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Vichentito
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Nahuatl_Espanol_v1

Text2Text Generation
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t5
Generated from Trainer
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README.md CHANGED
@@ -1,4 +1,6 @@
1
  ---
 
 
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  tags:
3
  - generated_from_trainer
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  metrics:
@@ -13,11 +15,11 @@ should probably proofread and complete it, then remove this comment. -->
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  # Nahuatl_Espanol_v1
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- This model was trained from scratch on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.7731
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- - Bleu: 2.855
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- - Gen Len: 16.4731
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  ## Model description
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@@ -36,204 +38,92 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0002
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- - train_batch_size: 24
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- - eval_batch_size: 24
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 30
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  ### Training results
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49
- | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
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- |:-------------:|:-------:|:-----:|:---------------:|:------:|:-------:|
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- | No log | 0.1610 | 100 | 2.5463 | 0.3374 | 17.6013 |
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- | No log | 0.3221 | 200 | 2.3620 | 0.5422 | 17.5578 |
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- | No log | 0.4831 | 300 | 2.2535 | 0.5657 | 17.2797 |
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- | No log | 0.6441 | 400 | 2.1754 | 0.7341 | 17.2977 |
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- | 2.6455 | 0.8052 | 500 | 2.1126 | 0.8894 | 16.9874 |
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- | 2.6455 | 0.9662 | 600 | 2.0582 | 0.9524 | 17.036 |
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- | 2.6455 | 1.1272 | 700 | 2.0159 | 1.0109 | 16.9672 |
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- | 2.6455 | 1.2882 | 800 | 1.9775 | 1.1543 | 16.8598 |
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- | 2.6455 | 1.4493 | 900 | 1.9452 | 1.1861 | 16.8724 |
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- | 2.1536 | 1.6103 | 1000 | 1.9148 | 1.2127 | 16.9035 |
61
- | 2.1536 | 1.7713 | 1100 | 1.8855 | 1.3266 | 16.9366 |
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- | 2.1536 | 1.9324 | 1200 | 1.8609 | 1.3655 | 16.878 |
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- | 2.1536 | 2.0934 | 1300 | 1.8449 | 1.4542 | 16.7461 |
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- | 2.1536 | 2.2544 | 1400 | 1.8221 | 1.4619 | 16.6625 |
65
- | 1.9509 | 2.4155 | 1500 | 1.8114 | 1.5078 | 16.8248 |
66
- | 1.9509 | 2.5765 | 1600 | 1.7913 | 1.6021 | 16.756 |
67
- | 1.9509 | 2.7375 | 1700 | 1.7632 | 1.5406 | 16.8348 |
68
- | 1.9509 | 2.8986 | 1800 | 1.7524 | 1.6257 | 16.7233 |
69
- | 1.9509 | 3.0596 | 1900 | 1.7450 | 1.6093 | 16.7668 |
70
- | 1.8086 | 3.2206 | 2000 | 1.7397 | 1.6759 | 16.7077 |
71
- | 1.8086 | 3.3816 | 2100 | 1.7152 | 1.7204 | 16.7182 |
72
- | 1.8086 | 3.5427 | 2200 | 1.7080 | 1.6942 | 16.6916 |
73
- | 1.8086 | 3.7037 | 2300 | 1.7020 | 1.7501 | 16.5768 |
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- | 1.8086 | 3.8647 | 2400 | 1.6805 | 1.7556 | 16.7566 |
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- | 1.6943 | 4.0258 | 2500 | 1.6866 | 1.795 | 16.586 |
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- | 1.6943 | 4.1868 | 2600 | 1.6717 | 1.835 | 16.6588 |
77
- | 1.6943 | 4.3478 | 2700 | 1.6658 | 1.824 | 16.5852 |
78
- | 1.6943 | 4.5089 | 2800 | 1.6573 | 1.8731 | 16.6032 |
79
- | 1.6943 | 4.6699 | 2900 | 1.6483 | 1.8997 | 16.6628 |
80
- | 1.5847 | 4.8309 | 3000 | 1.6405 | 1.86 | 16.6362 |
81
- | 1.5847 | 4.9919 | 3100 | 1.6217 | 1.9496 | 16.7004 |
82
- | 1.5847 | 5.1530 | 3200 | 1.6342 | 1.9594 | 16.6405 |
83
- | 1.5847 | 5.3140 | 3300 | 1.6340 | 1.9784 | 16.6155 |
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- | 1.5847 | 5.4750 | 3400 | 1.6271 | 2.0696 | 16.5838 |
85
- | 1.5035 | 5.6361 | 3500 | 1.6177 | 1.9757 | 16.5895 |
86
- | 1.5035 | 5.7971 | 3600 | 1.6119 | 2.0183 | 16.5551 |
87
- | 1.5035 | 5.9581 | 3700 | 1.6002 | 2.0347 | 16.5473 |
88
- | 1.5035 | 6.1192 | 3800 | 1.6029 | 2.0596 | 16.5951 |
89
- | 1.5035 | 6.2802 | 3900 | 1.6064 | 2.0672 | 16.5529 |
90
- | 1.4234 | 6.4412 | 4000 | 1.6070 | 2.2163 | 16.6067 |
91
- | 1.4234 | 6.6023 | 4100 | 1.5889 | 2.1004 | 16.6212 |
92
- | 1.4234 | 6.7633 | 4200 | 1.5925 | 2.1498 | 16.5591 |
93
- | 1.4234 | 6.9243 | 4300 | 1.5911 | 2.1569 | 16.6832 |
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- | 1.4234 | 7.0853 | 4400 | 1.5902 | 2.1138 | 16.5618 |
95
- | 1.3688 | 7.2464 | 4500 | 1.5970 | 2.151 | 16.5849 |
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- | 1.3688 | 7.4074 | 4600 | 1.5906 | 2.1259 | 16.5553 |
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- | 1.3688 | 7.5684 | 4700 | 1.5857 | 2.2305 | 16.5443 |
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- | 1.3688 | 7.7295 | 4800 | 1.5672 | 2.2411 | 16.547 |
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- | 1.3688 | 7.8905 | 4900 | 1.5596 | 2.2849 | 16.5562 |
100
- | 1.3144 | 8.0515 | 5000 | 1.5777 | 2.256 | 16.5828 |
101
- | 1.3144 | 8.2126 | 5100 | 1.5855 | 2.2882 | 16.5489 |
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- | 1.3144 | 8.3736 | 5200 | 1.5834 | 2.2823 | 16.5682 |
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- | 1.3144 | 8.5346 | 5300 | 1.5723 | 2.2694 | 16.5328 |
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- | 1.3144 | 8.6957 | 5400 | 1.5713 | 2.2923 | 16.5478 |
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- | 1.244 | 8.8567 | 5500 | 1.5638 | 2.3811 | 16.5425 |
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- | 1.244 | 9.0177 | 5600 | 1.5842 | 2.3126 | 16.5776 |
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- | 1.244 | 9.1787 | 5700 | 1.5777 | 2.357 | 16.5226 |
108
- | 1.244 | 9.3398 | 5800 | 1.5618 | 2.3822 | 16.4809 |
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- | 1.244 | 9.5008 | 5900 | 1.5710 | 2.3494 | 16.5038 |
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- | 1.1933 | 9.6618 | 6000 | 1.5728 | 2.3299 | 16.5287 |
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- | 1.1933 | 9.8229 | 6100 | 1.5520 | 2.4029 | 16.5317 |
112
- | 1.1933 | 9.9839 | 6200 | 1.5572 | 2.3852 | 16.5411 |
113
- | 1.1933 | 10.1449 | 6300 | 1.5791 | 2.3908 | 16.4973 |
114
- | 1.1933 | 10.3060 | 6400 | 1.5676 | 2.4364 | 16.5336 |
115
- | 1.1507 | 10.4670 | 6500 | 1.5796 | 2.4361 | 16.4925 |
116
- | 1.1507 | 10.6280 | 6600 | 1.5560 | 2.4666 | 16.5239 |
117
- | 1.1507 | 10.7890 | 6700 | 1.5596 | 2.4446 | 16.55 |
118
- | 1.1507 | 10.9501 | 6800 | 1.5521 | 2.4664 | 16.5459 |
119
- | 1.1507 | 11.1111 | 6900 | 1.5784 | 2.5296 | 16.5196 |
120
- | 1.1065 | 11.2721 | 7000 | 1.5680 | 2.4907 | 16.5454 |
121
- | 1.1065 | 11.4332 | 7100 | 1.5717 | 2.4572 | 16.449 |
122
- | 1.1065 | 11.5942 | 7200 | 1.5628 | 2.4754 | 16.4715 |
123
- | 1.1065 | 11.7552 | 7300 | 1.5531 | 2.5194 | 16.5411 |
124
- | 1.1065 | 11.9163 | 7400 | 1.5634 | 2.536 | 16.5247 |
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- | 1.0746 | 12.0773 | 7500 | 1.5911 | 2.5297 | 16.5271 |
126
- | 1.0746 | 12.2383 | 7600 | 1.5856 | 2.5618 | 16.514 |
127
- | 1.0746 | 12.3994 | 7700 | 1.5821 | 2.5151 | 16.5024 |
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- | 1.0746 | 12.5604 | 7800 | 1.5799 | 2.5401 | 16.4895 |
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- | 1.0746 | 12.7214 | 7900 | 1.5702 | 2.5466 | 16.4798 |
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- | 1.0266 | 12.8824 | 8000 | 1.5721 | 2.584 | 16.4952 |
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- | 1.0266 | 13.0435 | 8100 | 1.5895 | 2.6024 | 16.5406 |
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- | 1.0266 | 13.2045 | 8200 | 1.5863 | 2.5719 | 16.5395 |
133
- | 1.0266 | 13.3655 | 8300 | 1.5887 | 2.5903 | 16.4742 |
134
- | 1.0266 | 13.5266 | 8400 | 1.6005 | 2.5751 | 16.5457 |
135
- | 0.9806 | 13.6876 | 8500 | 1.5757 | 2.6075 | 16.4782 |
136
- | 0.9806 | 13.8486 | 8600 | 1.5767 | 2.6331 | 16.4887 |
137
- | 0.9806 | 14.0097 | 8700 | 1.5863 | 2.5818 | 16.4785 |
138
- | 0.9806 | 14.1707 | 8800 | 1.6043 | 2.6117 | 16.5408 |
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- | 0.9806 | 14.3317 | 8900 | 1.5829 | 2.5749 | 16.45 |
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- | 0.9577 | 14.4928 | 9000 | 1.6059 | 2.5734 | 16.5019 |
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- | 0.9577 | 14.6538 | 9100 | 1.5926 | 2.6662 | 16.4831 |
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- | 0.9577 | 14.8148 | 9200 | 1.5854 | 2.6131 | 16.5116 |
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- | 0.9577 | 14.9758 | 9300 | 1.5714 | 2.6157 | 16.4954 |
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- | 0.9577 | 15.1369 | 9400 | 1.6011 | 2.6916 | 16.442 |
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- | 0.9266 | 15.2979 | 9500 | 1.6128 | 2.6445 | 16.5242 |
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- | 0.9266 | 15.4589 | 9600 | 1.6070 | 2.6538 | 16.4847 |
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- | 0.9266 | 15.6200 | 9700 | 1.6060 | 2.5989 | 16.5059 |
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- | 0.9266 | 15.7810 | 9800 | 1.6178 | 2.5933 | 16.5266 |
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- | 0.9266 | 15.9420 | 9900 | 1.6260 | 2.6454 | 16.4979 |
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- | 0.8936 | 16.1031 | 10000 | 1.6296 | 2.6559 | 16.461 |
151
- | 0.8936 | 16.2641 | 10100 | 1.6290 | 2.6873 | 16.4997 |
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- | 0.8936 | 16.4251 | 10200 | 1.6076 | 2.6861 | 16.4694 |
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- | 0.8936 | 16.5862 | 10300 | 1.6201 | 2.6822 | 16.4524 |
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- | 0.8936 | 16.7472 | 10400 | 1.6161 | 2.7133 | 16.4833 |
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- | 0.8613 | 16.9082 | 10500 | 1.6229 | 2.686 | 16.4554 |
156
- | 0.8613 | 17.0692 | 10600 | 1.6395 | 2.7024 | 16.4213 |
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- | 0.8613 | 17.2303 | 10700 | 1.6463 | 2.6706 | 16.4331 |
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- | 0.8613 | 17.3913 | 10800 | 1.6549 | 2.6859 | 16.4484 |
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- | 0.8613 | 17.5523 | 10900 | 1.6331 | 2.6857 | 16.4863 |
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- | 0.8385 | 17.7134 | 11000 | 1.6305 | 2.6936 | 16.4441 |
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- | 0.8385 | 17.8744 | 11100 | 1.6322 | 2.6882 | 16.4503 |
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- | 0.8385 | 18.0354 | 11200 | 1.6516 | 2.7029 | 16.4175 |
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- | 0.8385 | 18.1965 | 11300 | 1.6568 | 2.6676 | 16.4602 |
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- | 0.8385 | 18.3575 | 11400 | 1.6554 | 2.6773 | 16.4954 |
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- | 0.8034 | 18.5185 | 11500 | 1.6494 | 2.7066 | 16.4213 |
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- | 0.8034 | 18.6795 | 11600 | 1.6497 | 2.6936 | 16.4436 |
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- | 0.8034 | 18.8406 | 11700 | 1.6394 | 2.7009 | 16.4833 |
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- | 0.8034 | 19.0016 | 11800 | 1.6493 | 2.7471 | 16.4549 |
169
- | 0.8034 | 19.1626 | 11900 | 1.6696 | 2.6889 | 16.417 |
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- | 0.7883 | 19.3237 | 12000 | 1.6540 | 2.6753 | 16.4382 |
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- | 0.7883 | 19.4847 | 12100 | 1.6597 | 2.7349 | 16.4557 |
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- | 0.7883 | 19.6457 | 12200 | 1.6558 | 2.7191 | 16.4393 |
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- | 0.7883 | 19.8068 | 12300 | 1.6557 | 2.7646 | 16.4484 |
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- | 0.7883 | 19.9678 | 12400 | 1.6599 | 2.7076 | 16.4444 |
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- | 0.7761 | 20.1288 | 12500 | 1.6873 | 2.7427 | 16.4653 |
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- | 0.7761 | 20.2899 | 12600 | 1.6976 | 2.7498 | 16.4704 |
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- | 0.7761 | 20.4509 | 12700 | 1.6969 | 2.7683 | 16.4331 |
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- | 0.7761 | 20.6119 | 12800 | 1.6710 | 2.7009 | 16.4347 |
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- | 0.7761 | 20.7729 | 12900 | 1.6614 | 2.7105 | 16.4038 |
180
- | 0.7438 | 20.9340 | 13000 | 1.6864 | 2.7188 | 16.425 |
181
- | 0.7438 | 21.0950 | 13100 | 1.6923 | 2.7656 | 16.4909 |
182
- | 0.7438 | 21.2560 | 13200 | 1.7034 | 2.745 | 16.4675 |
183
- | 0.7438 | 21.4171 | 13300 | 1.6972 | 2.7237 | 16.428 |
184
- | 0.7438 | 21.5781 | 13400 | 1.6992 | 2.7556 | 16.4242 |
185
- | 0.7251 | 21.7391 | 13500 | 1.6785 | 2.8106 | 16.45 |
186
- | 0.7251 | 21.9002 | 13600 | 1.6830 | 2.7098 | 16.4108 |
187
- | 0.7251 | 22.0612 | 13700 | 1.7122 | 2.733 | 16.4656 |
188
- | 0.7251 | 22.2222 | 13800 | 1.7046 | 2.7216 | 16.4331 |
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- | 0.7251 | 22.3833 | 13900 | 1.6968 | 2.7373 | 16.4326 |
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- | 0.7049 | 22.5443 | 14000 | 1.7202 | 2.7242 | 16.4777 |
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- | 0.7049 | 22.7053 | 14100 | 1.6988 | 2.7873 | 16.4804 |
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- | 0.7049 | 22.8663 | 14200 | 1.6913 | 2.7961 | 16.446 |
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- | 0.7049 | 23.0274 | 14300 | 1.7020 | 2.7455 | 16.4575 |
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- | 0.7049 | 23.1884 | 14400 | 1.7156 | 2.7819 | 16.4436 |
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- | 0.6917 | 23.3494 | 14500 | 1.7248 | 2.7571 | 16.4436 |
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- | 0.6917 | 23.5105 | 14600 | 1.7131 | 2.7771 | 16.4573 |
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- | 0.6917 | 23.6715 | 14700 | 1.7140 | 2.7973 | 16.4495 |
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- | 0.6917 | 23.8325 | 14800 | 1.7135 | 2.8346 | 16.4672 |
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- | 0.6917 | 23.9936 | 14900 | 1.7135 | 2.8071 | 16.4471 |
200
- | 0.6802 | 24.1546 | 15000 | 1.7296 | 2.8281 | 16.4433 |
201
- | 0.6802 | 24.3156 | 15100 | 1.7336 | 2.8261 | 16.479 |
202
- | 0.6802 | 24.4767 | 15200 | 1.7373 | 2.8372 | 16.4567 |
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- | 0.6802 | 24.6377 | 15300 | 1.7263 | 2.8396 | 16.4938 |
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- | 0.6802 | 24.7987 | 15400 | 1.7388 | 2.8184 | 16.4498 |
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- | 0.6645 | 24.9597 | 15500 | 1.7182 | 2.8107 | 16.4398 |
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- | 0.6645 | 25.1208 | 15600 | 1.7427 | 2.7916 | 16.4207 |
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- | 0.6645 | 25.2818 | 15700 | 1.7491 | 2.824 | 16.4242 |
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- | 0.6645 | 25.4428 | 15800 | 1.7390 | 2.8197 | 16.4132 |
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- | 0.6645 | 25.6039 | 15900 | 1.7498 | 2.8252 | 16.449 |
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- | 0.651 | 25.7649 | 16000 | 1.7379 | 2.8417 | 16.4895 |
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- | 0.651 | 25.9259 | 16100 | 1.7447 | 2.8191 | 16.4637 |
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- | 0.651 | 26.0870 | 16200 | 1.7522 | 2.8645 | 16.4554 |
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- | 0.651 | 26.2480 | 16300 | 1.7513 | 2.8656 | 16.4672 |
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- | 0.651 | 26.4090 | 16400 | 1.7593 | 2.8727 | 16.478 |
215
- | 0.6385 | 26.5700 | 16500 | 1.7589 | 2.8586 | 16.4514 |
216
- | 0.6385 | 26.7311 | 16600 | 1.7620 | 2.8671 | 16.4731 |
217
- | 0.6385 | 26.8921 | 16700 | 1.7587 | 2.8518 | 16.4691 |
218
- | 0.6385 | 27.0531 | 16800 | 1.7714 | 2.8843 | 16.4527 |
219
- | 0.6385 | 27.2142 | 16900 | 1.7713 | 2.8654 | 16.45 |
220
- | 0.6317 | 27.3752 | 17000 | 1.7681 | 2.8745 | 16.4785 |
221
- | 0.6317 | 27.5362 | 17100 | 1.7579 | 2.831 | 16.4621 |
222
- | 0.6317 | 27.6973 | 17200 | 1.7668 | 2.8836 | 16.4672 |
223
- | 0.6317 | 27.8583 | 17300 | 1.7606 | 2.8513 | 16.4817 |
224
- | 0.6317 | 28.0193 | 17400 | 1.7564 | 2.8436 | 16.4782 |
225
- | 0.6276 | 28.1804 | 17500 | 1.7735 | 2.8623 | 16.4592 |
226
- | 0.6276 | 28.3414 | 17600 | 1.7751 | 2.8616 | 16.4597 |
227
- | 0.6276 | 28.5024 | 17700 | 1.7665 | 2.8474 | 16.4592 |
228
- | 0.6276 | 28.6634 | 17800 | 1.7666 | 2.8633 | 16.4707 |
229
- | 0.6276 | 28.8245 | 17900 | 1.7733 | 2.8409 | 16.4694 |
230
- | 0.6169 | 28.9855 | 18000 | 1.7709 | 2.8444 | 16.4557 |
231
- | 0.6169 | 29.1465 | 18100 | 1.7766 | 2.8485 | 16.4527 |
232
- | 0.6169 | 29.3076 | 18200 | 1.7745 | 2.8587 | 16.4661 |
233
- | 0.6169 | 29.4686 | 18300 | 1.7751 | 2.8552 | 16.4688 |
234
- | 0.6169 | 29.6296 | 18400 | 1.7755 | 2.8574 | 16.4699 |
235
- | 0.6112 | 29.7907 | 18500 | 1.7737 | 2.8521 | 16.4694 |
236
- | 0.6112 | 29.9517 | 18600 | 1.7731 | 2.855 | 16.4731 |
237
 
238
 
239
  ### Framework versions
 
1
  ---
2
+ license: apache-2.0
3
+ base_model: google/flan-t5-base
4
  tags:
5
  - generated_from_trainer
6
  metrics:
 
15
 
16
  # Nahuatl_Espanol_v1
17
 
18
+ This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
20
+ - Loss: 1.5569
21
+ - Bleu: 11.3722
22
+ - Gen Len: 50.2618
23
 
24
  ## Model description
25
 
 
38
  ### Training hyperparameters
39
 
40
  The following hyperparameters were used during training:
41
+ - learning_rate: 0.0003
42
+ - train_batch_size: 32
43
+ - eval_batch_size: 32
44
  - seed: 42
45
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
46
  - lr_scheduler_type: linear
47
+ - num_epochs: 8
48
 
49
  ### Training results
50
 
51
+ | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
52
+ |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|
53
+ | No log | 0.1071 | 100 | 2.5723 | 1.5414 | 66.3629 |
54
+ | No log | 0.2141 | 200 | 2.4126 | 2.2691 | 57.963 |
55
+ | No log | 0.3212 | 300 | 2.3108 | 3.1973 | 58.5399 |
56
+ | No log | 0.4283 | 400 | 2.2388 | 3.287 | 62.4842 |
57
+ | 2.6546 | 0.5353 | 500 | 2.1836 | 4.2131 | 62.4095 |
58
+ | 2.6546 | 0.6424 | 600 | 2.1288 | 4.5412 | 59.577 |
59
+ | 2.6546 | 0.7495 | 700 | 2.0843 | 4.9761 | 58.3334 |
60
+ | 2.6546 | 0.8565 | 800 | 2.0521 | 5.3677 | 55.6142 |
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+ | 2.6546 | 0.9636 | 900 | 2.0155 | 5.7455 | 58.4085 |
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+ | 2.2633 | 1.0707 | 1000 | 1.9911 | 5.7546 | 55.7296 |
63
+ | 2.2633 | 1.1777 | 1100 | 1.9598 | 6.1048 | 56.0984 |
64
+ | 2.2633 | 1.2848 | 1200 | 1.9519 | 6.5473 | 53.637 |
65
+ | 2.2633 | 1.3919 | 1300 | 1.9216 | 6.7154 | 56.1837 |
66
+ | 2.2633 | 1.4989 | 1400 | 1.9029 | 7.0137 | 53.0228 |
67
+ | 2.0618 | 1.6060 | 1500 | 1.8773 | 7.1217 | 53.8373 |
68
+ | 2.0618 | 1.7131 | 1600 | 1.8583 | 7.2656 | 53.5391 |
69
+ | 2.0618 | 1.8201 | 1700 | 1.8431 | 7.3096 | 55.2832 |
70
+ | 2.0618 | 1.9272 | 1800 | 1.8293 | 7.8435 | 52.0387 |
71
+ | 2.0618 | 2.0343 | 1900 | 1.8255 | 7.8638 | 52.7891 |
72
+ | 1.9543 | 2.1413 | 2000 | 1.8023 | 7.9867 | 52.4633 |
73
+ | 1.9543 | 2.2484 | 2100 | 1.7893 | 8.1977 | 52.3648 |
74
+ | 1.9543 | 2.3555 | 2200 | 1.7791 | 8.117 | 52.2424 |
75
+ | 1.9543 | 2.4625 | 2300 | 1.7606 | 8.2898 | 51.9746 |
76
+ | 1.9543 | 2.5696 | 2400 | 1.7504 | 8.3719 | 52.2516 |
77
+ | 1.8436 | 2.6767 | 2500 | 1.7390 | 8.4477 | 52.0948 |
78
+ | 1.8436 | 2.7837 | 2600 | 1.7274 | 8.5875 | 52.6693 |
79
+ | 1.8436 | 2.8908 | 2700 | 1.7133 | 8.5885 | 53.2887 |
80
+ | 1.8436 | 2.9979 | 2800 | 1.7044 | 8.7356 | 52.3006 |
81
+ | 1.8436 | 3.1049 | 2900 | 1.7054 | 9.0792 | 51.414 |
82
+ | 1.7834 | 3.2120 | 3000 | 1.7031 | 9.1291 | 50.6498 |
83
+ | 1.7834 | 3.3191 | 3100 | 1.6909 | 9.3481 | 51.7598 |
84
+ | 1.7834 | 3.4261 | 3200 | 1.6855 | 9.3196 | 51.568 |
85
+ | 1.7834 | 3.5332 | 3300 | 1.6777 | 9.5455 | 51.1409 |
86
+ | 1.7834 | 3.6403 | 3400 | 1.6633 | 9.4966 | 51.7201 |
87
+ | 1.7053 | 3.7473 | 3500 | 1.6564 | 9.4615 | 51.7318 |
88
+ | 1.7053 | 3.8544 | 3600 | 1.6512 | 9.758 | 50.6543 |
89
+ | 1.7053 | 3.9615 | 3700 | 1.6458 | 9.8421 | 51.4519 |
90
+ | 1.7053 | 4.0685 | 3800 | 1.6473 | 9.8202 | 51.6808 |
91
+ | 1.7053 | 4.1756 | 3900 | 1.6428 | 9.9752 | 51.6946 |
92
+ | 1.6525 | 4.2827 | 4000 | 1.6334 | 9.9772 | 50.9325 |
93
+ | 1.6525 | 4.3897 | 4100 | 1.6398 | 10.2281 | 50.3922 |
94
+ | 1.6525 | 4.4968 | 4200 | 1.6277 | 10.1332 | 50.6855 |
95
+ | 1.6525 | 4.6039 | 4300 | 1.6182 | 10.2265 | 50.787 |
96
+ | 1.6525 | 4.7109 | 4400 | 1.6103 | 10.2743 | 50.9466 |
97
+ | 1.6085 | 4.8180 | 4500 | 1.6091 | 10.3769 | 50.8686 |
98
+ | 1.6085 | 4.9251 | 4600 | 1.6040 | 10.3912 | 50.8964 |
99
+ | 1.6085 | 5.0321 | 4700 | 1.6106 | 10.477 | 50.8413 |
100
+ | 1.6085 | 5.1392 | 4800 | 1.6061 | 10.4855 | 50.4158 |
101
+ | 1.6085 | 5.2463 | 4900 | 1.5994 | 10.4867 | 50.289 |
102
+ | 1.5533 | 5.3533 | 5000 | 1.5996 | 10.6141 | 50.4866 |
103
+ | 1.5533 | 5.4604 | 5100 | 1.5915 | 10.6853 | 50.511 |
104
+ | 1.5533 | 5.5675 | 5200 | 1.5909 | 10.6245 | 50.6536 |
105
+ | 1.5533 | 5.6745 | 5300 | 1.5831 | 10.7335 | 50.8369 |
106
+ | 1.5533 | 5.7816 | 5400 | 1.5825 | 10.9312 | 50.8341 |
107
+ | 1.5405 | 5.8887 | 5500 | 1.5774 | 10.812 | 50.8858 |
108
+ | 1.5405 | 5.9957 | 5600 | 1.5741 | 10.8934 | 50.6563 |
109
+ | 1.5405 | 6.1028 | 5700 | 1.5793 | 11.0778 | 49.9203 |
110
+ | 1.5405 | 6.2099 | 5800 | 1.5747 | 10.899 | 50.7741 |
111
+ | 1.5405 | 6.3169 | 5900 | 1.5740 | 10.9393 | 50.37 |
112
+ | 1.4986 | 6.4240 | 6000 | 1.5750 | 10.9161 | 50.542 |
113
+ | 1.4986 | 6.5310 | 6100 | 1.5686 | 11.108 | 50.5564 |
114
+ | 1.4986 | 6.6381 | 6200 | 1.5686 | 11.1873 | 50.0757 |
115
+ | 1.4986 | 6.7452 | 6300 | 1.5652 | 11.0949 | 49.9321 |
116
+ | 1.4986 | 6.8522 | 6400 | 1.5611 | 11.0787 | 50.3984 |
117
+ | 1.4796 | 6.9593 | 6500 | 1.5606 | 11.2271 | 50.4241 |
118
+ | 1.4796 | 7.0664 | 6600 | 1.5635 | 11.319 | 50.3903 |
119
+ | 1.4796 | 7.1734 | 6700 | 1.5629 | 11.3525 | 50.0731 |
120
+ | 1.4796 | 7.2805 | 6800 | 1.5635 | 11.3429 | 50.1856 |
121
+ | 1.4796 | 7.3876 | 6900 | 1.5610 | 11.3293 | 50.1604 |
122
+ | 1.4508 | 7.4946 | 7000 | 1.5599 | 11.2761 | 50.1051 |
123
+ | 1.4508 | 7.6017 | 7100 | 1.5594 | 11.2963 | 50.2512 |
124
+ | 1.4508 | 7.7088 | 7200 | 1.5582 | 11.2863 | 50.1589 |
125
+ | 1.4508 | 7.8158 | 7300 | 1.5588 | 11.3195 | 50.1687 |
126
+ | 1.4508 | 7.9229 | 7400 | 1.5569 | 11.3722 | 50.2618 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
127
 
128
 
129
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