Hugging Face's logo

craa
/
100M_high_10_6910

Text Generation
Transformers
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Model card Files
xet
 Community
craa commited on
Commit
07f8420
·
verified ·
1 Parent(s): 46e69bd

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: 3.3078
20
- - Accuracy: 0.3941
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
- | 5.1085 | 0.1076 | 1000 | 5.0539 | 0.2231 |
54
- | 4.6345 | 0.2153 | 2000 | 4.5576 | 0.2653 |
55
- | 4.3214 | 0.3229 | 3000 | 4.2625 | 0.2962 |
56
- | 4.1974 | 0.4305 | 4000 | 4.1103 | 0.3101 |
57
- | 4.0529 | 0.5382 | 5000 | 4.0090 | 0.3196 |
58
- | 4.0151 | 0.6458 | 6000 | 3.9343 | 0.3263 |
59
- | 3.9373 | 0.7534 | 7000 | 3.8774 | 0.3317 |
60
- | 3.9002 | 0.8610 | 8000 | 3.8270 | 0.3365 |
61
- | 3.8393 | 0.9687 | 9000 | 3.7905 | 0.3401 |
62
- | 3.7744 | 1.0763 | 10000 | 3.7595 | 0.3432 |
63
- | 3.7534 | 1.1839 | 11000 | 3.7296 | 0.3462 |
64
- | 3.7543 | 1.2916 | 12000 | 3.7070 | 0.3489 |
65
- | 3.7094 | 1.3992 | 13000 | 3.6848 | 0.3506 |
66
- | 3.7048 | 1.5068 | 14000 | 3.6649 | 0.3530 |
67
- | 3.679 | 1.6145 | 15000 | 3.6440 | 0.3545 |
68
- | 3.6858 | 1.7221 | 16000 | 3.6285 | 0.3566 |
69
- | 3.6496 | 1.8297 | 17000 | 3.6119 | 0.3580 |
70
- | 3.6412 | 1.9374 | 18000 | 3.5962 | 0.3598 |
71
- | 3.5425 | 2.0450 | 19000 | 3.5833 | 0.3611 |
72
- | 3.5503 | 2.1526 | 20000 | 3.5737 | 0.3622 |
73
- | 3.5643 | 2.2603 | 21000 | 3.5636 | 0.3635 |
74
- | 3.567 | 2.3679 | 22000 | 3.5522 | 0.3644 |
75
- | 3.5654 | 2.4755 | 23000 | 3.5436 | 0.3654 |
76
- | 3.5634 | 2.5831 | 24000 | 3.5358 | 0.3662 |
77
- | 3.5495 | 2.6908 | 25000 | 3.5234 | 0.3672 |
78
- | 3.5343 | 2.7984 | 26000 | 3.5159 | 0.3687 |
79
- | 3.5113 | 2.9060 | 27000 | 3.5090 | 0.3688 |
80
- | 3.4213 | 3.0137 | 28000 | 3.4993 | 0.3704 |
81
- | 3.4504 | 3.1213 | 29000 | 3.4976 | 0.3707 |
82
- | 3.456 | 3.2289 | 30000 | 3.4912 | 0.3713 |
83
- | 3.4391 | 3.3366 | 31000 | 3.4859 | 0.3720 |
84
- | 3.4683 | 3.4442 | 32000 | 3.4771 | 0.3728 |
85
- | 3.4697 | 3.5518 | 33000 | 3.4712 | 0.3736 |
86
- | 3.4563 | 3.6595 | 34000 | 3.4643 | 0.3742 |
87
- | 3.4365 | 3.7671 | 35000 | 3.4570 | 0.3751 |
88
- | 3.4522 | 3.8747 | 36000 | 3.4505 | 0.3754 |
89
- | 3.4452 | 3.9823 | 37000 | 3.4460 | 0.3764 |
90
- | 3.3889 | 4.0900 | 38000 | 3.4473 | 0.3765 |
91
- | 3.3858 | 4.1976 | 39000 | 3.4451 | 0.3768 |
92
- | 3.3859 | 4.3052 | 40000 | 3.4385 | 0.3777 |
93
- | 3.3802 | 4.4129 | 41000 | 3.4353 | 0.3780 |
94
- | 3.3993 | 4.5205 | 42000 | 3.4270 | 0.3785 |
95
- | 3.3736 | 4.6281 | 43000 | 3.4234 | 0.3789 |
96
- | 3.3786 | 4.7358 | 44000 | 3.4175 | 0.3798 |
97
- | 3.3819 | 4.8434 | 45000 | 3.4125 | 0.3802 |
98
- | 3.3887 | 4.9510 | 46000 | 3.4097 | 0.3803 |
99
- | 3.2862 | 5.0587 | 47000 | 3.4116 | 0.3806 |
100
- | 3.3055 | 5.1663 | 48000 | 3.4106 | 0.3810 |
101
- | 3.302 | 5.2739 | 49000 | 3.4073 | 0.3816 |
102
- | 3.3289 | 5.3816 | 50000 | 3.4017 | 0.3819 |
103
- | 3.3209 | 5.4892 | 51000 | 3.3965 | 0.3825 |
104
- | 3.3229 | 5.5968 | 52000 | 3.3938 | 0.3830 |
105
- | 3.3251 | 5.7044 | 53000 | 3.3888 | 0.3832 |
106
- | 3.3349 | 5.8121 | 54000 | 3.3831 | 0.3837 |
107
- | 3.3285 | 5.9197 | 55000 | 3.3801 | 0.3841 |
108
- | 3.233 | 6.0273 | 56000 | 3.3838 | 0.3840 |
109
- | 3.2755 | 6.1350 | 57000 | 3.3851 | 0.3843 |
110
- | 3.2801 | 6.2426 | 58000 | 3.3820 | 0.3849 |
111
- | 3.2813 | 6.3502 | 59000 | 3.3768 | 0.3853 |
112
- | 3.2974 | 6.4579 | 60000 | 3.3712 | 0.3856 |
113
- | 3.2941 | 6.5655 | 61000 | 3.3675 | 0.3862 |
114
- | 3.2819 | 6.6731 | 62000 | 3.3645 | 0.3866 |
115
- | 3.2896 | 6.7808 | 63000 | 3.3575 | 0.3868 |
116
- | 3.2969 | 6.8884 | 64000 | 3.3574 | 0.3869 |
117
- | 3.2875 | 6.9960 | 65000 | 3.3510 | 0.3876 |
118
- | 3.207 | 7.1036 | 66000 | 3.3583 | 0.3872 |
119
- | 3.2326 | 7.2113 | 67000 | 3.3557 | 0.3877 |
120
- | 3.2289 | 7.3189 | 68000 | 3.3547 | 0.3879 |
121
- | 3.2421 | 7.4265 | 69000 | 3.3468 | 0.3885 |
122
- | 3.2287 | 7.5342 | 70000 | 3.3427 | 0.3889 |
123
- | 3.2358 | 7.6418 | 71000 | 3.3413 | 0.3892 |
124
- | 3.224 | 7.7494 | 72000 | 3.3377 | 0.3897 |
125
- | 3.2265 | 7.8571 | 73000 | 3.3341 | 0.3898 |
126
- | 3.2039 | 7.9647 | 74000 | 3.3297 | 0.3904 |
127
- | 3.1745 | 8.0723 | 75000 | 3.3360 | 0.3899 |
128
- | 3.1758 | 8.1800 | 76000 | 3.3342 | 0.3903 |
129
- | 3.172 | 8.2876 | 77000 | 3.3309 | 0.3908 |
130
- | 3.1808 | 8.3952 | 78000 | 3.3283 | 0.3910 |
131
- | 3.1774 | 8.5029 | 79000 | 3.3244 | 0.3914 |
132
- | 3.1726 | 8.6105 | 80000 | 3.3215 | 0.3917 |
133
- | 3.1791 | 8.7181 | 81000 | 3.3189 | 0.3920 |
134
- | 3.185 | 8.8257 | 82000 | 3.3138 | 0.3925 |
135
- | 3.182 | 8.9334 | 83000 | 3.3114 | 0.3928 |
136
- | 3.1128 | 9.0410 | 84000 | 3.3157 | 0.3928 |
137
- | 3.1474 | 9.1486 | 85000 | 3.3130 | 0.3928 |
138
- | 3.1503 | 9.2563 | 86000 | 3.3113 | 0.3932 |
139
- | 3.1263 | 9.3639 | 87000 | 3.3093 | 0.3935 |
140
- | 3.1202 | 9.4715 | 88000 | 3.3074 | 0.3937 |
141
- | 3.1363 | 9.5792 | 89000 | 3.3046 | 0.3940 |
142
- | 3.1077 | 9.6868 | 90000 | 3.3027 | 0.3943 |
143
- | 3.1283 | 9.7944 | 91000 | 3.3010 | 0.3945 |
144
- | 3.1229 | 9.9021 | 92000 | 3.2988 | 0.3947 |
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: 3.3016
20
+ - Accuracy: 0.3945
21
 
22
  ## Model description
23
 
 
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:------:|:-----:|:---------------:|:--------:|
53
+ | 5.1125 | 0.1078 | 1000 | 5.0240 | 0.2273 |
54
+ | 4.5843 | 0.2156 | 2000 | 4.5046 | 0.2714 |
55
+ | 4.3107 | 0.3235 | 3000 | 4.2323 | 0.2990 |
56
+ | 4.1545 | 0.4313 | 4000 | 4.0870 | 0.3125 |
57
+ | 4.0621 | 0.5391 | 5000 | 4.0006 | 0.3208 |
58
+ | 4.001 | 0.6469 | 6000 | 3.9160 | 0.3281 |
59
+ | 3.9268 | 0.7547 | 7000 | 3.8637 | 0.3332 |
60
+ | 3.882 | 0.8625 | 8000 | 3.8152 | 0.3379 |
61
+ | 3.8406 | 0.9704 | 9000 | 3.7795 | 0.3405 |
62
+ | 3.7776 | 1.0782 | 10000 | 3.7567 | 0.3441 |
63
+ | 3.7378 | 1.1860 | 11000 | 3.7236 | 0.3475 |
64
+ | 3.7355 | 1.2938 | 12000 | 3.7023 | 0.3491 |
65
+ | 3.7107 | 1.4016 | 13000 | 3.6805 | 0.3514 |
66
+ | 3.7005 | 1.5094 | 14000 | 3.6586 | 0.3534 |
67
+ | 3.6782 | 1.6173 | 15000 | 3.6409 | 0.3553 |
68
+ | 3.6771 | 1.7251 | 16000 | 3.6200 | 0.3569 |
69
+ | 3.6631 | 1.8329 | 17000 | 3.6075 | 0.3583 |
70
+ | 3.6446 | 1.9407 | 18000 | 3.5916 | 0.3598 |
71
+ | 3.5595 | 2.0485 | 19000 | 3.5814 | 0.3615 |
72
+ | 3.5597 | 2.1563 | 20000 | 3.5742 | 0.3624 |
73
+ | 3.5616 | 2.2642 | 21000 | 3.5634 | 0.3635 |
74
+ | 3.5446 | 2.3720 | 22000 | 3.5524 | 0.3644 |
75
+ | 3.5515 | 2.4798 | 23000 | 3.5413 | 0.3653 |
76
+ | 3.5416 | 2.5876 | 24000 | 3.5337 | 0.3665 |
77
+ | 3.5269 | 2.6954 | 25000 | 3.5241 | 0.3671 |
78
+ | 3.5393 | 2.8032 | 26000 | 3.5148 | 0.3685 |
79
+ | 3.5236 | 2.9111 | 27000 | 3.5071 | 0.3690 |
80
+ | 3.4462 | 3.0189 | 28000 | 3.5028 | 0.3702 |
81
+ | 3.4405 | 3.1267 | 29000 | 3.4963 | 0.3704 |
82
+ | 3.468 | 3.2345 | 30000 | 3.4904 | 0.3716 |
83
+ | 3.4749 | 3.3423 | 31000 | 3.4830 | 0.3723 |
84
+ | 3.4634 | 3.4501 | 32000 | 3.4755 | 0.3731 |
85
+ | 3.4541 | 3.5580 | 33000 | 3.4708 | 0.3732 |
86
+ | 3.4549 | 3.6658 | 34000 | 3.4651 | 0.3743 |
87
+ | 3.4706 | 3.7736 | 35000 | 3.4588 | 0.3750 |
88
+ | 3.4377 | 3.8814 | 36000 | 3.4547 | 0.3753 |
89
+ | 3.4194 | 3.9892 | 37000 | 3.4472 | 0.3756 |
90
+ | 3.3615 | 4.0970 | 38000 | 3.4490 | 0.3762 |
91
+ | 3.3852 | 4.2049 | 39000 | 3.4475 | 0.3766 |
92
+ | 3.4006 | 4.3127 | 40000 | 3.4418 | 0.3768 |
93
+ | 3.4161 | 4.4205 | 41000 | 3.4352 | 0.3779 |
94
+ | 3.3901 | 4.5283 | 42000 | 3.4316 | 0.3782 |
95
+ | 3.4087 | 4.6361 | 43000 | 3.4247 | 0.3790 |
96
+ | 3.3934 | 4.7439 | 44000 | 3.4211 | 0.3793 |
97
+ | 3.391 | 4.8518 | 45000 | 3.4142 | 0.3800 |
98
+ | 3.3877 | 4.9596 | 46000 | 3.4123 | 0.3802 |
99
+ | 3.3146 | 5.0674 | 47000 | 3.4130 | 0.3806 |
100
+ | 3.3089 | 5.1752 | 48000 | 3.4128 | 0.3808 |
101
+ | 3.3293 | 5.2830 | 49000 | 3.4085 | 0.3813 |
102
+ | 3.3418 | 5.3908 | 50000 | 3.4017 | 0.3818 |
103
+ | 3.3174 | 5.4987 | 51000 | 3.3982 | 0.3820 |
104
+ | 3.329 | 5.6065 | 52000 | 3.3947 | 0.3824 |
105
+ | 3.3429 | 5.7143 | 53000 | 3.3912 | 0.3830 |
106
+ | 3.3239 | 5.8221 | 54000 | 3.3861 | 0.3835 |
107
+ | 3.3279 | 5.9299 | 55000 | 3.3820 | 0.3839 |
108
+ | 3.239 | 6.0377 | 56000 | 3.3849 | 0.3838 |
109
+ | 3.2688 | 6.1456 | 57000 | 3.3839 | 0.3842 |
110
+ | 3.2762 | 6.2534 | 58000 | 3.3817 | 0.3844 |
111
+ | 3.2943 | 6.3612 | 59000 | 3.3772 | 0.3848 |
112
+ | 3.2847 | 6.4690 | 60000 | 3.3734 | 0.3852 |
113
+ | 3.2762 | 6.5768 | 61000 | 3.3690 | 0.3857 |
114
+ | 3.2792 | 6.6846 | 62000 | 3.3652 | 0.3860 |
115
+ | 3.2876 | 6.7925 | 63000 | 3.3613 | 0.3865 |
116
+ | 3.3024 | 6.9003 | 64000 | 3.3561 | 0.3868 |
117
+ | 3.2097 | 7.0081 | 65000 | 3.3606 | 0.3867 |
118
+ | 3.2216 | 7.1159 | 66000 | 3.3589 | 0.3875 |
119
+ | 3.2324 | 7.2237 | 67000 | 3.3576 | 0.3874 |
120
+ | 3.2145 | 7.3315 | 68000 | 3.3548 | 0.3878 |
121
+ | 3.2498 | 7.4394 | 69000 | 3.3506 | 0.3881 |
122
+ | 3.2347 | 7.5472 | 70000 | 3.3484 | 0.3886 |
123
+ | 3.2366 | 7.6550 | 71000 | 3.3433 | 0.3890 |
124
+ | 3.2311 | 7.7628 | 72000 | 3.3418 | 0.3894 |
125
+ | 3.2317 | 7.8706 | 73000 | 3.3353 | 0.3896 |
126
+ | 3.2455 | 7.9784 | 74000 | 3.3325 | 0.3900 |
127
+ | 3.1594 | 8.0863 | 75000 | 3.3395 | 0.3897 |
128
+ | 3.1872 | 8.1941 | 76000 | 3.3382 | 0.3898 |
129
+ | 3.1757 | 8.3019 | 77000 | 3.3341 | 0.3905 |
130
+ | 3.193 | 8.4097 | 78000 | 3.3314 | 0.3909 |
131
+ | 3.1788 | 8.5175 | 79000 | 3.3288 | 0.3910 |
132
+ | 3.1767 | 8.6253 | 80000 | 3.3249 | 0.3916 |
133
+ | 3.1896 | 8.7332 | 81000 | 3.3198 | 0.3921 |
134
+ | 3.1942 | 8.8410 | 82000 | 3.3171 | 0.3922 |
135
+ | 3.1726 | 8.9488 | 83000 | 3.3140 | 0.3926 |
136
+ | 3.1085 | 9.0566 | 84000 | 3.3178 | 0.3926 |
137
+ | 3.1471 | 9.1644 | 85000 | 3.3154 | 0.3928 |
138
+ | 3.1328 | 9.2722 | 86000 | 3.3146 | 0.3931 |
139
+ | 3.122 | 9.3801 | 87000 | 3.3118 | 0.3932 |
140
+ | 3.1391 | 9.4879 | 88000 | 3.3092 | 0.3936 |
141
+ | 3.1324 | 9.5957 | 89000 | 3.3066 | 0.3939 |
142
+ | 3.1338 | 9.7035 | 90000 | 3.3049 | 0.3941 |
143
+ | 3.1342 | 9.8113 | 91000 | 3.3029 | 0.3943 |
144
+ | 3.1426 | 9.9191 | 92000 | 3.3016 | 0.3945 |
145
 
146
 
147
  ### Framework versions
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:22d134ca76b894d8a614c7cd52da68e13432aec01443d26dee4619ab06758d55
3
  size 503128704
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:dc127a0c8cad6002bc2ca557013edf489450faaad70e2e6783a8f84fdb38f032
3
  size 503128704