 | negative_73 | negative_74 | negative_75 | negative_76 | negative_77 | negative_78 | negative_79 | negative_80 | negative_81 | negative_82 | negative_83 | negative_84 | negative_85 | negative_86 | negative_87 | negative_88 | negative_89 | negative_90 | negative_91 | negative_92 | negative_93 | negative_94 | negative_95 | negative_96 | negative_97 | negative_98 | negative_99 | negative_scores |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list |
| details | min: 7 tokens, mean: 27.05 tokens, max: 256 tokens | min: 8 tokens, mean: 81.53 tokens, max: 256 tokens | min: 7 tokens, mean: 69.02 tokens, max: 256 tokens | min: 7 tokens, mean: 70.71 tokens, max: 256 tokens | min: 7 tokens, mean: 67.95 tokens, max: 256 tokens | min: 7 tokens, mean: 69.89 tokens, max: 256 tokens | min: 7 tokens, mean: 65.69 tokens, max: 256 tokens | min: 7 tokens, mean: 69.16 tokens, max: 256 tokens | min: 7 tokens, mean: 67.47 tokens, max: 256 tokens | min: 7 tokens, mean: 68.72 tokens, max: 256 tokens | min: 7 tokens, mean: 70.4 tokens, max: 256 tokens | min: 7 tokens, mean: 69.84 tokens, max: 256 tokens | min: 7 tokens, mean: 72.83 tokens, max: 256 tokens | min: 7 tokens, mean: 68.37 tokens, max: 256 tokens | min: 7 tokens, mean: 71.14 tokens, max: 256 tokens | min: 7 tokens, mean: 72.14 tokens, max: 256 tokens | min: 7 tokens, mean: 69.19 tokens, max: 256 tokens | min: 7 tokens, mean: 71.39 tokens, max: 256 tokens | min: 7 tokens, mean: 70.7 tokens, max: 256 tokens | min: 7 tokens, mean: 68.28 tokens, max: 256 tokens | min: 7 tokens, mean: 67.56 tokens, max: 256 tokens | min: 7 tokens, mean: 69.9 tokens, max: 256 tokens | min: 7 tokens, mean: 71.59 tokens, max: 256 tokens | min: 7 tokens, mean: 72.27 tokens, max: 256 tokens | min: 7 tokens, mean: 69.55 tokens, max: 256 tokens | min: 7 tokens, mean: 69.9 tokens, max: 256 tokens | min: 7 tokens, mean: 69.97 tokens, max: 256 tokens | min: 7 tokens, mean: 71.98 tokens, max: 256 tokens | min: 7 tokens, mean: 71.81 tokens, max: 256 tokens | min: 7 tokens, mean: 70.69 tokens, max: 256 tokens | min: 7 tokens, mean: 72.99 tokens, max: 256 tokens | min: 7 tokens, mean: 70.93 tokens, max: 256 tokens | min: 7 tokens, mean: 70.75 tokens, max: 256 tokens | min: 7 tokens, mean: 74.68 tokens, max: 256 tokens | min: 7 tokens, mean: 69.44 tokens, max: 256 tokens | min: 7 tokens, mean: 71.7 tokens, max: 256 tokens | min: 7 tokens, mean: 71.09 tokens, max: 256 tokens | min: 7 tokens, mean: 71.35 tokens, max: 256 tokens | min: 7 tokens, mean: 72.7 tokens, max: 256 tokens | min: 7 tokens, mean: 74.9 tokens, max: 256 tokens | min: 7 tokens, mean: 74.32 tokens, max: 256 tokens | min: 7 tokens, mean: 76.96 tokens, max: 256 tokens | min: 7 tokens, mean: 73.02 tokens, max: 256 tokens | min: 7 tokens, mean: 68.75 tokens, max: 256 tokens | min: 7 tokens, mean: 72.28 tokens, max: 256 tokens | min: 7 tokens, mean: 72.49 tokens, max: 256 tokens | min: 7 tokens, mean: 73.83 tokens, max: 256 tokens | min: 7 tokens, mean: 70.28 tokens, max: 256 tokens | min: 7 tokens, mean: 72.02 tokens, max: 256 tokens | min: 7 tokens, mean: 73.17 tokens, max: 256 tokens | min: 7 tokens, mean: 73.49 tokens, max: 256 tokens | min: 7 tokens, mean: 71.15 tokens, max: 256 tokens | min: 7 tokens, mean: 74.28 tokens, max: 256 tokens | min: 7 tokens, mean: 71.97 tokens, max: 256 tokens | min: 7 tokens, mean: 75.13 tokens, max: 256 tokens | min: 7 tokens, mean: 75.39 tokens, max: 256 tokens | min: 7 tokens, mean: 71.9 tokens, max: 256 tokens | min: 7 tokens, mean: 72.95 tokens, max: 256 tokens | min: 7 tokens, mean: 75.97 tokens, max: 256 tokens | min: 7 tokens, mean: 72.86 tokens, max: 256 tokens | min: 7 tokens, mean: 75.5 tokens, max: 256 tokens | min: 7 tokens, mean: 72.36 tokens, max: 256 tokens | min: 7 tokens, mean: 70.49 tokens, max: 256 tokens | min: 7 tokens, mean: 68.93 tokens, max: 256 tokens | min: 7 tokens, mean: 69.85 tokens, max: 256 tokens | min: 7 tokens, mean: 72.19 tokens, max: 256 tokens | min: 7 tokens, mean: 72.8 tokens, max: 256 tokens | min: 7 tokens, mean: 72.15 tokens, max: 256 tokens | min: 7 tokens, mean: 73.03 tokens, max: 256 tokens | min: 7 tokens, mean: 72.78 tokens, max: 256 tokens | min: 7 tokens, mean: 71.82 tokens, max: 256 tokens | min: 7 tokens, mean: 70.84 tokens, max: 256 tokens | min: 7 tokens, mean: 70.99 tokens, max: 256 tokens | min: 7 tokens, mean: 71.77 tokens, max: 256 tokens | min: 7 tokens, mean: 72.01 tokens, max: 256 tokens | min: 7 tokens, mean: 73.07 tokens, max: 256 tokens | min: 7 tokens, mean: 74.55 tokens, max: 256 tokens | min: 7 tokens, mean: 72.25 tokens, max: 256 tokens | min: 7 tokens, mean: 75.45 tokens, max: 256 tokens | min: 7 tokens, mean: 75.16 tokens, max: 256 tokens | min: 7 tokens, mean: 71.09 tokens, max: 256 tokens | min: 7 tokens, mean: 72.39 tokens, max: 256 tokens | min: 7 tokens, mean: 70.39 tokens, max: 256 tokens | min: 7 tokens, mean: 72.62 tokens, max: 256 tokens | min: 7 tokens, mean: 72.57 tokens, max: 256 tokens | min: 7 tokens, mean: 72.55 tokens, max: 256 tokens | min: 7 tokens, mean: 72.69 tokens, max: 256 tokens | min: 7 tokens, mean: 74.38 tokens, max: 256 tokens | min: 7 tokens, mean: 73.11 tokens, max: 256 tokens | min: 7 tokens, mean: 72.3 tokens, max: 256 tokens | min: 7 tokens, mean: 72.77 tokens, max: 256 tokens | min: 7 tokens, mean: 69.29 tokens, max: 256 tokens | min: 7 tokens, mean: 71.55 tokens, max: 256 tokens | min: 7 tokens, mean: 71.25 tokens, max: 256 tokens | min: 7 tokens, mean: 72.35 tokens, max: 256 tokens | min: 7 tokens, mean: 70.07 tokens, max: 256 tokens | min: 7 tokens, mean: 73.3 tokens, max: 256 tokens | min: 7 tokens, mean: 72.86 tokens, max: 256 tokens | min: 7 tokens, mean: 73.71 tokens, max: 256 tokens | min: 7 tokens, mean: 73.45 tokens, max: 256 tokens | min: 7 tokens, mean: 74.06 tokens, max: 256 tokens | min: 7 tokens, mean: 74.93 tokens, max: 256 tokens | size: 100 elements |
* Samples:
| query | document | negative_0 |
|:------|:---------|:-----------|
| <code>GET /property_between_floor_slaps GET /property_between_floor_slaps.json</code> | <code>def index<br> @property_between_floor_slaps = PropertyBetweenFloorSlap.all<br> end</code> | <code>def set_property_between_floor_slap<br> @property_between_floor_slap = PropertyBetweenFloorSlap.find(params[:id])<br> end</code> |
* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
#### javascript
* Dataset: [javascript](https://huggingface.co/datasets/lightonai/cornstack) at [d821c55](https://huggingface.co/datasets/lightonai/cornstack/tree/d821c5591c1397bce8e0c59efa7af64ad0fbbeab)
* Size: 1,386,353 training samples
* Approximate statistics based on the first 1000 samples:
| | query | document | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | negative_50 | negative_51 | negative_52 | negative_53 | negative_54 | negative_55 | negative_56 | negative_57 | negative_58 | negative_59 | negative_60 | negative_61 | negative_62 | negative_63 | negative_64 | negative_65 | negative_66 | negative_67 | negative_68 | negative_69 | negative_70 | negative_71 | negative_72 | negative_73 | negative_74 | negative_75 | negative_76 | negative_77 | negative_78 | negative_79 | negative_80 | negative_81 | negative_82 | negative_83 | negative_84 | negative_85 | negative_86 | negative_87 | negative_88 | negative_89 | negative_90 | negative_91 | negative_92 | negative_93 | negative_94 | negative_95 | negative_96 | negative_97 | negative_98 | negative_99 | negative_scores |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list |
| details | min: 7 tokens, mean: 23.44 tokens, max: 256 tokens | min: 11 tokens, mean: 123.26 tokens, max: 256 tokens | min: 7 tokens, mean: 108.69 tokens, max: 256 tokens | min: 6 tokens, mean: 112.6 tokens, max: 256 tokens | min: 6 tokens, mean: 109.73 tokens, max: 256 tokens | min: 7 tokens, mean: 111.32 tokens, max: 256 tokens | min: 7 tokens, mean: 110.19 tokens, max: 256 tokens | min: 6 tokens, mean: 113.09 tokens, max: 256 tokens | min: 6 tokens, mean: 109.3 tokens, max: 256 tokens | min: 6 tokens, mean: 108.64 tokens, max: 256 tokens | min: 7 tokens, mean: 111.45 tokens, max: 256 tokens | min: 6 tokens, mean: 108.68 tokens, max: 256 tokens | min: 7 tokens, mean: 109.11 tokens, max: 256 tokens | min: 6 tokens, mean: 111.44 tokens, max: 256 tokens | min: 6 tokens, mean: 110.8 tokens, max: 256 tokens | min: 7 tokens, mean: 108.59 tokens, max: 256 tokens | min: 7 tokens, mean: 109.79 tokens, max: 256 tokens | min: 6 tokens, mean: 111.66 tokens, max: 256 tokens | min: 6 tokens, mean: 111.99 tokens, max: 256 tokens | min: 6 tokens, mean: 110.94 tokens, max: 256 tokens | min: 6 tokens, mean: 110.95 tokens, max: 256 tokens | min: 7 tokens, mean: 114.86 tokens, max: 256 tokens | min: 7 tokens, mean: 113.17 tokens, max: 256 tokens | min: 7 tokens, mean: 114.22 tokens, max: 256 tokens | min: 7 tokens, mean: 110.79 tokens, max: 256 tokens | min: 7 tokens, mean: 113.14 tokens, max: 256 tokens | min: 7 tokens, mean: 109.93 tokens, max: 256 tokens | min: 7 tokens, mean: 114.33 tokens, max: 256 tokens | min: 7 tokens, mean: 109.24 tokens, max: 256 tokens | min: 7 tokens, mean: 108.27 tokens, max: 256 tokens | min: 7 tokens, mean: 113.35 tokens, max: 256 tokens | min: 6 tokens, mean: 111.46 tokens, max: 256 tokens | min: 6 tokens, mean: 107.44 tokens, max: 256 tokens | min: 6 tokens, mean: 110.61 tokens, max: 256 tokens | min: 6 tokens, mean: 112.36 tokens, max: 256 tokens | min: 6 tokens, mean: 114.32 tokens, max: 256 tokens | min: 7 tokens, mean: 111.15 tokens, max: 256 tokens | min: 7 tokens, mean: 115.82 tokens, max: 256 tokens | min: 7 tokens, mean: 112.35 tokens, max: 256 tokens | min: 6 tokens, mean: 116.12 tokens, max: 256 tokens | min: 6 tokens, mean: 116.65 tokens, max: 256 tokens | min: 6 tokens, mean: 114.54 tokens, max: 256 tokens | min: 6 tokens, mean: 114.71 tokens, max: 256 tokens | min: 6 tokens, mean: 116.68 tokens, max: 256 tokens | min: 6 tokens, mean: 114.22 tokens, max: 256 tokens | min: 7 tokens, mean: 116.89 tokens, max: 256 tokens | min: 7 tokens, mean: 115.25 tokens, max: 256 tokens | min: 8 tokens, mean: 115.23 tokens, max: 256 tokens | min: 6 tokens, mean: 113.56 tokens, max: 256 tokens | min: 7 tokens, mean: 113.22 tokens, max: 256 tokens | min: 7 tokens, mean: 113.26 tokens, max: 256 tokens | min: 6 tokens, mean: 113.47 tokens, max: 256 tokens | min: 6 tokens, mean: 112.82 tokens, max: 256 tokens | min: 7 tokens, mean: 115.75 tokens, max: 256 tokens | min: 7 tokens, mean: 116.48 tokens, max: 256 tokens | min: 7 tokens, mean: 118.39 tokens, max: 256 tokens | min: 6 tokens, mean: 113.04 tokens, max: 256 tokens | min: 6 tokens, mean: 113.02 tokens, max: 256 tokens | min: 6 tokens, mean: 111.43 tokens, max: 256 tokens | min: 6 tokens, mean: 112.15 tokens, max: 256 tokens | min: 7 tokens, mean: 113.1 tokens, max: 256 tokens | min: 7 tokens, mean: 118.14 tokens, max: 256 tokens | min: 6 tokens, mean: 111.18 tokens, max: 256 tokens | min: 6 tokens, mean: 117.35 tokens, max: 256 tokens | min: 6 tokens, mean: 120.87 tokens, max: 256 tokens | min: 6 tokens, mean: 113.66 tokens, max: 256 tokens | min: 6 tokens, mean: 111.91 tokens, max: 256 tokens | min: 6 tokens, mean: 112.84 tokens, max: 256 tokens | min: 6 tokens, mean: 116.42 tokens, max: 256 tokens | min: 6 tokens, mean: 107.84 tokens, max: 256 tokens | min: 6 tokens, mean: 113.34 tokens, max: 256 tokens | min: 6 tokens, mean: 114.5 tokens, max: 256 tokens | min: 6 tokens, mean: 116.62 tokens, max: 256 tokens | min: 6 tokens, mean: 115.67 tokens, max: 256 tokens | min: 6 tokens, mean: 118.16 tokens, max: 256 tokens | min: 7 tokens, mean: 110.66 tokens, max: 256 tokens | min: 7 tokens, mean: 111.98 tokens, max: 256 tokens | min: 6 tokens, mean: 115.11 tokens, max: 256 tokens | min: 7 tokens, mean: 115.62 tokens, max: 256 tokens | min: 7 tokens, mean: 115.22 tokens, max: 256 tokens | min: 6 tokens, mean: 115.56 tokens, max: 256 tokens | min: 6 tokens, mean: 114.04 tokens, max: 256 tokens | min: 6 tokens, mean: 112.88 tokens, max: 256 tokens | min: 7 tokens, mean: 114.54 tokens, max: 256 tokens | min: 7 tokens, mean: 111.37 tokens, max: 256 tokens | min: 7 tokens, mean: 115.61 tokens, max: 256 tokens | min: 7 tokens, mean: 116.21 tokens, max: 256 tokens | min: 7 tokens, mean: 113.79 tokens, max: 256 tokens | min: 7 tokens, mean: 114.63 tokens, max: 256 tokens | min: 7 tokens, mean: 117.35 tokens, max: 256 tokens | min: 7 tokens, mean: 114.45 tokens, max: 256 tokens | min: 7 tokens, mean: 114.6 tokens, max: 256 tokens | min: 6 tokens, mean: 112.9 tokens, max: 256 tokens | min: 6 tokens, mean: 114.72 tokens, max: 256 tokens | min: 7 tokens, mean: 118.15 tokens, max: 256 tokens | min: 6 tokens, mean: 115.93 tokens, max: 256 tokens | min: 6 tokens, mean: 116.82 tokens, max: 256 tokens | min: 6 tokens, mean: 114.43 tokens, max: 256 tokens | min: 6 tokens, mean: 115.04 tokens, max: 256 tokens | min: 6 tokens, mean: 112.67 tokens, max: 256 tokens | min: 6 tokens, mean: 116.39 tokens, max: 256 tokens | min: 7 tokens, mean: 116.13 tokens, max: 256 tokens | size: 100 elements |
* Samples:
| query | document | negative_0 |
|:------|:---------|:-----------|
| <code>Example binToHex(["0111110", "1000000", "1000000", "1111110", "1000001", "1000001", "0111110"])</code> | <code>function binToHex(bins) {<br> return bins.map(bin => ("00" + (parseInt(bin, 2).toString(16))).substr(-2).toUpperCase()).join("");<br>}</code> | <code>function binToHex(a) {<br> var newVal = "";<br> for (i = 0; i < a.length/8; i++)<br> newVal += ("00" + parseInt(a.slice(8*i, 8*i+8),2).toString(16)).slice(-2);<br> return newVal;<br>}</code> |
* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
#### java
* Dataset: [java](https://huggingface.co/datasets/lightonai/cornstack) at [d821c55](https://huggingface.co/datasets/lightonai/cornstack/tree/d821c5591c1397bce8e0c59efa7af64ad0fbbeab)
* Size: 4,103,086 training samples
* Approximate statistics based on the first 1000 samples:
| | query | document | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | negative_21 | negative_22 | negative_23 | negative_24 | negative_25 | negative_26 | negative_27 | negative_28 | negative_29 | negative_30 | negative_31 | negative_32 | negative_33 | negative_34 | negative_35 | negative_36 | negative_37 | negative_38 | negative_39 | negative_40 | negative_41 | negative_42 | negative_43 | negative_44 | negative_45 | negative_46 | negative_47 | negative_48 | negative_49 | negative_50 | negative_51 | negative_52 | negative_53 | negative_54 | negative_55 | negative_56 | negative_57 | negative_58 | negative_59 | negative_60 | negative_61 | negative_62 | negative_63 | negative_64 | negative_65 | negative_66 | negative_67 | negative_68 | negative_69 | negative_70 | negative_71 | negative_72 | negative_73 | negative_74 | negative_75 | negative_76 | negative_77 | negative_78 | negative_79 | negative_80 | negative_81 | negative_82 | negative_83 | negative_84 | negative_85 | negative_86 | negative_87 | negative_88 | negative_89 | negative_90 | negative_91 | negative_92 | negative_93 | negative_94 | negative_95 | negative_96 | negative_97 | negative_98 | negative_99 | negative_scores |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | list |
| details | min: 7 tokens, mean: 21.71 tokens, max: 256 tokens | min: 7 tokens, mean: 77.42 tokens, max: 256 tokens | min: 6 tokens, mean: 71.37 tokens, max: 256 tokens | min: 6 tokens, mean: 69.8 tokens, max: 256 tokens | min: 6 tokens, mean: 72.44 tokens, max: 256 tokens | min: 6 tokens, mean: 69.17 tokens, max: 256 tokens | min: 6 tokens, mean: 71.06 tokens, max: 256 tokens | min: 6 tokens, mean: 67.69 tokens, max: 256 tokens | min: 6 tokens, mean: 69.77 tokens, max: 256 tokens | min: 6 tokens, mean: 72.27 tokens, max: 256 tokens | min: 7 tokens, mean: 69.94 tokens, max: 256 tokens | min: 6 tokens, mean: 74.24 tokens, max: 256 tokens | min: 6 tokens, mean: 69.71 tokens, max: 256 tokens | min: 6 tokens, mean: 76.42 tokens, max: 256 tokens | min: 6 tokens, mean: 69.51 tokens, max: 256 tokens | min: 6 tokens, mean: 75.04 tokens, max: 256 tokens | min: 6 tokens, mean: 74.85 tokens, max: 256 tokens | min: 6 tokens, mean: 70.02 tokens, max: 256 tokens | min: 6 tokens, mean: 73.83 tokens, max: 256 tokens | min: 6 tokens, mean: 75.32 tokens, max: 256 tokens | min: 6 tokens, mean: 72.47 tokens, max: 256 tokens | min: 6 tokens, mean: 73.81 tokens, max: 256 tokens | min: 6 tokens, mean: 74.18 tokens, max: 256 tokens | min: 6 tokens, mean: 74.24 tokens, max: 256 tokens | min: 7 tokens, mean: 75.02 tokens, max: 256 tokens | min: 6 tokens, mean: 74.13 tokens, max: 256 tokens | min: 6 tokens, mean: 74.43 tokens, max: 256 tokens | min: 6 tokens, mean: 73.84 tokens, max: 256 tokens | min: 6 tokens, mean: 74.28 tokens, max: 256 tokens | min: 6 tokens, mean: 73.97 tokens, max: 256 tokens | min: 6 tokens, mean: 76.87 tokens, max: 256 tokens | min: 6 tokens, mean: 73.57 tokens, max: 256 tokens | min: 6 tokens, mean: 73.81 tokens, max: 256 tokens | min: 6 tokens, mean: 73.33 tokens, max: 256 tokens | min: 6 tokens, mean: 73.83 tokens, max: 256 tokens | min: 6 tokens, mean: 74.7 tokens, max: 256 tokens | min: 6 tokens, mean: 76.17 tokens, max: 256 tokens | min: 6 tokens, mean: 71.5 tokens, max: 256 tokens | min: 6 tokens, mean: 72.62 tokens, max: 256 tokens | min: 6 tokens, mean: 74.7 tokens, max: 256 tokens | min: 7 tokens, mean: 76.15 tokens, max: 256 tokens | min: 7 tokens, mean: 73.69 tokens, max: 256 tokens | min: 7 tokens, mean: 76.57 tokens, max: 256 tokens | min: 6 tokens, mean: 79.51 tokens, max: 256 tokens | min: 6 tokens, mean: 74.77 tokens, max: 256 tokens | min: 6 tokens, mean: 75.84 tokens, max: 256 tokens | min: 6 tokens, mean: 78.24 tokens, max: 256 tokens | min: 6 tokens, mean: 76.83 tokens, max: 256 tokens | min: 6 tokens, mean: 74.52 tokens, max: 256 tokens | min: 6 tokens, mean: 77.46 tokens, max: 256 tokens | min: 6 tokens, mean: 77.25 tokens, max: 256 tokens | min: 6 tokens, mean: 76.99 tokens, max: 256 tokens | min: 6 tokens, mean: 71.1 tokens, max: 256 tokens | min: 6 tokens, mean: 75.7 tokens, max: 256 tokens | min: 6 tokens, mean: 74.21 tokens, max: 256 tokens | min: 6 tokens, mean: 77.32 tokens, max: 256 tokens | min: 6 tokens, mean: 74.31 tokens, max: 256 tokens | min: 6 tokens, mean: 75.1 tokens, max: 256 tokens | min: 6 tokens, mean: 75.92 tokens, max: 256 tokens | min: 6 tokens, mean: 73.22 tokens, max: 256 tokens | min: 6 tokens, mean: 77.19 tokens, max: 256 tokens | min: 6 tokens, mean: 77.92 tokens, max: 256 tokens | min: 6 tokens, mean: 76.66 tokens, max: 256 tokens | min: 6 tokens, mean: 78.32 tokens, max: 256 tokens | min: 6 tokens, mean: 75.27 tokens, max: 256 tokens | min: 6 tokens, mean: 75.74 tokens, max: 256 tokens | min: 6 tokens, mean: 75.07 tokens, max: 256 tokens | min: 6 tokens, mean: 77.43 tokens, max: 256 tokens | min: 6 tokens, mean: 75.44 tokens, max: 256 tokens | min: 6 tokens, mean: 78.1 tokens, max: 256 tokens | min: 6 tokens, mean: 72.81 tokens, max: 256 tokens | min: 6 tokens, mean: 77.38 tokens, max: 256 tokens | min: 6 tokens, mean: 73.94 tokens, max: 256 tokens | min: 6 tokens, mean: 82.52 tokens, max: 256 tokens | min: 6 tokens, mean: 75.75 tokens, max: 256 tokens | min: 6 tokens, mean: 76.62 tokens, max: 256 tokens | min: 6 tokens, mean: 76.24 tokens, max: 256 tokens | min: 6 tokens, mean: 75.22 tokens, max: 256 tokens | min: 6 tokens, mean: 78.36 tokens, max: 256 tokens | min: 6 tokens, mean: 76.99 tokens, max: 256 tokens | min: 6 tokens, mean: 78.3 tokens, max: 256 tokens | min: 6 tokens, mean: 76.42 tokens, max: 256 tokens | min: 6 tokens, mean: 73.43 tokens, max: 256 tokens | min: 6 tokens, mean: 75.98 tokens, max: 256 tokens | min: 6 tokens, mean: 76.06 tokens, max: 256 tokens | min: 6 tokens, mean: 78.0 tokens, max: 256 tokens | min: 6 tokens, mean: 75.96 tokens, max: 256 tokens | min: 6 tokens, mean: 77.93 tokens, max: 256 tokens | min: 6 tokens, mean: 75.44 tokens, max: 256 tokens | min: 6 tokens, mean: 76.37 tokens, max: 256 tokens | min: 6 tokens, mean: 76.35 tokens, max: 256 tokens | min: 6 tokens, mean: 79.68 tokens, max: 256 tokens | min: 6 tokens, mean: 77.05 tokens, max: 256 tokens | min: 6 tokens, mean: 74.21 tokens, max: 256 tokens | min: 6 tokens, mean: 77.08 tokens, max: 256 tokens | min: 6 tokens, mean: 76.61 tokens, max: 256 tokens | min: 6 tokens, mean: 75.78 tokens, max: 256 tokens | min: 6 tokens, mean: 81.28 tokens, max: 256 tokens | min: 6 tokens, mean: 73.4 tokens, max: 256 tokens | min: 6 tokens, mean: 79.05 tokens, max: 256 tokens | min: 6 tokens, mean: 80.35 tokens, max: 256 tokens | min: 6 tokens, mean: 74.41 tokens, max: 256 tokens | size: 100 elements |
* Samples:
| query | document | negative_0 |
|:------|:---------|:-----------|
| <code>private void signSetter(String[] lines, Player p, Block s)</code> | <code>private void signSetter(Block b, Player p, String[] lines) <br> { <br> //TODO: virer debug<br> //p.sendMessage("dbg1");<br> <br> <br> if(b==null) <br> return;<br> <br> BoutiqueSign bs = new BoutiqueSign();<br> <br> bs.setOwner(p);<br> bs.setLocation(b.getLocation());<br> bs.setLines(lines);<br><br> //TODO: virer debug<br> /*<br> p.sendMessage("dbg1 : line1 = " + bs.getLine1());<br> p.sendMessage("dbg1 : line2 = " + bs.getLine2());<br> p.sendMessage("dbg1 : line3 = " + bs.getLine3());<br> p.sendMessage("dbg1 : line4 = " + bs.getLine4()); <br> p.sendMessage("dbg2 : type = " + bs.getType());<br> */<br> <br> if(bs.isSignServer())<br> {<br> <br> if (!PermissionsHandler.canSetGlobalSign(p))<br> {<br> p.sendMessage(PermissionsHandler.permissionErr);<br> return;<br> }<br> <br> if(!bs.checkLines(p))<br> {<br> return;<br> }<br> <br> p.sendMessage(plugin.chatPrefix + Messages.getString("Sign.SERVERSIGNADDED")); //$NON-NLS-1$<br> }<br> <br> else if(bs.isSignChest())<br> {<br> if (!PermissionsHandler.canSetPersonalSign(p))<br> {<br> p.sendMessage(plugin.chatPrefix +...</code> | <code>void updateSignToPlayer(Player player, Location location, String[] lines);</code> |
* Loss: <code>pylate.losses.cached_contrastive.CachedContrastive</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 6e-05
- `num_train_epochs`: 1
- `bf16`: True
- `dataloader_num_workers`: 8
- `accelerator_config`: {'split_batches': True, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 6e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 6
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 8
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': True, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | CodeSearchNetPython_MaxSim_ndcg@10 | CodeSearchNetJavascript_MaxSim_ndcg@10 | CodeSearchNetGo_MaxSim_ndcg@10 | CodeSearchNetRuby_MaxSim_ndcg@10 | CodeSearchNetJava_MaxSim_ndcg@10 | CodeSearchNetPhp_MaxSim_ndcg@10 | CodeSearchNet_mean_MaxSim_ndcg@10 |
|:------:|:------:|:-------------:|:----------------------------------:|:--------------------------------------:|:------------------------------:|:--------------------------------:|:--------------------------------:|:-------------------------------:|:---------------------------------:|
| 0.0000 | 1 | 1.7028 | - | - | - | - | - | - | - |
| 0.0298 | 5000 | 0.3527 | 0.9190 | 0.7719 | 0.9532 | 0.8199 | 0.8163 | 0.8655 | 0.8576 |
| 0.0595 | 10000 | 0.3463 | 0.9223 | 0.7770 | 0.9576 | 0.8351 | 0.8507 | 0.8670 | 0.8683 |
| 0.0893 | 15000 | 0.3659 | 0.9267 | 0.7768 | 0.9587 | 0.8347 | 0.8358 | 0.8669 | 0.8666 |
| 0.1191 | 20000 | 0.2052 | 0.9288 | 0.7777 | 0.9566 | 0.8307 | 0.8425 | 0.8681 | 0.8674 |
| 0.1488 | 25000 | 0.4141 | 0.9322 | 0.7815 | 0.9589 | 0.8336 | 0.8516 | 0.8756 | 0.8722 |
| 0.1786 | 30000 | 0.1963 | 0.9320 | 0.7844 | 0.9637 | 0.8393 | 0.8588 | 0.8764 | 0.8758 |
| 0.2084 | 35000 | 0.177 | 0.9311 | 0.7838 | 0.9595 | 0.8416 | 0.8647 | 0.8778 | 0.8764 |
| 0.2381 | 40000 | 0.2023 | 0.9316 | 0.7882 | 0.9590 | 0.8448 | 0.8431 | 0.8710 | 0.8729 |
| 0.2679 | 45000 | 0.2902 | 0.9321 | 0.7864 | 0.9611 | 0.8426 | 0.8507 | 0.8665 | 0.8732 |
| 0.2976 | 50000 | 0.3503 | 0.9316 | 0.7825 | 0.9610 | 0.8399 | 0.8588 | 0.8731 | 0.8745 |
| 0.3274 | 55000 | 0.2677 | 0.9363 | 0.7904 | 0.9630 | 0.8441 | 0.8667 | 0.8755 | 0.8793 |
| 0.3572 | 60000 | 0.2907 | 0.9391 | 0.7909 | 0.9650 | 0.8434 | 0.8497 | 0.8787 | 0.8778 |
| 0.3869 | 65000 | 0.3091 | 0.9395 | 0.7905 | 0.9630 | 0.8477 | 0.8611 | 0.8787 | 0.8801 |
| 0.4167 | 70000 | 0.3065 | 0.9358 | 0.7904 | 0.9636 | 0.8484 | 0.8686 | 0.8764 | 0.8805 |
| 0.4465 | 75000 | 0.192 | 0.9385 | 0.7910 | 0.9641 | 0.8527 | 0.8864 | 0.8794 | 0.8854 |
| 0.4762 | 80000 | 0.2751 | 0.9414 | 0.7936 | 0.9620 | 0.8462 | 0.8729 | 0.8769 | 0.8822 |
| 0.5060 | 85000 | 0.4214 | 0.9399 | 0.7887 | 0.9630 | 0.8503 | 0.8722 | 0.8774 | 0.8819 |
| 0.5358 | 90000 | 0.3068 | 0.9355 | 0.7999 | 0.9659 | 0.8461 | 0.8739 | 0.8817 | 0.8838 |
| 0.5655 | 95000 | 0.4011 | 0.9370 | 0.7953 | 0.9660 | 0.8502 | 0.8662 | 0.8809 | 0.8826 |
| 0.5953 | 100000 | 0.3784 | 0.9401 | 0.7951 | 0.9650 | 0.8473 | 0.8687 | 0.8769 | 0.8822 |
| 0.6251 | 105000 | 0.3102 | 0.9397 | 0.7979 | 0.9647 | 0.8517 | 0.8771 | 0.8802 | 0.8852 |
| 0.6548 | 110000 | 0.1732 | 0.9383 | 0.7957 | 0.9627 | 0.8551 | 0.8765 | 0.8798 | 0.8847 |
| 0.6846 | 115000 | 0.1759 | 0.9419 | 0.7986 | 0.9635 | 0.8510 | 0.8760 | 0.8770 | 0.8847 |
| 0.7143 | 120000 | 0.2477 | 0.9381 | 0.7962 | 0.9669 | 0.8534 | 0.8607 | 0.8796 | 0.8825 |
| 0.7441 | 125000 | 0.2555 | 0.9395 | 0.7998 | 0.9653 | 0.8526 | 0.8714 | 0.8805 | 0.8848 |
| 0.7739 | 130000 | 0.2151 | 0.9408 | 0.7975 | 0.9657 | 0.8538 | 0.8795 | 0.8810 | 0.8864 |
| 0.8036 | 135000 | 0.2073 | 0.9428 | 0.7987 | 0.9646 | 0.8558 | 0.8767 | 0.8806 | 0.8865 |
| 0.8334 | 140000 | 0.1641 | 0.9367 | 0.7957 | 0.9664 | 0.8556 | 0.8782 | 0.8832 | 0.8860 |
| 0.8632 | 145000 | 0.1639 | 0.9418 | 0.7980 | 0.9642 | 0.8536 | 0.8821 | 0.8807 | 0.8867 |
| 0.8929 | 150000 | 0.2177 | 0.9427 | 0.7979 | 0.9639 | 0.8560 | 0.8905 | 0.8804 | 0.8886 |
| 0.9227 | 155000 | 0.1416 | 0.9412 | 0.7999 | 0.9646 | 0.8550 | 0.8820 | 0.8810 | 0.8873 |
| 0.9525 | 160000 | 0.285 | 0.9417 | 0.7985 | 0.9664 | 0.8561 | 0.8837 | 0.8813 | 0.8879 |
| 0.9822 | 165000 | 0.6056 | 0.9420 | 0.8002 | 0.9659 | 0.8574 | 0.8841 | 0.8806 | 0.8884 |
| 1.0 | 167985 | 0.2891 | - | - | - | - | - | - | - |
</details>
### Framework Versions
- Python: 3.12.11
- Sentence Transformers: 5.1.1
- PyLate: 1.3.4
- Transformers: 4.52.3
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.1
- Datasets: 4.4.1
- Tokenizers: 0.21.4
## Citation
### BibTeX
#### LateOn-Code
```bibtex
@misc{LateOn-Code,
title = {LateOn-Code: a Family of State-Of-The-Art Late Interaction Code Retrieval Models},
author = {Chaffin, Antoine},
url = {https://huggingface.co/collections/lightonai/lateon-code},
year = {2026}
}
```
#### ColGrep
```bibtex
@software{next-plaid,
title = {NextPlaid, ColGREP: Multi-vector search, from database to coding agents.},
url = {https://github.com/lightonai/next-plaid},
author = {Raphaël Sourty},
year = {2026},
}
```
#### CoRNStack
```bibtex
@inproceedings{DBLP:conf/iclr/SureshRXNMDJ25,
author = {Tarun Suresh and
Revanth Gangi Reddy and
Yifei Xu and
Zach Nussbaum and
Andriy Mulyar and
Brandon Duderstadt and
Heng Ji},
title = {CoRNStack: High-Quality Contrastive Data for Better Code Retrieval
and Reranking},
booktitle = {The Thirteenth International Conference on Learning Representations,
{ICLR} 2025, Singapore, April 24-28, 2025},
publisher = {OpenReview.net},
year = {2025},
url = {https://openreview.net/forum?id=iyJOUELYir},
timestamp = {Sun, 25 May 2025 21:25:19 +0200},
biburl = {https://dblp.org/rec/conf/iclr/SureshRXNMDJ25.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
#### CoIR
```bibtex
@inproceedings{li2025coir,
title = {Coir: A comprehensive benchmark for code information retrieval models},
author = {Li, Xiangyang and Dong, Kuicai and Lee, Yi Quan and Xia, Wei and Zhang, Hao and Dai, Xinyi and Wang, Yasheng and Tang, Ruiming},
booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages = {22074--22091},
year = {2025}
}
```
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084"
}
```
#### PyLate
```bibtex
@inproceedings{DBLP:conf/cikm/ChaffinS25,
author = {Antoine Chaffin and
Rapha{"{e}}l Sourty},
editor = {Meeyoung Cha and
Chanyoung Park and
Noseong Park and
Carl Yang and
Senjuti Basu Roy and
Jessie Li and
Jaap Kamps and
Kijung Shin and
Bryan Hooi and
Lifang He},
title = {PyLate: Flexible Training and Retrieval for Late Interaction Models},
booktitle = {Proceedings of the 34th {ACM} International Conference on Information
and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November
10-14, 2025},
pages = {6334--6339},
publisher = {{ACM}},
year = {2025},
url = {https://github.com/lightonai/pylate},
doi = {10.1145/3746252.3761608},
}
```
#### CachedContrastive
```bibtex
@misc{gao2021scaling,
title = {Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author = {Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year = {2021},
eprint = {2101.06983},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |