CocoRoF / KMB_SimCSE

huggingface.co
Total runs: 4
24-hour runs: 0
7-day runs: -4
30-day runs: -2
Model's Last Updated: February 02 2025

Introduction of KMB_SimCSE

Model Details of KMB_SimCSE

KMB_SimCSE

This model is a fine-tuned version of x2bee/KoModernBERT-base-mlm on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0387
  • Pearson Cosine: 0.7824
  • Spearman Cosine: 0.7845
  • Pearson Manhattan: 0.7335
  • Spearman Manhattan: 0.7460
  • Pearson Euclidean: 0.7337
  • Spearman Euclidean: 0.7463
  • Pearson Dot: 0.6362
  • Spearman Dot: 0.6532
Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure
Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10.0
Training results
Training Loss Epoch Step Validation Loss Pearson Cosine Spearman Cosine Pearson Manhattan Spearman Manhattan Pearson Euclidean Spearman Euclidean Pearson Dot Spearman Dot
1.0084 0.1172 250 0.1579 0.6838 0.6994 0.6615 0.6693 0.6621 0.6694 0.3480 0.3442
0.7072 0.2343 500 0.1364 0.7226 0.7375 0.7207 0.7263 0.7214 0.7271 0.4002 0.3910
0.6207 0.3515 750 0.1194 0.7371 0.7509 0.7295 0.7398 0.7300 0.7401 0.4517 0.4462
0.5767 0.4686 1000 0.1147 0.7508 0.7636 0.7395 0.7502 0.7400 0.7511 0.5170 0.5181
0.5026 0.5858 1250 0.1047 0.7507 0.7635 0.7455 0.7558 0.7459 0.7564 0.5487 0.5531
0.5192 0.7029 1500 0.1166 0.7522 0.7673 0.7487 0.7591 0.7489 0.7594 0.5055 0.5053
0.5046 0.8201 1750 0.1110 0.7555 0.7675 0.7582 0.7675 0.7581 0.7672 0.5303 0.5391
0.5055 0.9372 2000 0.1062 0.7546 0.7726 0.7501 0.7650 0.7502 0.7651 0.5638 0.5710
0.4177 1.0544 2250 0.0942 0.7577 0.7709 0.7511 0.7635 0.7510 0.7633 0.5577 0.5635
0.4136 1.1715 2500 0.0915 0.7612 0.7727 0.7584 0.7696 0.7586 0.7696 0.5554 0.5595
0.4425 1.2887 2750 0.0928 0.7605 0.7726 0.7461 0.7591 0.7463 0.7592 0.5498 0.5512
0.3708 1.4058 3000 0.0819 0.7670 0.7783 0.7478 0.7634 0.7481 0.7637 0.5834 0.5847
0.3934 1.5230 3250 0.0848 0.7709 0.7814 0.7539 0.7692 0.7542 0.7689 0.5655 0.5668
0.3203 1.6401 3500 0.0781 0.7706 0.7810 0.7529 0.7689 0.7531 0.7691 0.5871 0.5891
0.4052 1.7573 3750 0.0824 0.7705 0.7816 0.7628 0.7771 0.7628 0.7771 0.5909 0.5989
0.3723 1.8744 4000 0.0819 0.7720 0.7840 0.7515 0.7679 0.7520 0.7685 0.5711 0.5713
0.3645 1.9916 4250 0.0802 0.7676 0.7804 0.7560 0.7704 0.7560 0.7703 0.5685 0.5701
0.3007 2.1087 4500 0.0662 0.7682 0.7799 0.7572 0.7721 0.7574 0.7721 0.5973 0.5981
0.2397 2.2259 4750 0.0617 0.7693 0.7782 0.7501 0.7655 0.7502 0.7652 0.5855 0.5898
0.28 2.3430 5000 0.0645 0.7654 0.7760 0.7567 0.7705 0.7569 0.7705 0.5925 0.5970
0.2631 2.4602 5250 0.0639 0.7712 0.7798 0.7561 0.7705 0.7562 0.7705 0.5715 0.5731
0.2488 2.5773 5500 0.0636 0.7736 0.7838 0.7537 0.7687 0.7538 0.7685 0.5835 0.5861
0.2557 2.6945 5750 0.0614 0.7739 0.7830 0.7570 0.7716 0.7571 0.7717 0.6008 0.6041
0.2699 2.8116 6000 0.0636 0.7722 0.7795 0.7570 0.7699 0.7572 0.7701 0.5844 0.5864
0.2794 2.9288 6250 0.0639 0.7704 0.7800 0.7582 0.7745 0.7581 0.7746 0.5817 0.5793
0.1778 3.0459 6500 0.0526 0.7738 0.7811 0.7574 0.7739 0.7573 0.7739 0.6193 0.6255
0.1791 3.1631 6750 0.0519 0.7728 0.7783 0.7540 0.7704 0.7538 0.7700 0.6116 0.6182
0.201 3.2802 7000 0.0511 0.7755 0.7825 0.7506 0.7671 0.7503 0.7670 0.6039 0.6071
0.225 3.3974 7250 0.0513 0.7684 0.7749 0.7515 0.7689 0.7514 0.7692 0.5867 0.5894
0.1748 3.5145 7500 0.0502 0.7752 0.7801 0.7459 0.7630 0.7461 0.7636 0.5877 0.5949
0.2045 3.6317 7750 0.0512 0.7787 0.7856 0.7457 0.7636 0.7460 0.7642 0.6113 0.6156
0.1821 3.7488 8000 0.0502 0.7782 0.7842 0.7543 0.7707 0.7545 0.7710 0.6045 0.6069
0.1783 3.8660 8250 0.0491 0.7772 0.7829 0.7455 0.7630 0.7459 0.7637 0.5915 0.5984
0.2055 3.9831 8500 0.0504 0.7776 0.7832 0.7476 0.7658 0.7480 0.7662 0.5959 0.6017
0.1345 4.1003 8750 0.0467 0.7762 0.7802 0.7429 0.7606 0.7435 0.7611 0.6206 0.6303
0.1506 4.2174 9000 0.0477 0.7711 0.7759 0.7466 0.7625 0.7473 0.7631 0.5978 0.6025
0.1565 4.3346 9250 0.0477 0.7717 0.7768 0.7481 0.7641 0.7486 0.7645 0.6026 0.6102
0.1577 4.4517 9500 0.0442 0.7794 0.7824 0.7439 0.7627 0.7444 0.7630 0.6182 0.6291
0.1463 4.5689 9750 0.0456 0.7764 0.7821 0.7401 0.7602 0.7405 0.7604 0.5941 0.5991
0.16 4.6860 10000 0.0460 0.7749 0.7793 0.7495 0.7658 0.7498 0.7660 0.6140 0.6192
0.148 4.8032 10250 0.0436 0.7817 0.7855 0.7421 0.7596 0.7425 0.7601 0.6171 0.6239
0.1382 4.9203 10500 0.0446 0.7824 0.7872 0.7437 0.7620 0.7443 0.7625 0.6330 0.6424
0.1109 5.0375 10750 0.0426 0.7796 0.7846 0.7431 0.7600 0.7434 0.7602 0.6195 0.6249
0.1009 5.1546 11000 0.0431 0.7807 0.7835 0.7423 0.7591 0.7428 0.7591 0.6237 0.6377
0.1082 5.2718 11250 0.0438 0.7774 0.7818 0.7430 0.7591 0.7433 0.7593 0.6039 0.6129
0.1138 5.3889 11500 0.0415 0.7829 0.7870 0.7405 0.7560 0.7410 0.7561 0.6347 0.6464
0.1015 5.5061 11750 0.0420 0.7778 0.7811 0.7437 0.7592 0.7435 0.7589 0.6249 0.6370
0.1153 5.6232 12000 0.0448 0.7730 0.7784 0.7451 0.7598 0.7453 0.7596 0.6141 0.6214
0.1269 5.7404 12250 0.0420 0.7802 0.7840 0.7413 0.7562 0.7417 0.7564 0.6217 0.6311
0.0888 5.8575 12500 0.0414 0.7805 0.7841 0.7408 0.7567 0.7412 0.7568 0.6245 0.6365
0.1202 5.9747 12750 0.0431 0.7793 0.7835 0.7412 0.7572 0.7414 0.7575 0.6261 0.6405
0.0941 6.0918 13000 0.0399 0.7838 0.7873 0.7388 0.7527 0.7391 0.7530 0.6493 0.6642
0.081 6.2090 13250 0.0405 0.7814 0.7854 0.7353 0.7513 0.7355 0.7514 0.6356 0.6478
0.0807 6.3261 13500 0.0401 0.7838 0.7879 0.7339 0.7510 0.7344 0.7513 0.6450 0.6615
0.0863 6.4433 13750 0.0405 0.7814 0.7841 0.7404 0.7587 0.7408 0.7589 0.6324 0.6479
0.0948 6.5604 14000 0.0397 0.7830 0.7866 0.7410 0.7578 0.7415 0.7579 0.6308 0.6460
0.0919 6.6776 14250 0.0409 0.7820 0.7858 0.7402 0.7545 0.7403 0.7544 0.6341 0.6459
0.0784 6.7948 14500 0.0408 0.7794 0.7839 0.7308 0.7495 0.7312 0.7494 0.6306 0.6427
0.0821 6.9119 14750 0.0406 0.7789 0.7822 0.7265 0.7446 0.7270 0.7446 0.6377 0.6567
0.0792 7.0291 15000 0.0401 0.7800 0.7833 0.7398 0.7569 0.7405 0.7572 0.6338 0.6467
0.0698 7.1462 15250 0.0396 0.7822 0.7855 0.7341 0.7507 0.7346 0.7509 0.6381 0.6552
0.0699 7.2634 15500 0.0392 0.7820 0.7851 0.7322 0.7502 0.7325 0.7502 0.6466 0.6629
0.0739 7.3805 15750 0.0389 0.7865 0.7886 0.7323 0.7491 0.7328 0.7495 0.6412 0.6589
0.0745 7.4977 16000 0.0397 0.7794 0.7827 0.7366 0.7524 0.7373 0.7524 0.6380 0.6504
0.0779 7.6148 16250 0.0391 0.7826 0.7846 0.7326 0.7462 0.7333 0.7467 0.6372 0.6532
0.078 7.7320 16500 0.0397 0.7810 0.7826 0.7299 0.7461 0.7300 0.7457 0.6364 0.6555
0.0699 7.8491 16750 0.0405 0.7811 0.7837 0.7308 0.7468 0.7312 0.7470 0.6315 0.6426
0.0735 7.9663 17000 0.0394 0.7804 0.7823 0.7320 0.7455 0.7326 0.7462 0.6468 0.6607
0.0682 8.0834 17250 0.0386 0.7845 0.7869 0.7306 0.7447 0.7311 0.7449 0.6431 0.6613
0.0526 8.2006 17500 0.0389 0.7824 0.7832 0.7272 0.7431 0.7275 0.7431 0.6370 0.6539
0.0558 8.3177 17750 0.0385 0.7856 0.7865 0.7370 0.7513 0.7376 0.7518 0.6517 0.6679
0.0633 8.4349 18000 0.0392 0.7822 0.7845 0.7388 0.7537 0.7395 0.7542 0.6512 0.6664
0.0568 8.5520 18250 0.0389 0.7826 0.7831 0.7358 0.7510 0.7362 0.7509 0.6378 0.6536
0.0645 8.6692 18500 0.0377 0.7888 0.7892 0.7315 0.7495 0.7319 0.7499 0.6514 0.6704
0.0563 8.7863 18750 0.0376 0.7870 0.7878 0.7285 0.7451 0.7289 0.7454 0.6393 0.6606
0.0669 8.9035 19000 0.0383 0.7850 0.7866 0.7238 0.7433 0.7244 0.7437 0.6359 0.6571
0.0436 9.0206 19250 0.0377 0.7855 0.7856 0.7289 0.7462 0.7293 0.7465 0.6489 0.6696
0.047 9.1378 19500 0.0377 0.7870 0.7882 0.7249 0.7414 0.7254 0.7413 0.6459 0.6694
0.0482 9.2549 19750 0.0377 0.7863 0.7871 0.7296 0.7442 0.7306 0.7449 0.6498 0.6690
0.0529 9.3721 20000 0.0377 0.7873 0.7888 0.7285 0.7423 0.7290 0.7426 0.6490 0.6690
0.0429 9.4892 20250 0.0378 0.7868 0.7883 0.7286 0.7426 0.7292 0.7431 0.6503 0.6684
0.0534 9.6064 20500 0.0380 0.7861 0.7881 0.7300 0.7443 0.7305 0.7451 0.6446 0.6635
0.0531 9.7235 20750 0.0375 0.7886 0.7894 0.7350 0.7492 0.7356 0.7498 0.6442 0.6634
0.0464 9.8407 21000 0.0380 0.7861 0.7871 0.7314 0.7464 0.7320 0.7468 0.6415 0.6600
0.0406 9.9578 21250 0.0387 0.7824 0.7845 0.7335 0.7460 0.7337 0.7463 0.6362 0.6532
Framework versions
  • Transformers 4.48.0.dev0
  • Pytorch 2.5.1+cu121
  • Datasets 3.1.0
  • Tokenizers 0.21.0

Runs of CocoRoF KMB_SimCSE on huggingface.co

4
Total runs
0
24-hour runs
-1
3-day runs
-4
7-day runs
-2
30-day runs

More Information About KMB_SimCSE huggingface.co Model

More KMB_SimCSE license Visit here:

https://choosealicense.com/licenses/apache-2.0

KMB_SimCSE huggingface.co

KMB_SimCSE huggingface.co is an AI model on huggingface.co that provides KMB_SimCSE's model effect (), which can be used instantly with this CocoRoF KMB_SimCSE model. huggingface.co supports a free trial of the KMB_SimCSE model, and also provides paid use of the KMB_SimCSE. Support call KMB_SimCSE model through api, including Node.js, Python, http.

KMB_SimCSE huggingface.co Url

https://huggingface.co/CocoRoF/KMB_SimCSE

CocoRoF KMB_SimCSE online free

KMB_SimCSE huggingface.co is an online trial and call api platform, which integrates KMB_SimCSE's modeling effects, including api services, and provides a free online trial of KMB_SimCSE, you can try KMB_SimCSE online for free by clicking the link below.

CocoRoF KMB_SimCSE online free url in huggingface.co:

https://huggingface.co/CocoRoF/KMB_SimCSE

KMB_SimCSE install

KMB_SimCSE is an open source model from GitHub that offers a free installation service, and any user can find KMB_SimCSE on GitHub to install. At the same time, huggingface.co provides the effect of KMB_SimCSE install, users can directly use KMB_SimCSE installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

KMB_SimCSE install url in huggingface.co:

https://huggingface.co/CocoRoF/KMB_SimCSE

Url of KMB_SimCSE

KMB_SimCSE huggingface.co Url

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