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gpt3_model
This model is a fine-tuned version of MJ199999/gpt3_model on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.4905
- Train Lr: 0.0009999999
- Epoch: 199
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adagrad', 'learning_rate': 0.0009999999, 'decay': 0.0, 'initial_accumulator_value': 0.1, 'epsilon': 1e-07}
- training_precision: float32
Training results
Train Loss | Train Lr | Epoch |
---|---|---|
5.1583 | 0.01 | 0 |
3.9477 | 0.01 | 1 |
2.9332 | 0.01 | 2 |
2.1581 | 0.01 | 3 |
1.6918 | 0.01 | 4 |
1.3929 | 0.01 | 5 |
1.2062 | 0.01 | 6 |
1.0955 | 0.01 | 7 |
1.0068 | 0.01 | 8 |
0.9528 | 0.01 | 9 |
0.9051 | 0.01 | 10 |
0.8710 | 0.01 | 11 |
0.8564 | 0.01 | 12 |
0.8094 | 0.01 | 13 |
0.8143 | 0.01 | 14 |
0.7853 | 0.01 | 15 |
0.7625 | 0.01 | 16 |
0.7508 | 0.01 | 17 |
0.7449 | 0.01 | 18 |
0.7319 | 0.01 | 19 |
0.7144 | 0.01 | 20 |
0.7045 | 0.01 | 21 |
0.7029 | 0.01 | 22 |
0.6937 | 0.01 | 23 |
0.6898 | 0.01 | 24 |
0.6745 | 0.01 | 25 |
0.6767 | 0.01 | 26 |
0.6692 | 0.01 | 27 |
0.6604 | 0.01 | 28 |
0.6573 | 0.01 | 29 |
0.6524 | 0.01 | 30 |
0.6508 | 0.01 | 31 |
0.6443 | 0.01 | 32 |
0.6452 | 0.01 | 33 |
0.6371 | 0.01 | 34 |
0.6362 | 0.01 | 35 |
0.6304 | 0.01 | 36 |
0.6317 | 0.01 | 37 |
0.6270 | 0.01 | 38 |
0.6257 | 0.01 | 39 |
0.6208 | 0.01 | 40 |
0.6227 | 0.01 | 41 |
0.6154 | 0.01 | 42 |
0.6126 | 0.01 | 43 |
0.6149 | 0.01 | 44 |
0.6075 | 0.01 | 45 |
0.6084 | 0.01 | 46 |
0.6078 | 0.01 | 47 |
0.6057 | 0.01 | 48 |
0.6033 | 0.01 | 49 |
0.6040 | 0.01 | 50 |
0.5989 | 0.01 | 51 |
0.5967 | 0.01 | 52 |
0.5952 | 0.01 | 53 |
0.5911 | 0.01 | 54 |
0.5904 | 0.01 | 55 |
0.5888 | 0.01 | 56 |
0.5886 | 0.01 | 57 |
0.5883 | 0.01 | 58 |
0.5838 | 0.01 | 59 |
0.5856 | 0.01 | 60 |
0.5850 | 0.01 | 61 |
0.5801 | 0.01 | 62 |
0.5821 | 0.01 | 63 |
0.5781 | 0.01 | 64 |
0.5786 | 0.01 | 65 |
0.5835 | 0.01 | 66 |
0.5808 | 0.01 | 67 |
0.5754 | 0.01 | 68 |
0.5742 | 0.01 | 69 |
0.5733 | 0.01 | 70 |
0.5700 | 0.01 | 71 |
0.5738 | 0.01 | 72 |
0.5678 | 0.01 | 73 |
0.5695 | 0.01 | 74 |
0.5684 | 0.01 | 75 |
0.5696 | 0.01 | 76 |
0.5688 | 0.01 | 77 |
0.5648 | 0.01 | 78 |
0.5592 | 0.01 | 79 |
0.5622 | 0.01 | 80 |
0.5660 | 0.01 | 81 |
0.5636 | 0.01 | 82 |
0.5602 | 0.01 | 83 |
0.5613 | 0.01 | 84 |
0.5608 | 0.01 | 85 |
0.5589 | 0.01 | 86 |
0.5580 | 0.01 | 87 |
0.5566 | 0.01 | 88 |
0.5531 | 0.01 | 89 |
0.5571 | 0.01 | 90 |
0.5541 | 0.01 | 91 |
0.5576 | 0.01 | 92 |
0.5560 | 0.01 | 93 |
0.5517 | 0.01 | 94 |
0.5508 | 0.01 | 95 |
0.5554 | 0.01 | 96 |
0.5539 | 0.01 | 97 |
0.5493 | 0.01 | 98 |
0.5499 | 0.01 | 99 |
0.4999 | 0.0009999999 | 100 |
0.4981 | 0.0009999999 | 101 |
0.4983 | 0.0009999999 | 102 |
0.4984 | 0.0009999999 | 103 |
0.4974 | 0.0009999999 | 104 |
0.4957 | 0.0009999999 | 105 |
0.4966 | 0.0009999999 | 106 |
0.4975 | 0.0009999999 | 107 |
0.4962 | 0.0009999999 | 108 |
0.4932 | 0.0009999999 | 109 |
0.4983 | 0.0009999999 | 110 |
0.4937 | 0.0009999999 | 111 |
0.4926 | 0.0009999999 | 112 |
0.4944 | 0.0009999999 | 113 |
0.4947 | 0.0009999999 | 114 |
0.4953 | 0.0009999999 | 115 |
0.4934 | 0.0009999999 | 116 |
0.4929 | 0.0009999999 | 117 |
0.4925 | 0.0009999999 | 118 |
0.4948 | 0.0009999999 | 119 |
0.4947 | 0.0009999999 | 120 |
0.4936 | 0.0009999999 | 121 |
0.4909 | 0.0009999999 | 122 |
0.4960 | 0.0009999999 | 123 |
0.4952 | 0.0009999999 | 124 |
0.4923 | 0.0009999999 | 125 |
0.4930 | 0.0009999999 | 126 |
0.4942 | 0.0009999999 | 127 |
0.4927 | 0.0009999999 | 128 |
0.4917 | 0.0009999999 | 129 |
0.4926 | 0.0009999999 | 130 |
0.4927 | 0.0009999999 | 131 |
0.4932 | 0.0009999999 | 132 |
0.4925 | 0.0009999999 | 133 |
0.4928 | 0.0009999999 | 134 |
0.4936 | 0.0009999999 | 135 |
0.4908 | 0.0009999999 | 136 |
0.4936 | 0.0009999999 | 137 |
0.4916 | 0.0009999999 | 138 |
0.4906 | 0.0009999999 | 139 |
0.4904 | 0.0009999999 | 140 |
0.4920 | 0.0009999999 | 141 |
0.4924 | 0.0009999999 | 142 |
0.4902 | 0.0009999999 | 143 |
0.4903 | 0.0009999999 | 144 |
0.4903 | 0.0009999999 | 145 |
0.4924 | 0.0009999999 | 146 |
0.4889 | 0.0009999999 | 147 |
0.4896 | 0.0009999999 | 148 |
0.4919 | 0.0009999999 | 149 |
0.4896 | 0.0009999999 | 150 |
0.4906 | 0.0009999999 | 151 |
0.4923 | 0.0009999999 | 152 |
0.4899 | 0.0009999999 | 153 |
0.4925 | 0.0009999999 | 154 |
0.4901 | 0.0009999999 | 155 |
0.4910 | 0.0009999999 | 156 |
0.4904 | 0.0009999999 | 157 |
0.4912 | 0.0009999999 | 158 |
0.4937 | 0.0009999999 | 159 |
0.4894 | 0.0009999999 | 160 |
0.4913 | 0.0009999999 | 161 |
0.4899 | 0.0009999999 | 162 |
0.4894 | 0.0009999999 | 163 |
0.4904 | 0.0009999999 | 164 |
0.4900 | 0.0009999999 | 165 |
0.4890 | 0.0009999999 | 166 |
0.4919 | 0.0009999999 | 167 |
0.4909 | 0.0009999999 | 168 |
0.4891 | 0.0009999999 | 169 |
0.4900 | 0.0009999999 | 170 |
0.4910 | 0.0009999999 | 171 |
0.4901 | 0.0009999999 | 172 |
0.4914 | 0.0009999999 | 173 |
0.4913 | 0.0009999999 | 174 |
0.4897 | 0.0009999999 | 175 |
0.4892 | 0.0009999999 | 176 |
0.4929 | 0.0009999999 | 177 |
0.4881 | 0.0009999999 | 178 |
0.4920 | 0.0009999999 | 179 |
0.4888 | 0.0009999999 | 180 |
0.4901 | 0.0009999999 | 181 |
0.4875 | 0.0009999999 | 182 |
0.4930 | 0.0009999999 | 183 |
0.4867 | 0.0009999999 | 184 |
0.4890 | 0.0009999999 | 185 |
0.4898 | 0.0009999999 | 186 |
0.4880 | 0.0009999999 | 187 |
0.4899 | 0.0009999999 | 188 |
0.4881 | 0.0009999999 | 189 |
0.4897 | 0.0009999999 | 190 |
0.4876 | 0.0009999999 | 191 |
0.4873 | 0.0009999999 | 192 |
0.4901 | 0.0009999999 | 193 |
0.4898 | 0.0009999999 | 194 |
0.4898 | 0.0009999999 | 195 |
0.4861 | 0.0009999999 | 196 |
0.4878 | 0.0009999999 | 197 |
0.4880 | 0.0009999999 | 198 |
0.4905 | 0.0009999999 | 199 |
Framework versions
- Transformers 4.21.3
- TensorFlow 2.8.2
- Tokenizers 0.12.1