Hugging Face's logo

CocoRoF
/
KMB_SimCSE

Transformers
Safetensors
modernbert
Generated from Trainer
Model card Files
xet
Community
CocoRoF commited on
Commit
47e083a
·
verified ·
1 Parent(s): d2d93d9

test Done

Browse files
Files changed (1) hide show
  1. README.md +103 -52
README.md CHANGED
@@ -1,30 +1,30 @@
1
  ---
2
  library_name: transformers
3
  license: apache-2.0
4
- base_model: CocoRoF/KoModernBERT-chp-11
5
  tags:
6
  - generated_from_trainer
7
  model-index:
8
- - name: KMB_SimCSE_test
9
  results: []
10
  ---
11
 
12
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
  should probably proofread and complete it, then remove this comment. -->
14
 
15
- # KMB_SimCSE_test
16
 
17
- This model is a fine-tuned version of [CocoRoF/KoModernBERT-chp-11](https://huggingface.co/CocoRoF/KoModernBERT-chp-11) on an unknown dataset.
18
  It achieves the following results on the evaluation set:
19
- - Loss: 0.0438
20
- - Pearson Cosine: 0.7947
21
- - Spearman Cosine: 0.7992
22
- - Pearson Manhattan: 0.7493
23
- - Spearman Manhattan: 0.7655
24
- - Pearson Euclidean: 0.7507
25
- - Spearman Euclidean: 0.7666
26
- - Pearson Dot: 0.6408
27
- - Spearman Dot: 0.6472
28
 
29
  ## Model description
30
 
@@ -43,9 +43,9 @@ More information needed
43
  ### Training hyperparameters
44
 
45
  The following hyperparameters were used during training:
46
- - learning_rate: 2e-05
47
  - train_batch_size: 16
48
- - eval_batch_size: 16
49
  - seed: 42
50
  - distributed_type: multi-GPU
51
  - gradient_accumulation_steps: 8
@@ -53,46 +53,97 @@ The following hyperparameters were used during training:
53
  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
54
  - lr_scheduler_type: linear
55
  - lr_scheduler_warmup_ratio: 0.1
56
- - num_epochs: 4.0
57
 
58
  ### Training results
59
 
60
- | Training Loss | Epoch | Step | Validation Loss | Pearson Cosine | Spearman Cosine | Pearson Manhattan | Spearman Manhattan | Pearson Euclidean | Spearman Euclidean | Pearson Dot | Spearman Dot |
61
- |:-------------:|:------:|:----:|:---------------:|:--------------:|:---------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:-----------:|:------------:|
62
- | 0.761 | 0.1172 | 250 | 0.1397 | 0.7191 | 0.7366 | 0.7129 | 0.7205 | 0.7135 | 0.7210 | 0.4342 | 0.4302 |
63
- | 0.6275 | 0.2343 | 500 | 0.1240 | 0.7535 | 0.7638 | 0.7442 | 0.7505 | 0.7442 | 0.7506 | 0.4527 | 0.4533 |
64
- | 0.5326 | 0.3515 | 750 | 0.1149 | 0.7540 | 0.7698 | 0.7320 | 0.7461 | 0.7327 | 0.7466 | 0.4786 | 0.4737 |
65
- | 0.4917 | 0.4686 | 1000 | 0.1028 | 0.7630 | 0.7778 | 0.7395 | 0.7532 | 0.7395 | 0.7531 | 0.5428 | 0.5404 |
66
- | 0.4451 | 0.5858 | 1250 | 0.0959 | 0.7634 | 0.7803 | 0.7505 | 0.7649 | 0.7508 | 0.7652 | 0.5909 | 0.5929 |
67
- | 0.4682 | 0.7029 | 1500 | 0.1057 | 0.7687 | 0.7855 | 0.7541 | 0.7681 | 0.7545 | 0.7685 | 0.5271 | 0.5190 |
68
- | 0.4489 | 0.8201 | 1750 | 0.0994 | 0.7658 | 0.7800 | 0.7505 | 0.7624 | 0.7514 | 0.7627 | 0.5765 | 0.5760 |
69
- | 0.4696 | 0.9372 | 2000 | 0.1055 | 0.7618 | 0.7835 | 0.7514 | 0.7669 | 0.7526 | 0.7675 | 0.5910 | 0.5835 |
70
- | 0.3474 | 1.0544 | 2250 | 0.0818 | 0.7663 | 0.7777 | 0.7527 | 0.7636 | 0.7536 | 0.7642 | 0.5774 | 0.5748 |
71
- | 0.319 | 1.1715 | 2500 | 0.0752 | 0.7753 | 0.7858 | 0.7589 | 0.7692 | 0.7592 | 0.7692 | 0.5929 | 0.5919 |
72
- | 0.3682 | 1.2887 | 2750 | 0.0767 | 0.7736 | 0.7851 | 0.7556 | 0.7667 | 0.7564 | 0.7671 | 0.5784 | 0.5785 |
73
- | 0.3033 | 1.4058 | 3000 | 0.0716 | 0.7836 | 0.7962 | 0.7590 | 0.7723 | 0.7600 | 0.7727 | 0.5987 | 0.5976 |
74
- | 0.3247 | 1.5230 | 3250 | 0.0768 | 0.7779 | 0.7911 | 0.7613 | 0.7731 | 0.7621 | 0.7735 | 0.5638 | 0.5623 |
75
- | 0.26 | 1.6401 | 3500 | 0.0686 | 0.7792 | 0.7902 | 0.7615 | 0.7733 | 0.7623 | 0.7734 | 0.6004 | 0.5998 |
76
- | 0.3216 | 1.7573 | 3750 | 0.0707 | 0.7851 | 0.7950 | 0.7668 | 0.7787 | 0.7677 | 0.7791 | 0.6098 | 0.6136 |
77
- | 0.3166 | 1.8744 | 4000 | 0.0719 | 0.7799 | 0.7911 | 0.7550 | 0.7693 | 0.7563 | 0.7701 | 0.5737 | 0.5754 |
78
- | 0.315 | 1.9916 | 4250 | 0.0710 | 0.7818 | 0.7925 | 0.7657 | 0.7780 | 0.7672 | 0.7790 | 0.5918 | 0.5930 |
79
- | 0.2117 | 2.1087 | 4500 | 0.0545 | 0.7772 | 0.7890 | 0.7551 | 0.7702 | 0.7567 | 0.7712 | 0.6059 | 0.6096 |
80
- | 0.1725 | 2.2259 | 4750 | 0.0544 | 0.7780 | 0.7868 | 0.7593 | 0.7714 | 0.7605 | 0.7721 | 0.6065 | 0.6128 |
81
- | 0.1985 | 2.3430 | 5000 | 0.0540 | 0.7818 | 0.7916 | 0.7621 | 0.7733 | 0.7626 | 0.7734 | 0.6017 | 0.6078 |
82
- | 0.1871 | 2.4602 | 5250 | 0.0527 | 0.7830 | 0.7898 | 0.7576 | 0.7718 | 0.7587 | 0.7724 | 0.5843 | 0.5894 |
83
- | 0.17 | 2.5773 | 5500 | 0.0521 | 0.7877 | 0.7959 | 0.7621 | 0.7746 | 0.7633 | 0.7753 | 0.6240 | 0.6246 |
84
- | 0.174 | 2.6945 | 5750 | 0.0528 | 0.7876 | 0.7949 | 0.7594 | 0.7713 | 0.7603 | 0.7716 | 0.6196 | 0.6234 |
85
- | 0.1896 | 2.8116 | 6000 | 0.0506 | 0.7848 | 0.7891 | 0.7595 | 0.7712 | 0.7606 | 0.7718 | 0.6052 | 0.6083 |
86
- | 0.1897 | 2.9288 | 6250 | 0.0549 | 0.7819 | 0.7902 | 0.7521 | 0.7664 | 0.7533 | 0.7667 | 0.5957 | 0.5981 |
87
- | 0.105 | 3.0459 | 6500 | 0.0450 | 0.7887 | 0.7931 | 0.7516 | 0.7669 | 0.7527 | 0.7675 | 0.6385 | 0.6450 |
88
- | 0.1055 | 3.1631 | 6750 | 0.0460 | 0.7875 | 0.7927 | 0.7515 | 0.7652 | 0.7525 | 0.7657 | 0.6256 | 0.6332 |
89
- | 0.1145 | 3.2802 | 7000 | 0.0453 | 0.7925 | 0.7977 | 0.7548 | 0.7671 | 0.7559 | 0.7678 | 0.6316 | 0.6408 |
90
- | 0.1252 | 3.3974 | 7250 | 0.0470 | 0.7889 | 0.7947 | 0.7561 | 0.7683 | 0.7571 | 0.7693 | 0.6257 | 0.6283 |
91
- | 0.1058 | 3.5145 | 7500 | 0.0446 | 0.7913 | 0.7958 | 0.7572 | 0.7714 | 0.7578 | 0.7715 | 0.6221 | 0.6338 |
92
- | 0.1144 | 3.6317 | 7750 | 0.0433 | 0.7939 | 0.7989 | 0.7534 | 0.7673 | 0.7542 | 0.7677 | 0.6519 | 0.6583 |
93
- | 0.0971 | 3.7488 | 8000 | 0.0438 | 0.7952 | 0.7993 | 0.7537 | 0.7675 | 0.7547 | 0.7679 | 0.6345 | 0.6383 |
94
- | 0.1107 | 3.8660 | 8250 | 0.0432 | 0.7953 | 0.7992 | 0.7507 | 0.7673 | 0.7518 | 0.7675 | 0.6355 | 0.6411 |
95
- | 0.1232 | 3.9831 | 8500 | 0.0438 | 0.7947 | 0.7992 | 0.7493 | 0.7655 | 0.7507 | 0.7666 | 0.6408 | 0.6472 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
96
 
97
 
98
  ### Framework versions
 
1
  ---
2
  library_name: transformers
3
  license: apache-2.0
4
+ base_model: x2bee/KoModernBERT-base-mlm
5
  tags:
6
  - generated_from_trainer
7
  model-index:
8
+ - name: KMB_SimCSE
9
  results: []
10
  ---
11
 
12
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
  should probably proofread and complete it, then remove this comment. -->
14
 
15
+ # KMB_SimCSE
16
 
17
+ This model is a fine-tuned version of [x2bee/KoModernBERT-base-mlm](https://huggingface.co/x2bee/KoModernBERT-base-mlm) on an unknown dataset.
18
  It achieves the following results on the evaluation set:
19
+ - Loss: 0.0387
20
+ - Pearson Cosine: 0.7824
21
+ - Spearman Cosine: 0.7845
22
+ - Pearson Manhattan: 0.7335
23
+ - Spearman Manhattan: 0.7460
24
+ - Pearson Euclidean: 0.7337
25
+ - Spearman Euclidean: 0.7463
26
+ - Pearson Dot: 0.6362
27
+ - Spearman Dot: 0.6532
28
 
29
  ## Model description
30
 
 
43
  ### Training hyperparameters
44
 
45
  The following hyperparameters were used during training:
46
+ - learning_rate: 1e-05
47
  - train_batch_size: 16
48
+ - eval_batch_size: 1
49
  - seed: 42
50
  - distributed_type: multi-GPU
51
  - gradient_accumulation_steps: 8
 
53
  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
54
  - lr_scheduler_type: linear
55
  - lr_scheduler_warmup_ratio: 0.1
56
+ - num_epochs: 10.0
57
 
58
  ### Training results
59
 
60
+ | Training Loss | Epoch | Step | Validation Loss | Pearson Cosine | Spearman Cosine | Pearson Manhattan | Spearman Manhattan | Pearson Euclidean | Spearman Euclidean | Pearson Dot | Spearman Dot |
61
+ |:-------------:|:------:|:-----:|:---------------:|:--------------:|:---------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:-----------:|:------------:|
62
+ | 1.0084 | 0.1172 | 250 | 0.1579 | 0.6838 | 0.6994 | 0.6615 | 0.6693 | 0.6621 | 0.6694 | 0.3480 | 0.3442 |
63
+ | 0.7072 | 0.2343 | 500 | 0.1364 | 0.7226 | 0.7375 | 0.7207 | 0.7263 | 0.7214 | 0.7271 | 0.4002 | 0.3910 |
64
+ | 0.6207 | 0.3515 | 750 | 0.1194 | 0.7371 | 0.7509 | 0.7295 | 0.7398 | 0.7300 | 0.7401 | 0.4517 | 0.4462 |
65
+ | 0.5767 | 0.4686 | 1000 | 0.1147 | 0.7508 | 0.7636 | 0.7395 | 0.7502 | 0.7400 | 0.7511 | 0.5170 | 0.5181 |
66
+ | 0.5026 | 0.5858 | 1250 | 0.1047 | 0.7507 | 0.7635 | 0.7455 | 0.7558 | 0.7459 | 0.7564 | 0.5487 | 0.5531 |
67
+ | 0.5192 | 0.7029 | 1500 | 0.1166 | 0.7522 | 0.7673 | 0.7487 | 0.7591 | 0.7489 | 0.7594 | 0.5055 | 0.5053 |
68
+ | 0.5046 | 0.8201 | 1750 | 0.1110 | 0.7555 | 0.7675 | 0.7582 | 0.7675 | 0.7581 | 0.7672 | 0.5303 | 0.5391 |
69
+ | 0.5055 | 0.9372 | 2000 | 0.1062 | 0.7546 | 0.7726 | 0.7501 | 0.7650 | 0.7502 | 0.7651 | 0.5638 | 0.5710 |
70
+ | 0.4177 | 1.0544 | 2250 | 0.0942 | 0.7577 | 0.7709 | 0.7511 | 0.7635 | 0.7510 | 0.7633 | 0.5577 | 0.5635 |
71
+ | 0.4136 | 1.1715 | 2500 | 0.0915 | 0.7612 | 0.7727 | 0.7584 | 0.7696 | 0.7586 | 0.7696 | 0.5554 | 0.5595 |
72
+ | 0.4425 | 1.2887 | 2750 | 0.0928 | 0.7605 | 0.7726 | 0.7461 | 0.7591 | 0.7463 | 0.7592 | 0.5498 | 0.5512 |
73
+ | 0.3708 | 1.4058 | 3000 | 0.0819 | 0.7670 | 0.7783 | 0.7478 | 0.7634 | 0.7481 | 0.7637 | 0.5834 | 0.5847 |
74
+ | 0.3934 | 1.5230 | 3250 | 0.0848 | 0.7709 | 0.7814 | 0.7539 | 0.7692 | 0.7542 | 0.7689 | 0.5655 | 0.5668 |
75
+ | 0.3203 | 1.6401 | 3500 | 0.0781 | 0.7706 | 0.7810 | 0.7529 | 0.7689 | 0.7531 | 0.7691 | 0.5871 | 0.5891 |
76
+ | 0.4052 | 1.7573 | 3750 | 0.0824 | 0.7705 | 0.7816 | 0.7628 | 0.7771 | 0.7628 | 0.7771 | 0.5909 | 0.5989 |
77
+ | 0.3723 | 1.8744 | 4000 | 0.0819 | 0.7720 | 0.7840 | 0.7515 | 0.7679 | 0.7520 | 0.7685 | 0.5711 | 0.5713 |
78
+ | 0.3645 | 1.9916 | 4250 | 0.0802 | 0.7676 | 0.7804 | 0.7560 | 0.7704 | 0.7560 | 0.7703 | 0.5685 | 0.5701 |
79
+ | 0.3007 | 2.1087 | 4500 | 0.0662 | 0.7682 | 0.7799 | 0.7572 | 0.7721 | 0.7574 | 0.7721 | 0.5973 | 0.5981 |
80
+ | 0.2397 | 2.2259 | 4750 | 0.0617 | 0.7693 | 0.7782 | 0.7501 | 0.7655 | 0.7502 | 0.7652 | 0.5855 | 0.5898 |
81
+ | 0.28 | 2.3430 | 5000 | 0.0645 | 0.7654 | 0.7760 | 0.7567 | 0.7705 | 0.7569 | 0.7705 | 0.5925 | 0.5970 |
82
+ | 0.2631 | 2.4602 | 5250 | 0.0639 | 0.7712 | 0.7798 | 0.7561 | 0.7705 | 0.7562 | 0.7705 | 0.5715 | 0.5731 |
83
+ | 0.2488 | 2.5773 | 5500 | 0.0636 | 0.7736 | 0.7838 | 0.7537 | 0.7687 | 0.7538 | 0.7685 | 0.5835 | 0.5861 |
84
+ | 0.2557 | 2.6945 | 5750 | 0.0614 | 0.7739 | 0.7830 | 0.7570 | 0.7716 | 0.7571 | 0.7717 | 0.6008 | 0.6041 |
85
+ | 0.2699 | 2.8116 | 6000 | 0.0636 | 0.7722 | 0.7795 | 0.7570 | 0.7699 | 0.7572 | 0.7701 | 0.5844 | 0.5864 |
86
+ | 0.2794 | 2.9288 | 6250 | 0.0639 | 0.7704 | 0.7800 | 0.7582 | 0.7745 | 0.7581 | 0.7746 | 0.5817 | 0.5793 |
87
+ | 0.1778 | 3.0459 | 6500 | 0.0526 | 0.7738 | 0.7811 | 0.7574 | 0.7739 | 0.7573 | 0.7739 | 0.6193 | 0.6255 |
88
+ | 0.1791 | 3.1631 | 6750 | 0.0519 | 0.7728 | 0.7783 | 0.7540 | 0.7704 | 0.7538 | 0.7700 | 0.6116 | 0.6182 |
89
+ | 0.201 | 3.2802 | 7000 | 0.0511 | 0.7755 | 0.7825 | 0.7506 | 0.7671 | 0.7503 | 0.7670 | 0.6039 | 0.6071 |
90
+ | 0.225 | 3.3974 | 7250 | 0.0513 | 0.7684 | 0.7749 | 0.7515 | 0.7689 | 0.7514 | 0.7692 | 0.5867 | 0.5894 |
91
+ | 0.1748 | 3.5145 | 7500 | 0.0502 | 0.7752 | 0.7801 | 0.7459 | 0.7630 | 0.7461 | 0.7636 | 0.5877 | 0.5949 |
92
+ | 0.2045 | 3.6317 | 7750 | 0.0512 | 0.7787 | 0.7856 | 0.7457 | 0.7636 | 0.7460 | 0.7642 | 0.6113 | 0.6156 |
93
+ | 0.1821 | 3.7488 | 8000 | 0.0502 | 0.7782 | 0.7842 | 0.7543 | 0.7707 | 0.7545 | 0.7710 | 0.6045 | 0.6069 |
94
+ | 0.1783 | 3.8660 | 8250 | 0.0491 | 0.7772 | 0.7829 | 0.7455 | 0.7630 | 0.7459 | 0.7637 | 0.5915 | 0.5984 |
95
+ | 0.2055 | 3.9831 | 8500 | 0.0504 | 0.7776 | 0.7832 | 0.7476 | 0.7658 | 0.7480 | 0.7662 | 0.5959 | 0.6017 |
96
+ | 0.1345 | 4.1003 | 8750 | 0.0467 | 0.7762 | 0.7802 | 0.7429 | 0.7606 | 0.7435 | 0.7611 | 0.6206 | 0.6303 |
97
+ | 0.1506 | 4.2174 | 9000 | 0.0477 | 0.7711 | 0.7759 | 0.7466 | 0.7625 | 0.7473 | 0.7631 | 0.5978 | 0.6025 |
98
+ | 0.1565 | 4.3346 | 9250 | 0.0477 | 0.7717 | 0.7768 | 0.7481 | 0.7641 | 0.7486 | 0.7645 | 0.6026 | 0.6102 |
99
+ | 0.1577 | 4.4517 | 9500 | 0.0442 | 0.7794 | 0.7824 | 0.7439 | 0.7627 | 0.7444 | 0.7630 | 0.6182 | 0.6291 |
100
+ | 0.1463 | 4.5689 | 9750 | 0.0456 | 0.7764 | 0.7821 | 0.7401 | 0.7602 | 0.7405 | 0.7604 | 0.5941 | 0.5991 |
101
+ | 0.16 | 4.6860 | 10000 | 0.0460 | 0.7749 | 0.7793 | 0.7495 | 0.7658 | 0.7498 | 0.7660 | 0.6140 | 0.6192 |
102
+ | 0.148 | 4.8032 | 10250 | 0.0436 | 0.7817 | 0.7855 | 0.7421 | 0.7596 | 0.7425 | 0.7601 | 0.6171 | 0.6239 |
103
+ | 0.1382 | 4.9203 | 10500 | 0.0446 | 0.7824 | 0.7872 | 0.7437 | 0.7620 | 0.7443 | 0.7625 | 0.6330 | 0.6424 |
104
+ | 0.1109 | 5.0375 | 10750 | 0.0426 | 0.7796 | 0.7846 | 0.7431 | 0.7600 | 0.7434 | 0.7602 | 0.6195 | 0.6249 |
105
+ | 0.1009 | 5.1546 | 11000 | 0.0431 | 0.7807 | 0.7835 | 0.7423 | 0.7591 | 0.7428 | 0.7591 | 0.6237 | 0.6377 |
106
+ | 0.1082 | 5.2718 | 11250 | 0.0438 | 0.7774 | 0.7818 | 0.7430 | 0.7591 | 0.7433 | 0.7593 | 0.6039 | 0.6129 |
107
+ | 0.1138 | 5.3889 | 11500 | 0.0415 | 0.7829 | 0.7870 | 0.7405 | 0.7560 | 0.7410 | 0.7561 | 0.6347 | 0.6464 |
108
+ | 0.1015 | 5.5061 | 11750 | 0.0420 | 0.7778 | 0.7811 | 0.7437 | 0.7592 | 0.7435 | 0.7589 | 0.6249 | 0.6370 |
109
+ | 0.1153 | 5.6232 | 12000 | 0.0448 | 0.7730 | 0.7784 | 0.7451 | 0.7598 | 0.7453 | 0.7596 | 0.6141 | 0.6214 |
110
+ | 0.1269 | 5.7404 | 12250 | 0.0420 | 0.7802 | 0.7840 | 0.7413 | 0.7562 | 0.7417 | 0.7564 | 0.6217 | 0.6311 |
111
+ | 0.0888 | 5.8575 | 12500 | 0.0414 | 0.7805 | 0.7841 | 0.7408 | 0.7567 | 0.7412 | 0.7568 | 0.6245 | 0.6365 |
112
+ | 0.1202 | 5.9747 | 12750 | 0.0431 | 0.7793 | 0.7835 | 0.7412 | 0.7572 | 0.7414 | 0.7575 | 0.6261 | 0.6405 |
113
+ | 0.0941 | 6.0918 | 13000 | 0.0399 | 0.7838 | 0.7873 | 0.7388 | 0.7527 | 0.7391 | 0.7530 | 0.6493 | 0.6642 |
114
+ | 0.081 | 6.2090 | 13250 | 0.0405 | 0.7814 | 0.7854 | 0.7353 | 0.7513 | 0.7355 | 0.7514 | 0.6356 | 0.6478 |
115
+ | 0.0807 | 6.3261 | 13500 | 0.0401 | 0.7838 | 0.7879 | 0.7339 | 0.7510 | 0.7344 | 0.7513 | 0.6450 | 0.6615 |
116
+ | 0.0863 | 6.4433 | 13750 | 0.0405 | 0.7814 | 0.7841 | 0.7404 | 0.7587 | 0.7408 | 0.7589 | 0.6324 | 0.6479 |
117
+ | 0.0948 | 6.5604 | 14000 | 0.0397 | 0.7830 | 0.7866 | 0.7410 | 0.7578 | 0.7415 | 0.7579 | 0.6308 | 0.6460 |
118
+ | 0.0919 | 6.6776 | 14250 | 0.0409 | 0.7820 | 0.7858 | 0.7402 | 0.7545 | 0.7403 | 0.7544 | 0.6341 | 0.6459 |
119
+ | 0.0784 | 6.7948 | 14500 | 0.0408 | 0.7794 | 0.7839 | 0.7308 | 0.7495 | 0.7312 | 0.7494 | 0.6306 | 0.6427 |
120
+ | 0.0821 | 6.9119 | 14750 | 0.0406 | 0.7789 | 0.7822 | 0.7265 | 0.7446 | 0.7270 | 0.7446 | 0.6377 | 0.6567 |
121
+ | 0.0792 | 7.0291 | 15000 | 0.0401 | 0.7800 | 0.7833 | 0.7398 | 0.7569 | 0.7405 | 0.7572 | 0.6338 | 0.6467 |
122
+ | 0.0698 | 7.1462 | 15250 | 0.0396 | 0.7822 | 0.7855 | 0.7341 | 0.7507 | 0.7346 | 0.7509 | 0.6381 | 0.6552 |
123
+ | 0.0699 | 7.2634 | 15500 | 0.0392 | 0.7820 | 0.7851 | 0.7322 | 0.7502 | 0.7325 | 0.7502 | 0.6466 | 0.6629 |
124
+ | 0.0739 | 7.3805 | 15750 | 0.0389 | 0.7865 | 0.7886 | 0.7323 | 0.7491 | 0.7328 | 0.7495 | 0.6412 | 0.6589 |
125
+ | 0.0745 | 7.4977 | 16000 | 0.0397 | 0.7794 | 0.7827 | 0.7366 | 0.7524 | 0.7373 | 0.7524 | 0.6380 | 0.6504 |
126
+ | 0.0779 | 7.6148 | 16250 | 0.0391 | 0.7826 | 0.7846 | 0.7326 | 0.7462 | 0.7333 | 0.7467 | 0.6372 | 0.6532 |
127
+ | 0.078 | 7.7320 | 16500 | 0.0397 | 0.7810 | 0.7826 | 0.7299 | 0.7461 | 0.7300 | 0.7457 | 0.6364 | 0.6555 |
128
+ | 0.0699 | 7.8491 | 16750 | 0.0405 | 0.7811 | 0.7837 | 0.7308 | 0.7468 | 0.7312 | 0.7470 | 0.6315 | 0.6426 |
129
+ | 0.0735 | 7.9663 | 17000 | 0.0394 | 0.7804 | 0.7823 | 0.7320 | 0.7455 | 0.7326 | 0.7462 | 0.6468 | 0.6607 |
130
+ | 0.0682 | 8.0834 | 17250 | 0.0386 | 0.7845 | 0.7869 | 0.7306 | 0.7447 | 0.7311 | 0.7449 | 0.6431 | 0.6613 |
131
+ | 0.0526 | 8.2006 | 17500 | 0.0389 | 0.7824 | 0.7832 | 0.7272 | 0.7431 | 0.7275 | 0.7431 | 0.6370 | 0.6539 |
132
+ | 0.0558 | 8.3177 | 17750 | 0.0385 | 0.7856 | 0.7865 | 0.7370 | 0.7513 | 0.7376 | 0.7518 | 0.6517 | 0.6679 |
133
+ | 0.0633 | 8.4349 | 18000 | 0.0392 | 0.7822 | 0.7845 | 0.7388 | 0.7537 | 0.7395 | 0.7542 | 0.6512 | 0.6664 |
134
+ | 0.0568 | 8.5520 | 18250 | 0.0389 | 0.7826 | 0.7831 | 0.7358 | 0.7510 | 0.7362 | 0.7509 | 0.6378 | 0.6536 |
135
+ | 0.0645 | 8.6692 | 18500 | 0.0377 | 0.7888 | 0.7892 | 0.7315 | 0.7495 | 0.7319 | 0.7499 | 0.6514 | 0.6704 |
136
+ | 0.0563 | 8.7863 | 18750 | 0.0376 | 0.7870 | 0.7878 | 0.7285 | 0.7451 | 0.7289 | 0.7454 | 0.6393 | 0.6606 |
137
+ | 0.0669 | 8.9035 | 19000 | 0.0383 | 0.7850 | 0.7866 | 0.7238 | 0.7433 | 0.7244 | 0.7437 | 0.6359 | 0.6571 |
138
+ | 0.0436 | 9.0206 | 19250 | 0.0377 | 0.7855 | 0.7856 | 0.7289 | 0.7462 | 0.7293 | 0.7465 | 0.6489 | 0.6696 |
139
+ | 0.047 | 9.1378 | 19500 | 0.0377 | 0.7870 | 0.7882 | 0.7249 | 0.7414 | 0.7254 | 0.7413 | 0.6459 | 0.6694 |
140
+ | 0.0482 | 9.2549 | 19750 | 0.0377 | 0.7863 | 0.7871 | 0.7296 | 0.7442 | 0.7306 | 0.7449 | 0.6498 | 0.6690 |
141
+ | 0.0529 | 9.3721 | 20000 | 0.0377 | 0.7873 | 0.7888 | 0.7285 | 0.7423 | 0.7290 | 0.7426 | 0.6490 | 0.6690 |
142
+ | 0.0429 | 9.4892 | 20250 | 0.0378 | 0.7868 | 0.7883 | 0.7286 | 0.7426 | 0.7292 | 0.7431 | 0.6503 | 0.6684 |
143
+ | 0.0534 | 9.6064 | 20500 | 0.0380 | 0.7861 | 0.7881 | 0.7300 | 0.7443 | 0.7305 | 0.7451 | 0.6446 | 0.6635 |
144
+ | 0.0531 | 9.7235 | 20750 | 0.0375 | 0.7886 | 0.7894 | 0.7350 | 0.7492 | 0.7356 | 0.7498 | 0.6442 | 0.6634 |
145
+ | 0.0464 | 9.8407 | 21000 | 0.0380 | 0.7861 | 0.7871 | 0.7314 | 0.7464 | 0.7320 | 0.7468 | 0.6415 | 0.6600 |
146
+ | 0.0406 | 9.9578 | 21250 | 0.0387 | 0.7824 | 0.7845 | 0.7335 | 0.7460 | 0.7337 | 0.7463 | 0.6362 | 0.6532 |
147
 
148
 
149
  ### Framework versions