Hugging Face's logo

craa
/
100M_high_2000_495

Text Generation
Transformers
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Model card Files
xet
Community
craa commited on
Commit
6aa82b7
·
verified ·
1 Parent(s): cd96ee2

Model save

Browse files
Files changed (2) hide show
  1. README.md +94 -94
  2. model.safetensors +1 -1
README.md CHANGED
@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
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: 3.3018
20
- - Accuracy: 0.3945
21
 
22
  ## Model description
23
 
@@ -50,98 +50,98 @@ The following hyperparameters were used during training:
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:------:|:-----:|:---------------:|:--------:|
53
- | 5.0644 | 0.1076 | 1000 | 5.0158 | 0.2280 |
54
- | 4.5877 | 0.2153 | 2000 | 4.5086 | 0.2704 |
55
- | 4.2989 | 0.3229 | 3000 | 4.2294 | 0.2990 |
56
- | 4.1677 | 0.4305 | 4000 | 4.0851 | 0.3131 |
57
- | 4.0729 | 0.5382 | 5000 | 3.9909 | 0.3210 |
58
- | 3.995 | 0.6458 | 6000 | 3.9144 | 0.3291 |
59
- | 3.9517 | 0.7534 | 7000 | 3.8637 | 0.3331 |
60
- | 3.8717 | 0.8610 | 8000 | 3.8143 | 0.3380 |
61
- | 3.8378 | 0.9687 | 9000 | 3.7806 | 0.3416 |
62
- | 3.7573 | 1.0763 | 10000 | 3.7458 | 0.3451 |
63
- | 3.7359 | 1.1839 | 11000 | 3.7223 | 0.3476 |
64
- | 3.7189 | 1.2916 | 12000 | 3.6969 | 0.3495 |
65
- | 3.7161 | 1.3992 | 13000 | 3.6729 | 0.3519 |
66
- | 3.7014 | 1.5068 | 14000 | 3.6550 | 0.3535 |
67
- | 3.684 | 1.6145 | 15000 | 3.6347 | 0.3553 |
68
- | 3.6546 | 1.7221 | 16000 | 3.6192 | 0.3573 |
69
- | 3.6475 | 1.8297 | 17000 | 3.6017 | 0.3592 |
70
- | 3.6584 | 1.9374 | 18000 | 3.5917 | 0.3605 |
71
- | 3.5579 | 2.0450 | 19000 | 3.5779 | 0.3617 |
72
- | 3.5664 | 2.1526 | 20000 | 3.5689 | 0.3628 |
73
- | 3.5668 | 2.2603 | 21000 | 3.5577 | 0.3640 |
74
- | 3.5642 | 2.3679 | 22000 | 3.5505 | 0.3648 |
75
- | 3.5787 | 2.4755 | 23000 | 3.5385 | 0.3662 |
76
- | 3.5471 | 2.5831 | 24000 | 3.5304 | 0.3670 |
77
- | 3.5407 | 2.6908 | 25000 | 3.5202 | 0.3680 |
78
- | 3.5461 | 2.7984 | 26000 | 3.5130 | 0.3686 |
79
- | 3.5291 | 2.9060 | 27000 | 3.5044 | 0.3698 |
80
- | 3.4265 | 3.0137 | 28000 | 3.5000 | 0.3701 |
81
- | 3.4472 | 3.1213 | 29000 | 3.4976 | 0.3712 |
82
- | 3.4667 | 3.2289 | 30000 | 3.4887 | 0.3717 |
83
- | 3.4857 | 3.3366 | 31000 | 3.4819 | 0.3728 |
84
- | 3.4601 | 3.4442 | 32000 | 3.4770 | 0.3728 |
85
- | 3.4542 | 3.5518 | 33000 | 3.4676 | 0.3739 |
86
- | 3.4527 | 3.6595 | 34000 | 3.4630 | 0.3743 |
87
- | 3.4603 | 3.7671 | 35000 | 3.4550 | 0.3753 |
88
- | 3.4545 | 3.8747 | 36000 | 3.4517 | 0.3756 |
89
- | 3.456 | 3.9823 | 37000 | 3.4446 | 0.3764 |
90
- | 3.376 | 4.0900 | 38000 | 3.4467 | 0.3766 |
91
- | 3.3961 | 4.1976 | 39000 | 3.4436 | 0.3772 |
92
- | 3.3729 | 4.3052 | 40000 | 3.4377 | 0.3778 |
93
- | 3.3866 | 4.4129 | 41000 | 3.4327 | 0.3781 |
94
- | 3.3773 | 4.5205 | 42000 | 3.4274 | 0.3784 |
95
- | 3.4146 | 4.6281 | 43000 | 3.4233 | 0.3791 |
96
- | 3.3892 | 4.7358 | 44000 | 3.4198 | 0.3791 |
97
- | 3.401 | 4.8434 | 45000 | 3.4125 | 0.3802 |
98
- | 3.3919 | 4.9510 | 46000 | 3.4081 | 0.3806 |
99
- | 3.2957 | 5.0587 | 47000 | 3.4127 | 0.3808 |
100
- | 3.3146 | 5.1663 | 48000 | 3.4106 | 0.3808 |
101
- | 3.3387 | 5.2739 | 49000 | 3.4057 | 0.3813 |
102
- | 3.3388 | 5.3816 | 50000 | 3.4009 | 0.3819 |
103
- | 3.3306 | 5.4892 | 51000 | 3.3981 | 0.3825 |
104
- | 3.3177 | 5.5968 | 52000 | 3.3930 | 0.3828 |
105
- | 3.3337 | 5.7044 | 53000 | 3.3885 | 0.3832 |
106
- | 3.344 | 5.8121 | 54000 | 3.3852 | 0.3834 |
107
- | 3.3345 | 5.9197 | 55000 | 3.3790 | 0.3840 |
108
- | 3.2586 | 6.0273 | 56000 | 3.3835 | 0.3845 |
109
- | 3.2657 | 6.1350 | 57000 | 3.3805 | 0.3849 |
110
- | 3.2792 | 6.2426 | 58000 | 3.3793 | 0.3846 |
111
- | 3.2714 | 6.3502 | 59000 | 3.3757 | 0.3853 |
112
- | 3.2827 | 6.4579 | 60000 | 3.3696 | 0.3858 |
113
- | 3.2671 | 6.5655 | 61000 | 3.3685 | 0.3860 |
114
- | 3.3058 | 6.6731 | 62000 | 3.3634 | 0.3863 |
115
- | 3.2775 | 6.7808 | 63000 | 3.3596 | 0.3868 |
116
- | 3.2919 | 6.8884 | 64000 | 3.3541 | 0.3874 |
117
- | 3.2603 | 6.9960 | 65000 | 3.3519 | 0.3877 |
118
- | 3.1971 | 7.1036 | 66000 | 3.3571 | 0.3874 |
119
- | 3.2284 | 7.2113 | 67000 | 3.3560 | 0.3878 |
120
- | 3.2006 | 7.3189 | 68000 | 3.3514 | 0.3882 |
121
- | 3.2225 | 7.4265 | 69000 | 3.3473 | 0.3890 |
122
- | 3.2258 | 7.5342 | 70000 | 3.3456 | 0.3892 |
123
- | 3.2407 | 7.6418 | 71000 | 3.3400 | 0.3894 |
124
- | 3.228 | 7.7494 | 72000 | 3.3369 | 0.3898 |
125
- | 3.2466 | 7.8571 | 73000 | 3.3320 | 0.3903 |
126
- | 3.2126 | 7.9647 | 74000 | 3.3308 | 0.3905 |
127
- | 3.1488 | 8.0723 | 75000 | 3.3340 | 0.3904 |
128
- | 3.1626 | 8.1800 | 76000 | 3.3353 | 0.3904 |
129
- | 3.1706 | 8.2876 | 77000 | 3.3312 | 0.3909 |
130
- | 3.1875 | 8.3952 | 78000 | 3.3270 | 0.3912 |
131
- | 3.1783 | 8.5029 | 79000 | 3.3256 | 0.3915 |
132
- | 3.1571 | 8.6105 | 80000 | 3.3212 | 0.3921 |
133
- | 3.2066 | 8.7181 | 81000 | 3.3176 | 0.3923 |
134
- | 3.1893 | 8.8257 | 82000 | 3.3133 | 0.3927 |
135
- | 3.197 | 8.9334 | 83000 | 3.3111 | 0.3929 |
136
- | 3.1188 | 9.0410 | 84000 | 3.3146 | 0.3929 |
137
- | 3.1232 | 9.1486 | 85000 | 3.3147 | 0.3932 |
138
- | 3.1126 | 9.2563 | 86000 | 3.3120 | 0.3934 |
139
- | 3.1101 | 9.3639 | 87000 | 3.3084 | 0.3936 |
140
- | 3.1343 | 9.4715 | 88000 | 3.3068 | 0.3940 |
141
- | 3.1383 | 9.5792 | 89000 | 3.3037 | 0.3943 |
142
- | 3.1249 | 9.6868 | 90000 | 3.3018 | 0.3945 |
143
- | 3.1263 | 9.7944 | 91000 | 3.2996 | 0.3947 |
144
- | 3.1164 | 9.9021 | 92000 | 3.2985 | 0.3949 |
145
 
146
 
147
  ### 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: 3.3055
20
+ - Accuracy: 0.3940
21
 
22
  ## Model description
23
 
 
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:------:|:-----:|:---------------:|:--------:|
53
+ | 5.0973 | 0.1078 | 1000 | 5.0241 | 0.2268 |
54
+ | 4.5905 | 0.2156 | 2000 | 4.5331 | 0.2683 |
55
+ | 4.3425 | 0.3235 | 3000 | 4.2444 | 0.2971 |
56
+ | 4.1711 | 0.4313 | 4000 | 4.0994 | 0.3116 |
57
+ | 4.0584 | 0.5391 | 5000 | 3.9989 | 0.3207 |
58
+ | 4.0004 | 0.6469 | 6000 | 3.9262 | 0.3268 |
59
+ | 3.9359 | 0.7547 | 7000 | 3.8700 | 0.3328 |
60
+ | 3.8808 | 0.8625 | 8000 | 3.8258 | 0.3369 |
61
+ | 3.8477 | 0.9704 | 9000 | 3.7849 | 0.3407 |
62
+ | 3.7666 | 1.0782 | 10000 | 3.7532 | 0.3441 |
63
+ | 3.7793 | 1.1860 | 11000 | 3.7287 | 0.3461 |
64
+ | 3.7434 | 1.2938 | 12000 | 3.7050 | 0.3484 |
65
+ | 3.7161 | 1.4016 | 13000 | 3.6793 | 0.3512 |
66
+ | 3.7004 | 1.5094 | 14000 | 3.6627 | 0.3528 |
67
+ | 3.691 | 1.6173 | 15000 | 3.6420 | 0.3550 |
68
+ | 3.6662 | 1.7251 | 16000 | 3.6244 | 0.3570 |
69
+ | 3.667 | 1.8329 | 17000 | 3.6124 | 0.3581 |
70
+ | 3.6416 | 1.9407 | 18000 | 3.5950 | 0.3594 |
71
+ | 3.5877 | 2.0485 | 19000 | 3.5851 | 0.3610 |
72
+ | 3.5652 | 2.1563 | 20000 | 3.5803 | 0.3618 |
73
+ | 3.5516 | 2.2642 | 21000 | 3.5687 | 0.3632 |
74
+ | 3.5532 | 2.3720 | 22000 | 3.5571 | 0.3645 |
75
+ | 3.5482 | 2.4798 | 23000 | 3.5448 | 0.3653 |
76
+ | 3.5416 | 2.5876 | 24000 | 3.5368 | 0.3664 |
77
+ | 3.5535 | 2.6954 | 25000 | 3.5240 | 0.3674 |
78
+ | 3.5548 | 2.8032 | 26000 | 3.5174 | 0.3680 |
79
+ | 3.5409 | 2.9111 | 27000 | 3.5087 | 0.3690 |
80
+ | 3.4391 | 3.0189 | 28000 | 3.5021 | 0.3698 |
81
+ | 3.4565 | 3.1267 | 29000 | 3.4997 | 0.3706 |
82
+ | 3.4718 | 3.2345 | 30000 | 3.4929 | 0.3711 |
83
+ | 3.4614 | 3.3423 | 31000 | 3.4889 | 0.3722 |
84
+ | 3.4684 | 3.4501 | 32000 | 3.4804 | 0.3726 |
85
+ | 3.4553 | 3.5580 | 33000 | 3.4752 | 0.3733 |
86
+ | 3.4793 | 3.6658 | 34000 | 3.4694 | 0.3737 |
87
+ | 3.4554 | 3.7736 | 35000 | 3.4621 | 0.3745 |
88
+ | 3.4401 | 3.8814 | 36000 | 3.4566 | 0.3750 |
89
+ | 3.4633 | 3.9892 | 37000 | 3.4528 | 0.3756 |
90
+ | 3.3788 | 4.0970 | 38000 | 3.4522 | 0.3764 |
91
+ | 3.3878 | 4.2049 | 39000 | 3.4478 | 0.3763 |
92
+ | 3.3898 | 4.3127 | 40000 | 3.4442 | 0.3768 |
93
+ | 3.4054 | 4.4205 | 41000 | 3.4364 | 0.3776 |
94
+ | 3.4062 | 4.5283 | 42000 | 3.4326 | 0.3782 |
95
+ | 3.3955 | 4.6361 | 43000 | 3.4265 | 0.3785 |
96
+ | 3.4009 | 4.7439 | 44000 | 3.4228 | 0.3795 |
97
+ | 3.4055 | 4.8518 | 45000 | 3.4179 | 0.3795 |
98
+ | 3.3862 | 4.9596 | 46000 | 3.4133 | 0.3802 |
99
+ | 3.3108 | 5.0674 | 47000 | 3.4191 | 0.3804 |
100
+ | 3.3153 | 5.1752 | 48000 | 3.4132 | 0.3807 |
101
+ | 3.3394 | 5.2830 | 49000 | 3.4108 | 0.3809 |
102
+ | 3.3424 | 5.3908 | 50000 | 3.4119 | 0.3810 |
103
+ | 3.3462 | 5.4987 | 51000 | 3.4031 | 0.3819 |
104
+ | 3.3532 | 5.6065 | 52000 | 3.3986 | 0.3822 |
105
+ | 3.3446 | 5.7143 | 53000 | 3.3940 | 0.3827 |
106
+ | 3.3335 | 5.8221 | 54000 | 3.3920 | 0.3827 |
107
+ | 3.3552 | 5.9299 | 55000 | 3.3870 | 0.3837 |
108
+ | 3.2536 | 6.0377 | 56000 | 3.3894 | 0.3838 |
109
+ | 3.2704 | 6.1456 | 57000 | 3.3884 | 0.3836 |
110
+ | 3.2897 | 6.2534 | 58000 | 3.3816 | 0.3845 |
111
+ | 3.2734 | 6.3612 | 59000 | 3.3801 | 0.3847 |
112
+ | 3.2902 | 6.4690 | 60000 | 3.3772 | 0.3848 |
113
+ | 3.2797 | 6.5768 | 61000 | 3.3762 | 0.3852 |
114
+ | 3.301 | 6.6846 | 62000 | 3.3677 | 0.3858 |
115
+ | 3.2762 | 6.7925 | 63000 | 3.3649 | 0.3863 |
116
+ | 3.2801 | 6.9003 | 64000 | 3.3606 | 0.3868 |
117
+ | 3.2107 | 7.0081 | 65000 | 3.3624 | 0.3868 |
118
+ | 3.2054 | 7.1159 | 66000 | 3.3629 | 0.3869 |
119
+ | 3.2306 | 7.2237 | 67000 | 3.3623 | 0.3875 |
120
+ | 3.2293 | 7.3315 | 68000 | 3.3557 | 0.3875 |
121
+ | 3.2277 | 7.4394 | 69000 | 3.3539 | 0.3875 |
122
+ | 3.2367 | 7.5472 | 70000 | 3.3519 | 0.3880 |
123
+ | 3.2321 | 7.6550 | 71000 | 3.3482 | 0.3884 |
124
+ | 3.2335 | 7.7628 | 72000 | 3.3431 | 0.3892 |
125
+ | 3.2454 | 7.8706 | 73000 | 3.3407 | 0.3892 |
126
+ | 3.2569 | 7.9784 | 74000 | 3.3366 | 0.3899 |
127
+ | 3.1679 | 8.0863 | 75000 | 3.3423 | 0.3896 |
128
+ | 3.1915 | 8.1941 | 76000 | 3.3415 | 0.3897 |
129
+ | 3.1884 | 8.3019 | 77000 | 3.3404 | 0.3899 |
130
+ | 3.1803 | 8.4097 | 78000 | 3.3354 | 0.3904 |
131
+ | 3.1798 | 8.5175 | 79000 | 3.3344 | 0.3906 |
132
+ | 3.2136 | 8.6253 | 80000 | 3.3270 | 0.3911 |
133
+ | 3.1662 | 8.7332 | 81000 | 3.3245 | 0.3914 |
134
+ | 3.1835 | 8.8410 | 82000 | 3.3233 | 0.3916 |
135
+ | 3.1983 | 8.9488 | 83000 | 3.3167 | 0.3921 |
136
+ | 3.1333 | 9.0566 | 84000 | 3.3234 | 0.3919 |
137
+ | 3.1284 | 9.1644 | 85000 | 3.3207 | 0.3922 |
138
+ | 3.1299 | 9.2722 | 86000 | 3.3178 | 0.3927 |
139
+ | 3.1326 | 9.3801 | 87000 | 3.3172 | 0.3927 |
140
+ | 3.1286 | 9.4879 | 88000 | 3.3145 | 0.3930 |
141
+ | 3.1328 | 9.5957 | 89000 | 3.3118 | 0.3933 |
142
+ | 3.1369 | 9.7035 | 90000 | 3.3088 | 0.3936 |
143
+ | 3.1481 | 9.8113 | 91000 | 3.3071 | 0.3939 |
144
+ | 3.1488 | 9.9191 | 92000 | 3.3055 | 0.3940 |
145
 
146
 
147
  ### Framework versions
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:36d003b9ef241fdf3e7ad5152e2e99f12fb33478d345f62a63b7ceb2180e1d5f
3
  size 503128704
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d81fd2a855a54316c9d5e144af39460efb05ece4b8b8c1410cc5084740fa387a
3
  size 503128704