K-12BERT-reward-neurallinguisticpioneers-3
This model is a fine-tuned version of vasugoel/K-12BERT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5952
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:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
11.0121 | 0.01 | 1 | 8.7103 |
8.6653 | 0.01 | 2 | 5.3654 |
4.4755 | 0.02 | 3 | 2.9993 |
3.5791 | 0.03 | 4 | 2.0453 |
2.1693 | 0.03 | 5 | 1.8209 |
1.8131 | 0.04 | 6 | 1.9799 |
2.2419 | 0.05 | 7 | 1.9990 |
1.3732 | 0.06 | 8 | 1.6069 |
1.8928 | 0.06 | 9 | 2.3442 |
2.0764 | 0.07 | 10 | 2.4528 |
1.967 | 0.08 | 11 | 1.7570 |
1.641 | 0.08 | 12 | 1.3257 |
1.7557 | 0.09 | 13 | 1.2695 |
0.9002 | 0.1 | 14 | 1.3756 |
1.2707 | 0.1 | 15 | 1.3182 |
0.8898 | 0.11 | 16 | 1.3691 |
0.7869 | 0.12 | 17 | 1.1321 |
1.0837 | 0.12 | 18 | 1.0468 |
0.6809 | 0.13 | 19 | 1.0298 |
0.8359 | 0.14 | 20 | 1.0701 |
1.532 | 0.15 | 21 | 1.1456 |
0.4573 | 0.15 | 22 | 1.2005 |
1.5712 | 0.16 | 23 | 1.5595 |
0.7703 | 0.17 | 24 | 1.4326 |
1.0659 | 0.17 | 25 | 1.0713 |
1.2147 | 0.18 | 26 | 0.9409 |
0.8567 | 0.19 | 27 | 0.9517 |
0.7758 | 0.19 | 28 | 1.1403 |
1.1653 | 0.2 | 29 | 1.2216 |
0.7928 | 0.21 | 30 | 1.1482 |
1.5701 | 0.22 | 31 | 0.9704 |
1.006 | 0.22 | 32 | 0.8644 |
0.6701 | 0.23 | 33 | 0.8915 |
0.8564 | 0.24 | 34 | 0.8800 |
0.4669 | 0.24 | 35 | 0.8653 |
0.6119 | 0.25 | 36 | 0.8946 |
0.6012 | 0.26 | 37 | 0.9236 |
0.3485 | 0.26 | 38 | 0.9712 |
1.0116 | 0.27 | 39 | 1.1866 |
0.5167 | 0.28 | 40 | 1.3035 |
0.6579 | 0.28 | 41 | 1.2122 |
0.4849 | 0.29 | 42 | 1.0500 |
0.9278 | 0.3 | 43 | 0.9969 |
0.6447 | 0.31 | 44 | 0.9311 |
0.5996 | 0.31 | 45 | 0.8287 |
1.2452 | 0.32 | 46 | 0.7565 |
1.0734 | 0.33 | 47 | 0.9004 |
0.8317 | 0.33 | 48 | 0.9884 |
1.5887 | 0.34 | 49 | 0.9487 |
0.8313 | 0.35 | 50 | 0.8382 |
0.7197 | 0.35 | 51 | 0.8620 |
1.2268 | 0.36 | 52 | 0.8625 |
0.7911 | 0.37 | 53 | 0.7812 |
1.0787 | 0.38 | 54 | 0.7301 |
0.159 | 0.38 | 55 | 0.7704 |
0.2064 | 0.39 | 56 | 0.7715 |
0.1974 | 0.4 | 57 | 0.7977 |
0.5762 | 0.4 | 58 | 0.9158 |
1.0812 | 0.41 | 59 | 0.9906 |
0.2053 | 0.42 | 60 | 1.0119 |
0.7374 | 0.42 | 61 | 0.9580 |
1.1598 | 0.43 | 62 | 0.9101 |
0.8277 | 0.44 | 63 | 0.9434 |
1.3316 | 0.44 | 64 | 0.9062 |
1.2475 | 0.45 | 65 | 0.8304 |
0.4621 | 0.46 | 66 | 0.7518 |
1.047 | 0.47 | 67 | 0.7238 |
1.439 | 0.47 | 68 | 0.7726 |
0.4821 | 0.48 | 69 | 0.9050 |
0.6916 | 0.49 | 70 | 0.9246 |
2.1817 | 0.49 | 71 | 0.8534 |
1.3539 | 0.5 | 72 | 0.7835 |
0.4156 | 0.51 | 73 | 0.6983 |
0.3394 | 0.51 | 74 | 0.6919 |
0.6992 | 0.52 | 75 | 0.6941 |
0.5135 | 0.53 | 76 | 0.7190 |
0.4799 | 0.53 | 77 | 0.7536 |
1.3763 | 0.54 | 78 | 0.7912 |
0.5585 | 0.55 | 79 | 0.7875 |
0.4962 | 0.56 | 80 | 0.7185 |
0.5592 | 0.56 | 81 | 0.6845 |
0.5033 | 0.57 | 82 | 0.6537 |
0.7098 | 0.58 | 83 | 0.6501 |
0.3739 | 0.58 | 84 | 0.6543 |
0.3426 | 0.59 | 85 | 0.6953 |
0.7321 | 0.6 | 86 | 0.7230 |
0.5637 | 0.6 | 87 | 0.7387 |
1.0959 | 0.61 | 88 | 0.7226 |
0.3723 | 0.62 | 89 | 0.7004 |
1.3296 | 0.62 | 90 | 0.6756 |
1.2918 | 0.63 | 91 | 0.6348 |
0.878 | 0.64 | 92 | 0.6189 |
0.832 | 0.65 | 93 | 0.6899 |
0.4342 | 0.65 | 94 | 0.8381 |
1.1652 | 0.66 | 95 | 0.8777 |
0.8581 | 0.67 | 96 | 0.7764 |
0.7468 | 0.67 | 97 | 0.8387 |
0.8492 | 0.68 | 98 | 0.7698 |
0.667 | 0.69 | 99 | 0.6798 |
1.0306 | 0.69 | 100 | 0.6889 |
0.4181 | 0.7 | 101 | 0.7197 |
0.8002 | 0.71 | 102 | 0.7047 |
0.4183 | 0.72 | 103 | 0.6576 |
0.256 | 0.72 | 104 | 0.6398 |
0.7134 | 0.73 | 105 | 0.6309 |
0.4752 | 0.74 | 106 | 0.6322 |
1.9373 | 0.74 | 107 | 0.7256 |
0.9749 | 0.75 | 108 | 0.8439 |
0.9102 | 0.76 | 109 | 0.8159 |
0.802 | 0.76 | 110 | 0.6732 |
0.3826 | 0.77 | 111 | 0.6126 |
0.2151 | 0.78 | 112 | 0.7456 |
0.3858 | 0.78 | 113 | 0.9544 |
0.8457 | 0.79 | 114 | 1.0739 |
1.6663 | 0.8 | 115 | 1.0784 |
1.6755 | 0.81 | 116 | 1.0225 |
27.0617 | 0.81 | 117 | 1.0269 |
2.0955 | 0.82 | 118 | 0.9601 |
0.7422 | 0.83 | 119 | 0.8699 |
0.5386 | 0.83 | 120 | 0.8449 |
0.6455 | 0.84 | 121 | 0.8467 |
3.3079 | 0.85 | 122 | 0.9692 |
0.7571 | 0.85 | 123 | 0.9999 |
0.9167 | 0.86 | 124 | 0.9253 |
0.639 | 0.87 | 125 | 0.7434 |
0.8654 | 0.88 | 126 | 0.6060 |
1.0687 | 0.88 | 127 | 0.5836 |
0.3017 | 0.89 | 128 | 0.5929 |
0.42 | 0.9 | 129 | 0.6492 |
0.9606 | 0.9 | 130 | 0.6870 |
1.2006 | 0.91 | 131 | 0.7251 |
0.8671 | 0.92 | 132 | 0.7631 |
0.6252 | 0.92 | 133 | 0.7495 |
1.725 | 0.93 | 134 | 0.7139 |
0.7052 | 0.94 | 135 | 0.6708 |
0.615 | 0.94 | 136 | 0.6332 |
0.5124 | 0.95 | 137 | 0.6807 |
0.6559 | 0.96 | 138 | 0.6876 |
0.6609 | 0.97 | 139 | 0.6254 |
0.3095 | 0.97 | 140 | 0.5933 |
0.7292 | 0.98 | 141 | 0.5738 |
0.8383 | 0.99 | 142 | 0.5779 |
0.5622 | 0.99 | 143 | 0.5934 |
0.307 | 1.0 | 144 | 0.5952 |
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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