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reverse_add_replicate_eval20_small_1layer_d1_50

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nanogpt
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  1. README.md +156 -156
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -16,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
16
 
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.3789
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  - Accuracy: 0.0
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  ## Model description
@@ -51,161 +51,161 @@ The following hyperparameters were used during training:
51
  |:-------------:|:------:|:-----:|:---------------:|:--------:|
52
  | No log | 0 | 0 | 2.6391 | 0.0 |
53
  | 2.639 | 0.0064 | 100 | 2.6390 | 0.0 |
54
- | 2.6388 | 0.0128 | 200 | 2.6387 | 0.0 |
55
- | 2.6377 | 0.0192 | 300 | 2.6374 | 0.0 |
56
- | 2.6347 | 0.0256 | 400 | 2.6338 | 0.0 |
57
- | 2.6286 | 0.032 | 500 | 2.6264 | 0.0 |
58
- | 2.6184 | 0.0384 | 600 | 2.6141 | 0.0 |
59
- | 2.6028 | 0.0448 | 700 | 2.5965 | 0.0 |
60
- | 2.5822 | 0.0512 | 800 | 2.5744 | 0.0 |
61
- | 2.5577 | 0.0576 | 900 | 2.5484 | 0.0 |
62
- | 2.5314 | 0.064 | 1000 | 2.5208 | 0.0 |
63
- | 2.5057 | 0.0704 | 1100 | 2.4938 | 0.0 |
64
- | 2.4826 | 0.0768 | 1200 | 2.4697 | 0.0 |
65
- | 2.4621 | 0.0832 | 1300 | 2.4497 | 0.0 |
66
- | 2.4472 | 0.0896 | 1400 | 2.4337 | 0.0 |
67
- | 2.4364 | 0.096 | 1500 | 2.4210 | 0.0 |
68
- | 2.4261 | 0.1024 | 1600 | 2.4116 | 0.0 |
69
- | 2.4177 | 0.1088 | 1700 | 2.4052 | 0.0 |
70
- | 2.4137 | 0.1152 | 1800 | 2.3997 | 0.0 |
71
- | 2.4103 | 0.1216 | 1900 | 2.3963 | 0.0 |
72
- | 2.4083 | 0.128 | 2000 | 2.3936 | 0.0 |
73
- | 2.4066 | 0.1344 | 2100 | 2.3913 | 0.0 |
74
- | 2.4043 | 0.1408 | 2200 | 2.3894 | 0.0 |
75
- | 2.4028 | 0.1472 | 2300 | 2.3879 | 0.0 |
76
- | 2.4015 | 0.1536 | 2400 | 2.3873 | 0.0 |
77
- | 2.4013 | 0.16 | 2500 | 2.3859 | 0.0 |
78
- | 2.4006 | 0.1664 | 2600 | 2.3851 | 0.0 |
79
- | 2.3998 | 0.1728 | 2700 | 2.3846 | 0.0 |
80
- | 2.3987 | 0.1792 | 2800 | 2.3842 | 0.0 |
81
- | 2.3984 | 0.1856 | 2900 | 2.3838 | 0.0 |
82
- | 2.3973 | 0.192 | 3000 | 2.3832 | 0.0 |
83
- | 2.3988 | 0.1984 | 3100 | 2.3827 | 0.0 |
84
- | 2.3972 | 0.2048 | 3200 | 2.3821 | 0.0 |
85
- | 2.3978 | 0.2112 | 3300 | 2.3819 | 0.0 |
86
- | 2.3978 | 0.2176 | 3400 | 2.3819 | 0.0 |
87
- | 2.3973 | 0.224 | 3500 | 2.3814 | 0.0 |
88
- | 2.396 | 0.2304 | 3600 | 2.3815 | 0.0 |
89
- | 2.3961 | 0.2368 | 3700 | 2.3811 | 0.0 |
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- | 2.3955 | 0.2432 | 3800 | 2.3809 | 0.0 |
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- | 2.3952 | 0.2496 | 3900 | 2.3810 | 0.0 |
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- | 2.3947 | 0.256 | 4000 | 2.3811 | 0.0 |
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- | 2.396 | 0.2624 | 4100 | 2.3806 | 0.0 |
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- | 2.3949 | 0.2688 | 4200 | 2.3802 | 0.0 |
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- | 2.3953 | 0.2752 | 4300 | 2.3806 | 0.0 |
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- | 2.3963 | 0.2816 | 4400 | 2.3803 | 0.0 |
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- | 2.3961 | 0.288 | 4500 | 2.3798 | 0.0 |
98
- | 2.3942 | 0.2944 | 4600 | 2.3803 | 0.0 |
99
- | 2.3944 | 0.3008 | 4700 | 2.3800 | 0.0 |
100
- | 2.3961 | 0.3072 | 4800 | 2.3797 | 0.0 |
101
- | 2.3936 | 0.3136 | 4900 | 2.3795 | 0.0 |
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- | 2.3955 | 0.32 | 5000 | 2.3795 | 0.0 |
103
- | 2.395 | 0.3264 | 5100 | 2.3796 | 0.0 |
104
- | 2.3959 | 0.3328 | 5200 | 2.3796 | 0.0 |
105
- | 2.3959 | 0.3392 | 5300 | 2.3799 | 0.0 |
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- | 2.3925 | 0.3456 | 5400 | 2.3799 | 0.0 |
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- | 2.3941 | 0.352 | 5500 | 2.3795 | 0.0 |
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- | 2.3944 | 0.3584 | 5600 | 2.3792 | 0.0 |
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- | 2.3939 | 0.3648 | 5700 | 2.3798 | 0.0 |
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- | 2.3941 | 0.3712 | 5800 | 2.3795 | 0.0 |
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- | 2.3944 | 0.3776 | 5900 | 2.3793 | 0.0 |
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- | 2.3947 | 0.384 | 6000 | 2.3793 | 0.0 |
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- | 2.3945 | 0.3904 | 6100 | 2.3794 | 0.0 |
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- | 2.3946 | 0.3968 | 6200 | 2.3793 | 0.0 |
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- | 2.3965 | 0.4032 | 6300 | 2.3793 | 0.0 |
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- | 2.3941 | 0.4096 | 6400 | 2.3791 | 0.0 |
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- | 2.3937 | 0.416 | 6500 | 2.3796 | 0.0 |
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- | 2.395 | 0.4224 | 6600 | 2.3790 | 0.0 |
119
- | 2.3955 | 0.4288 | 6700 | 2.3794 | 0.0 |
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- | 2.3936 | 0.4352 | 6800 | 2.3794 | 0.0 |
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- | 2.3933 | 0.4416 | 6900 | 2.3790 | 0.0 |
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- | 2.3938 | 0.448 | 7000 | 2.3794 | 0.0 |
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- | 2.394 | 0.4544 | 7100 | 2.3793 | 0.0 |
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- | 2.3933 | 0.4608 | 7200 | 2.3793 | 0.0 |
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- | 2.3949 | 0.4672 | 7300 | 2.3792 | 0.0 |
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- | 2.3938 | 0.4736 | 7400 | 2.3792 | 0.0 |
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- | 2.3934 | 0.48 | 7500 | 2.3793 | 0.0 |
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- | 2.3947 | 0.4864 | 7600 | 2.3790 | 0.0 |
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- | 2.3952 | 0.4928 | 7700 | 2.3793 | 0.0 |
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- | 2.3944 | 0.4992 | 7800 | 2.3795 | 0.0 |
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- | 2.3936 | 0.5056 | 7900 | 2.3793 | 0.0 |
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- | 2.3943 | 0.512 | 8000 | 2.3796 | 0.0 |
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- | 2.394 | 0.5184 | 8100 | 2.3797 | 0.0 |
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- | 2.395 | 0.5248 | 8200 | 2.3791 | 0.0 |
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- | 2.3937 | 0.5312 | 8300 | 2.3795 | 0.0 |
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- | 2.3937 | 0.5376 | 8400 | 2.3793 | 0.0 |
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- | 2.3931 | 0.544 | 8500 | 2.3791 | 0.0 |
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- | 2.3945 | 0.5504 | 8600 | 2.3789 | 0.0 |
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- | 2.3958 | 0.5568 | 8700 | 2.3792 | 0.0 |
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- | 2.3943 | 0.5632 | 8800 | 2.3789 | 0.0 |
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- | 2.3947 | 0.5696 | 8900 | 2.3794 | 0.0 |
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- | 2.3946 | 0.576 | 9000 | 2.3790 | 0.0 |
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- | 2.3944 | 0.5824 | 9100 | 2.3791 | 0.0 |
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- | 2.3932 | 0.5888 | 9200 | 2.3790 | 0.0 |
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- | 2.3949 | 0.5952 | 9300 | 2.3788 | 0.0 |
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- | 2.3939 | 0.6016 | 9400 | 2.3790 | 0.0 |
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- | 2.3941 | 0.608 | 9500 | 2.3791 | 0.0 |
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- | 2.3931 | 0.6144 | 9600 | 2.3787 | 0.0 |
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- | 2.3943 | 0.6208 | 9700 | 2.3790 | 0.0 |
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- | 2.3949 | 0.6272 | 9800 | 2.3792 | 0.0 |
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- | 2.3948 | 0.6336 | 9900 | 2.3790 | 0.0 |
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- | 2.3947 | 0.64 | 10000 | 2.3790 | 0.0 |
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- | 2.3927 | 0.6464 | 10100 | 2.3788 | 0.0 |
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- | 2.3941 | 0.6528 | 10200 | 2.3793 | 0.0 |
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- | 2.394 | 0.6592 | 10300 | 2.3789 | 0.0 |
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- | 2.3946 | 0.6656 | 10400 | 2.3791 | 0.0 |
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- | 2.3927 | 0.672 | 10500 | 2.3792 | 0.0 |
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- | 2.3939 | 0.6784 | 10600 | 2.3791 | 0.0 |
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- | 2.3949 | 0.6848 | 10700 | 2.3793 | 0.0 |
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- | 2.3941 | 0.6912 | 10800 | 2.3790 | 0.0 |
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- | 2.3944 | 0.6976 | 10900 | 2.3789 | 0.0 |
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- | 2.3947 | 0.704 | 11000 | 2.3786 | 0.0 |
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- | 2.3927 | 0.7104 | 11100 | 2.3787 | 0.0 |
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- | 2.3947 | 0.7168 | 11200 | 2.3789 | 0.0 |
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- | 2.3928 | 0.7232 | 11300 | 2.3789 | 0.0 |
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- | 2.394 | 0.7296 | 11400 | 2.3788 | 0.0 |
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- | 2.3929 | 0.736 | 11500 | 2.3788 | 0.0 |
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- | 2.3939 | 0.7424 | 11600 | 2.3787 | 0.0 |
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- | 2.3927 | 0.7488 | 11700 | 2.3790 | 0.0 |
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- | 2.3943 | 0.7552 | 11800 | 2.3791 | 0.0 |
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- | 2.3945 | 0.7616 | 11900 | 2.3790 | 0.0 |
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- | 2.3948 | 0.768 | 12000 | 2.3791 | 0.0 |
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- | 2.3934 | 0.7744 | 12100 | 2.3792 | 0.0 |
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- | 2.3937 | 0.7808 | 12200 | 2.3791 | 0.0 |
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- | 2.3934 | 0.7872 | 12300 | 2.3790 | 0.0 |
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- | 2.394 | 0.7936 | 12400 | 2.3791 | 0.0 |
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- | 2.3958 | 0.8 | 12500 | 2.3791 | 0.0 |
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- | 2.3942 | 0.8064 | 12600 | 2.3791 | 0.0 |
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- | 2.3941 | 0.8128 | 12700 | 2.3788 | 0.0 |
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- | 2.3951 | 0.8192 | 12800 | 2.3789 | 0.0 |
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- | 2.3926 | 0.8256 | 12900 | 2.3789 | 0.0 |
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- | 2.3939 | 0.832 | 13000 | 2.3789 | 0.0 |
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- | 2.3937 | 0.8384 | 13100 | 2.3791 | 0.0 |
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- | 2.3943 | 0.8448 | 13200 | 2.3790 | 0.0 |
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- | 2.3938 | 0.8512 | 13300 | 2.3789 | 0.0 |
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- | 2.3952 | 0.8576 | 13400 | 2.3790 | 0.0 |
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- | 2.3927 | 0.864 | 13500 | 2.3791 | 0.0 |
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- | 2.3943 | 0.8704 | 13600 | 2.3790 | 0.0 |
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- | 2.3942 | 0.8768 | 13700 | 2.3789 | 0.0 |
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- | 2.3949 | 0.8832 | 13800 | 2.3790 | 0.0 |
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- | 2.3938 | 0.8896 | 13900 | 2.3790 | 0.0 |
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- | 2.3944 | 0.896 | 14000 | 2.3789 | 0.0 |
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- | 2.3949 | 0.9024 | 14100 | 2.3789 | 0.0 |
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- | 2.3951 | 0.9088 | 14200 | 2.3789 | 0.0 |
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- | 2.3954 | 0.9152 | 14300 | 2.3789 | 0.0 |
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- | 2.3946 | 0.9216 | 14400 | 2.3789 | 0.0 |
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- | 2.3942 | 0.928 | 14500 | 2.3789 | 0.0 |
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- | 2.3929 | 0.9344 | 14600 | 2.3789 | 0.0 |
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- | 2.3937 | 0.9408 | 14700 | 2.3789 | 0.0 |
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- | 2.393 | 0.9472 | 14800 | 2.3789 | 0.0 |
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- | 2.3942 | 0.9536 | 14900 | 2.3789 | 0.0 |
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- | 2.394 | 0.96 | 15000 | 2.3789 | 0.0 |
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- | 2.394 | 0.9664 | 15100 | 2.3789 | 0.0 |
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- | 2.3944 | 0.9728 | 15200 | 2.3789 | 0.0 |
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- | 2.3929 | 0.9792 | 15300 | 2.3789 | 0.0 |
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- | 2.3932 | 0.9856 | 15400 | 2.3789 | 0.0 |
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- | 2.394 | 0.992 | 15500 | 2.3789 | 0.0 |
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- | 2.3947 | 0.9984 | 15600 | 2.3789 | 0.0 |
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  ### Framework versions
 
16
 
17
  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
18
  It achieves the following results on the evaluation set:
19
+ - Loss: 2.4121
20
  - Accuracy: 0.0
21
 
22
  ## Model description
 
51
  |:-------------:|:------:|:-----:|:---------------:|:--------:|
52
  | No log | 0 | 0 | 2.6391 | 0.0 |
53
  | 2.639 | 0.0064 | 100 | 2.6390 | 0.0 |
54
+ | 2.6388 | 0.0128 | 200 | 2.6389 | 0.0 |
55
+ | 2.6378 | 0.0192 | 300 | 2.6380 | 0.0 |
56
+ | 2.6347 | 0.0256 | 400 | 2.6357 | 0.0 |
57
+ | 2.6286 | 0.032 | 500 | 2.6309 | 0.0 |
58
+ | 2.6183 | 0.0384 | 600 | 2.6227 | 0.0 |
59
+ | 2.6028 | 0.0448 | 700 | 2.6098 | 0.0 |
60
+ | 2.5821 | 0.0512 | 800 | 2.5914 | 0.0 |
61
+ | 2.5578 | 0.0576 | 900 | 2.5689 | 0.0 |
62
+ | 2.5314 | 0.064 | 1000 | 2.5440 | 0.0 |
63
+ | 2.5058 | 0.0704 | 1100 | 2.5198 | 0.0 |
64
+ | 2.4828 | 0.0768 | 1200 | 2.4975 | 0.0 |
65
+ | 2.462 | 0.0832 | 1300 | 2.4785 | 0.0 |
66
+ | 2.4471 | 0.0896 | 1400 | 2.4634 | 0.0 |
67
+ | 2.4364 | 0.096 | 1500 | 2.4522 | 0.0 |
68
+ | 2.4261 | 0.1024 | 1600 | 2.4435 | 0.0 |
69
+ | 2.4177 | 0.1088 | 1700 | 2.4366 | 0.0 |
70
+ | 2.4137 | 0.1152 | 1800 | 2.4325 | 0.0 |
71
+ | 2.4103 | 0.1216 | 1900 | 2.4287 | 0.0 |
72
+ | 2.4083 | 0.128 | 2000 | 2.4258 | 0.0 |
73
+ | 2.4066 | 0.1344 | 2100 | 2.4239 | 0.0 |
74
+ | 2.4044 | 0.1408 | 2200 | 2.4223 | 0.0 |
75
+ | 2.4028 | 0.1472 | 2300 | 2.4209 | 0.0 |
76
+ | 2.4015 | 0.1536 | 2400 | 2.4194 | 0.0 |
77
+ | 2.4013 | 0.16 | 2500 | 2.4190 | 0.0 |
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+ | 2.4006 | 0.1664 | 2600 | 2.4182 | 0.0 |
79
+ | 2.3998 | 0.1728 | 2700 | 2.4173 | 0.0 |
80
+ | 2.3987 | 0.1792 | 2800 | 2.4168 | 0.0 |
81
+ | 2.3984 | 0.1856 | 2900 | 2.4161 | 0.0 |
82
+ | 2.3973 | 0.192 | 3000 | 2.4157 | 0.0 |
83
+ | 2.3988 | 0.1984 | 3100 | 2.4156 | 0.0 |
84
+ | 2.3972 | 0.2048 | 3200 | 2.4158 | 0.0 |
85
+ | 2.3978 | 0.2112 | 3300 | 2.4153 | 0.0 |
86
+ | 2.3978 | 0.2176 | 3400 | 2.4145 | 0.0 |
87
+ | 2.3973 | 0.224 | 3500 | 2.4147 | 0.0 |
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+ | 2.396 | 0.2304 | 3600 | 2.4142 | 0.0 |
89
+ | 2.3961 | 0.2368 | 3700 | 2.4143 | 0.0 |
90
+ | 2.3955 | 0.2432 | 3800 | 2.4141 | 0.0 |
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+ | 2.3952 | 0.2496 | 3900 | 2.4138 | 0.0 |
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+ | 2.3947 | 0.256 | 4000 | 2.4132 | 0.0 |
93
+ | 2.396 | 0.2624 | 4100 | 2.4138 | 0.0 |
94
+ | 2.3949 | 0.2688 | 4200 | 2.4138 | 0.0 |
95
+ | 2.3953 | 0.2752 | 4300 | 2.4133 | 0.0 |
96
+ | 2.3963 | 0.2816 | 4400 | 2.4133 | 0.0 |
97
+ | 2.3961 | 0.288 | 4500 | 2.4139 | 0.0 |
98
+ | 2.3942 | 0.2944 | 4600 | 2.4130 | 0.0 |
99
+ | 2.3944 | 0.3008 | 4700 | 2.4132 | 0.0 |
100
+ | 2.3961 | 0.3072 | 4800 | 2.4134 | 0.0 |
101
+ | 2.3936 | 0.3136 | 4900 | 2.4134 | 0.0 |
102
+ | 2.3955 | 0.32 | 5000 | 2.4134 | 0.0 |
103
+ | 2.395 | 0.3264 | 5100 | 2.4129 | 0.0 |
104
+ | 2.3959 | 0.3328 | 5200 | 2.4129 | 0.0 |
105
+ | 2.3959 | 0.3392 | 5300 | 2.4125 | 0.0 |
106
+ | 2.3925 | 0.3456 | 5400 | 2.4124 | 0.0 |
107
+ | 2.3941 | 0.352 | 5500 | 2.4128 | 0.0 |
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+ | 2.3944 | 0.3584 | 5600 | 2.4132 | 0.0 |
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+ | 2.3939 | 0.3648 | 5700 | 2.4122 | 0.0 |
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+ | 2.3941 | 0.3712 | 5800 | 2.4126 | 0.0 |
111
+ | 2.3944 | 0.3776 | 5900 | 2.4129 | 0.0 |
112
+ | 2.3947 | 0.384 | 6000 | 2.4125 | 0.0 |
113
+ | 2.3945 | 0.3904 | 6100 | 2.4126 | 0.0 |
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+ | 2.3946 | 0.3968 | 6200 | 2.4125 | 0.0 |
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+ | 2.3965 | 0.4032 | 6300 | 2.4123 | 0.0 |
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+ | 2.3941 | 0.4096 | 6400 | 2.4127 | 0.0 |
117
+ | 2.3937 | 0.416 | 6500 | 2.4122 | 0.0 |
118
+ | 2.395 | 0.4224 | 6600 | 2.4127 | 0.0 |
119
+ | 2.3955 | 0.4288 | 6700 | 2.4122 | 0.0 |
120
+ | 2.3936 | 0.4352 | 6800 | 2.4122 | 0.0 |
121
+ | 2.3933 | 0.4416 | 6900 | 2.4126 | 0.0 |
122
+ | 2.3938 | 0.448 | 7000 | 2.4120 | 0.0 |
123
+ | 2.394 | 0.4544 | 7100 | 2.4121 | 0.0 |
124
+ | 2.3933 | 0.4608 | 7200 | 2.4121 | 0.0 |
125
+ | 2.3949 | 0.4672 | 7300 | 2.4125 | 0.0 |
126
+ | 2.3938 | 0.4736 | 7400 | 2.4124 | 0.0 |
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+ | 2.3934 | 0.48 | 7500 | 2.4120 | 0.0 |
128
+ | 2.3947 | 0.4864 | 7600 | 2.4124 | 0.0 |
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+ | 2.3952 | 0.4928 | 7700 | 2.4120 | 0.0 |
130
+ | 2.3944 | 0.4992 | 7800 | 2.4118 | 0.0 |
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+ | 2.3936 | 0.5056 | 7900 | 2.4122 | 0.0 |
132
+ | 2.3943 | 0.512 | 8000 | 2.4116 | 0.0 |
133
+ | 2.394 | 0.5184 | 8100 | 2.4117 | 0.0 |
134
+ | 2.395 | 0.5248 | 8200 | 2.4122 | 0.0 |
135
+ | 2.3937 | 0.5312 | 8300 | 2.4116 | 0.0 |
136
+ | 2.3937 | 0.5376 | 8400 | 2.4122 | 0.0 |
137
+ | 2.3931 | 0.544 | 8500 | 2.4124 | 0.0 |
138
+ | 2.3945 | 0.5504 | 8600 | 2.4125 | 0.0 |
139
+ | 2.3958 | 0.5568 | 8700 | 2.4120 | 0.0 |
140
+ | 2.3943 | 0.5632 | 8800 | 2.4124 | 0.0 |
141
+ | 2.3947 | 0.5696 | 8900 | 2.4118 | 0.0 |
142
+ | 2.3946 | 0.576 | 9000 | 2.4124 | 0.0 |
143
+ | 2.3944 | 0.5824 | 9100 | 2.4121 | 0.0 |
144
+ | 2.3932 | 0.5888 | 9200 | 2.4121 | 0.0 |
145
+ | 2.3949 | 0.5952 | 9300 | 2.4124 | 0.0 |
146
+ | 2.3939 | 0.6016 | 9400 | 2.4121 | 0.0 |
147
+ | 2.3941 | 0.608 | 9500 | 2.4120 | 0.0 |
148
+ | 2.3931 | 0.6144 | 9600 | 2.4124 | 0.0 |
149
+ | 2.3943 | 0.6208 | 9700 | 2.4120 | 0.0 |
150
+ | 2.3949 | 0.6272 | 9800 | 2.4120 | 0.0 |
151
+ | 2.3948 | 0.6336 | 9900 | 2.4121 | 0.0 |
152
+ | 2.3947 | 0.64 | 10000 | 2.4121 | 0.0 |
153
+ | 2.3927 | 0.6464 | 10100 | 2.4124 | 0.0 |
154
+ | 2.3941 | 0.6528 | 10200 | 2.4117 | 0.0 |
155
+ | 2.394 | 0.6592 | 10300 | 2.4122 | 0.0 |
156
+ | 2.3946 | 0.6656 | 10400 | 2.4121 | 0.0 |
157
+ | 2.3927 | 0.672 | 10500 | 2.4119 | 0.0 |
158
+ | 2.3939 | 0.6784 | 10600 | 2.4121 | 0.0 |
159
+ | 2.3949 | 0.6848 | 10700 | 2.4118 | 0.0 |
160
+ | 2.3941 | 0.6912 | 10800 | 2.4122 | 0.0 |
161
+ | 2.3944 | 0.6976 | 10900 | 2.4122 | 0.0 |
162
+ | 2.3947 | 0.704 | 11000 | 2.4125 | 0.0 |
163
+ | 2.3927 | 0.7104 | 11100 | 2.4124 | 0.0 |
164
+ | 2.3947 | 0.7168 | 11200 | 2.4122 | 0.0 |
165
+ | 2.3928 | 0.7232 | 11300 | 2.4122 | 0.0 |
166
+ | 2.394 | 0.7296 | 11400 | 2.4123 | 0.0 |
167
+ | 2.3929 | 0.736 | 11500 | 2.4123 | 0.0 |
168
+ | 2.3939 | 0.7424 | 11600 | 2.4124 | 0.0 |
169
+ | 2.3927 | 0.7488 | 11700 | 2.4120 | 0.0 |
170
+ | 2.3943 | 0.7552 | 11800 | 2.4119 | 0.0 |
171
+ | 2.3945 | 0.7616 | 11900 | 2.4121 | 0.0 |
172
+ | 2.3948 | 0.768 | 12000 | 2.4119 | 0.0 |
173
+ | 2.3934 | 0.7744 | 12100 | 2.4118 | 0.0 |
174
+ | 2.3937 | 0.7808 | 12200 | 2.4119 | 0.0 |
175
+ | 2.3934 | 0.7872 | 12300 | 2.4121 | 0.0 |
176
+ | 2.394 | 0.7936 | 12400 | 2.4118 | 0.0 |
177
+ | 2.3958 | 0.8 | 12500 | 2.4119 | 0.0 |
178
+ | 2.3942 | 0.8064 | 12600 | 2.4118 | 0.0 |
179
+ | 2.3941 | 0.8128 | 12700 | 2.4122 | 0.0 |
180
+ | 2.3951 | 0.8192 | 12800 | 2.4122 | 0.0 |
181
+ | 2.3926 | 0.8256 | 12900 | 2.4121 | 0.0 |
182
+ | 2.3939 | 0.832 | 13000 | 2.4121 | 0.0 |
183
+ | 2.3937 | 0.8384 | 13100 | 2.4119 | 0.0 |
184
+ | 2.3943 | 0.8448 | 13200 | 2.4119 | 0.0 |
185
+ | 2.3938 | 0.8512 | 13300 | 2.4121 | 0.0 |
186
+ | 2.3952 | 0.8576 | 13400 | 2.4120 | 0.0 |
187
+ | 2.3927 | 0.864 | 13500 | 2.4119 | 0.0 |
188
+ | 2.3943 | 0.8704 | 13600 | 2.4120 | 0.0 |
189
+ | 2.3942 | 0.8768 | 13700 | 2.4121 | 0.0 |
190
+ | 2.3949 | 0.8832 | 13800 | 2.4121 | 0.0 |
191
+ | 2.3938 | 0.8896 | 13900 | 2.4120 | 0.0 |
192
+ | 2.3944 | 0.896 | 14000 | 2.4121 | 0.0 |
193
+ | 2.3949 | 0.9024 | 14100 | 2.4121 | 0.0 |
194
+ | 2.3951 | 0.9088 | 14200 | 2.4121 | 0.0 |
195
+ | 2.3954 | 0.9152 | 14300 | 2.4121 | 0.0 |
196
+ | 2.3946 | 0.9216 | 14400 | 2.4121 | 0.0 |
197
+ | 2.3942 | 0.928 | 14500 | 2.4121 | 0.0 |
198
+ | 2.3929 | 0.9344 | 14600 | 2.4121 | 0.0 |
199
+ | 2.3937 | 0.9408 | 14700 | 2.4121 | 0.0 |
200
+ | 2.393 | 0.9472 | 14800 | 2.4120 | 0.0 |
201
+ | 2.3942 | 0.9536 | 14900 | 2.4120 | 0.0 |
202
+ | 2.394 | 0.96 | 15000 | 2.4120 | 0.0 |
203
+ | 2.394 | 0.9664 | 15100 | 2.4121 | 0.0 |
204
+ | 2.3944 | 0.9728 | 15200 | 2.4121 | 0.0 |
205
+ | 2.3929 | 0.9792 | 15300 | 2.4121 | 0.0 |
206
+ | 2.3932 | 0.9856 | 15400 | 2.4121 | 0.0 |
207
+ | 2.394 | 0.992 | 15500 | 2.4121 | 0.0 |
208
+ | 2.3947 | 0.9984 | 15600 | 2.4121 | 0.0 |
209
 
210
 
211
  ### Framework versions
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