Hugging Face's logo

zera09
/
long_t5_4

Text2Text Generation
Transformers
Safetensors
longt5
Generated from Trainer
Inference Endpoints
Model card Files Community
zera09 commited on
Commit
e725e12
•
1 Parent(s): f302076

End of training

Browse files
Files changed (1) hide show
  1. README.md +110 -60
README.md CHANGED
@@ -18,12 +18,12 @@ should probably proofread and complete it, then remove this comment. -->
18
 
19
  This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on an unknown dataset.
20
  It achieves the following results on the evaluation set:
21
- - Loss: 3.0847
22
- - Rouge1: 0.5303
23
- - Rouge2: 0.3398
24
- - Rougel: 0.477
25
- - Rougelsum: 0.477
26
- - Gen Len: 31.974
27
 
28
  ## Model description
29
 
@@ -42,68 +42,118 @@ More information needed
42
  ### Training hyperparameters
43
 
44
  The following hyperparameters were used during training:
45
- - learning_rate: 0.0001
46
  - train_batch_size: 8
47
  - eval_batch_size: 8
48
  - seed: 42
49
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
  - lr_scheduler_type: linear
51
- - num_epochs: 50
52
 
53
  ### Training results
54
 
55
- | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
56
- |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
57
- | 2.0147 | 1.0 | 1000 | 1.5675 | 0.4907 | 0.3059 | 0.4453 | 0.4454 | 25.7975 |
58
- | 1.7618 | 2.0 | 2000 | 1.5138 | 0.5037 | 0.3169 | 0.4578 | 0.458 | 26.608 |
59
- | 1.5904 | 3.0 | 3000 | 1.5015 | 0.5091 | 0.3239 | 0.4645 | 0.4648 | 25.5405 |
60
- | 1.4555 | 4.0 | 4000 | 1.5083 | 0.5183 | 0.3335 | 0.4727 | 0.4732 | 26.777 |
61
- | 1.3579 | 5.0 | 5000 | 1.5363 | 0.5205 | 0.3353 | 0.4743 | 0.4744 | 27.916 |
62
- | 1.2345 | 6.0 | 6000 | 1.5543 | 0.5193 | 0.338 | 0.4772 | 0.4769 | 25.6475 |
63
- | 1.1663 | 7.0 | 7000 | 1.5570 | 0.5299 | 0.3449 | 0.4837 | 0.4837 | 26.9075 |
64
- | 1.0754 | 8.0 | 8000 | 1.5953 | 0.5289 | 0.3422 | 0.4804 | 0.4804 | 29.1995 |
65
- | 0.9901 | 9.0 | 9000 | 1.6392 | 0.5333 | 0.3443 | 0.483 | 0.4831 | 28.9815 |
66
- | 0.9321 | 10.0 | 10000 | 1.6641 | 0.5269 | 0.3361 | 0.4764 | 0.4765 | 28.8695 |
67
- | 0.87 | 11.0 | 11000 | 1.7062 | 0.5299 | 0.3409 | 0.4793 | 0.4794 | 29.366 |
68
- | 0.8062 | 12.0 | 12000 | 1.7558 | 0.5287 | 0.342 | 0.4794 | 0.4798 | 29.29 |
69
- | 0.7595 | 13.0 | 13000 | 1.8033 | 0.5256 | 0.3402 | 0.4784 | 0.4783 | 29.204 |
70
- | 0.7195 | 14.0 | 14000 | 1.8229 | 0.5293 | 0.3425 | 0.4802 | 0.4803 | 30.156 |
71
- | 0.668 | 15.0 | 15000 | 1.8817 | 0.5288 | 0.3421 | 0.4791 | 0.4792 | 30.1525 |
72
- | 0.6283 | 16.0 | 16000 | 1.9278 | 0.5294 | 0.3404 | 0.478 | 0.4778 | 29.942 |
73
- | 0.5957 | 17.0 | 17000 | 1.9536 | 0.5312 | 0.3416 | 0.4807 | 0.4809 | 29.525 |
74
- | 0.5496 | 18.0 | 18000 | 2.0396 | 0.5309 | 0.3403 | 0.4788 | 0.479 | 30.359 |
75
- | 0.5208 | 19.0 | 19000 | 2.0539 | 0.5312 | 0.3442 | 0.4813 | 0.481 | 30.173 |
76
- | 0.491 | 20.0 | 20000 | 2.0836 | 0.5297 | 0.3395 | 0.4794 | 0.4792 | 29.554 |
77
- | 0.4522 | 21.0 | 21000 | 2.1548 | 0.5282 | 0.3396 | 0.4751 | 0.4753 | 31.565 |
78
- | 0.4339 | 22.0 | 22000 | 2.2076 | 0.5264 | 0.338 | 0.476 | 0.476 | 30.0425 |
79
- | 0.4095 | 23.0 | 23000 | 2.2331 | 0.5258 | 0.3366 | 0.4751 | 0.475 | 31.307 |
80
- | 0.3818 | 24.0 | 24000 | 2.3036 | 0.5275 | 0.3371 | 0.4756 | 0.4753 | 31.8185 |
81
- | 0.362 | 25.0 | 25000 | 2.3462 | 0.529 | 0.3374 | 0.4739 | 0.4741 | 32.9885 |
82
- | 0.3414 | 26.0 | 26000 | 2.3989 | 0.5335 | 0.3444 | 0.482 | 0.4819 | 30.4255 |
83
- | 0.3188 | 27.0 | 27000 | 2.4419 | 0.5257 | 0.3367 | 0.4745 | 0.4744 | 30.6095 |
84
- | 0.2976 | 28.0 | 28000 | 2.4965 | 0.5256 | 0.3336 | 0.4702 | 0.4701 | 33.6375 |
85
- | 0.2896 | 29.0 | 29000 | 2.4841 | 0.5254 | 0.3341 | 0.4725 | 0.4725 | 32.7325 |
86
- | 0.2702 | 30.0 | 30000 | 2.5704 | 0.5298 | 0.3399 | 0.4775 | 0.4778 | 31.307 |
87
- | 0.2583 | 31.0 | 31000 | 2.6376 | 0.5306 | 0.3411 | 0.4773 | 0.4774 | 31.0695 |
88
- | 0.2472 | 32.0 | 32000 | 2.6134 | 0.5266 | 0.3376 | 0.4729 | 0.473 | 32.3075 |
89
- | 0.2361 | 33.0 | 33000 | 2.6922 | 0.5294 | 0.3391 | 0.4763 | 0.4764 | 31.5785 |
90
- | 0.2242 | 34.0 | 34000 | 2.7246 | 0.5292 | 0.3383 | 0.4745 | 0.4747 | 32.823 |
91
- | 0.2173 | 35.0 | 35000 | 2.7647 | 0.5294 | 0.3386 | 0.4754 | 0.4754 | 32.0915 |
92
- | 0.2057 | 36.0 | 36000 | 2.7717 | 0.5297 | 0.343 | 0.4781 | 0.4781 | 32.132 |
93
- | 0.1957 | 37.0 | 37000 | 2.8077 | 0.5257 | 0.3372 | 0.4729 | 0.4728 | 32.147 |
94
- | 0.1895 | 38.0 | 38000 | 2.8661 | 0.5268 | 0.3375 | 0.4733 | 0.4734 | 32.156 |
95
- | 0.1818 | 39.0 | 39000 | 2.8841 | 0.5272 | 0.3388 | 0.4747 | 0.475 | 31.3275 |
96
- | 0.1749 | 40.0 | 40000 | 2.9060 | 0.5278 | 0.3395 | 0.4752 | 0.4751 | 31.835 |
97
- | 0.1705 | 41.0 | 41000 | 2.9260 | 0.5262 | 0.3365 | 0.4729 | 0.4732 | 32.3635 |
98
- | 0.163 | 42.0 | 42000 | 2.9924 | 0.5284 | 0.3383 | 0.4754 | 0.4754 | 31.4935 |
99
- | 0.163 | 43.0 | 43000 | 2.9798 | 0.5299 | 0.3403 | 0.4762 | 0.4765 | 31.8165 |
100
- | 0.1583 | 44.0 | 44000 | 2.9919 | 0.5291 | 0.3397 | 0.4755 | 0.4759 | 31.6065 |
101
- | 0.1537 | 45.0 | 45000 | 3.0308 | 0.5281 | 0.3381 | 0.4748 | 0.4749 | 31.447 |
102
- | 0.1493 | 46.0 | 46000 | 3.0491 | 0.5287 | 0.339 | 0.4753 | 0.4755 | 31.944 |
103
- | 0.1437 | 47.0 | 47000 | 3.0595 | 0.5282 | 0.3383 | 0.4744 | 0.4746 | 31.833 |
104
- | 0.1437 | 48.0 | 48000 | 3.0804 | 0.5307 | 0.3401 | 0.477 | 0.4771 | 31.837 |
105
- | 0.1435 | 49.0 | 49000 | 3.0782 | 0.5312 | 0.3406 | 0.4772 | 0.4772 | 31.798 |
106
- | 0.1392 | 50.0 | 50000 | 3.0847 | 0.5303 | 0.3398 | 0.477 | 0.477 | 31.974 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
107
 
108
 
109
  ### Framework versions
 
18
 
19
  This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on an unknown dataset.
20
  It achieves the following results on the evaluation set:
21
+ - Loss: 1.5697
22
+ - Rouge1: 0.5326
23
+ - Rouge2: 0.3464
24
+ - Rougel: 0.4843
25
+ - Rougelsum: 0.4839
26
+ - Gen Len: 27.734
27
 
28
  ## Model description
29
 
 
42
  ### Training hyperparameters
43
 
44
  The following hyperparameters were used during training:
45
+ - learning_rate: 1e-05
46
  - train_batch_size: 8
47
  - eval_batch_size: 8
48
  - seed: 42
49
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
  - lr_scheduler_type: linear
51
+ - num_epochs: 100
52
 
53
  ### Training results
54
 
55
+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
56
+ |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
57
+ | 2.4555 | 1.0 | 1000 | 1.7755 | 0.4481 | 0.2666 | 0.402 | 0.4019 | 26.852 |
58
+ | 2.2467 | 2.0 | 2000 | 1.7041 | 0.4657 | 0.2823 | 0.421 | 0.4205 | 27.304 |
59
+ | 2.1352 | 3.0 | 3000 | 1.6575 | 0.4752 | 0.2902 | 0.4295 | 0.4292 | 27.117 |
60
+ | 2.0543 | 4.0 | 4000 | 1.6314 | 0.4788 | 0.295 | 0.4348 | 0.4341 | 26.2045 |
61
+ | 2.0058 | 5.0 | 5000 | 1.6106 | 0.4856 | 0.3016 | 0.4415 | 0.441 | 26.3885 |
62
+ | 1.9365 | 6.0 | 6000 | 1.5924 | 0.4882 | 0.3037 | 0.4431 | 0.4425 | 26.048 |
63
+ | 1.9234 | 7.0 | 7000 | 1.5743 | 0.4882 | 0.3049 | 0.4435 | 0.443 | 26.207 |
64
+ | 1.8728 | 8.0 | 8000 | 1.5649 | 0.4925 | 0.3094 | 0.4479 | 0.4474 | 26.4 |
65
+ | 1.814 | 9.0 | 9000 | 1.5558 | 0.495 | 0.3113 | 0.4498 | 0.4495 | 26.383 |
66
+ | 1.8025 | 10.0 | 10000 | 1.5436 | 0.4966 | 0.3121 | 0.4517 | 0.4512 | 25.8435 |
67
+ | 1.7683 | 11.0 | 11000 | 1.5424 | 0.4987 | 0.3143 | 0.4535 | 0.453 | 25.8365 |
68
+ | 1.7299 | 12.0 | 12000 | 1.5308 | 0.4994 | 0.3147 | 0.4543 | 0.4537 | 25.718 |
69
+ | 1.7308 | 13.0 | 13000 | 1.5245 | 0.5002 | 0.3168 | 0.4554 | 0.4548 | 25.7385 |
70
+ | 1.7075 | 14.0 | 14000 | 1.5218 | 0.5028 | 0.3176 | 0.4569 | 0.4564 | 25.87 |
71
+ | 1.6969 | 15.0 | 15000 | 1.5171 | 0.5042 | 0.3194 | 0.4586 | 0.4583 | 25.7615 |
72
+ | 1.6618 | 16.0 | 16000 | 1.5138 | 0.5073 | 0.3216 | 0.4617 | 0.4609 | 25.772 |
73
+ | 1.6658 | 17.0 | 17000 | 1.5089 | 0.5051 | 0.3198 | 0.4602 | 0.4596 | 25.6465 |
74
+ | 1.6249 | 18.0 | 18000 | 1.5073 | 0.5052 | 0.3199 | 0.4604 | 0.4599 | 25.4575 |
75
+ | 1.6098 | 19.0 | 19000 | 1.5055 | 0.5068 | 0.321 | 0.4619 | 0.4614 | 26.0035 |
76
+ | 1.6018 | 20.0 | 20000 | 1.5015 | 0.5098 | 0.3244 | 0.4648 | 0.4644 | 25.4315 |
77
+ | 1.5637 | 21.0 | 21000 | 1.5027 | 0.5087 | 0.3243 | 0.4635 | 0.4633 | 26.032 |
78
+ | 1.5664 | 22.0 | 22000 | 1.5029 | 0.5118 | 0.3268 | 0.4672 | 0.4668 | 25.6305 |
79
+ | 1.561 | 23.0 | 23000 | 1.4968 | 0.5115 | 0.3255 | 0.4667 | 0.4661 | 25.7905 |
80
+ | 1.5388 | 24.0 | 24000 | 1.4997 | 0.5112 | 0.3259 | 0.4657 | 0.4653 | 26.007 |
81
+ | 1.5173 | 25.0 | 25000 | 1.4981 | 0.5129 | 0.3273 | 0.4683 | 0.4679 | 25.9415 |
82
+ | 1.5057 | 26.0 | 26000 | 1.4995 | 0.5134 | 0.3289 | 0.4692 | 0.4687 | 26.128 |
83
+ | 1.4967 | 27.0 | 27000 | 1.4973 | 0.5149 | 0.3308 | 0.4704 | 0.4701 | 25.7005 |
84
+ | 1.4755 | 28.0 | 28000 | 1.5033 | 0.5155 | 0.3304 | 0.4703 | 0.4699 | 26.4255 |
85
+ | 1.4673 | 29.0 | 29000 | 1.4995 | 0.5174 | 0.3319 | 0.4727 | 0.4725 | 25.891 |
86
+ | 1.4515 | 30.0 | 30000 | 1.5012 | 0.5158 | 0.3309 | 0.4712 | 0.4709 | 25.668 |
87
+ | 1.4502 | 31.0 | 31000 | 1.5021 | 0.518 | 0.3336 | 0.4739 | 0.4737 | 25.8405 |
88
+ | 1.4369 | 32.0 | 32000 | 1.4996 | 0.5176 | 0.333 | 0.4732 | 0.4729 | 26.093 |
89
+ | 1.4347 | 33.0 | 33000 | 1.5033 | 0.5184 | 0.3334 | 0.4731 | 0.4726 | 26.225 |
90
+ | 1.4014 | 34.0 | 34000 | 1.5044 | 0.5185 | 0.3333 | 0.4735 | 0.4733 | 26.1955 |
91
+ | 1.399 | 35.0 | 35000 | 1.5061 | 0.5192 | 0.3341 | 0.4733 | 0.473 | 26.5095 |
92
+ | 1.3941 | 36.0 | 36000 | 1.5067 | 0.5193 | 0.3343 | 0.4739 | 0.4735 | 26.2715 |
93
+ | 1.3646 | 37.0 | 37000 | 1.5060 | 0.5201 | 0.335 | 0.4753 | 0.4751 | 25.932 |
94
+ | 1.3677 | 38.0 | 38000 | 1.5046 | 0.5213 | 0.3354 | 0.4757 | 0.4751 | 26.1425 |
95
+ | 1.3623 | 39.0 | 39000 | 1.5084 | 0.5202 | 0.3342 | 0.4747 | 0.4743 | 25.9125 |
96
+ | 1.3438 | 40.0 | 40000 | 1.5103 | 0.5204 | 0.3356 | 0.4756 | 0.4752 | 26.231 |
97
+ | 1.3476 | 41.0 | 41000 | 1.5083 | 0.5203 | 0.3357 | 0.4748 | 0.4746 | 26.4745 |
98
+ | 1.3258 | 42.0 | 42000 | 1.5135 | 0.5195 | 0.3349 | 0.4744 | 0.474 | 26.265 |
99
+ | 1.3484 | 43.0 | 43000 | 1.5110 | 0.5222 | 0.3375 | 0.4762 | 0.476 | 26.4365 |
100
+ | 1.324 | 44.0 | 44000 | 1.5136 | 0.5229 | 0.3386 | 0.4781 | 0.4777 | 26.192 |
101
+ | 1.3225 | 45.0 | 45000 | 1.5148 | 0.5233 | 0.3377 | 0.477 | 0.4767 | 26.3725 |
102
+ | 1.2867 | 46.0 | 46000 | 1.5160 | 0.5224 | 0.3372 | 0.4762 | 0.4758 | 26.565 |
103
+ | 1.296 | 47.0 | 47000 | 1.5170 | 0.5224 | 0.3363 | 0.4757 | 0.4755 | 26.8325 |
104
+ | 1.2834 | 48.0 | 48000 | 1.5165 | 0.5227 | 0.3382 | 0.4772 | 0.477 | 26.5355 |
105
+ | 1.2908 | 49.0 | 49000 | 1.5216 | 0.5255 | 0.3391 | 0.4784 | 0.4782 | 26.835 |
106
+ | 1.2719 | 50.0 | 50000 | 1.5234 | 0.525 | 0.3392 | 0.4779 | 0.4775 | 26.4905 |
107
+ | 1.2768 | 51.0 | 51000 | 1.5257 | 0.5262 | 0.34 | 0.4789 | 0.4785 | 27.0845 |
108
+ | 1.2703 | 52.0 | 52000 | 1.5216 | 0.5262 | 0.3408 | 0.4798 | 0.4793 | 26.578 |
109
+ | 1.2599 | 53.0 | 53000 | 1.5270 | 0.5279 | 0.3409 | 0.4811 | 0.4809 | 26.7485 |
110
+ | 1.2502 | 54.0 | 54000 | 1.5250 | 0.5276 | 0.3412 | 0.4797 | 0.4794 | 26.8205 |
111
+ | 1.2207 | 55.0 | 55000 | 1.5278 | 0.5259 | 0.3408 | 0.4792 | 0.4789 | 26.477 |
112
+ | 1.238 | 56.0 | 56000 | 1.5276 | 0.5281 | 0.3423 | 0.4812 | 0.4809 | 26.2345 |
113
+ | 1.2199 | 57.0 | 57000 | 1.5303 | 0.5262 | 0.3413 | 0.4792 | 0.4788 | 26.818 |
114
+ | 1.2193 | 58.0 | 58000 | 1.5335 | 0.528 | 0.3421 | 0.4804 | 0.4802 | 27.0625 |
115
+ | 1.2075 | 59.0 | 59000 | 1.5330 | 0.5275 | 0.3405 | 0.4793 | 0.4791 | 27.1185 |
116
+ | 1.2096 | 60.0 | 60000 | 1.5401 | 0.5283 | 0.3421 | 0.4807 | 0.4805 | 27.2025 |
117
+ | 1.2032 | 61.0 | 61000 | 1.5377 | 0.5281 | 0.342 | 0.4806 | 0.4803 | 26.784 |
118
+ | 1.2165 | 62.0 | 62000 | 1.5378 | 0.5288 | 0.3423 | 0.4804 | 0.4802 | 27.143 |
119
+ | 1.2025 | 63.0 | 63000 | 1.5391 | 0.5275 | 0.3415 | 0.4799 | 0.4797 | 27.172 |
120
+ | 1.199 | 64.0 | 64000 | 1.5415 | 0.5303 | 0.3445 | 0.4821 | 0.4819 | 27.1665 |
121
+ | 1.1847 | 65.0 | 65000 | 1.5445 | 0.5289 | 0.3432 | 0.4815 | 0.4812 | 27.115 |
122
+ | 1.1815 | 66.0 | 66000 | 1.5482 | 0.5286 | 0.3428 | 0.4802 | 0.4801 | 27.408 |
123
+ | 1.1828 | 67.0 | 67000 | 1.5468 | 0.5299 | 0.3443 | 0.4823 | 0.4819 | 27.2485 |
124
+ | 1.1823 | 68.0 | 68000 | 1.5484 | 0.5297 | 0.3441 | 0.4813 | 0.4809 | 27.3335 |
125
+ | 1.1771 | 69.0 | 69000 | 1.5488 | 0.5305 | 0.3441 | 0.4811 | 0.4808 | 27.6115 |
126
+ | 1.1748 | 70.0 | 70000 | 1.5475 | 0.5296 | 0.3439 | 0.4814 | 0.4811 | 27.2955 |
127
+ | 1.1732 | 71.0 | 71000 | 1.5493 | 0.5304 | 0.3444 | 0.482 | 0.4818 | 27.504 |
128
+ | 1.1504 | 72.0 | 72000 | 1.5529 | 0.5305 | 0.3449 | 0.4826 | 0.4824 | 27.313 |
129
+ | 1.1497 | 73.0 | 73000 | 1.5528 | 0.5318 | 0.3466 | 0.4838 | 0.4835 | 27.463 |
130
+ | 1.1589 | 74.0 | 74000 | 1.5543 | 0.5312 | 0.3452 | 0.4826 | 0.4823 | 27.482 |
131
+ | 1.1453 | 75.0 | 75000 | 1.5561 | 0.5309 | 0.3447 | 0.4826 | 0.4822 | 27.5885 |
132
+ | 1.1451 | 76.0 | 76000 | 1.5577 | 0.5305 | 0.3445 | 0.4825 | 0.4822 | 27.3815 |
133
+ | 1.154 | 77.0 | 77000 | 1.5571 | 0.5303 | 0.3449 | 0.4828 | 0.4822 | 27.3945 |
134
+ | 1.152 | 78.0 | 78000 | 1.5572 | 0.5311 | 0.3456 | 0.4832 | 0.4828 | 27.473 |
135
+ | 1.1205 | 79.0 | 79000 | 1.5598 | 0.5317 | 0.3458 | 0.4839 | 0.4835 | 27.355 |
136
+ | 1.1376 | 80.0 | 80000 | 1.5619 | 0.5325 | 0.347 | 0.4846 | 0.4843 | 27.483 |
137
+ | 1.1391 | 81.0 | 81000 | 1.5614 | 0.5321 | 0.3465 | 0.4839 | 0.4835 | 27.7635 |
138
+ | 1.1293 | 82.0 | 82000 | 1.5632 | 0.5329 | 0.3472 | 0.4847 | 0.4842 | 27.777 |
139
+ | 1.1551 | 83.0 | 83000 | 1.5616 | 0.5323 | 0.3468 | 0.4842 | 0.4837 | 27.7005 |
140
+ | 1.1312 | 84.0 | 84000 | 1.5628 | 0.5318 | 0.3459 | 0.4835 | 0.4832 | 27.772 |
141
+ | 1.1109 | 85.0 | 85000 | 1.5654 | 0.5327 | 0.3469 | 0.4847 | 0.4843 | 27.5055 |
142
+ | 1.1371 | 86.0 | 86000 | 1.5653 | 0.534 | 0.3478 | 0.4856 | 0.4852 | 27.666 |
143
+ | 1.1355 | 87.0 | 87000 | 1.5642 | 0.5336 | 0.3481 | 0.4858 | 0.4855 | 27.617 |
144
+ | 1.1133 | 88.0 | 88000 | 1.5667 | 0.5333 | 0.3478 | 0.485 | 0.4847 | 27.725 |
145
+ | 1.1143 | 89.0 | 89000 | 1.5674 | 0.5329 | 0.3471 | 0.4849 | 0.4845 | 27.781 |
146
+ | 1.1203 | 90.0 | 90000 | 1.5673 | 0.5331 | 0.3474 | 0.4851 | 0.4846 | 27.7695 |
147
+ | 1.121 | 91.0 | 91000 | 1.5681 | 0.5333 | 0.3471 | 0.4849 | 0.4845 | 27.7595 |
148
+ | 1.0999 | 92.0 | 92000 | 1.5680 | 0.533 | 0.347 | 0.4845 | 0.4842 | 27.8525 |
149
+ | 1.1179 | 93.0 | 93000 | 1.5691 | 0.533 | 0.3473 | 0.485 | 0.4846 | 27.74 |
150
+ | 1.1057 | 94.0 | 94000 | 1.5688 | 0.5333 | 0.3476 | 0.4852 | 0.4847 | 27.6195 |
151
+ | 1.1186 | 95.0 | 95000 | 1.5687 | 0.5334 | 0.3474 | 0.4853 | 0.4849 | 27.677 |
152
+ | 1.1063 | 96.0 | 96000 | 1.5688 | 0.5329 | 0.3468 | 0.4844 | 0.4842 | 27.6925 |
153
+ | 1.0992 | 97.0 | 97000 | 1.5692 | 0.5332 | 0.3471 | 0.4849 | 0.4846 | 27.6885 |
154
+ | 1.1114 | 98.0 | 98000 | 1.5696 | 0.5328 | 0.3467 | 0.4845 | 0.4842 | 27.744 |
155
+ | 1.1085 | 99.0 | 99000 | 1.5697 | 0.5328 | 0.3468 | 0.4846 | 0.4842 | 27.744 |
156
+ | 1.101 | 100.0 | 100000 | 1.5697 | 0.5326 | 0.3464 | 0.4843 | 0.4839 | 27.734 |
157
 
158
 
159
  ### Framework versions